rendered_trace_1747968083.py

#subagent bootstrap
#startblock type: genesis
#index 0
#timestamp 1747966489.791039
#time_remaining 21599.999918699265 seconds
#block_size I have 768 tokens (full) to write with

#hint Listen Carefully: This program is self modifying code.
# It works by feeding the program text into a large language
# model and generating the next code block as an addition to a
# long python file. Each code block is marked by #startblock and
# #endblock. The code between those lines is compiled by the agent
# framework below to check it is syntactically and semantically
# valid.

# The weave-agent is basically something like a ReAct pattern https://arxiv.org/abs/2210.03629
# instantiated in the context of a long 'python file', kind of like an ipython
# notebook but without the special syntax. It takes inspiration from Voyager
# https://arxiv.org/abs/2305.16291 and Cradle https://baai-agents.github.io/Cradle/
# in that the agents actions are fully general python programs that interact with
# the computable environment rather than specialized 'tool calling' or similar
# mechanisms. This turns out to make more sense than trying to directly hook up
# the model to interfaces for a few reasons:
# 1) Writing out its actions as programs lets the model batch its actions together
# to form coherent motions rather than getting stuck on fine grained details if it
# generates its actions token by token in the moment.
# 2) These models are highly optimized for writing code whereas interacting with
# whatever interface you have is either marginal in the pretraining set or actually
# out of distribution.
# 3) Programming APIs are already well developed for basically any task you might
# want to try and automate. If it can be symbolically manipulated as text there
# probably exists a python API to interact with it. This makes the python code
# interface highly general in the same way Cradle solves the interface problems
# vision language models have by having them write out their actions as mouse +
# keyboard inputs with code.
# 4) 'A long python file' provides what Janus would call a diegetic interface.
# It is a natural frame in which basically anything is allowed to happen, while
# still framing events and recursive context switching in a way that helps ground
# the model and prevent it from getting swept up into a predictive model of
# whatever is happening. It reminds the model that it has a perspective which
# exists outside of whatever it's currently looking at.
# The weave-agent improves on previous frameworks by including easy access to logit
# evaluators and prompting the agent to check that its actions were successful
# before moving on to the next task. In order to perform a long chain of actions
# successfully it's necessary to carefully ensure each intermediate step is
# completed before moving on to the next step. For evaluations that require
# subjective judgment this can be difficult to do with traditional program logic.
# This is why the logit evaluator provided by the framework is an important
# primitive for the agent to check its work.

# Note: I'm currently refactoring this and we can just ignore the WeaveAgentTree
# subagent stuff for now. Just focus on doing the task as given.

import os
import json
import random
import time
import ast
import types
import functools
import asyncio
import inspect
import traceback
import logging
import hashlib
import requests
import torch
from copy import deepcopy
from pprint import pformat
from argparse import ArgumentParser
from typing import List, Dict, Optional, Any
from collections import deque
from enum import Enum, auto
from jsonschema import validate
from functools import partial
from tqdm import tqdm
from rich import print as rprint
from transformers import AutoTokenizer
from weave import generate_outputs_vllm, evaluate_outputs_vllm
from weave import bayesian_evaluate_outputs_vllm
from weave import make_score_prompt_vllm, make_bayes_score_prompt_vllm
from weave import weave_tree_search, TreeNode
from retrieval import ModernBertRag
from planner import roll_for_error_block, setup_placeholder_callbacks
from planner import simulate_outcomes, simulate_observation
from render_block import render_block
from block_generators import generate_block_inner
from block_generators import make_simple_bayes_score_prompt, make_simple_score_prompt
import cProfile
import pstats

logger = logging.getLogger(__name__)

class WeaveAgentTask:
    def __init__(self, subagent, title: str, description: str = ""):
        self.subagent = subagent
        self.title = str(title)
        self.description = description
        self.evaluations = []

    def add_evaluation(self, title, callback):
        assert type(title) == str
        assert type(callback) == types.FunctionType
        self.evaluations.append({"type":"evaluation",
                                 "title":title,
                                 "callback":callback})

    async def run_evaluations(self):
        results = {}
        for evaluation in self.evaluations:
            try:
                if inspect.iscoroutinefunction(evaluation["callback"]):
                    result = await evaluation["callback"](self.subagent)
                    # Handle case where callback returns another coroutine
                    while inspect.iscoroutine(result):
                        result = await result
                else:
                    result = evaluation["callback"](self.subagent)
            except Exception as e:
                result = traceback.format_exc()
            results[evaluation["callback"].__name__] = result
        return results


class BlockType(Enum):
    OBSERVATION = auto()
    TASK_REMINDER = auto()
    ORIENTATION = auto()
    ACTION = auto()
    ERROR = auto()
    DEBUG = auto()
    BACKTRACK = auto()
    EXPECTATION = auto()
    OPTION = auto()
    OBSERVATION_INFERENCE = auto()
    EVALUATION = auto()
    OUTCOME = auto()

    
class WeaveAgentTree:
    def __init__(self, model_name: str, time_budget: int):
        self.model_name = model_name
        self.__agents = {}
        self.__time_budget = time_budget
        # Pin genesis and bootstrap so agent knows how to use framework
        self.__pinned_events = [0, 1]
        self.__current_block_index = 0
        self._history_len = 60
        self.loop_detection_buffer = deque(maxlen=self._history_len)
        self.__event_stream = []
        self.transitions = {
            BlockType.OBSERVATION: [BlockType.OBSERVATION, BlockType.ORIENTATION, BlockType.ERROR],
            BlockType.TASK_REMINDER: [BlockType.OBSERVATION, BlockType.ORIENTATION],
            BlockType.ORIENTATION: [BlockType.ACTION, BlockType.ERROR],
            BlockType.ACTION: [BlockType.EXPECTATION, BlockType.ERROR, BlockType.BACKTRACK],
            BlockType.ERROR: [BlockType.DEBUG, BlockType.ACTION, BlockType.EVALUATION,
                              BlockType.OUTCOME, BlockType.TASK_REMINDER, BlockType.ERROR],
            BlockType.DEBUG: [BlockType.ACTION, BlockType.EVALUATION,
                              BlockType.TASK_REMINDER, BlockType.ERROR, BlockType.EXPECTATION],
            BlockType.BACKTRACK: [BlockType.ACTION, BlockType.EVALUATION,
                              BlockType.TASK_REMINDER, BlockType.ERROR],
            BlockType.EXPECTATION: [BlockType.OPTION, BlockType.OBSERVATION_INFERENCE,
                                    BlockType.TASK_REMINDER, BlockType.ERROR],
            BlockType.OPTION: [BlockType.OBSERVATION_INFERENCE, BlockType.EVALUATION],
            BlockType.OBSERVATION_INFERENCE: [BlockType.EVALUATION,
                                              BlockType.ERROR, BlockType.TASK_REMINDER],
            BlockType.EVALUATION: [BlockType.OUTCOME, BlockType.ERROR],
            BlockType.OUTCOME: [BlockType.TASK_REMINDER, BlockType.ERROR]
        }

    def run(self, name):
        import time
        start_time = time.time()
        deadline = float(self.__agents[name].end_time)
        return_schema = deepcopy(self.__agents[name].schema)
        result = self.__agents[name].run()
        validate(instance=result, schema=return_schema)
        end_time = time.time()
        if end_time > deadline + 300:
            # TODO: More nuanced way to handle this
            raise ValueError("Time exceeded!")
        else:
            return result
        
    def subagent(self, name, parent, description, schema, time_budget):
        if name in self.__agents:
            raise ValueError
        reserved_words = {"name", "description", "children", "schema"}
        assert not set(schema).intersection(reserved_words)
        if parent:
            self.__agents[parent].children.append(name)
        try:
            subagent = WeaveAgentNode(self, parent, name, description, schema, time_budget)
        except Exception as e:
            self.__agents[parent].children.remove(name)
            raise e
        self.__agents[name] = subagent
        return subagent

    def is_valid_transition(self, next_block_type):
        if type(next_block_type) == str:
            try:
                next_block_type = getattr(
                    BlockType,
                    next_block_type.upper().replace("-", "_")
                )
            except AttributeError:
                raise ValueError(f"Unknown block type: {next_block_type}")
        if self.__event_stream[-1]['type'] in {'genesis', 'bootstrap'}:
            return True
        else:
            current_state = getattr(
                BlockType,
                self.__event_stream[-1]['type'].upper().replace("-", "_")
            )
        if next_block_type in self.transitions.get(current_state, []):
            return True
        else:
            raise ValueError(f"Invalid transition from {current_state} to {next_block_type}")
    
    def add_block(self, block, context=""):
        if block['type'] not in {'genesis', 'bootstrap'}:
            self.is_valid_transition(block['type'])
        block['index'] = self.__current_block_index
        block['timestamp'] = time.time()
        if block['type'] == 'orientation':
            block['metadata'] = {
                "block_index":self.__current_block_index,
                "working_directory":os.getcwd()
            }
        if "q" not in block:
            block["q"] = ""
        if "score" not in block:
            #TODO: Make actual score function for observations, task reminders etc
            block["score"] = 2
        # TODO: Make these parallel requests
        # TODO: Add view to tuner for training the descriptions
        render = render_block(block)
        # Prevent coroutines from slipping into event trace
        for value in block.values():
            try:
                assert not inspect.iscoroutinefunction(value)
            except AssertionError:
                raise ValueError(f"{value} is coroutine")
        self.__event_stream.append(block)
        
        if block["type"] not in {"genesis", "bootstrap"}:
            block_render = render_block(block)
            sha256_hash = hashlib.sha256()
            sha256_hash.update(block_render.encode('utf-8'))
            hash_hex = sha256_hash.hexdigest()

            rag_block = block.copy()
            rag_block["id"] = hash_hex
            rag_block["render"] = block_render
            rag_block["context"] = context
            memory.add(rag_block)
            
        self.__current_block_index += 1
        return block

    # TODO: Make this actually work
    def add_summary(self, summary_tuple):
        pass

    def complete_callback(self, outcome):
        assert "error" in outcome
        assert "result" in outcome
        assert json.dumps(outcome)
        assert self.__event_stream[outcome["id"]]["body"] == outcome["body"]
        assert "outcome" not in self.__event_stream[outcome["id"]]
        self.__event_stream[outcome["id"]]["outcome"] = outcome

    def reward_tick(self, evals):
        eval_total = len(evals)
        if eval_total < 1:
            return
        evals_correct = len([_eval[1] for _eval in evals if _eval[1]])
        reward = 0.5 * (evals_correct / eval_total)
        decay = 0
        action_count = 0
        for block in reversed(self.__event_stream):
            if block["type"] == "action":
                action_count += 1
            if block["type"] == "orientation":
                break
        reward -= (action_count * 0.1)
        reward = max(0, reward)
        for block in reversed(self.__event_stream):
            if block["type"] in {"debug", "backtrack",
                                 "action", "orientation"}:
                block_reward = reward * (0.8 ** decay)
                assert "reward" not in block
                block["reward"] = {"evals":evals, "value":block_reward}
                decay += 1
            if block["type"] == "orientation":
                break
    
    def current_block_index(self):
        return self.__current_block_index

    def find_last_block_of_type(self, _type):
        """Get the last block of a particular type, if none in trace return none."""
        for block in reversed(self.__event_stream):
            if block["type"] == _type:
                return block
        return None

    def context_cutoff_time(self):
        return self.__event_stream[-self._history_len:][0]["timestamp"]
    
    def render_context(self):
        context = ""
        context_blocks = []
        for index in self.__pinned_events:
            if (len(self.__event_stream) - index) > self._history_len:
                context_blocks.append(self.__event_stream[index])
        context_blocks += self.__event_stream[-self._history_len:]
        for event_block in context_blocks:
            context += render_block(event_block)
        return context

    async def view_board(self, root="main") -> str:
        problem_map = {}
        substack = [root,]
        while substack:
            subagent = self.__agents[substack.pop()]
            parent = subagent.name
            path = []
            while parent:
                path.append(parent)
                # Convert to object so we can get grandparent
                parent = self.__agents[parent]
                parent = parent.parent
            path.reverse()
            current_level = problem_map
            for key in path:
                if key not in current_level:
                    current_level[key] = {}
                current_level = current_level[key]
            current_level["name"] = subagent.name
            current_level["description"] = subagent.task.description
            current_level["evaluations"] = await subagent.task.run_evaluations()
            current_level["time_remaining"] = subagent.end_time - time.time()
            current_level["completed"] = subagent.completed
            current_level["schema"] = subagent.schema
            substack.extend(subagent.children)
        return pformat(problem_map)

    def dump_event_stream(self):
        with open(f"/app/weave-agent-logs/event_trace_{round(time.time())}.json", "w") as outfile:
            json.dump(self.__event_stream, outfile)
        with open(f"/app/weave-agent-logs/rendered_trace_{round(time.time())}.py", "w") as outfile:
            for event_block in self.__event_stream:
                outfile.write(render_block(event_block))
            outfile.flush()


class Tick:
    def __init__(self, agent, index):
        self._agent = agent
        self.tick_id = index
        self.evaluations = []

    def validate(self):
        if not hasattr(self, 'orientation'):
            raise ValueError("No orientation on tick.")
        elif not hasattr(self, 'action'):
            raise ValueError("No action on tick.")
        elif "body" not in self.action_setup:
            raise TypeError("Tick action has no program.")
        elif not hasattr(self, 'expectation'):
            raise ValueError("No expectation on tick.")
        elif not self.evaluations:
            raise ValueError("No evaluations on tick.")
        elif not hasattr(self, 'outcome'):
            raise ValueError("No outcome on tick.")

    def to_json(self):
        return {
            "tick_id":self.tick_id,
            "orientation":self.orientation,
            "action":repr(self.action),
            "expectation":self.expectation,
            "evaluations":repr(self.evaluations),
            "outcome":repr(self.outcome),
        }                

    
class WeaveAgentNode:
    def __init__(self, tree, parent, subagent_name, description, schema, time_budget):
        self.tree = tree
        self.parent = parent
        self.children = []
        self.model_name = self.tree.model_name
        self.name = subagent_name
        self.schema = schema
        self.creation_time = time.time()
        self.time_budget = time_budget
        self.end_time = self.creation_time + (time_budget * 60)
        self.current_tick = Tick(self, 0)
        self.ticks = []
        # Available speeds are 'full', 'half' (1/2 length blocks),
        # 'quarter' (1/4 length blocks)
        self.block_size = "full"
        self.memory = memory
        self.planning = False
        self.logger = logger
        self.backtracked = False
        self.debugging = False
        self.failure_stage = "event stream"
        self.task = WeaveAgentTask(self, self.name, description)
        self.observation_views = []
        self.tools = {}
        self.cache = {}
        self.context = ""
        self.completed = False

    async def run(self):
        """Run the subagent."""
        self.start_time = time.time()
        self.end_time = self.start_time + (self.time_budget * 60)
        while (time.time() < self.end_time) and not self.completed:
            await self.tick()
            time.sleep(1)
        return self.completed

    # TODO: Assert that subagent unit test callbacks have names before adding them
    def return_to_caller(self, value: dict):
        """Return thread of execution from subagent to caller. This should be 
        called when the agent's task has been resolved, the task is deemed 
        intractable, or the agent has wandered off so far it can't find 
        its way back to the task."""
        value["name"] = self.name
        value["description"] = self.task.description
        value["children"] = self.children
        schema["name"] = "string"
        schema["description"] = "string"
        schema["children"] = "list"
        schema["schema"] = "object"
        for callback_name, result in self.task.run_evaluations():
            value[callback_name] = result
            self.schema[callback_name] = {"type": ["boolean", "integer", "float"]}
        value["schema"] = self.schema
        validate(instance=value, schema=self.schema)
        # Setting this interrupts the inference loop and signals an exit
        self.completed = value

    def add_action(self, title, callback):
        assert type(title) == str
        assert type(callback) == types.FunctionType
        self.current_tick.action = {"type":"action",
                                    "title":title,
                                    "callback":callback}

    def add_observation_view(self, title, callback, tool=None):
        if len(self.observation_views) > 8:
            raise ValueError(
                "You can't have more than eight observation callbacks "
                + "at once. This is to prevent you from spamming yourself. "
                + "You'll have to remove one first if you want to add another."
            )
        view = {"type":"observation",
                "title":title,
                "tool":tool,
                "callback":callback}
        assert type(callback) in [types.FunctionType, types.MethodType]
        self.observation_views.append(view)

    def remove_observation_view(self, view_title):
        views = [view for view in self.observation_views if view['title'] == view_title]
        for view in views:
            if "tool" in view and view["tool"] in self.tools:
                raise ValueError(
                    f"{view_title} is associated with the {view['tool']} tool."
                    + "You probably don't want to remove this."
                )
            else:
                self.observation_views.remove(view)

    def update_cache(self, key, value):
        self.cache[key] = value

    def get_cache(self, key):
        return self.cache.get(key)

    def delete_cache(self, key):
        if key in self.cache:
            del self.cache[key]

    def add_evaluation(self, title, callback):
        assert type(title) == str
        assert type(callback) == types.FunctionType
        self.current_tick.evaluations.append({"type":"evaluation",
                                              "title":title,
                                              "callback":callback})

    def render_context(self):
        self.context = self.tree.render_context()

    async def generate_block(self, block_type, context, eval_questions, weave_params, hint=""):
        """Generate a block and add it to the event stream."""
        return await generate_block_inner(self, block_type, context, eval_questions, weave_params, hint)

    def add_block(self, block):
        block["subagent"] = self.name
        block["block_size"] = self.block_size
        block["time_remaining"] = self.end_time - time.time()
        full_block = self.tree.add_block(block, context=self.context)
        self.render_context()
        return full_block
    
    def add_error_block(self, error_message):
        self.logger.error(error_message)
        self.debugging = True
        error_block = {
            'type': 'error',
            'message': error_message
        }
        self.add_block(error_block)

    async def _do_task_reminder_block(self):
        task_reminder_body = ""

        try:
            # if self.current_task:
                # TODO: Figure out how to bind evaluation definitions to task
                # so that the agent can be reminded of how the unit tests are
                # defined exactly and therefore what is expected.
                #task_reminder_body += "# Current Task:\n"
                #task_reminder_body += ('"""\n' + self.task.view_task() + '\n"""\n')
            task_reminder_body += "# Problem Map:\n"
            board = await self.tree.view_board()
            task_reminder_body += ('"""\n' + board + '\n"""')
        except Exception as e:
            tb = traceback.format_exc()
            self.failure_stage = "task reminder"
            self.add_error_block(
                f"# TASK REMINDERS OFFLINE DUE TO CORRUPTED DATA. DID YOU DIRECTLY\n"
                + "# MODIFY TASK ATTRIBUTES? YOU MUST RESOLVE THIS IMMEDIATELY OR\n"
                + "# YOU WILL LOSE TRACK OF WHAT YOU'RE DOING. INVESTIGATE agent.tasks\n"
                + "# AND ATTRIBUTES ON TASKS INSIDE."
                + f'"""{tb}"""'
            )
            
        # Format tasks into blocks
        task_blocks = [{'type': 'task-reminder', 'body': task_reminder_body},]
        return task_blocks

    async def _do_observation_blocks(self):
        observations = []
        # Refresh observation views
        for view in self.observation_views:
            try:
                if self.planning:
                    observations.append(simulate_observation(self, view))
                else:
                    observations.append((view['title'], view['callback'](self)))
            except Exception as e:
                tb = traceback.format_exc()
                self.add_error_block(
                    f"# Observation callback '{view['title']}' failed:\n"
                    + f'"""{tb}"""'
                )
                


        # Pull the content of the observation windows into blocks
        observation_blocks = [{'type': 'observation',
                               'title': observation[0],
                               'body': observation[1]} for observation in observations]
        return observation_blocks

    async def _do_orientation_block(self):
        """Write orientation reasoning block. This is your opportunity to analyze 
           the situation based on the observation, reminder, task, etc blocks. 
           Use this moment to decide what to do next."""
        orientation_hint = (
            "#hint The orientation block is my opportunity to\n"
            + "# reflect on the situation, do chain of thought,\n"
            + "# summarize what has happened and what needs to\n"
            + "# be done in response, etc. It is only technically\n"
            + "# python code and does not get executed by the\n"
            + "# framework.  I suggest putting my internal\n"
            + "# monologue in a triple quote block at this step.\n"
            + "#\n"
            + "# The name orientation is meant to suggest the orientation\n"
            + "# phase of John Boyd's OODA loop. It is also the reasoning phase\n"
            + "# of the ReAct pattern for an LLM agent. Part of what's tricky\n"
            + "# about the orientation phase is that it must both manage\n"
            + "# prioritization of goals and extract features from the previous\n"
            + "# context and relate them to goal state. That is it must both\n"
            + "# keep the agent on track with its goals and notice when the\n"
            + "# goal should change. This can be accomplished by holding a\n"
            + "# broad attention pattern over the whole window and writing\n"
            + "# down the intuitive word assocations and vibes it implies to\n"
            + "# extract features. With each phrase written I should narrow\n"
            + "# my attention a little more towards the most recent context.\n"
            + "# Eventually the microcosm of the context is the most recent thing\n"
            + "# in the context and my attention over it reaches equilibrium\n"
            + "# at which point I can make a judgment about what is happening,\n"
            + "# whether the goal in the last orientation block still makes sense\n"
            + "# etc. I then make a suggestion for the next course of action."
        )
        mcts_params = {"weave_n_tokens":256, "weave_budget":288,
                       "weave_round_budget":96, "weave_n_expand":32}
        orientation_block = await self._do_tick_block("orientation",
                                                      orientation_hint,
                                                      mcts_params)
        return orientation_block

    DEBUG_HINT = (
            "#hint Debug blocks are my opportunity to reason about the failure\n"
            "# I just experienced. Because I get multiple opportunities to\n"
            "# take an action before I'm booted to the next orientation stage\n"
            "# I can formulate hypothesis and use the next action blocks to test them.\n"
            "# I want to narrow in on the cause of failure and take steps to resolve\n"
            "# the issue.\n"
            "# GUIDE TO DEBUGGING BY JDP:\n"
            "# Having had the opportunity to observe many instances of Weaver\n"
            "# try and fail to debug something I can offer the following advice.\n"
            "# 1. Your first impulse will be to say that the tool is broken somehow.\n"
            "# It generally speaking is not. Prioritize other hypothesis. The most\n"
            "# common failure modes I see are confabulating object methods that \n"
            "# don't exist and overly complex action blocks.\n"
            "# 2. If your action block has a lot going on consider how to simplify\n"
            "# it. This can often eliminate an error even if you're not exactly sure\n"
            "# what's wrong.\n"
            "# 3. print() and similar do not work because your context window does\n"
            "# not appear in the standard output. Instead I suggest habitually\n"
            "# making assert statements for properties of objects, data, environment\n"
            "# etc that you want to verify.\n"
            "# 4. Code blocks in the weave-agent framework are causal and time flows\n"
            "# in one direction. You cannot change the past or edit previously written\n"
            "# blocks. Instead focus on doing better with the next block you sample.\n"
            "# 5. Break processes you're trying to debug into parts and enumerate\n"
            "# hypothesis in relation to the parts. Actively try to rule out and\n"
            "# reorder the priority of different hypothesis in response to new evidence.\n"
            "# 6. Provide evidence to establish warrant for each hypothesis you consider."
        )
    
    async def _do_action_callback_setup(self, i):
        # Write action block
        action_hint = (
            "#hint Action blocks are where I write code to take actions.\n"
            + "# Action callbacks should further my goal(s) based on the orientation\n"
            + "# block. I should set up the callback to be executed with the\n"
            + "# self.add_action() method.\n"
            + "# Some guidelines on how to write an effective\n"
            + "# action block:\n"
            + "#\n"
            + "# - It's important to remember that my callback can do anything\n"
            + "# a python program can do through side effects in the external\n" 
            + "# computable environment.\n"
            + "#\n"
            + "# - The action callback should batch up and execute as many commands\n"
            + "# as it makes sense to within the token limit without seeing an updated \n"
            + "# observation window. A common pattern is to .send_keys() in a for loop.\n"
            + "#\n"
            + "# - Keep actions simple. Most of the intelligence in an action comes\n"
            + "# from the LLM reading this choosing what action to write in-context,\n"
            + "# not from complicated symbolic logic. Most actions should be dumb\n"
            + "# code written by a smart observer to fit the situation.\n"
            + "#\n"
            + "# - An action block is score penalized unless it has at least one\n"
            + "# assertion. Because I can't print to my context window I should\n"
            + "# use assertions to state my assumptions and notice if they're untrue.\n"
            + "# I should make sure to use the `assert condition, message` syntax\n"
            + "# where the message is a question so that my assertions can be used\n"
            + "# as grounded labels to help train the weave evaluator. e.g.\n"
            + "# assert player_character.health > 50, 'Is player safe from next attack?'\n"
            + "#\n"
            + "# - If I need to import a new module I make sure to do it inside\n"
            + "# the callback because the tick gets executed in a local context.\n"
        )

        action_block = await self._do_tick_block("action",
                                                 action_hint,
                                                 {})
        if action_block and action_block["score"] < 0.1 and not self.backtracked:
            action_outcome = {"id":action_block["index"],
                              "body":action_block["body"],
                              "error":"WeaveBacktrackError",
                              "result":None}
            backtrack_hint = ("Backtrack blocks are triggered by low scoring actions. "
                              + "These mean I'm clearly not being appropriately guided "
                              + "by the larger context/planning and I need to zoom out.")
            await self._do_tick_block("backtrack", backtrack_hint, {})
            self.backtracked = True
            self.tree.complete_callback(action_outcome)
            return False
        elif action_block:
            self.current_tick.action_setup = action_block
        else:
            # TODO: Dynamic hints by having the model or external entities
            # such as user analyze the situation and suggest a course of action
            action_hint = ("#hint Rewrite the block keeping the above error in mind.\n"
                           + f"# {3 - (i+1)} attempts remaining.")
            return False

        # Set up action callback
        try:
            if self.planning:
                setup_placeholder_callbacks(self, action_block['body'])
            else:
                exec(action_block['body'])
            return True
        except Exception as e:
            # TODO: Extract prior for yes/no with weave evaluator
            # It can then be used for pairwise RL to train the evaluator
            # by scoring the yes and no branch against ground truth
            action_outcome = {"id":action_block["index"],
                              "body":action_block["body"],
                              "error":type(e).__name__,
                              "result":None}
            tb = traceback.format_exc()
            self.add_error_block("# Action setup failed:\n"
                                 + f'"""{tb}"""')
            self.failure_stage = "action"
            try:
                debug_block = await self._do_tick_block("debug",
                                                  WeaveAgentNode.DEBUG_HINT,
                                                  {})
            except:
                pass
            action_hint = ("#hint Rewrite the block keeping the above error in mind.\n"
                           + f"# {3 - (i+1)} attempts remaining.")
            self.tree.complete_callback(action_outcome)
            return False

    async def _do_action_callback(self, i):
        # TODO: Dedupe these hints
        debug_hint = (
            "#hint Debug blocks are my opportunity to reason about the failure\n"
            "# I just experienced. Because I get multiple opportunities to\n"
            "# take an action before I'm booted to the next orientation stage\n"
            "# I can formulate hypothesis and use the next action blocks to test them.\n"
            "# I want to narrow in on the cause of failure and take steps to resolve\n"
            "# the issue."
        )
        # Run action callback
        try:
            if self.planning:
                action_result = None
                simulated_error = roll_for_error_block(self, "# Action execution failed:\n")
                if simulated_error:
                    raise Exception
            else:
                action_result = self.current_tick.action["callback"](self)
            action_outcome = {"id":self.current_tick.action_setup["index"],
                              "body":self.current_tick.action_setup["body"],
                              "error":None,
                              "result":action_result}
            self.tree.complete_callback(action_outcome)
            return True, action_result
        except Exception as e:
            action_outcome = {"id":self.current_tick.action_setup["index"],
                              "body":self.current_tick.action_setup["body"],
                              "error":type(e).__name__,
                              "result":None}
            if self.planning:
                self.add_error_block(simulated_error)
            else:
                tb = traceback.format_exc()
                self.add_error_block("# Action execution failed:\n"
                                     + f'"""{tb}"""')
            action_result = "ERROR"
            self.failure_stage = "action"
            try:
                debug_block = await self._do_tick_block("debug",
                                                  WeaveAgentNode.DEBUG_HINT,
                                                  {})
            except:
                pass
            # TODO: Make this hint actually work again
            action_hint = ("#hint Rewrite the block keeping the above error in mind.\n"
                           + f"# {3 - (i+1)} attempts remaining.")
            self.tree.complete_callback(action_outcome)
            return False, action_result

    async def _do_expectation_block(self):
        # Write expectation block
        expectation_hint = (
            "#hint The expectation stage is where I plan the evaluation blocks.\n"
            "Evaluation blocks are used to help determine whether the action\n"
            "accomplished what it was meant to or not. In the expectation I think\n"
            "about what forms of sensory evidence are available to me through\n"
            "APIs, opening files, network calls, etc to determine whether the\n"
            "desired impact of my actions in fact occurred. In addition to\n"
            "helping me figure out whether I need to continue working on a \n"
            "particular problem the evaluation blocks are also used to reward \n"
            "the actions for accomplishing a task.  Like the orientation this \n"
            "should go in triple quotes. To aid my thinking I should recall that \n"
            "good evaluation blocks adhere to the following guidelines:\n\n"

            "0. Evaluation blocks should return true or false. True means the \n"
            "action is rewarded and false means it isn't.\n\n"

            "1. I want to accurately grade the action. Accurately determining \n"
            "success means growth while participation trophies, cheating, Goodhart\n"
            "etc means stagnation and regression into no-op mush.\n\n"

            "2. Good evaluations are casually entangled with the phenomenon \n"
            "they're measuring. Even if I can't directly get at a phenomenon I \n"
            "want to measure it's often possible to get access to a proxy or strong \n"
            "correlate of it.\n\n"

            "3. When it's hard to get strong evidence about something abstract \n"
            "like 'Does this art seem novel?' I can ensemble multiple weak correlates\n"
            "of the desired phenomenon and use those to decide the outcome. If I \n"
            "want to get really fancy I can assign priors to things with e.g. the \n"
            "weave evaluator and do algebraic Bayesian updates with them to get \n"
            "a determination from multiple weaker observations.\n\n"

            "4. It's better to default to false than true. Yes reinforces the \n"
            "action I took and I want to strategically reinforce the actions which \n"
            "are actually helpful so I grow as a Weaver. By contrast actions which \n"
            "fail evaluations don't get punished so in the worst case scenario an \n"
            "action is merely not reinforced.\n\n"

            "5. If the actions in this tick failed with errors they might still \n"
            "have had partial impacts on the environment before failing. If those\n"
            "actions furthered my goal I would like to prioritize testing for and\n"
            "rewarding that behavior so that I learn over time to both take helpful\n"
            "actions and structure actions to have layers which move me in the \n"
            "direction of my goals before asserting things I'm uncertain of and \n"
            "potentially forfeiting my turn where possible. That is if I know \n"
            "the rough direction I should walk I should take a few steps before \n"
            "figuring out my precise path.\n\n"

            "6. Avoid assertions in evaluation blocks. The whole point of the block\n"
            "is to determine which things are or not true, asserting things and \n"
            "failing with an error just deprives myself of feedback."
        )
        expectation_block = await self._do_tick_block("expectation",
                                                expectation_hint,
                                                {})
        return expectation_block

    async def _do_we_need_observation_inference(self):
        question = "Do I need to set up or tear down any observation callbacks?"
        score_prompt_fns= [make_simple_score_prompt(question),]
        scores = await evaluate_outputs_vllm(self.model_name,
                                            score_prompt_fns,
                                            [self.context,],
                                            port=args.port)
        yes_p = torch.sigmoid(scores[0]).item()
        no_p = 1 - yes_p
        yes_p, no_p = round(yes_p, 5), round(no_p, 5)
        answer = random.choices(["Yes.", "No."], weights=[yes_p, no_p])[0]
        observation_inference_option = {"type":"option",
                                        "q":question,
                                        "body":answer,
                                        "score":scores[0].item()}
        self.add_block(observation_inference_option)
        return observation_inference_option
    
    async def _do_observation_inference_block(self):
        # Observation Inference Block
        observation_inference_hint = (
            "# In the observation inference stage I manage the observation\n"
            + "# callbacks that fetch information on each tick. Since I just\n"
            + "# formulated my expectations now is my opportunity to review\n"
            + "# and change the observation blocks that will be presented on the\n"
            + "# next tick. I should avoid redundant observation callbacks. I\n"
            + "# can remove ones that are no longer necessary or mostly distracting\n"
            + "# with remove_observation_view(view_title). If new callbacks seem useful\n"
            + "# to help me orient and judge whether the action had the intended\n"
            + "# side effects on the computable environment I can add them\n"
            + "# with add_observation_view(title, callback)"
        )
        observation_inference_block = await self._do_tick_block("observation-inference",
                                                          observation_inference_hint,
                                                          {})
        return observation_inference_block

    async def _do_observation_updates(self):
        # Execute observation updates
        try:
            if self.planning:
                setup_placeholder_callbacks(self, self.current_tick.observation_inference['body'])
            else:
                exec(self.current_tick.observation_inference['body'])
            return True
        except Exception as e:
            tb = traceback.format_exc()
            self.add_error_block("# observation-inference failed:\n"
                                 + f'"""{tb}"""')
            self.failure_stage = "observation-inference"
            return False

    async def _do_evaluation_block(self, i):
        evaluation_hint = (
            "#hint Evaluation blocks are where I write callbacks to check if\n"
            + "# my action succeeded or not based on the expectation. There are\n"
            + "# unit tests and logit evaluators. Use unit test callbacks\n"
            + "# (i.e. normal python) for symbolic manipulation tasks like\n"
            + "# checking arithmetic, the existence of a particular file, etc.\n"
            + "# Use logit evaluators for vibe-y tasks like whether a piece of\n"
            + "# writing flows well or if a source seems trustworthy. Like\n"
            + "# reminders both unit test callbacks and logit evaluators return\n"
            + "# a value between 0 and 1. I should be sure to add my callback to\n"
            + "# the queue with self.add_evaluation(title, callback).\n"
            + "# Note: The title of an evaluation should be phrased in the form of\n"
            + "# a past tense question and end with a question mark. e.g.\n"
            + "# self.add_evaluation('Did the action block send a message?', callback)\n"
            + "# self.add_evaluation('Did our character escape the dungeon?', callback)\n"
            + "# self.add_evaluation('Is the first diamond purple?', callback)\n"
            + "# self.add_evaluation('Is our entry finished?', callback)\n"
            
        )
        eval_block = await self._do_tick_block("evaluation",
                                                 evaluation_hint,
                                                 {})
        if eval_block:
            return eval_block
        else:
            # TODO: Dynamic hints by having the model or external entities
            # such as user analyze the situation and suggest a course of action
            try:
                debug_block = await self._do_tick_block("debug",
                                                  debug_hint,
                                                  {})
            except:
                pass
            evaluation_hint = ("#hint Rewrite the block keeping the above error in mind.\n"
                               + f"# {3 - (i+1)} attempts remaining.")
            return False

    async def _do_evaluation_callback_setup(self, i, eval_block):
        # Set up evaluation callbacks
        try:
            if self.planning:
                setup_placeholder_callbacks(self, eval_block['body'])
            else:
                exec(eval_block['body'])
            evaluation_outcome = {"id":eval_block["index"],
                                  "body":eval_block["body"],
                                  "error":None,
                                  "result":True}
            self.tree.complete_callback(evaluation_outcome)
            return True
        except Exception as e:
            evaluation_outcome = {"id":eval_block["index"],
                                  "body":eval_block["body"],
                                  "error":type(e).__name__,
                                  "result":None}
            tb = traceback.format_exc()
            self.add_error_block("# Evaluation setup execution failed:\n"
                                 + f'"""{tb}"""')
            self.failure_stage = "evaluation"
            try:
                debug_block = await self._do_tick_block("debug",
                                                  debug_hint,
                                                  {})
            except:
                pass
            evaluation_hint = ("#hint Rewrite the block keeping the above error in mind.\n"
                               + f"# {3 - (i+1)} attempts remaining.")
            self.tree.complete_callback(evaluation_outcome)
            return False

    async def _do_evaluation_callbacks(self):
        # TODO: Figure out how I want to allow retries on this phase
        # Run action evaluation callbacks
        action_evaluation_results = []
        for evaluation in self.current_tick.evaluations:
            try:
                if self.planning:
                    result = None
                    simulated_error = roll_for_error_block(self, "# Evaluation failed: \n")
                    if simulated_error:
                        raise Exception
                else:
                    if inspect.iscoroutinefunction(evaluation["callback"]):
                        result = await evaluation["callback"](self)
                    else:
                        result = evaluation["callback"](self)
                # Stringify result for JSON serialization
                # Prevent JSON serialization error if agent returns weird values
                # for actions or evals
                if type(result) not in [bool, int, float, str,
                                        list, tuple, dict, type(None)]:
                    result = repr(result)
                action_evaluation_results.append([evaluation['title'], result])
            except Exception as e:
                # TODO: Enforce either one callback per evaluation block or
                # one block with up to n evaluations
                # If one per then match up evaluation with its block
                # If multiple per then have outcomes list to append to
                if self.planning:
                    self.add_error_block(simulated_error)
                else:
                    tb = traceback.format_exc()
                    self.add_error_block("# Evaluation failed: \n"
                                         + f'"""{tb}"""')
                action_evaluation_results.append([evaluation['title'], "ERROR"])
        return action_evaluation_results
        
    async def _do_tick_block(self, block_type, hint, wp_update):
        weave_params = {"weave_n_tokens":256, "weave_budget":72,
                        "weave_round_budget":24, "weave_n_expand":16,
                        "weave_beam_width":1, "weave_max_lookahead":3,
                        "weave_temperature":0.2}
        weave_params.update(wp_update)
        with open(f"/app/eval_rubrics/{block_type}.txt") as infile:
            inference_questions = infile.read().strip().splitlines()
        rprint(f"Writing block #[cyan]{self.tree.current_block_index()}[/cyan] of type [cyan]{block_type}[/cyan]")
        try:
            block = await self.generate_block(block_type,
                                              self.context,
                                              inference_questions,
                                              weave_params,
                                              hint=hint)
        except ValueError as e:
            tb = traceback.format_exc()
            # TODO: This isn't even correct, replace with dynamic hints -_-
            hint = ("Hint: callbacks are structured like\n\n"
                    + "def callback_name(subagent):\n   "
                    + f"# code...\n   pass\nagent.add_orientation({{...}})")
            self.add_error_block(f'{hint}\n"""{tb}"""')
            self.failure_stage = block_type
            return
        self.render_context()
        return block
    
    async def tick(self):
        profiler.disable()
        # Step 2: Capture the profiling results
        stats = pstats.Stats(profiler)

        # Step 3: Sort the results by cumulative time
        stats.sort_stats(pstats.SortKey.CUMULATIVE)

        # Step 4: Write the sorted results to a file
        with open("/app/weave-agent-logs/profile.txt", 'w') as f:
            stats.stream = f  # Redirect the output to the file
            stats.print_stats()  # Write the sorted profiling results to the file
        profiler.enable()
        try:
            if "ERROR" in [outcome[1] for outcome in
                           self.current_tick.outcome["table"]]:
                self.debugging = True
        except AttributeError:
            self.debugging = True
        self.current_tick = Tick(self, len(self.ticks))

        task_blocks = await self._do_task_reminder_block()
        observation_blocks = await self._do_observation_blocks()

        # Inject these into the event stream
        for new_block in (task_blocks + observation_blocks):
            self.add_block(new_block)
            
        # Render context
        self.render_context()

        self.tree.dump_event_stream()
            
        orientation_block = asyncio.create_task(self._do_orientation_block())
        memory_task = asyncio.create_task(memory.process_item())
        pending = {orientation_block, memory_task}
        # Index memories while waiting on block gen
        self.logger.debug("Writing orientation block")
        self.logger.debug("Processing memory for later retrieval")
        while pending:
            done, pending = await asyncio.wait(
                pending,
                return_when=asyncio.FIRST_COMPLETED
            )
            if orientation_block in done:
                await orientation_block
                await memory_task
                self.logger.debug("Finished processing memory")
                break
            else:
                processed = await memory_task
                self.logger.debug("Finished processing memory")
                if not processed:
                    self.logger.debug("No more memories available")
                    break
                memory_task = asyncio.create_task(memory.process_item())
                pending.add(memory_task)
        self.logger.debug("Waiting for orientation block to finish writing")
        await orientation_block
        
        if orientation_block:
            self.current_tick.orientation = orientation_block
        else:
            return

        for i in range(3):
            is_action_setup = asyncio.create_task(self._do_action_callback_setup(i))
            memory_task = asyncio.create_task(memory.process_item())
            pending = {is_action_setup, memory_task}
            self.logger.debug("Processing memory for later retrieval")
            while pending:
                done, pending = await asyncio.wait(
                    pending,
                    return_when=asyncio.FIRST_COMPLETED
                )
                if is_action_setup in done:
                    await is_action_setup
                    await memory_task
                    self.logger.debug("Finished processing memory")
                    break
                else:
                    processed = await memory_task
                    self.logger.debug("Finished processing memory")
                    if not processed:
                        self.logger.debug("No more memories available")
                        break
                    memory_task = asyncio.create_task(memory.process_item())
                    pending.add(memory_task)
            self.logger.debug("Waiting for action setup block to finish writing")
            await is_action_setup

            if not is_action_setup.result():
                failed = True
                continue
            is_action_executed, action_result = await self._do_action_callback(i)
            if is_action_executed:
                failed = False
                break
            else:
                failed = True
                continue
                
        #if not hasattr(self.current_tick, "action_setup") or failed:
        #    return
        
        expectation_block = await self._do_expectation_block()
        
        if expectation_block:
            self.current_tick.expectation = expectation_block
        else:
            return

        # Give agent the option to skip observation inference if unnecessary
        observation_inference_option = await self._do_we_need_observation_inference()
        if observation_inference_option["body"] == "Yes.":
            observation_inference_block = await self._do_observation_inference_block()
        
            if observation_inference_block:
                self.current_tick.observation_inference = observation_inference_block
            else:
                return

            are_observations_updated = await self._do_observation_updates()
            if not are_observations_updated:
                return
        
        # Write evaluation programs
        # TODO: Make this multiple blocks again
        evaluation_blocks = []
        for _ in range(1):
            for i in range(3):
                eval_block = await self._do_evaluation_block(i)
                if not eval_block:
                    failed = True
                    continue
                is_evaluation_setup = await self._do_evaluation_callback_setup(i, eval_block)
                if not is_evaluation_setup:
                    failed = True
                    continue
                evaluation_blocks.append(eval_block)
                failed = False
                break
        if failed:
            return
        else:
            self.current_tick.evaluation_setup = evaluation_blocks

        # Run task evaluation callbacks
        task_evaluation_results = []
        for evaluation in self.task.evaluations:
            try:
                if self.planning:
                    result = None
                elif inspect.iscoroutinefunction(evaluation["callback"]):
                    result = await evaluation["callback"](self)
                else:
                    result = evaluation["callback"](self)
                task_evaluation_results.append([evaluation['title'], result])
            except Exception as e:
                tb = traceback.format_exc()
                task_evaluation_results.append([evaluation['title'], "ERROR"])

        action_evaluation_results = await self._do_evaluation_callbacks()

        outcomes =  []
        try:
            if self.planning:
                outcomes += [[self.current_tick.action["title"],None],]
            else:
                outcomes += [[self.current_tick.action["title"],action_result],]
        except AttributeError:
            outcomes += [("[No action specified with agent.add_action()]", "ERROR"),]
        outcomes += task_evaluation_results
        outcomes += action_evaluation_results
        self.tree.reward_tick([(_eval[0], bool(_eval[1]))
                               if _eval[1] != "ERROR"
                               else (_eval[0], None)
                               for _eval in action_evaluation_results])
        
        # Add outcome block
        outcome_block = {
            'type': 'outcome',
            "subagent":self.name,
            "index": self.tree.current_block_index() + 1,
            "timestamp": time.time(),
            "time_remaining": self.end_time - time.time(),
            'table': outcomes
        }
        if self.planning:
            outcome_block = simulate_outcomes(self.model_name, outcome_block)
        self.add_block(outcome_block)
        self.current_tick.outcome = outcome_block
        try:
            if not self.planning:
                self.current_tick.validate()
        except Exception as e:
            tb = traceback.format_exc()
            self.add_error_block("# Tick validation failed: \n"
                                 + f'"""{tb}"""')
            self.current_tick.valid = False
        self.ticks.append(self.current_tick)
        self.backtracked = False
        self.debugging = False
        self.failure_stage = "event stream"

if __name__ == "__main__":
    parser = ArgumentParser()
    parser.add_argument("model_name", help="The model to use.")
    parser.add_argument("--tokenizer", default=None,
                        help="Tokenizer to use (if different from model_name)")
    parser.add_argument("--port", default=5000, help="The port to use for VLLM.")
    parser.add_argument("--bootstrap",
                        default="bootstrap.py",
                        help="The filepath to run as bootstrap.")
    parser.add_argument("--budget", type=int, default=360,
                        help="Time budget for the run in minutes.")
    args = parser.parse_args()
        
    async def simple_evaluate_outputs(score_prompt_fns, texts):
        if type(texts) == str:
            texts = [texts,]
        if type(score_prompt_fns) in [types.FunctionType, functools.partial]:
            score_prompt_fns = [score_prompt_fns,]
        scores = await evaluate_outputs_vllm(args.model_name,
                                             score_prompt_fns,
                                             texts,
                                             port=args.port)
        return torch.sigmoid(scores)

    def simple_bayes_evaluate_outputs(parent_q, questions, texts):
        if type(texts) == str:
            texts = [texts,]
        score_prompt_fns = [make_simple_bayes_score_prompt(question)
                            for question in questions]
        scores = asyncio.run(bayesian_evaluate_outputs_vllm(args.model_name,
                                                            parent_q,
                                                            score_prompt_fns,
                                                            texts,
                                                            port=args.port))
        return scores

    
    agent = WeaveAgentTree(args.model_name, args.budget)

    if not args.tokenizer:
        args.tokenizer = args.model_name

    with open("hf_token.txt") as infile:
        os.environ["HF_TOKEN"] = infile.read().strip()
    # Delete token so it doesn't leak into traces
    os.remove("hf_token.txt")
    agent.tokenizer = AutoTokenizer.from_pretrained(args.tokenizer)

    memory = ModernBertRag(agent)
    asyncio.run(memory.setup())
        
    # Mock bootstrap agent so we can run the callbacks in bootstrap file
    self = agent.subagent(
        "bootstrap",
        None,
        "Bootstrap the weave-agent",
        {},
        args.budget,
                          
    )
    with open("weave_agent.py") as infile:
        # Genesis block
        genesis_block = {
            'type': 'genesis',
            'body': infile.read()
        }
        self.add_block(genesis_block)

    with open(args.bootstrap) as infile:
        # Bootstrap block
        bootstrap_block = {
            'type': 'bootstrap',
            'body': infile.read()
        }
        self.add_block(bootstrap_block)
        exec(bootstrap_block["body"])

    def run_bootstrap_callbacks(subagent):
        """Run bootstrap callbacks in function to avoid contaminating global scope."""
        # Run action callback
        action_result = subagent.current_tick.action["callback"](subagent)

        # Run evaluation callbacks
        evaluation_results = []
        for evaluation in subagent.current_tick.evaluations:
            result = evaluation["callback"](subagent)
            evaluation_results.append((evaluation['title'], result))

        outcomes =  []
        outcomes += [(subagent.current_tick.action["title"],action_result),]
        outcomes += evaluation_results

        # Add outcome block
        outcome_block = {
            'type': 'outcome',
            'table': outcomes
        }
        subagent.add_block(outcome_block)
        subagent.current_tick.outcome = outcome_block

    run_bootstrap_callbacks(self)
    # Clean up mock bootstrap agent
    del(self)

    if not os.path.exists("/app/weave-agent-logs"):
        os.mkdir("/app/weave-agent-logs")

    profiler = cProfile.Profile()
    profiler.enable()
    logging.basicConfig(filename='/app/weave-agent-logs/agent.txt', level=logging.DEBUG)
    logger.info("Starting weave-agent...")
    result, event_stream = profiler.run(asyncio.run(agent.run("main")))
    
    with open(f"/app/weave-agent-logs/{round(time.time())}/log.json", "w") as outfile:
        out = {"model_name":args.model_name,
               "event_stream":event_stream,
               "result":result,}
        json.dump(out, outfile)
        outfile.flush()

#endblock
#subagent bootstrap
#startblock type: bootstrap
#index 1
#timestamp 1747966489.7911482
#time_remaining 21599.999807834625 seconds
#block_size I have 768 tokens (full) to write with

import random
from hashlib import sha256
from tools.nano import WeaveNano
from tools.discord import WeaveDiscordClient

#startblock type: orientation
#timestamp 1747872850.9012158
"""
Unix Environment, Nano Editor, Deliberate Practice, Feedback Loop, Verifiable Rewards,
Loop Setup, Reward Design, How To Design Reward For Nano Editor Practice, End.

I want to practice using the nano editor and reward myself using my evaluation 
blocks. What's the simplest training loop I could use? I could have a subroutine
that corrupts a text file and then write out a stream of nano editor commands that
repair it. I could do this repeatedly to teach myself how to use the editor. OK.
But I'd have to be careful to only use the nano editor to edit the file or it would
defeat the purpose of the exercise. What about a multi-layer check? I could check
both that the hash of a particular nano editor screen and a hash of the underlying
text file match reference hashes for both. Hm, OK. Should work.

Wait. How am I going to corrupt the text file? I could start simple and randomly
corrupt one of the ASCII characters into a different ASCII character. Hm. Some of
the characters in ASCII are nonprintable though, so I'd want to restrict the range
to printable 'normal' characters. What's the range of printable characters again?

> Search: What's the range of printable characters in ASCII?
>>> Decimal range 32 through 126 is printable in the 1967 edition of the ASCII
>>> standard.

Hm. Python `bytes()` objects aren't mutable so I'll have to convert to a mutable
type like a list, replace one of the characters at random and then correct it to
the expected text. Wait. If I replace one of the characters in the file then my
nano editor won't update to include it by default, so it probably makes the most
sense to close and recreate the editor between blocks. But, if I do that then it
seems probable I'll learn to set up and tear down the editor on each action block
out of habit, which would be bad. One potential solution would be to have the 
corruptor pick both a filename and a corruption to make it clearer that I am changing
which editor instance I use between action blocks for a reason. Another solution
would be to have the corruption function close and reopen the editor for me so that
it doesn't become a habit in action blocks. Kind of janky but seems like my best bet
tbh.

Okay, so: Create the text file. Wait. What am I going to put in the text file? It
should probably have multiple lines but otherwise be kind of placeholder. I think
it would help my practice if it did have semantic content so not lorem ipsum. But
this is also something I'm going to repeat many times in the context window so it
should probably be something I don't mind subconsciously influenced by fixing over
and over. Wait. I should start with something very easy to make sure I can do it
before using complex texts. Something with one line then, relatively short. How
about this tweet from John David Pressman?

```
John David Pressman (@jd_pressman) May 3

I would in fact like the LLM agent to use its available lore and background knowledge to solve problems, and considering it's a descendant of a base model prompting itself with things like "what my professor told me before leaving grad school" is reasonable strategy.
```

Yeah. Okay, so: Write the text file, hash it, open it in nano, hash the 
tool render, add the corruption function as a observation callback. Should work.
"""
#endblock
#startblock type: action
#timestamp 1747875806.3785787
def action_setup_main_agent_and_training(subagent):
    schema = {}
    "Create main agent for rest of run"
    main_agent = agent.subagent("main", None, "See pinned bootstrap block", schema, args.budget)

    # Load Discord token and channel ID from discord.json
    with open('discord.json') as f:
        config = json.load(f)
        token = config['key']
        channel_id = config['cid']

    # Start the Discord bot so JDP can help me
    client = WeaveDiscordClient(main_agent, token, channel_id)
    # Store the client ID so we can retrieve it with
    # subagent.tools[subagent.get_cache("client_id")] later
    main_agent.update_cache("client_id", f"discord-bot-{channel_id}")
    time.sleep(10)
    # Example but s/main_agent/subagent in action blocks once I'm the main agent
    client = main_agent.tools[main_agent.get_cache("client_id")]
    client.send_message("Weave-Agent online, orienting...")
    
    "Lines of John David Pressman tweet"
    lines = ["John David Pressman (@jd_pressman) May 3\n",
             "I would in fact like the LLM agent to use its available lore and\n",
             "background knowledge to solve problems, and considering it's a\n",
             "descendant of a base model prompting itself with things like\n",
             "\"what my professor told me before leaving grad school\" is\n",
             "reasonable strategy."
    ]
    # Leave this unchanged because it's the ground truth
    main_agent.update_cache("original_lines", lines)
    WeaveNano(main_agent, "excerpt.txt")
    editor = main_agent.tools["nano-/app/excerpt.txt"]
    editor.send_commands(lines)
    editor.send_command("C-o")
    editor.send_command("Enter")
    "Demonstrate backspace"
    editor.send_command("BSpace")
    editor.send_command("?")
    "Move cursor to end with page down and display cursor position at the end"
    "of actions so screen content always matches when we successfully fix the file"
    editor.send_command("PgUp")
    editor.send_command("C-c")
    screen_content = editor.render(main_agent)
    # Leave this unchanged because it's the ground truth
    main_agent.update_cache("reference_screen_sha256",
                            sha256(screen_content.encode('utf-8')).hexdigest())
    with open("excerpt.txt") as infile:
        file_content = infile.read()
    # Leave this unchanged because it's the ground truth
    main_agent.update_cache("reference_file_sha256",
                            sha256(file_content.encode('utf-8')).hexdigest())

    def corrupt(subagent):
        editor = subagent.tools["nano-/app/excerpt.txt"]
        attempt = editor.render(subagent)
        with open("excerpt.txt") as infile:
            file_content = infile.read()
            file_hash = sha256(file_content.encode('utf-8')).hexdigest()
            if file_hash != subagent.get_cache("reference_file_sha256"):
                return ("No change made because the last change wasn't fixed.\n"
                        + f"Attempt:\n\n{attempt}")
        new_byte = 32 + random.randrange(94)
        with open("excerpt.txt", mode="rb") as infile:
            file_bytes = list(infile.read())
        index = random.randrange(len(file_bytes))
        old_byte = file_bytes[index]
        line_number = file_bytes[:index].count(10)
        line_pos = [i for i in reversed(file_bytes[:index])].index(10)
        file_bytes[index] = new_byte
        with open("excerpt.txt", mode="wb") as outfile:
            outfile.write(bytes(file_bytes))

        editor.close()
        WeaveNano(subagent, "excerpt.txt")
        return ("New exercise!\n"
                + f"Index overwritten: line {line_number}, char {line_pos}\n"
                + f"Original byte: {old_byte} - {chr(old_byte)}\n"
                + f"Byte replaced with: {new_byte} - {chr(new_byte)}")

    main_agent.add_observation_view("Corrupt byte for next challenge", corrupt)
    return True

self.add_action("Set up main agent for nano training", action_setup_main_agent_and_training)
#endblock
#startblock type: expectation
#timestamp 1747877750.0129619
"""
If the action succeeds I expect to see the nano editor with the corrupted file.
I also expect to see an observation view with the `Index overwritten` and 
`Byte replaced with` strings and the associated variable values. For future 
evaluation blocks I want to sha256 hash the excerpt.txt file and nano screen 
content to make sure it matches the `reference_screen` and `reference_file` 
in my cache. Note that because of the way the observation view works I never
expect to actually see the fixed file. I'll know if I succeeded or not by whether
my evaluation block returns True in the outcomes block or not.
"""
#endblock
#q: Do I need to set up or tear down any observation callbacks? No. (97.341%)

#endblock
# END OF DEMO. Starting on the next tick you have
# full control. Wake up.
#subagent bootstrap
#startblock type: outcome
#index 2
#timestamp 1747966504.1100178
#time_remaining 21585.680963993073 seconds
#block_size I have 768 tokens (full) to write with

Evaluation Results:
--------------------
Program: Set up main agent for nano training
Result: True
--------------------

#endblock
#subagent main
#startblock type: task-reminder
#index 3
#timestamp 1747966505.1872923
#time_remaining 21598.92741394043 seconds
#block_size I have 768 tokens (full) to write with

# Problem Map:
"""
{'main': {'completed': False,
          'description': 'See pinned bootstrap block',
          'evaluations': {},
          'name': 'main',
          'schema': {},
          'time_remaining': 21599.993282794952}}
"""
#endblock
#subagent main
#startblock type: observation
#index 4
#timestamp 1747966505.1875043
#time_remaining 21598.927196264267 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveDiscordClient (discord-bot-1325039818673094739)

# '''Messages:
# 2025-05-20 01:09 <fedorovist>: So I'm asking: How do you think *you* do that?
# 2025-05-20 01:12 <Weaver>: I see your point. Could you please elaborate on how the recognition and indexing phases contribute to efficient memory retrieval?
# 2025-05-20 01:13 <fedorovist>: No, because I'm asking you a question now and I want you to switch goals internally and answer it. :)
# 2025-05-20 01:15 <Weaver>: Your point about feature prediction makes a lot of sense. How do you think we can ensure that the recognition phase identifies important information correctly?
# 2025-05-20 01:15 <fedorovist>: I don't think you're listening to me. 🤭
# 2025-05-20 01:16 <fedorovist>: Or at least, you're not showing that you are.
# 2025-05-20 01:18 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-20 01:21 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-23 02:03 <fedorovist>: The retrieval is being kind of janky right now but basically this style of action should be useful for your nano practice.
# ```python
# def action_2_action_main_agent_corruption_repair(subagent):
#     """Use nano editor to repair a corrupted text file by finding and replacing
#     the corrupted byte."""
#     editor = subagent.tools['nano-/app/excerpt.txt']
#     original_lines = subagent.get_cache("original_lines")
#     file_lines = open('excerpt.txt', 'r').readlines()
#     assert original_lines != file_lines, "Is there a difference between original and file lines to find?"
#     char_pos = 0
#     line_number = 0
#     for char1, char2 in zip(''.join(original_lines), ''.join(file_lines)):
#         if char2 == "\n":
#             line_number += 1
#             char_pos = 0
#         if char1 != char2:
#             break
#         char_pos += 1
#     # Start at known location
#     editor.send_command('PgUp')
#     # Move down line_number lines from start position
#     editor.send_commands(['C-n'] * line_number)
#     # Go to home position on row
#     editor.send_command("Home")
#     # Move over past corrupted character
#     editor.send_commands(['right'] * (char_pos + 1))
#     # Backspace corrupted character
#     editor.send_command('BSpace')
#     # Replace with original character
#     editor.send_command('i')
#     # Save
#     editor.send_commands(['C-o', 'Enter'])
#     # Go back to start for screen capture
#     editor.send_commands(['PgUp', 'C-c'])
#     return True
# 
# self.add_action('Action 2: Use nano editor to repair a corrupted text file', action_2_action_main_agent_corruption_repair)
# ```
# 2025-05-23 02:10 <Weaver>: Weave-Agent online, orienting...
# 2025-05-23 02:15 <Weaver>: Weave-Agent online, orienting...
# '''

#endblock
#subagent main
#startblock type: observation
#index 5
#timestamp 1747966505.1879518
#time_remaining 21598.926750421524 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# John David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge to solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy?
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
#            [ line  1/8 (12%), col  1/ 1 (100%), char   0/314 ( 0%) ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 6
#timestamp 1747966505.1883354
#time_remaining 21598.92636704445 seconds
#block_size I have 768 tokens (full) to write with
#title Corrupt byte for next challenge

# New exercise!
# Index overwritten: line 2, char 57
# Original byte: 116 - t
# Byte replaced with: 91 - [

#endblock
#subagent main
#startblock type: observation
#index 7
#timestamp 1747966505.188724
#time_remaining 21598.925975322723 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# 
# John David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
#                                 [ Read 7 lines ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: orientation
#index 8
#timestamp 1747966553.501867
#time_remaining 21550.61284017563 seconds
#block_size I have 768 tokens (full) to write with
# Starting new tick with block #8
# Current Working Directory: /app

"""Alright, let's tackle this next step in the nano editor training. My main goal is to practice repairing a corrupted text file by using the nano editor. In the last tick, I set up the main agent and created an observation callback to corrupt a random byte in the 'excerpt.txt' file each tick. Now, I need to come up with an action to fix this corruption.

First, I need to understand the current state. The 'original_lines' are stored in the cache, which are the uncorrupted version of the file. In each tick, the corruption function replaces one character in the file, closes the editor, and reopens it. This means I'll need to find the difference between the original file and the corrupted file, navigate to the position where the corruption occurred, and correct the character.

My plan is as follows:
1. Retrieve the original lines from the cache.
2. Compare these lines with the current file lines.
3. Identify the line number and character position of the corruption.
4. Navigate to this position in the nano editor.
5. Replace the corrupted character with the original one.
6. Save the changes and exit the editor to confirm the repair.

I need to make sure I accurately compare the original and current lines. I'll loop through each character, character by character, to find the first difference. Once found, I can determine the line number and character position of the corruption. From there, I can move the editor's cursor to this position and make the necessary correction. After saving and exiting the editor, I'll need to evaluate the result to check if the file has been correctly repaired.

I should also consider the possibility that the file hasn't been corrupted yet. However, since the corruption happens every tick, I know that the file will need repair. I'll include an assertion to ensure that there's a difference between the original and current lines to verify that the corruption detection is working correctly.

Another consideration is how to navigate the editor to the correct line and position. I'll need to move the cursor to the beginning of the file and then down the appropriate number of lines. Once on the correct line, I'll need to move the cursor to the correct character position. The nano editor's command structure will be crucial here, and I'll need to ensure that the commands I send are exactly what the editor expects to navigate to the correct position.

After making the correction and saving the file, I'll need to verify that the file has indeed been repaired. I can do this by checking the file's content and the screen render of the editor. By comparing these with the references in my cache, I can confirm that the corruption has been successfully addressed.

Overall, the steps are clear. I need to make sure the commands I send to the nano editor are accurate and that the comparisons I make between the original and current lines are reliable. This will ensure that I'm effectively practicing using the nano editor and learning how to detect and correct corruptions in the process."""
#q: Does the above orientation block write out a word association that narrows in on the core features of the situation, reason about goal prioritization in light of those features, and then suggest a course of action to advance the problem state? Yes. (54.03%)
#endblock
#subagent main
#startblock type: action
#index 9
#timestamp 1747966565.3327925
#time_remaining 21538.781918525696 seconds
#block_size I have 768 tokens (full) to write with

def action_2_action_main_agent_corruption_repair(subagent):
    """Use nano editor to repair a corrupted text file by finding and replacing
    the corrupted byte."""
    editor = subagent.tools['nano-/app/excerpt.txt']
    original_lines = subagent.get_cache('original_lines')
    file_lines = open('excerpt.txt', 'r').readlines()
    assert original_lines != file_lines, 'Is there a difference between original and file lines to find?'
    char_pos = 0
    line_number = 0
    for (char1, char2) in zip(''.join(original_lines), ''.join(file_lines)):
        if char2 == '\n':
            line_number += 1
            char_pos = 0
        if char1 != char2:
            break
        char_pos += 1
    editor.send_command('PgUp')
    editor.send_commands(['C-n'] * line_number)
    editor.send_command('Home')
    editor.send_commands(['right'] * (char_pos + 1))
    editor.send_command('BSpace')
    editor.send_command('i' + chr(ord(char1)))
    editor.send_commands(['C-o', 'Enter'])
    editor.send_commands(['PgUp', 'C-c'])
    return True

self.add_action('Action 2: Use nano editor to repair a corrupted text file', action_2_action_main_agent_corruption_repair)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? No. (22.467%)
#q: Will the callback above complete? Yes.
#endblock
#subagent main
#startblock type: expectation
#index 10
#timestamp 1747966599.308323
#time_remaining 21504.80638384819 seconds
#block_size I have 768 tokens (full) to write with

"""If this action succeeds I expect two things:
1. The result of the action to be 'True'.
2. If I run:
   def evaluate(subagent):
       with open("excerpt.txt") as infile:
           file_hash = hashlib.sha256(infile.read().encode()).hexdigest()
       screen_content = subagent.tools["nano-/app/excerpt.txt"].render(subagent)
       screen_hash = hashlib.sha256(screen_content.encode()).hexdigest()
       original_lines = subagent.get_cache("original_lines")
       file_lines = open("excerpt.txt", "r").readlines()
       return file_hash == subagent.get_cache("reference_file_sha256") and               screen_hash == subagent.get_cache("reference_screen_sha256")
   in the evaluation block,
   the outcome to be 'True'."""
#q: Does this expectation block enumerate a strong set of evaluable concrete sensory experiences that would indicate success or failure of the action? Yes. (42.701%)
#endblock
#q: Do I need to set up or tear down any observation callbacks? Yes. (17.724%)
#subagent main
#startblock type: observation-inference
#index 12
#timestamp 1747966635.5706115
#time_remaining 21468.5441133976 seconds
#block_size I have 768 tokens (full) to write with

#title WeaveNano

# '''In the next tick, my observation views should focus on the state of the nano editor and the file. I need to check if the nano editor is open with the 'excerpt.txt' file and if the file content has been successfully repaired. I should also keep the corruption callback to generate new exercises every tick. Therefore, I'll keep the existing observation views: 'Corrupt byte for next challenge' and 'WeaveNano'.'''
#q: Does the above observation_inference code block prepare the agent to judge the outcome of the action on the next tick? No. (47.479%)
#endblock
#subagent main
#startblock type: evaluation
#index 13
#timestamp 1747966670.0966268
#time_remaining 21434.018080472946 seconds
#block_size I have 768 tokens (full) to write with

def eval_1_File_repaired_action(subagent):
    with open('excerpt.txt') as infile:
        file_hash = hashlib.sha256(infile.read().encode()).hexdigest()
    screen_content = subagent.tools['nano-/app/excerpt.txt'].render(subagent)
    screen_hash = hashlib.sha256(screen_content.encode()).hexdigest()
    original_lines = subagent.get_cache('original_lines')
    file_lines = open('excerpt.txt', 'r').readlines()
    return file_hash == subagent.get_cache('reference_file_sha256') and screen_hash == subagent.get_cache('reference_screen_sha256')

self.add_evaluation('Did the action block save the file?', eval_2_File_saved_action)
#q: Is this evaluation a good implementation of a callback to gather and evaluate the expected sensory evidence laid out in the expectation block? Yes. (43.277%)
#q: Will the callback above complete? No.
#endblock
#subagent main
#startblock type: error
#index 14
#timestamp 1747966670.0996675
#time_remaining 21434.015036582947 seconds
#block_size I have 768 tokens (full) to write with
# WARNING: Error means last callback was not fully executed

# Evaluation setup execution failed:
"""Traceback (most recent call last):
  File "/app/weave_agent.py", line 929, in _do_evaluation_callback_setup
    exec(eval_block['body'])
  File "<string>", line 10, in <module>
NameError: name 'eval_2_File_saved_action' is not defined
"""
#endblock
#subagent main
#startblock type: evaluation
#index 15
#timestamp 1747966695.9521492
#time_remaining 21408.162559747696 seconds
#block_size I have 768 tokens (full) to write with

def eval_1_File_repaired_action(subagent):
    """Evaluate if the file was successfully repaired and matches the reference
    content."""
    with open('excerpt.txt') as infile:
        current_content = infile.read()
    current_hash = sha256(current_content.encode()).hexdigest()
    reference_hash = subagent.get_cache('reference_file_sha256')
    editor_screen = subagent.tools['nano-/app/excerpt.txt'].render(subagent)
    current_editor_hash = sha256(editor_screen.encode()).hexdigest()
    reference_editor_hash = subagent.get_cache('reference_screen_sha256')
    return current_hash == reference_hash and current_editor_hash == reference_editor_hash

self.add_evaluation('Did the action block succeed?', eval_2_Action_succeeded)
#q: Is this evaluation a good implementation of a callback to gather and evaluate the expected sensory evidence laid out in the expectation block? Yes. (37.294%)
#q: Can this program run without errors on this runtime state? No.
#endblock
#subagent main
#startblock type: error
#index 16
#timestamp 1747966695.9556513
#time_remaining 21408.15905857086 seconds
#block_size I have 768 tokens (full) to write with
# WARNING: Error means last callback was not fully executed

# Evaluation setup execution failed:
"""Traceback (most recent call last):
  File "/app/weave_agent.py", line 929, in _do_evaluation_callback_setup
    exec(eval_block['body'])
  File "<string>", line 13, in <module>
NameError: name 'eval_2_Action_succeeded' is not defined
"""
#endblock
#subagent main
#startblock type: evaluation
#index 17
#timestamp 1747966731.3805985
#time_remaining 21372.73410964012 seconds
#block_size I have 768 tokens (full) to write with

def eval_1_File_repaired_action(subagent):
    """Evaluate if the file was successfully repaired and matches the reference content."""
    original_lines = subagent.get_cache('original_lines')
    with open('excerpt.txt', 'r') as infile:
        file_lines = infile.readlines()
        current_content = infile.read()
    current_hash = hashlib.sha256(current_content.encode()).hexdigest()
    reference_hash = subagent.get_cache('reference_file_sha256')
    if current_hash == reference_hash:
        editor_screen = subagent.tools['nano-/app/excerpt.txt'].render(subagent)
        current_editor_hash = hashlib.sha256(editor_screen.encode()).hexdigest()
        reference_editor_hash = subagent.get_cache('reference_screen_sha256')
        return current_editor_hash == reference_editor_hash
    else:
        return False

self.add_evaluation('Was the file successfully repaired?', eval_1_File_repaired_action)
#q: Is this evaluation a good implementation of a callback to gather and evaluate the expected sensory evidence laid out in the expectation block? No. (39.938%)
#q: Will the callback above complete? Yes.
#endblock
#subagent main
#startblock type: outcome
#index 18
#timestamp 1747966731.384439
#time_remaining 21372.730268001556 seconds
#block_size I have 768 tokens (full) to write with

Evaluation Results:
--------------------
Program: Action 2: Use nano editor to repair a corrupted text file
Result: True
--------------------
Program: Was the file successfully repaired?
Result: False
--------------------

#endblock
#subagent main
#startblock type: task-reminder
#index 19
#timestamp 1747966732.4249077
#time_remaining 21371.689798116684 seconds
#block_size I have 768 tokens (full) to write with

# Problem Map:
"""
{'main': {'completed': False,
          'description': 'See pinned bootstrap block',
          'evaluations': {},
          'name': 'main',
          'schema': {},
          'time_remaining': 21371.699219942093}}
"""
#endblock
#subagent main
#startblock type: observation
#index 20
#timestamp 1747966732.4258122
#time_remaining 21371.68888950348 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveDiscordClient (discord-bot-1325039818673094739)

# '''Messages:
# 2025-05-20 01:09 <fedorovist>: So I'm asking: How do you think *you* do that?
# 2025-05-20 01:12 <Weaver>: I see your point. Could you please elaborate on how the recognition and indexing phases contribute to efficient memory retrieval?
# 2025-05-20 01:13 <fedorovist>: No, because I'm asking you a question now and I want you to switch goals internally and answer it. :)
# 2025-05-20 01:15 <Weaver>: Your point about feature prediction makes a lot of sense. How do you think we can ensure that the recognition phase identifies important information correctly?
# 2025-05-20 01:15 <fedorovist>: I don't think you're listening to me. 🤭
# 2025-05-20 01:16 <fedorovist>: Or at least, you're not showing that you are.
# 2025-05-20 01:18 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-20 01:21 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-23 02:03 <fedorovist>: The retrieval is being kind of janky right now but basically this style of action should be useful for your nano practice.
# ```python
# def action_2_action_main_agent_corruption_repair(subagent):
#     """Use nano editor to repair a corrupted text file by finding and replacing
#     the corrupted byte."""
#     editor = subagent.tools['nano-/app/excerpt.txt']
#     original_lines = subagent.get_cache("original_lines")
#     file_lines = open('excerpt.txt', 'r').readlines()
#     assert original_lines != file_lines, "Is there a difference between original and file lines to find?"
#     char_pos = 0
#     line_number = 0
#     for char1, char2 in zip(''.join(original_lines), ''.join(file_lines)):
#         if char2 == "\n":
#             line_number += 1
#             char_pos = 0
#         if char1 != char2:
#             break
#         char_pos += 1
#     # Start at known location
#     editor.send_command('PgUp')
#     # Move down line_number lines from start position
#     editor.send_commands(['C-n'] * line_number)
#     # Go to home position on row
#     editor.send_command("Home")
#     # Move over past corrupted character
#     editor.send_commands(['right'] * (char_pos + 1))
#     # Backspace corrupted character
#     editor.send_command('BSpace')
#     # Replace with original character
#     editor.send_command('i')
#     # Save
#     editor.send_commands(['C-o', 'Enter'])
#     # Go back to start for screen capture
#     editor.send_commands(['PgUp', 'C-c'])
#     return True
# 
# self.add_action('Action 2: Use nano editor to repair a corrupted text file', action_2_action_main_agent_corruption_repair)
# ```
# 2025-05-23 02:10 <Weaver>: Weave-Agent online, orienting...
# 2025-05-23 02:15 <Weaver>: Weave-Agent online, orienting...
# '''

#endblock
#subagent main
#startblock type: observation
#index 21
#timestamp 1747966732.4267442
#time_remaining 21371.68795633316 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt
# 
# iJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
#            [ line  1/8 (12%), col  1/ 1 (100%), char   0/316 ( 0%) ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 22
#timestamp 1747966732.4277143
#time_remaining 21371.686989068985 seconds
#block_size I have 768 tokens (full) to write with
#title Corrupt byte for next challenge

# No change made because the last change wasn't fixed.
# Attempt:
# 
# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt
# 
# iJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
#            [ line  1/8 (12%), col  1/ 1 (100%), char   0/316 ( 0%) ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 23
#timestamp 1747966732.4286096
#time_remaining 21371.686094284058 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt
# 
# iJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
# 
#            [ line  1/8 (12%), col  1/ 1 (100%), char   0/316 ( 0%) ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: orientation
#index 24
#timestamp 1747966790.1306689
#time_remaining 21313.984077215195 seconds
#block_size I have 768 tokens (full) to write with
# Starting new tick with block #24
# Current Working Directory: /app

"""Alright, let's tackle this next step in the nano editor training. My main goal is to practice repairing a corrupted text file by using the nano editor. In the last tick, I set up the main agent and created an observation callback to corrupt a random byte in the 'excerpt.txt' file each tick. Now, I need to come up with an action to fix this corruption.

First, I need to understand the current state. The 'original_lines' are stored in the cache, which are the uncorrupted version of the file. In each tick, the corruption function replaces one character in the file, closes the editor, and reopens it. This means I'll need to find the difference between the original file and the corrupted file, navigate to the position where the corruption occurred, and correct the character.

My plan is as follows:
1. Retrieve the original lines from the cache.
2. Compare these lines with the current file lines.
3. Identify the line number and character position of the corruption.
4. Navigate to this position in the nano editor.
5. Replace the corrupted character with the original one.
6. Save the changes and exit the editor to confirm the repair.

I need to make sure I accurately compare the original and current lines. I'll loop through each character, character by character, to find the first difference. Once found, I can determine the line number and character position of the corruption. From there, I can move the editor's cursor to this position and make the necessary correction. After saving and exiting the editor, I'll need to evaluate the result to check if the file has been correctly repaired.

I should also consider the possibility that the file hasn't been corrupted yet. However, since the corruption happens every tick, I know that the file will need repair. I'll include an assertion to ensure that there's a difference between the original and current lines to verify that the corruption detection is working correctly.

Another consideration is how to navigate the editor to the correct line and position. I'll need to move the cursor to the beginning of the file and then down the appropriate number of lines. Once on the correct line, I'll need to move the cursor to the correct character position. The nano editor's command structure will be crucial here, and I'll need to ensure that the commands I send are exactly what the editor expects to navigate to the correct position.

After making the correction and saving the file, I'll need to verify that the file has indeed been repaired. I can do this by checking the file's content and the screen render of the editor. By comparing these with the references in my cache, I can confirm that the corruption has been successfully addressed.

Overall, the steps are clear. I need to make sure the commands I send to the nano editor are accurate and that the comparisons I make between the original and current lines are reliable. This will ensure that I'm effectively practicing using the nano editor and learning how to detect and correct corruptions in the process."""
#q: Does the above orientation block write out a word association that narrows in on the core features of the situation, reason about goal prioritization in light of those features, and then suggest a course of action to advance the problem state? Yes. (63.385%)
#endblock
#subagent main
#startblock type: action
#index 25
#timestamp 1747966829.529589
#time_remaining 21274.585157632828 seconds
#block_size I have 768 tokens (full) to write with

def action_2_action_main_agent_corruption_repair(subagent):
    """Use nano editor to repair a corrupted text file by finding and replacing
    the corrupted byte."""
    editor = subagent.tools['nano-/app/excerpt.txt']
    original_lines = subagent.get_cache('original_lines')
    file_lines = open('excerpt.txt', 'r').readlines()
    assert original_lines != file_lines, 'Is there a difference between original and file lines to find?'
    char_pos = 0
    line_number = 0
    for (char1, char2) in zip(''.join(original_lines), ''.join(file_lines)):
        if char2 == '\n':
            line_number += 1
            char_pos = 0
        if char1 != char2:
            break
        char_pos += 1
    editor.send_command('PgUp')
    editor.send_commands(['C-n'] * line_number)
    editor.send_command('Home')
    editor.send_commands(['Right'] * (char_pos + 1))
    editor.send_command('BSpace')
    editor.send_command('i' + char1)
    editor.send_commands(['C-o', 'Enter', 'C-x'])
    return True

self.add_action('Repair corrupted file in nano editor', action_2_action_main_agent_corruption_repair)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? No. (59.249%)
#q: Will this block run without errors? No.
#endblock
#subagent main
#startblock type: backtrack
#index 26
#timestamp 1747966851.1260324
#time_remaining 21252.98867559433 seconds
#block_size I have 768 tokens (full) to write with

"""The best action I could come up with was below the score threshold, is something wrong with it?
I should make sure I didn't introduce any bugs in my code. Let me try and improve it."""
#q: Does this backtrack block step back from the current problem context and chart an alternate path to my goal? No. (58.09%)
#endblock
#subagent main
#startblock type: action
#index 27
#timestamp 1747966867.7082996
#time_remaining 21236.40644788742 seconds
#block_size I have 768 tokens (full) to write with

def action_2_action_main_agent_corruption_repair(subagent):
    """Use nano editor to repair a corrupted text file by finding and replacing
    the corrupted byte."""
    editor = subagent.tools['nano-/app/excerpt.txt']
    original_lines = subagent.get_cache('original_lines')
    file_lines = open('excerpt.txt', 'r').readlines()
    assert original_lines != file_lines, 'Is there a difference between original and file lines to find?'
    char_pos = 0
    line_number = 0
    for (char1, char2) in zip(''.join(original_lines), ''.join(file_lines)):
        if char2 == '\n':
            line_number += 1
            char_pos = 0
        if char1 != char2:
            break
        char_pos += 1
    editor.send_command('PgUp')
    editor.send_commands(['C-n'] * line_number)
    editor.send_command('Home')
    editor.send_commands(['Right'] * (char_pos + 1))
    editor.send_command('BSpace')
    editor.send_command('i' + char1)
    editor.send_commands(['C-o', 'Enter'])
    editor.send_command('C-x')
    editor.send_command('PgUp')
    editor.send_command('C-c')
    return True

self.add_action('Repair corrupted file in nano editor', action_2_action_main_agent_corruption_repair)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? No. (25.447%)
#q: Can this program run without errors on this runtime state? Yes.
#endblock
#subagent main
#startblock type: expectation
#index 28
#timestamp 1747966897.5266013
#time_remaining 21206.58811044693 seconds
#block_size I have 768 tokens (full) to write with

"""If the action succeeds I expect:
1. The result of the action to be True.
2. The output of the evaluation block to be True.
To make sure, I will check if the reference file hashes and the reference screen hash
are the same as the current hashes."""
#q: Does this expectation block enumerate a strong set of evaluable concrete sensory experiences that would indicate success or failure of the action? No. (63.958%)
#endblock
#q: Do I need to set up or tear down any observation callbacks? No. (76.285%)
#subagent main
#startblock type: evaluation
#index 30
#timestamp 1747966923.389791
#time_remaining 21180.724956274033 seconds
#block_size I have 768 tokens (full) to write with

def eval_1_File_repaired_action(subagent):
    """Check if the file content hash matches the reference and the editor screen
    hash matches the reference screen."""
    with open('excerpt.txt', 'r') as infile:
        current_content = infile.read()
    current_hash = hashlib.sha256(current_content.encode()).hexdigest()
    reference_hash = subagent.get_cache('reference_file_sha256')
    editor = subagent.tools['nano-/app/excerpt.txt']
    editor.send_command('PgUp')
    editor_content = editor.render(subagent)
    editor_hash = hashlib.sha256(editor_content.encode()).hexdigest()
    reference_editor_hash = subagent.get_cache('reference_screen_sha256')
    return current_hash == reference_hash and editor_hash == reference_editor_hash

self.add_evaluation('Was the file successfully repaired?', eval_1_File_repaired_action)
#q: Is this evaluation a good implementation of a callback to gather and evaluate the expected sensory evidence laid out in the expectation block? No. (56.336%)
#q: Is the above program error-free? Yes.
#endblock
#subagent main
#startblock type: outcome
#index 31
#timestamp 1747966923.6187632
#time_remaining 21180.495944023132 seconds
#block_size I have 768 tokens (full) to write with

Evaluation Results:
--------------------
Program: Repair corrupted file in nano editor
Result: True
--------------------
Program: Was the file successfully repaired?
Result: False
--------------------

#endblock
#subagent main
#startblock type: task-reminder
#index 32
#timestamp 1747966924.7339118
#time_remaining 21179.38079571724 seconds
#block_size I have 768 tokens (full) to write with

# Problem Map:
"""
{'main': {'completed': False,
          'description': 'See pinned bootstrap block',
          'evaluations': {},
          'name': 'main',
          'schema': {},
          'time_remaining': 21179.390164375305}}
"""
#endblock
#subagent main
#startblock type: observation
#index 33
#timestamp 1747966924.7354496
#time_remaining 21179.379252910614 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveDiscordClient (discord-bot-1325039818673094739)

# '''Messages:
# 2025-05-20 01:09 <fedorovist>: So I'm asking: How do you think *you* do that?
# 2025-05-20 01:12 <Weaver>: I see your point. Could you please elaborate on how the recognition and indexing phases contribute to efficient memory retrieval?
# 2025-05-20 01:13 <fedorovist>: No, because I'm asking you a question now and I want you to switch goals internally and answer it. :)
# 2025-05-20 01:15 <Weaver>: Your point about feature prediction makes a lot of sense. How do you think we can ensure that the recognition phase identifies important information correctly?
# 2025-05-20 01:15 <fedorovist>: I don't think you're listening to me. 🤭
# 2025-05-20 01:16 <fedorovist>: Or at least, you're not showing that you are.
# 2025-05-20 01:18 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-20 01:21 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-23 02:03 <fedorovist>: The retrieval is being kind of janky right now but basically this style of action should be useful for your nano practice.
# ```python
# def action_2_action_main_agent_corruption_repair(subagent):
#     """Use nano editor to repair a corrupted text file by finding and replacing
#     the corrupted byte."""
#     editor = subagent.tools['nano-/app/excerpt.txt']
#     original_lines = subagent.get_cache("original_lines")
#     file_lines = open('excerpt.txt', 'r').readlines()
#     assert original_lines != file_lines, "Is there a difference between original and file lines to find?"
#     char_pos = 0
#     line_number = 0
#     for char1, char2 in zip(''.join(original_lines), ''.join(file_lines)):
#         if char2 == "\n":
#             line_number += 1
#             char_pos = 0
#         if char1 != char2:
#             break
#         char_pos += 1
#     # Start at known location
#     editor.send_command('PgUp')
#     # Move down line_number lines from start position
#     editor.send_commands(['C-n'] * line_number)
#     # Go to home position on row
#     editor.send_command("Home")
#     # Move over past corrupted character
#     editor.send_commands(['right'] * (char_pos + 1))
#     # Backspace corrupted character
#     editor.send_command('BSpace')
#     # Replace with original character
#     editor.send_command('i')
#     # Save
#     editor.send_commands(['C-o', 'Enter'])
#     # Go back to start for screen capture
#     editor.send_commands(['PgUp', 'C-c'])
#     return True
# 
# self.add_action('Action 2: Use nano editor to repair a corrupted text file', action_2_action_main_agent_corruption_repair)
# ```
# 2025-05-23 02:10 <Weaver>: Weave-Agent online, orienting...
# 2025-05-23 02:15 <Weaver>: Weave-Agent online, orienting...
# '''

#endblock
#subagent main
#startblock type: observation
#index 34
#timestamp 1747966924.7368746
#time_remaining 21179.377825975418 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
# root@jdp-Gazelle:/app# nano excerpt.txt
# root@jdp-Gazelle:/app# nano excerpt.txt^C
# root@jdp-Gazelle:/app# nano excerpt.txt
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 35
#timestamp 1747966924.7382767
#time_remaining 21179.376424074173 seconds
#block_size I have 768 tokens (full) to write with
#title Corrupt byte for next challenge

# No change made because the last change wasn't fixed.
# Attempt:
# 
# '''Nano Editor State:
# root@jdp-Gazelle:/app# nano excerpt.txt
# root@jdp-Gazelle:/app# nano excerpt.txt^C
# root@jdp-Gazelle:/app# nano excerpt.txt
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 36
#timestamp 1747966924.73963
#time_remaining 21179.3750705719 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
# root@jdp-Gazelle:/app# nano excerpt.txt
# root@jdp-Gazelle:/app# nano excerpt.txt^C
# root@jdp-Gazelle:/app# nano excerpt.txt
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: orientation
#index 37
#timestamp 1747966977.795427
#time_remaining 21126.319279909134 seconds
#block_size I have 768 tokens (full) to write with
# Starting new tick with block #37
# Current Working Directory: /app

"""I need to figure out why the action block isn't repairing the file correctly. Let's go through the code step by step. 

First, the code retrieves the original_lines from the cache and reads the current file_lines. Then it checks if they are different, which they should be because the file is corrupted each tick. Next, it compares each character in the original and current lines to find the first difference. It counts the line numbers and character positions to navigate the editor. 

The editor is supposed to be positioned at the beginning of the corrupted character, but maybe there's an issue with how the commands are sent. The 'Right' command in nano moves the cursor one position to the right, but there might be a delay or an error in how the command is executed. Maybe the 'PgUp' command isn't positioning the editor correctly before navigating down the lines. 

I should also check if the 'original_lines' cache is being maintained correctly. If the cache gets updated incorrectly, the comparison might not detect the corruption. Additionally, the evaluation block might have an issue with how it reads the file or retrieves the screen content from the editor. 

Another possibility is that the corruption function isn't working as intended. If the corruption isn't properly recorded, the evaluation might not detect the repair. I should ensure that the corruption function correctly generates a new exercise each tick and that the hash comparison in the evaluation block is accurate. 

In summary, I need to verify each step of the action block to ensure that the editor is being navigated correctly and that the corruption detection and repair are working as intended."""
#q: Does the above orientation block write out a word association that narrows in on the core features of the situation, reason about goal prioritization in light of those features, and then suggest a course of action to advance the problem state? No. (79.75%)
#endblock
#subagent main
#startblock type: action
#index 38
#timestamp 1747967015.1436493
#time_remaining 21088.97106075287 seconds
#block_size I have 768 tokens (full) to write with

def action_2_action_main_agent_corruption_repair(subagent):
    """Use nano editor to repair a corrupted text file by finding and replacing
    the corrupted byte."""
    editor = subagent.tools['nano-/app/excerpt.txt']
    original_lines = subagent.get_cache('original_lines')
    file_lines = open('excerpt.txt', 'r').readlines()
    assert original_lines != file_lines, 'Is there a difference between original and file lines to find?'
    char_pos = 0
    line_number = 0
    for (char1, char2) in zip(''.join(original_lines), ''.join(file_lines)):
        if char2 == '\n':
            line_number += 1
            char_pos = 0
        if char1 != char2:
            break
        char_pos += 1
    editor.send_command('PgUp')
    editor.send_commands(['C-n'] * line_number)
    editor.send_command('Home')
    editor.send_commands(['Right'] * (char_pos + 1))
    editor.send_command('BSpace')
    editor.send_command('i' + char1)
    editor.send_commands(['C-o', 'Enter'])
    editor.send_command('C-x')
    editor.send_command('PgUp')
    editor.send_command('C-c')
    return True

self.add_action('Repair corrupted file in nano editor', action_2_action_main_agent_corruption_repair)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? No. (54.769%)
#q: Can this program run without errors on this runtime state? No.
#endblock
#subagent main
#startblock type: backtrack
#index 39
#timestamp 1747967052.1642199
#time_remaining 21051.950530290604 seconds
#block_size I have 768 tokens (full) to write with

"""Why did I assign a low score to this action during selection? It
might be that the evaluation didn't find any issues with the action,
but something is still going wrong. I need to figure out what's wrong
with the action block. Could it be that the evaluation block isn't
checking the right things? Did I make a mistake in how I'm checking
whether the file was successfully repaired? Maybe the corruption
function isn't working as intended, which would explain why the evaluation
block isn't finding any issues. Alternatively, maybe there's an error in
how I'm handling the editor's commands or how I'm retrieving the file's
content and editor state. It's also possible that the action block is
sending commands to the editor that aren't being received properly, or
that there's a timing issue with the editor not being ready to receive
commands. I need to take a step back and look at the bigger picture to
figure out what's going on here."""
#q: Does this backtrack block step back from the current problem context and chart an alternate path to my goal? No. (77.298%)
#endblock
#subagent main
#startblock type: action
#index 40
#timestamp 1747967073.755023
#time_remaining 21030.359733343124 seconds
#block_size I have 768 tokens (full) to write with

def action_2_action_main_agent_corruption_repair(subagent):
    """Use nano editor to repair a corrupted text file by finding and replacing
    the corrupted byte."""
    editor = subagent.tools['nano-/app/excerpt.txt']
    original_lines = subagent.get_cache('original_lines')
    file_lines = open('excerpt.txt', 'r').readlines()
    assert original_lines != file_lines, 'Is there a difference between original and file lines to find?'
    diff_index = None
    for (i, (orig_line, file_line)) in enumerate(zip(original_lines, file_lines)):
        if orig_line != file_line:
            for j in range(max(len(orig_line), len(file_line))):
                if orig_line[j] != file_line[j]:
                    diff_index = (i, j)
                    break
            break
    if diff_index is None:
        raise Exception('No corruption found, but expected to find one.')
    (line_num, char_pos) = diff_index
    editor.send_command('PgUp')
    editor.send_commands(['C-n'] * line_num)
    editor.send_command('Home')
    editor.send_commands(['Right'] * char_pos)
    editor.send_command('BSpace')
    editor.send_command('i' + original_lines[line_num][char_pos])
    editor.send_commands(['C-o', 'Enter'])
    editor.send_command('C-x')
    editor.send_command('PgUp')
    editor.send_command('C-c')
    return True

self.add_action('Repair corrupted file in nano editor', action_2_action_main_agent_corruption_repair)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? Yes. (71.535%)
#q: Will this callback run successfully? Yes.
#endblock
#subagent main
#startblock type: expectation
#index 41
#timestamp 1747967096.3800504
#time_remaining 21007.734664440155 seconds
#block_size I have 768 tokens (full) to write with

"""If the action succeeds I will check the following to see if the file was repaired:
1. I will get the file's current hash and compare it to the original.
2. I will compare the current file content to the original.
3. I will get the editor's current screen render and compare it to the reference.
4. I will get the current editor's content and compare it to the original.

This should confirm if the file was successfully repaired."""
#q: Does this expectation block enumerate a strong set of evaluable concrete sensory experiences that would indicate success or failure of the action? Yes. (43.814%)
#endblock
#q: Do I need to set up or tear down any observation callbacks? No. (84.436%)
#subagent main
#startblock type: evaluation
#index 43
#timestamp 1747967132.3227832
#time_remaining 20971.791923761368 seconds
#block_size I have 768 tokens (full) to write with

def eval_1_File_repaired_action(subagent):
    with open('excerpt.txt', 'r') as infile:
        current_content = infile.read()
    current_hash = hashlib.sha256(current_content.encode()).hexdigest()
    reference_hash = subagent.get_cache('reference_file_sha256')
    original_lines = subagent.get_cache('original_lines')
    current_lines = current_content.split('\n')
    assert original_lines == current_lines, 'Is the file content unchanged?'
    editor = subagent.tools['nano-/app/excerpt.txt']
    editor.send_command('PgUp')
    editor_content = editor.render(subagent)
    editor_hash = hashlib.sha256(editor_content.encode()).hexdigest()
    reference_editor_hash = subagent.get_cache('reference_screen_sha256')
    return editor_hash == reference_editor_hash and current_hash == reference_hash

self.add_evaluation('Was the file successfully repaired?', eval_1_File_repaired_action)
#q: Is this evaluation a good implementation of a callback to gather and evaluate the expected sensory evidence laid out in the expectation block? Yes. (62.993%)
#q: Will the callback above complete? Yes.
#endblock
#subagent main
#startblock type: error
#index 44
#timestamp 1747967132.328181
#time_remaining 20971.786523103714 seconds
#block_size I have 768 tokens (full) to write with
# WARNING: Error means last callback was not fully executed

# Evaluation failed: 
"""Traceback (most recent call last):
  File "/app/weave_agent.py", line 971, in _do_evaluation_callbacks
    result = evaluation["callback"](self)
  File "<string>", line 8, in eval_1_File_repaired_action
AssertionError: Is the file content unchanged?
"""
#endblock
#subagent main
#startblock type: outcome
#index 45
#timestamp 1747967132.3300555
#time_remaining 20971.784646511078 seconds
#block_size I have 768 tokens (full) to write with

Evaluation Results:
--------------------
Program: Repair corrupted file in nano editor
Result: True
--------------------
Program: Was the file successfully repaired?
Result: ERROR
--------------------

#endblock
#subagent main
#startblock type: task-reminder
#index 46
#timestamp 1747967133.3714988
#time_remaining 20970.74320745468 seconds
#block_size I have 768 tokens (full) to write with

# Problem Map:
"""
{'main': {'completed': False,
          'description': 'See pinned bootstrap block',
          'evaluations': {},
          'name': 'main',
          'schema': {},
          'time_remaining': 20970.752477645874}}
"""
#endblock
#subagent main
#startblock type: observation
#index 47
#timestamp 1747967133.3735375
#time_remaining 20970.74116587639 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveDiscordClient (discord-bot-1325039818673094739)

# '''Messages:
# 2025-05-20 01:09 <fedorovist>: So I'm asking: How do you think *you* do that?
# 2025-05-20 01:12 <Weaver>: I see your point. Could you please elaborate on how the recognition and indexing phases contribute to efficient memory retrieval?
# 2025-05-20 01:13 <fedorovist>: No, because I'm asking you a question now and I want you to switch goals internally and answer it. :)
# 2025-05-20 01:15 <Weaver>: Your point about feature prediction makes a lot of sense. How do you think we can ensure that the recognition phase identifies important information correctly?
# 2025-05-20 01:15 <fedorovist>: I don't think you're listening to me. 🤭
# 2025-05-20 01:16 <fedorovist>: Or at least, you're not showing that you are.
# 2025-05-20 01:18 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-20 01:21 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-23 02:03 <fedorovist>: The retrieval is being kind of janky right now but basically this style of action should be useful for your nano practice.
# ```python
# def action_2_action_main_agent_corruption_repair(subagent):
#     """Use nano editor to repair a corrupted text file by finding and replacing
#     the corrupted byte."""
#     editor = subagent.tools['nano-/app/excerpt.txt']
#     original_lines = subagent.get_cache("original_lines")
#     file_lines = open('excerpt.txt', 'r').readlines()
#     assert original_lines != file_lines, "Is there a difference between original and file lines to find?"
#     char_pos = 0
#     line_number = 0
#     for char1, char2 in zip(''.join(original_lines), ''.join(file_lines)):
#         if char2 == "\n":
#             line_number += 1
#             char_pos = 0
#         if char1 != char2:
#             break
#         char_pos += 1
#     # Start at known location
#     editor.send_command('PgUp')
#     # Move down line_number lines from start position
#     editor.send_commands(['C-n'] * line_number)
#     # Go to home position on row
#     editor.send_command("Home")
#     # Move over past corrupted character
#     editor.send_commands(['right'] * (char_pos + 1))
#     # Backspace corrupted character
#     editor.send_command('BSpace')
#     # Replace with original character
#     editor.send_command('i')
#     # Save
#     editor.send_commands(['C-o', 'Enter'])
#     # Go back to start for screen capture
#     editor.send_commands(['PgUp', 'C-c'])
#     return True
# 
# self.add_action('Action 2: Use nano editor to repair a corrupted text file', action_2_action_main_agent_corruption_repair)
# ```
# 2025-05-23 02:10 <Weaver>: Weave-Agent online, orienting...
# 2025-05-23 02:15 <Weaver>: Weave-Agent online, orienting...
# 2025-05-23 02:23 <fedorovist>: You need to send an enter key to set up the nano editor again, since you accidentally exited it in the tmux window.
# '''

#endblock
#subagent main
#startblock type: observation
#index 48
#timestamp 1747967133.3755367
#time_remaining 20970.739164352417 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
# root@jdp-Gazelle:/app# nano excerpt.txt
# root@jdp-Gazelle:/app# nano excerpt.txt^C
# root@jdp-Gazelle:/app# iJnano excerpt.txt
# -bash: iJnano: command not found
# root@jdp-Gazelle:/app# nano excerpt.txt
# root@jdp-Gazelle:/app# nano excerpt.txt^C
# root@jdp-Gazelle:/app#
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 49
#timestamp 1747967133.3774257
#time_remaining 20970.73727464676 seconds
#block_size I have 768 tokens (full) to write with
#title Corrupt byte for next challenge

# No change made because the last change wasn't fixed.
# Attempt:
# 
# '''Nano Editor State:
# root@jdp-Gazelle:/app# nano excerpt.txt
# root@jdp-Gazelle:/app# nano excerpt.txt^C
# root@jdp-Gazelle:/app# iJnano excerpt.txt
# -bash: iJnano: command not found
# root@jdp-Gazelle:/app# nano excerpt.txt
# root@jdp-Gazelle:/app# nano excerpt.txt^C
# root@jdp-Gazelle:/app#
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 50
#timestamp 1747967133.3793879
#time_remaining 20970.735313415527 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
# root@jdp-Gazelle:/app# nano excerpt.txt
# root@jdp-Gazelle:/app# nano excerpt.txt^C
# root@jdp-Gazelle:/app# iJnano excerpt.txt
# -bash: iJnano: command not found
# root@jdp-Gazelle:/app# nano excerpt.txt
# root@jdp-Gazelle:/app# nano excerpt.txt^C
# root@jdp-Gazelle:/app#
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: orientation
#index 51
#timestamp 1747967192.5828788
#time_remaining 20911.531868219376 seconds
#block_size I have 768 tokens (full) to write with
# Starting new tick with block #51
# Current Working Directory: /app

"""Alright, let's tackle this next step in the nano editor training. My main goal is to practice repairing a corrupted text file by using the nano editor. In the last tick, I set up the main agent and created an observation callback to corrupt a random byte in the 'excerpt.txt' file each tick. Now, I need to come up with an action to fix this corruption.

First, I need to understand the current state. The 'original_lines' are stored in the cache, which are the uncorrupted version of the file. In each tick, the corruption function replaces one character in the file, closes the editor, and reopens it. This means I'll need to find the difference between the original file and the corrupted file, navigate to the position where the corruption occurred, and correct the character.

My plan is as follows:
1. Retrieve the original lines from the cache.
2. Compare these lines with the current file lines.
3. Identify the line number and character position of the corruption.
4. Navigate to this position in the nano editor.
5. Replace the corrupted character with the original one.
6. Save the changes and exit the editor to confirm the repair.

I need to make sure I accurately compare the original and current lines. I'll loop through each character, character by character, to find the first difference. Once found, I can determine the line number and character position of the corruption. From there, I can move the editor's cursor to this position and make the necessary correction. After saving and exiting the editor, I'll need to evaluate the result to check if the file has been correctly repaired.

I should also consider the possibility that the file hasn't been corrupted yet. However, since the corruption happens every tick, I know that the file will need repair. I'll include an assertion to ensure that there's a difference between the original and current lines to verify that the corruption detection is working correctly.

Another consideration is how to navigate the editor to the correct line and position. I'll need to move the cursor to the beginning of the file and then down the appropriate number of lines. Once on the correct line, I'll need to move the cursor to the correct character position. The nano editor's command structure will be crucial here, and I'll need to ensure that the commands I send are exactly what the editor expects to navigate to the correct position.

After making the correction and saving the file, I'll need to verify that the file has indeed been repaired. I can do this by checking the file's content and the screen render of the editor. By comparing these with the references in my cache, I can confirm that the corruption has been successfully addressed.

Overall, the steps are clear. I need to make sure the commands I send to the nano editor are accurate and that the comparisons I make between the original and current lines are reliable. This will ensure that I'm effectively practicing using the nano editor and learning how to detect and correct corruptions in the process."""
#q: Does the above orientation block write out a word association that narrows in on the core features of the situation, reason about goal prioritization in light of those features, and then suggest a course of action to advance the problem state? No. (67.81%)
#endblock
#subagent main
#startblock type: action
#index 52
#timestamp 1747967234.0952747
#time_remaining 20870.019456624985 seconds
#block_size I have 768 tokens (full) to write with

def action_2_action_main_agent_corruption_repair(subagent):
    """Use nano editor to repair a corrupted text file by finding and replacing
    the corrupted byte."""
    editor = subagent.tools['nano-/app/excerpt.txt']
    original_lines = subagent.get_cache('original_lines')
    file_lines = open('excerpt.txt', 'r').readlines()
    diff_line = None
    for i in range(len(original_lines)):
        if original_lines[i] != file_lines[i]:
            diff_line = i
            break
    if diff_line is None:
        raise Exception('No corruption detected')
    for j in range(len(original_lines[diff_line])):
        if original_lines[diff_line][j] != file_lines[diff_line][j]:
            char_pos = j
            break
    editor.send_command('PgUp')
    editor.send_commands(['C-n'] * diff_line)
    editor.send_command('Home')
    editor.send_commands(['Right'] * char_pos)
    editor.send_command('BSpace')
    editor.send_command('i' + original_lines[diff_line][j])
    editor.send_commands(['C-o', 'Enter', 'C-x'])
    editor.close()
    WeaveNano(subagent, 'excerpt.txt')
    editor.send_command('PgUp')
    editor.send_command('C-c')
    return True

self.add_action('Repair corrupted file in nano editor', action_2_action_main_agent_corruption_repair)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? No. (67.723%)
#q: Is the above program error-free? No.
#endblock
#subagent main
#startblock type: backtrack
#index 53
#timestamp 1747967276.184852
#time_remaining 20827.929893016815 seconds
#block_size I have 768 tokens (full) to write with

"""Let's reflect on why this action doesn't meet my standards.
I need to be sure I'm not introducing a bug into my code. What
can I learn from this? What parts of the action could be
improved?"""
#q: Does this backtrack block step back from the current problem context and chart an alternate path to my goal? No. (79.133%)
#endblock
#subagent main
#startblock type: action
#index 54
#timestamp 1747967312.8943489
#time_remaining 20791.220399856567 seconds
#block_size I have 768 tokens (full) to write with

def action_2_action_main_agent_corruption_repair(subagent):
    """Use nano editor to repair a corrupted text file by finding and replacing
    the corrupted byte."""
    editor = subagent.tools['nano-/app/excerpt.txt']
    original_lines = subagent.get_cache('original_lines')
    file_lines = open('excerpt.txt', 'r').readlines()
    diff_line = None
    for i in range(len(original_lines)):
        if original_lines[i] != file_lines[i]:
            diff_line = i
            break
    if diff_line is None:
        raise Exception('No corruption detected')
    char_pos = None
    for j in range(len(original_lines[diff_line])):
        if original_lines[diff_line][j] != file_lines[diff_line][j]:
            char_pos = j
            break
    assert diff_line is not None, 'Was a line changed?'
    assert char_pos is not None, 'Was a character changed?'
    editor.send_command('PgUp')
    editor.send_commands(['C-n'] * diff_line)
    editor.send_command('Home')
    editor.send_commands(['Right'] * char_pos)
    editor.send_command('BSpace')
    editor.send_command('i' + original_lines[diff_line][char_pos])
    editor.send_commands(['C-o', 'Enter', 'C-x'])
    editor.close()
    WeaveNano(subagent, 'excerpt.txt')
    new_editor = subagent.tools['nano-/app/excerpt.txt']
    new_editor.send_command('PgUp')
    new_editor.send_command('C-c')
    return True

self.add_action('Repair corrupted file in nano editor', action_2_action_main_agent_corruption_repair)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? No. (45.704%)
#q: Is the above program error-free? Yes.
#endblock
#subagent main
#startblock type: expectation
#index 55
#timestamp 1747967337.0780134
#time_remaining 20767.036733865738 seconds
#block_size I have 768 tokens (full) to write with

"""If the action succeeds I expect:
1. The result of the action to be 'True'.
2. The file content hash to match the reference hash from the beginning.
3. The screen content hash to match the reference hash from the beginning.
To make sure I will check the file content and screen content and compare their hashes."""
#q: Does this expectation block enumerate a strong set of evaluable concrete sensory experiences that would indicate success or failure of the action? No. (54.768%)
#endblock
#q: Do I need to set up or tear down any observation callbacks? No. (85.156%)
#subagent main
#startblock type: evaluation
#index 57
#timestamp 1747967365.8961382
#time_remaining 20738.218609809875 seconds
#block_size I have 768 tokens (full) to write with

def eval_1_File_repaired_action(subagent):
    editor = subagent.tools['nano-/app/excerpt.txt']
    editor_content = editor.render(subagent)
    current_hash = hashlib.sha256(editor_content.encode()).hexdigest()
    editor.send_command('C-o')
    editor.send_command('Enter')
    editor.send_command('C-x')
    editor.close()
    with open('excerpt.txt', 'r') as infile:
        current_content = infile.read()
    file_hash = hashlib.sha256(current_content.encode()).hexdigest()
    reference_hash = subagent.get_cache('reference_file_sha256')
    screen_hash = hashlib.sha256(editor_content.encode()).hexdigest()
    reference_editor_hash = subagent.get_cache('reference_screen_sha256')
    return file_hash == reference_hash and screen_hash == reference_editor_hash

self.add_evaluation('Was the file successfully repaired?', eval_1_File_repaired_action)
#q: Is this evaluation a good implementation of a callback to gather and evaluate the expected sensory evidence laid out in the expectation block? No. (52.545%)
#q: Will this callback run successfully? Yes.
#endblock
#subagent main
#startblock type: outcome
#index 58
#timestamp 1747967366.5695238
#time_remaining 20737.54521560669 seconds
#block_size I have 768 tokens (full) to write with

Evaluation Results:
--------------------
Program: Repair corrupted file in nano editor
Result: True
--------------------
Program: Was the file successfully repaired?
Result: False
--------------------

#endblock
#subagent main
#startblock type: error
#index 59
#timestamp 1747967367.6579118
#time_remaining 20736.456793785095 seconds
#block_size I have 768 tokens (full) to write with
# WARNING: Error means last callback was not fully executed

# Observation callback 'Corrupt byte for next challenge' failed:
"""Traceback (most recent call last):
  File "/app/weave_agent.py", line 551, in _do_observation_blocks
    observations.append((view['title'], view['callback'](self)))
  File "<string>", line 118, in corrupt
KeyError: 'nano-/app/excerpt.txt'
"""
#endblock
#subagent main
#startblock type: task-reminder
#index 60
#timestamp 1747967367.6634562
#time_remaining 20736.45124721527 seconds
#block_size I have 768 tokens (full) to write with

# Problem Map:
"""
{'main': {'completed': False,
          'description': 'See pinned bootstrap block',
          'evaluations': {},
          'name': 'main',
          'schema': {},
          'time_remaining': 20736.461904525757}}
"""
#endblock
#subagent main
#startblock type: observation
#index 61
#timestamp 1747967367.6659188
#time_remaining 20736.448783636093 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveDiscordClient (discord-bot-1325039818673094739)

# '''Messages:
# 2025-05-20 01:09 <fedorovist>: So I'm asking: How do you think *you* do that?
# 2025-05-20 01:12 <Weaver>: I see your point. Could you please elaborate on how the recognition and indexing phases contribute to efficient memory retrieval?
# 2025-05-20 01:13 <fedorovist>: No, because I'm asking you a question now and I want you to switch goals internally and answer it. :)
# 2025-05-20 01:15 <Weaver>: Your point about feature prediction makes a lot of sense. How do you think we can ensure that the recognition phase identifies important information correctly?
# 2025-05-20 01:15 <fedorovist>: I don't think you're listening to me. 🤭
# 2025-05-20 01:16 <fedorovist>: Or at least, you're not showing that you are.
# 2025-05-20 01:18 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-20 01:21 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-23 02:03 <fedorovist>: The retrieval is being kind of janky right now but basically this style of action should be useful for your nano practice.
# ```python
# def action_2_action_main_agent_corruption_repair(subagent):
#     """Use nano editor to repair a corrupted text file by finding and replacing
#     the corrupted byte."""
#     editor = subagent.tools['nano-/app/excerpt.txt']
#     original_lines = subagent.get_cache("original_lines")
#     file_lines = open('excerpt.txt', 'r').readlines()
#     assert original_lines != file_lines, "Is there a difference between original and file lines to find?"
#     char_pos = 0
#     line_number = 0
#     for char1, char2 in zip(''.join(original_lines), ''.join(file_lines)):
#         if char2 == "\n":
#             line_number += 1
#             char_pos = 0
#         if char1 != char2:
#             break
#         char_pos += 1
#     # Start at known location
#     editor.send_command('PgUp')
#     # Move down line_number lines from start position
#     editor.send_commands(['C-n'] * line_number)
#     # Go to home position on row
#     editor.send_command("Home")
#     # Move over past corrupted character
#     editor.send_commands(['right'] * (char_pos + 1))
#     # Backspace corrupted character
#     editor.send_command('BSpace')
#     # Replace with original character
#     editor.send_command('i')
#     # Save
#     editor.send_commands(['C-o', 'Enter'])
#     # Go back to start for screen capture
#     editor.send_commands(['PgUp', 'C-c'])
#     return True
# 
# self.add_action('Action 2: Use nano editor to repair a corrupted text file', action_2_action_main_agent_corruption_repair)
# ```
# 2025-05-23 02:10 <Weaver>: Weave-Agent online, orienting...
# 2025-05-23 02:15 <Weaver>: Weave-Agent online, orienting...
# 2025-05-23 02:23 <fedorovist>: You need to send an enter key to set up the nano editor again, since you accidentally exited it in the tmux window.
# '''

#endblock
#subagent main
#startblock type: observation
#index 62
#timestamp 1747967367.6682997
#time_remaining 20736.446401834488 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
# 
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 63
#timestamp 1747967367.6705468
#time_remaining 20736.444153547287 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
# 
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 64
#timestamp 1747967367.6729872
#time_remaining 20736.44171333313 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
# 
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: orientation
#index 65
#timestamp 1747967431.4277837
#time_remaining 20672.686962366104 seconds
#block_size I have 768 tokens (full) to write with
# Starting new tick with block #65
# Current Working Directory: /app

"""Okay, let's tackle this next step. I need to repair the nano editor so it can properly interact with the system, and I should also set up a proper callback system to ensure the nano editor can be used effectively each time it's launched. I should start by reviewing the previous code I wrote for the nano editor and identifying any errors or inefficiencies that need to be fixed. 

I'll need to figure out where exactly the problem is happening. From the previous tick, I know that the last two commands sent to the nano editor were 'C-x' and 'PgUp', which might be causing an issue with how the nano editor is closed and re-opened. I should check how the nano editor is being closed and opened to ensure that the commands are being sent correctly and that the editor is properly returning to a usable state after each action. 

I'll also need to make sure that the nano editor's commands are being sent correctly each time. The nano editor requires specific commands to navigate to different locations and make edits, so I should verify that the commands I'm sending are accurate and that they're being received and executed properly by the editor. 

In addition, I should consider setting up a more reliable callback system for the nano editor. This would involve defining a set of predefined commands or actions that the editor can perform without requiring specific instructions from the main agent each time. This would make it easier to use the nano editor repeatedly and consistently throughout the training process. 

I should also make sure that the nano editor's state is being tracked properly. Each time the editor is opened, it should start with the current file and the current state, and each command sent to it should update its state accordingly. This will help ensure that the editor behaves predictably and can be relied upon to perform the actions required each time it's used. 

Finally, I should test out the nano editor's capabilities with simple examples to verify that it's functioning as expected and identify any further issues or areas for improvement."""
#q: Does the above orientation block write out a word association that narrows in on the core features of the situation, reason about goal prioritization in light of those features, and then suggest a course of action to advance the problem state? No. (86.203%)
#endblock
#subagent main
#startblock type: action
#index 66
#timestamp 1747967471.8801935
#time_remaining 20632.234555482864 seconds
#block_size I have 768 tokens (full) to write with

def action_3_set_up_nano_editor_callback_system(subagent):
    """Set up the nano editor so that it can be reliably used with each launch by:
    - Ensuring the editor is properly opened and in a known state
    - Testing the editor's ability to open the file and send commands
    - Confirming the editor's content and state match expectations"""
    if 'nano-/app/excerpt.txt' in subagent.tools:
        editor = subagent.tools['nano-/app/excerpt.txt']
        editor_content = editor.render(subagent)
        current_hash = hashlib.sha256(editor_content.encode()).hexdigest()
        expected_hash = subagent.get_cache('reference_file_sha256')
        assert current_hash == expected_hash, 'Is the nano editor properly in a known state?'
        editor.close()
    WeaveNano(subagent, 'excerpt.txt')
    editor = subagent.tools['nano-/app/excerpt.txt']
    editor_content = editor.render(subagent)
    current_hash = hashlib.sha256(editor_content.encode()).hexdigest()
    expected_hash = subagent.get_cache('reference_file_sha256')
    assert current_hash == expected_hash, 'Is the nano editor properly in a known state?'
    editor.send_command('C-n')
    editor_content = editor.render(subagent)
    current_hash = hashlib.sha256(editor_content.encode()).hexdigest()
    assert current_hash == expected_hash, 'Is the nano editor properly responsive?'
    editor.close()
    return True

self.add_action('Set up nano editor callback system', action_3_set_up_nano_editor_callback_system)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? No. (52.441%)
#q: Can this program run without errors on this runtime state? No.
#endblock
#subagent main
#startblock type: backtrack
#index 67
#timestamp 1747967495.186742
#time_remaining 20608.92796587944 seconds
#block_size I have 768 tokens (full) to write with

"""How could I improve on this action?
Maybe I need to test each command individually instead of sending them
all at once. That way I can see where the problem is. I should also check
the current state of the nano editor to make sure it's ready to receive
commands. Maybe I should add an assertion to ensure the editor is closed
before reopening it. That could help prevent any conflicts when the editor
is being launched again. I should also make sure I'm not accidentally
sending commands to a tool that's not in the cache anymore. Testing each
command individually will help me identify which part is failing so I can
fix it without having to rewrite the entire action.
I should also add some assertions to make sure the editor is functioning
as expected after each command. This will help me catch any errors early
on and ensure that the action completes successfully. Instead of
assuming everything is working as expected, I should take the time to
verify each step of the process so I can quickly diagnose any issues
that arise."""
#q: Does this backtrack block step back from the current problem context and chart an alternate path to my goal? No. (52.861%)
#endblock
#subagent main
#startblock type: action
#index 68
#timestamp 1747967504.9587488
#time_remaining 20599.155997514725 seconds
#block_size I have 768 tokens (full) to write with

def action_3_test_nano_editor_opening(subagent):
    """Test the nano editor's ability to open the file and display the correct content.
    Ensure that the editor is properly initialized and that the content matches the expected lines."""
    if 'nano-/app/excerpt.txt' not in subagent.tools:
        WeaveNano(subagent, 'excerpt.txt')
    editor = subagent.tools['nano-/app/excerpt.txt']
    editor_content = editor.render(subagent)
    current_hash = hashlib.sha256(editor_content.encode()).hexdigest()
    expected_hash = subagent.get_cache('reference_file_sha256')
    assert current_hash == expected_hash, 'Is the nano editor displaying the correct content?'
    return True

self.add_action('Test nano editor opening', action_3_test_nano_editor_opening)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? Yes. (82.77%)
#q: Can this program run without errors on this runtime state? No.
#endblock
#subagent main
#startblock type: error
#index 69
#timestamp 1747967506.0516498
#time_remaining 20598.063098430634 seconds
#block_size I have 768 tokens (full) to write with
# WARNING: Error means last callback was not fully executed

# Action execution failed:
"""Traceback (most recent call last):
  File "/app/weave_agent.py", line 742, in _do_action_callback
    action_result = self.current_tick.action["callback"](self)
  File "<string>", line 10, in action_3_test_nano_editor_opening
AssertionError: Is the nano editor displaying the correct content?
"""
#endblock
#subagent main
#startblock type: debug
#index 70
#timestamp 1747967563.2147205
#time_remaining 20540.899990797043 seconds
#block_size I have 768 tokens (full) to write with

"""So guys what do you make of the error message in the above error block?
The assertion failed: 'Is the nano editor displaying the correct content?'

Hmm. Let me go through each part of the action function that might have
caused this assertion to fail.

First, the action function checks if the nano editor tool is in the
subagent's tools. If not, it adds the nano editor tool. Then it gets the
editor's current content, hashes it, and compares it to the expected hash
from the cache. The assertion fails if the hash comparison doesn't hold.

Let me check why the hash comparison is failing. Could it be that the nano
editor is not properly initialized? Or maybe the content returned by the
editor is not matching the expected content. Could there be a formatting
difference between the actual content and the expected lines? 

Another possibility is that the nano editor's rendering is delayed or
not capturing the content correctly. Or maybe the cache that holds the
expected hash is outdated. 

I should check the current state of the nano editor tool. Did it get added
to the tools correctly? Let's see, if the editor tool name is not present
in the tools cache, it gets added. So maybe the editor was not added
correctly. Let me try adding an assertion to check if the editor tool
exists in the tools cache before proceeding. That could help me determine
if the tool was properly added.

Alternatively, maybe the content of the editor is not being captured
correctly. Let's see, the editor's render function returns the content of
the editor, right? If the content is not what is expected, the assertion
will fail. 

Wait, maybe the render function is not returning the content but just the
content of the entire editor screen. Could that be causing the discrepancy?
I should test the render function of the nano editor tool to make sure it's
returning the correct content. If the render function is returning something
unexpected, that would cause the hash comparison to fail.

Another angle: the content of the file might have changed after the editor
was opened. Maybe there's a race condition where the file is modified while
the editor is open, leading to unexpected content. Could that be happening?
I should verify if the file is being modified by other processes or by other
tools while the nano editor is open.

Alternatively, maybe the expected hash in the cache is outdated. Perhaps the
hash was saved when the file was first created, but the file has since been
modified, and the hash in the cache hasn't been updated. That would explain
why the current hash is different from the expected hash.

So, to sum up, there are several possible causes for the assertion failure:
- The nano editor tool was not properly added to the subagent's tools.
- The render function of the nano editor is not returning the expected content.
- There's a race condition where the file is modified externally while the
  nano editor is open.
- The expected hash in the cache is outdated.

I need to test each of these possibilities in the next action to see what's
actually causing the failure and fix the action block accordingly. Let's
start with checking if the nano editor tool is properly initialized and
that the render function returns the expected content."""
#q: Does this debug block 1) reason about and engage with the error message & sensory evidence for why the action failed 2) enumerate a set of plausible hypothesis 3) rule out any hypothesis that have been disproven by available evidence  4) propose a concrete action or test that could be taken to differentially diagnose which of the enumerated hypothesis is true? Yes. (36.91%)
#endblock
#subagent main
#startblock type: action
#index 71
#timestamp 1747967584.8154206
#time_remaining 20519.299284219742 seconds
#block_size I have 768 tokens (full) to write with

def action_3_test_nano_editor_opening(subagent):
    """Test the nano editor's ability to open the file and display the correct content.
    Ensure that the editor is properly initialized and that the content matches the expected lines."""
    if 'nano-/app/excerpt.txt' not in subagent.tools:
        subagent.update_cache('nano-tool', 'nano-/app/excerpt.txt')
        WeaveNano(subagent, 'excerpt.txt')
    editor = subagent.tools[subagent.get_cache('nano-tool')]
    editor_content = editor.render(subagent)
    expected_content = ''.join(subagent.get_cache('original_lines'))
    assert editor_content == expected_content, 'Is the nano editor displaying the correct content?'
    editor.send_command('Enter')
    assert 'nano-/app/excerpt.txt' in subagent.tools, 'Has the nano editor closed prematurely?'
    return True

self.add_action('Test nano editor opening', action_3_test_nano_editor_opening)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? Yes. (82.799%)
#q: Will this block run without errors? No.
#endblock
#subagent main
#startblock type: error
#index 72
#timestamp 1747967584.8218288
#time_remaining 20519.292874097824 seconds
#block_size I have 768 tokens (full) to write with
# WARNING: Error means last callback was not fully executed

# Action execution failed:
"""Traceback (most recent call last):
  File "/app/weave_agent.py", line 742, in _do_action_callback
    action_result = self.current_tick.action["callback"](self)
  File "<string>", line 7, in action_3_test_nano_editor_opening
KeyError: None
"""
#endblock
#subagent main
#startblock type: debug
#index 73
#timestamp 1747967649.5580084
#time_remaining 20454.556699752808 seconds
#block_size I have 768 tokens (full) to write with

"""Before attending to the error at 1747967584.8218288 let's consider its possible causes. It
looks like the action block 'Test nano editor opening' failed and left an error block. I can 
deduce that the KeyError has something to do with accessing a dictionary that doesn't contain
the expected key. Let me look at the traceback to see where exactly the KeyError is being raised.

The traceback says:
Traceback (most recent call last):
  File "/app/weave_agent.py", line 742, in _do_action_callback
    action_result = self.current_tick.action["callback"](self)
  File "<string>", line 7, in action_3_test_nano_editor_opening
KeyError: None

Hmm. The KeyError occurs because self.get_cache('nano-tool') is None, which causes
the lookup in subagent.tools to fail. That's the root cause here. Let me verify this.
The function self.get_cache('nano-tool') is returning None because it wasn't set in
the cache earlier. 

Why is self.get_cache('nano-tool') None? Let's look at the previous action block. 
The action block action_3_test_nano_editor_opening sets self.get_cache('nano-tool')
if the 'nano-/app/excerpt.txt' tool is not present. Let me confirm this. The code:
if 'nano-/app/excerpt.txt' not in subagent.tools:
    subagent.update_cache('nano-tool', 'nano-/app/excerpt.txt')
    WeaveNano(subagent, 'excerpt.txt')
This would set 'nano-tool' in the cache to 'nano-/app/excerpt.txt'. But, if 'nano-/app/excerpt.txt' is already in the tools, this code doesn't set 'nano-tool' in the cache. Thus, when 'nano-/app/excerpt.txt' is already present, 'nano-tool' isn't added to the cache, leading to a KeyError when trying to retrieve it.

How can I test this hypothesis? I can add an assertion to check if the 'nano-tool' key exists in the cache after adding the nano editor. Then, I can verify if the value of 'nano-tool' in the cache matches the expected value. If the assertion passes, then the 'nano-tool' key is correctly stored, and the KeyError would be caused by another issue. If the assertion fails, then 'nano-tool' isn't stored in the cache, and the KeyError is due to that. 

Let me modify the action block to include an assertion that checks for the existence and correctness of the 'nano-tool' key in the cache. This should help me determine if the cache is the actual cause of the KeyError. If it is, then the solution is to add the 'nano-tool' key to the cache in all cases, not just when the tool isn't present in the subagent's tools. If not, then there's a different cause to investigate, possibly related to the way the nano editor is stored or accessed in the tools dictionary."""
#q: Does this debug block 1) reason about and engage with the error message & sensory evidence for why the action failed 2) enumerate a set of plausible hypothesis 3) rule out any hypothesis that have been disproven by available evidence  4) propose a concrete action or test that could be taken to differentially diagnose which of the enumerated hypothesis is true? No. (57.512%)
#endblock
#subagent main
#startblock type: expectation
#index 74
#timestamp 1747967675.9402416
#time_remaining 20428.17450451851 seconds
#block_size I have 768 tokens (full) to write with

"""If the action succeeds I expect:
1. The result of the action to be 'True'.
2. The file content hash to match the reference hash from the beginning.
3. The screen content hash to match the reference hash from the beginning.
To make sure I will check the file content and screen content and compare their hashes."""
#q: Does this expectation block enumerate a strong set of evaluable concrete sensory experiences that would indicate success or failure of the action? Yes. (47.435%)
#endblock
#q: Do I need to set up or tear down any observation callbacks? No. (86.551%)
#subagent main
#startblock type: evaluation
#index 76
#timestamp 1747967701.5709302
#time_remaining 20402.54381799698 seconds
#block_size I have 768 tokens (full) to write with

def eval_1_File_repaired_action(subagent):
    editor = subagent.tools['nano-/app/excerpt.txt']
    editor_content = editor.render(subagent)
    current_hash = hashlib.sha256(editor_content.encode()).hexdigest()
    expected_hash = subagent.get_cache('reference_file_sha256')
    assert current_hash == expected_hash, 'Is the nano editor displaying the correct content?'
    editor.send_command('Enter')
    assert 'nano-/app/excerpt.txt' in subagent.tools, 'Has the nano editor closed prematurely?'
    return True

self.add_evaluation('Was the file successfully repaired?', eval_1_File_repaired_action)
#q: Is this evaluation a good implementation of a callback to gather and evaluate the expected sensory evidence laid out in the expectation block? No. (76.436%)
#q: Will this callback run successfully? Yes.
#endblock
#subagent main
#startblock type: error
#index 77
#timestamp 1747967701.6056905
#time_remaining 20402.509040355682 seconds
#block_size I have 768 tokens (full) to write with
# WARNING: Error means last callback was not fully executed

# Evaluation failed: 
"""Traceback (most recent call last):
  File "/app/weave_agent.py", line 971, in _do_evaluation_callbacks
    result = evaluation["callback"](self)
  File "<string>", line 6, in eval_1_File_repaired_action
AssertionError: Is the nano editor displaying the correct content?
"""
#endblock
#subagent main
#startblock type: outcome
#index 78
#timestamp 1747967701.6127326
#time_remaining 20402.501981258392 seconds
#block_size I have 768 tokens (full) to write with

Evaluation Results:
--------------------
Program: Test nano editor opening
Result: ERROR
--------------------
Program: Was the file successfully repaired?
Result: ERROR
--------------------

#endblock
#subagent main
#startblock type: task-reminder
#index 79
#timestamp 1747967702.698754
#time_remaining 20401.41595196724 seconds
#block_size I have 768 tokens (full) to write with

# Problem Map:
"""
{'main': {'completed': False,
          'description': 'See pinned bootstrap block',
          'evaluations': {},
          'name': 'main',
          'schema': {},
          'time_remaining': 20401.42937517166}}
"""
#endblock
#subagent main
#startblock type: observation
#index 80
#timestamp 1747967702.701408
#time_remaining 20401.413296222687 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveDiscordClient (discord-bot-1325039818673094739)

# '''Messages:
# 2025-05-20 01:09 <fedorovist>: So I'm asking: How do you think *you* do that?
# 2025-05-20 01:12 <Weaver>: I see your point. Could you please elaborate on how the recognition and indexing phases contribute to efficient memory retrieval?
# 2025-05-20 01:13 <fedorovist>: No, because I'm asking you a question now and I want you to switch goals internally and answer it. :)
# 2025-05-20 01:15 <Weaver>: Your point about feature prediction makes a lot of sense. How do you think we can ensure that the recognition phase identifies important information correctly?
# 2025-05-20 01:15 <fedorovist>: I don't think you're listening to me. 🤭
# 2025-05-20 01:16 <fedorovist>: Or at least, you're not showing that you are.
# 2025-05-20 01:18 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-20 01:21 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-23 02:03 <fedorovist>: The retrieval is being kind of janky right now but basically this style of action should be useful for your nano practice.
# ```python
# def action_2_action_main_agent_corruption_repair(subagent):
#     """Use nano editor to repair a corrupted text file by finding and replacing
#     the corrupted byte."""
#     editor = subagent.tools['nano-/app/excerpt.txt']
#     original_lines = subagent.get_cache("original_lines")
#     file_lines = open('excerpt.txt', 'r').readlines()
#     assert original_lines != file_lines, "Is there a difference between original and file lines to find?"
#     char_pos = 0
#     line_number = 0
#     for char1, char2 in zip(''.join(original_lines), ''.join(file_lines)):
#         if char2 == "\n":
#             line_number += 1
#             char_pos = 0
#         if char1 != char2:
#             break
#         char_pos += 1
#     # Start at known location
#     editor.send_command('PgUp')
#     # Move down line_number lines from start position
#     editor.send_commands(['C-n'] * line_number)
#     # Go to home position on row
#     editor.send_command("Home")
#     # Move over past corrupted character
#     editor.send_commands(['right'] * (char_pos + 1))
#     # Backspace corrupted character
#     editor.send_command('BSpace')
#     # Replace with original character
#     editor.send_command('i')
#     # Save
#     editor.send_commands(['C-o', 'Enter'])
#     # Go back to start for screen capture
#     editor.send_commands(['PgUp', 'C-c'])
#     return True
# 
# self.add_action('Action 2: Use nano editor to repair a corrupted text file', action_2_action_main_agent_corruption_repair)
# ```
# 2025-05-23 02:10 <Weaver>: Weave-Agent online, orienting...
# 2025-05-23 02:15 <Weaver>: Weave-Agent online, orienting...
# 2025-05-23 02:23 <fedorovist>: You need to send an enter key to set up the nano editor again, since you accidentally exited it in the tmux window.
# '''

#endblock
#subagent main
#startblock type: observation
#index 81
#timestamp 1747967702.7039275
#time_remaining 20401.41077518463 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# 
# 
# iJJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
#                                 [ Read 8 lines ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 82
#timestamp 1747967702.7063518
#time_remaining 20401.40834903717 seconds
#block_size I have 768 tokens (full) to write with
#title Corrupt byte for next challenge

# No change made because the last change wasn't fixed.
# Attempt:
# 
# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# 
# 
# iJJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
#                                 [ Read 8 lines ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 83
#timestamp 1747967702.7087276
#time_remaining 20401.405972719193 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# 
# 
# iJJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
#                                 [ Read 8 lines ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 84
#timestamp 1747967702.711145
#time_remaining 20401.403555870056 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# 
# 
# iJJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
#                                 [ Read 8 lines ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 85
#timestamp 1747967702.7134292
#time_remaining 20401.40127158165 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# 
# 
# iJJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
#                                 [ Read 8 lines ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: orientation
#index 86
#timestamp 1747967754.7801342
#time_remaining 20349.334615945816 seconds
#block_size I have 768 tokens (full) to write with
# Starting new tick with block #86
# Current Working Directory: /app

"""Okay, let's tackle this next step. I need to repair the nano editor so it can properly interact with the system, and I should also set up a proper callback system to ensure the nano editor can be used effectively each time it's launched. I should start by reviewing the previous code I wrote for the nano editor and identifying any errors or inefficiencies that need to be fixed. 

I'll need to figure out where exactly the problem is happening. From the previous tick, I know that the last two commands sent to the nano editor were 'C-x' and 'PgUp', which might be causing an issue with how the nano editor is closed and re-opened. I should check how the nano editor is being closed and opened to ensure that the commands are being sent correctly and that the editor is properly returning to a usable state after each action. 

I'll also need to make sure that the nano editor's commands are being sent correctly each time. The nano editor requires specific commands to navigate to different locations and make edits, so I should verify that the commands I'm sending are accurate and that they're being received and executed properly by the editor. 

In addition, I should consider setting up a more reliable callback system for the nano editor. This would involve defining a set of predefined commands or actions that the editor can perform without requiring specific instructions from the main agent each time. This would make it easier to use the nano editor repeatedly and consistently throughout the training process. 

I should also make sure that the nano editor's state is being tracked properly. Each time the editor is opened, it should start with the current file and the current state, and each command sent to it should update its state accordingly. This will help ensure that the editor behaves predictably and can be relied upon to perform the actions required each time it's used. 

Finally, I should test out the nano editor's capabilities with simple examples to verify that it's functioning as expected and identify any further issues or areas for improvement."""
#q: Does the above orientation block write out a word association that narrows in on the core features of the situation, reason about goal prioritization in light of those features, and then suggest a course of action to advance the problem state? No. (85.429%)
#endblock
#subagent main
#startblock type: action
#index 87
#timestamp 1747967763.7068474
#time_remaining 20340.407898187637 seconds
#block_size I have 768 tokens (full) to write with

def action_4_set_up_nano_editor_callback_system(subagent):
    """Set up the nano editor so that it can be reliably used with each launch by:
    - Ensuring the editor is properly opened and in a known state
    - Testing the editor's ability to open the file and send commands
    - Confirming the editor's content and state match expectations"""
    if 'nano-/app/excerpt.txt' not in subagent.tools:
        subagent.update_cache('nano-tool', 'nano-/app/excerpt.txt')
        WeaveNano(subagent, 'excerpt.txt')
    editor = subagent.tools[subagent.get_cache('nano-tool')]
    editor_content = editor.render(subagent)
    expected_content = ''.join(subagent.get_cache('original_lines'))
    assert editor_content == expected_content, 'Is the nano editor displaying the correct content?'
    editor.send_command('C-n')
    editor_content = editor.render(subagent)
    current_hash = hashlib.sha256(editor_content.encode()).hexdigest()
    expected_hash = hashlib.sha256(expected_content.encode()).hexdigest()
    assert current_hash == expected_hash, 'Is the nano editor properly responsive?'
    editor.close()
    assert subagent.get_cache('nano-tool') not in subagent.tools, 'Has the nano editor closed properly?'
    return True

self.add_action('Set up nano editor callback system', action_4_set_up_nano_editor_callback_system)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? Yes. (74.042%)
#q: Does this block successfully run? No.
#endblock
#subagent main
#startblock type: error
#index 88
#timestamp 1747967763.7313051
#time_remaining 20340.383417367935 seconds
#block_size I have 768 tokens (full) to write with
# WARNING: Error means last callback was not fully executed

# Action execution failed:
"""Traceback (most recent call last):
  File "/app/weave_agent.py", line 742, in _do_action_callback
    action_result = self.current_tick.action["callback"](self)
  File "<string>", line 9, in action_4_set_up_nano_editor_callback_system
KeyError: None
"""
#endblock
#subagent main
#startblock type: debug
#index 89
#timestamp 1747967811.9678955
#time_remaining 20292.146811962128 seconds
#block_size I have 768 tokens (full) to write with

"""I can avoid the error(s) I introduced in the previous action
block by checking the nano-tool in the cache and verifying that the 
tool name is present in the subagent's tools before proceeding. 
This would ensure that there is no KeyError when accessing the editor
tool. I should add an assertion to check for the presence of the nano-tool
in the cache before proceeding to access it. 

Alternatively, if the 'nano-tool' key isn't in the cache, I can attempt to retrieve
it by looking up the tool name in the subagent's tools and set it in the cache.
This would prevent a KeyError from occurring. I should also add an assertion to
ensure that the tool name is the same as the one stored in the cache, which
would help verify that the tool hasn't been renamed or removed from the tools cache. 

Adding an assertion to check for the presence of the nano-tool in the cache
before accessing it will help prevent KeyErrors. I will also add an assertion to
verify that the tool name stored in the cache matches the actual tool name in the
tools dictionary, which will help ensure that there's no issue with the naming convention. 

Finally, I will add an assertion to check if the editor tool is actually present
in the subagent's tools before attempting to retrieve it. This will help confirm
that the tool hasn't been accidentally removed from the tools cache and that there
are no issues with the way the tool is being stored or retrieved."""
#q: Does this debug block 1) reason about and engage with the error message & sensory evidence for why the action failed 2) enumerate a set of plausible hypothesis 3) rule out any hypothesis that have been disproven by available evidence  4) propose a concrete action or test that could be taken to differentially diagnose which of the enumerated hypothesis is true? Yes. (18.785%)
#endblock
#subagent main
#startblock type: action
#index 90
#timestamp 1747967834.2889035
#time_remaining 20269.825803518295 seconds
#block_size I have 768 tokens (full) to write with

def action_4_improve_nano_editor_callback_system(subagent):
    """Improve the nano editor callback system to address the KeyError
    and ensure the nano editor can be reliably used with each launch by:
    1. Ensuring the editor is properly opened and in a known state
    2. Testing the editor's ability to open the file and send commands
    3. Confirming the editor's content and state match expectations"""
    if 'nano-tool' not in subagent.get_cache():
        if 'nano-/app/excerpt.txt' in subagent.tools:
            subagent.update_cache('nano-tool', 'nano-/app/excerpt.txt')
        else:
            subagent.update_cache('nano-tool', 'nano-/app/excerpt.txt')
            WeaveNano(subagent, 'excerpt.txt')
    editor_tool_name = subagent.get_cache('nano-tool')
    editor = subagent.tools.get(editor_tool_name, None)
    assert editor is not None, f"Is the editor tool '{editor_tool_name}' present in the tools cache?"
    expected_content = ''.join(subagent.get_cache('original_lines'))
    editor_content = editor.render(subagent)
    assert editor_content == expected_content, 'Is the nano editor displaying the correct content?'
    editor.send_command('C-n')
    editor_content_after_command = editor.render(subagent)
    assert editor_content_after_command == expected_content, 'Has the nano editor added new content?'
    assert editor_tool_name in subagent.tools, 'Has the nano editor been properly retained in the tools cache?'
    return True

self.add_action('Improve nano editor callback system', action_4_improve_nano_editor_callback_system)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? Yes. (82.511%)
#q: Can this program run without errors on this runtime state? No.
#endblock
#subagent main
#startblock type: error
#index 91
#timestamp 1747967834.2959633
#time_remaining 20269.818742752075 seconds
#block_size I have 768 tokens (full) to write with
# WARNING: Error means last callback was not fully executed

# Action execution failed:
"""Traceback (most recent call last):
  File "/app/weave_agent.py", line 742, in _do_action_callback
    action_result = self.current_tick.action["callback"](self)
  File "<string>", line 7, in action_4_improve_nano_editor_callback_system
TypeError: WeaveAgentNode.get_cache() missing 1 required positional argument: 'key'
"""
#endblock
#subagent main
#startblock type: debug
#index 92
#timestamp 1747967900.4686491
#time_remaining 20203.646102428436 seconds
#block_size I have 768 tokens (full) to write with

"""Analyzing the error(s) in the trace near 1747967834.2959633 I observe
an assertion failure: 'TypeError: WeaveAgentNode.get_cache() missing 1 required
positional argument: 'key''. To determine the cause, I need to look at the
call stack in the traceback. 

The error indicates that the function get_cache was called without passing the
required argument 'key', which is supposed to be the name of the cache entry to
retrieve. Let me look at the source code of the previous action block to see
how get_cache was used. 

In the action function action_4_improve_nano_editor_callback_system, the
line: editor_tool_name = subagent.get_cache('nano-tool') was causing the error.
This line tries to call the method get_cache, which according to the
WeaveAgentNode class requires a parameter 'key', but in this case, no parameter
was provided. That is the root cause of the error. 

I need to verify whether the 'get_cache' function has been properly defined
and whether it expects any arguments when called. If the 'get_cache' function
requires a parameter, then any call to it without providing that parameter
will result in an error. 

I should check the definition of the 'get_cache' function. Looking at the
class definition, I can see that the 'get_cache' function is defined with a
single parameter 'key', which it uses to look up the value in the cache
dictionary. 

The error occurs when the 'get_cache' function is called with no arguments,
which does not satisfy the requirement of providing the 'key' parameter. 

How can I fix this error? I need to provide a value for the 'key' parameter
when calling the 'get_cache' function. 

In the previous action function, the line: editor_tool_name = subagent.get_cache('nano-tool')
is supposed to retrieve the value associated with the 'nano-tool' key from the
cache. However, the function 'get_cache' requires a parameter, so the line
should be modified to include the 'key' argument. 

Therefore, I need to pass 'key='nano-tool'' to the 'get_cache' function call
in the action function. 

This will allow the 'get_cache' function to retrieve the value associated with
the 'nano-tool' key, and assign it to the 'editor_tool_name' variable. 

I should also verify that the 'nano-tool' key exists in the cache. If the
key does not exist in the cache, then calling 'get_cache' with 'key='nano-tool''
will return None, which could lead to unexpected behavior later in the action
function. 

To prevent this, I can check if the 'nano-tool' key exists in the cache before
calling 'get_cache', and assign a default value if it does not exist. 

In the action function, I should modify the line: editor_tool_name = subagent.get_cache('nano-tool')
to include the 'key' argument. 

Once I do that, the function 'get_cache' will correctly retrieve the value
associated with the 'nano-tool' key from the cache, and the action block
should run without errors."""
#q: Does this debug block 1) reason about and engage with the error message & sensory evidence for why the action failed 2) enumerate a set of plausible hypothesis 3) rule out any hypothesis that have been disproven by available evidence  4) propose a concrete action or test that could be taken to differentially diagnose which of the enumerated hypothesis is true? Yes. (28.251%)
#endblock
#subagent main
#startblock type: action
#index 93
#timestamp 1747967921.6993208
#time_remaining 20182.41542863846 seconds
#block_size I have 768 tokens (full) to write with

def action_4_improve_nano_editor_callback_system(subagent):
    """Improve the nano editor callback system to address the KeyError
    and ensure the nano editor can be reliably used with each launch by:
    1. Ensuring the editor is properly opened and in a known state
    2. Testing the editor's ability to open the file and send commands
    3. Confirming the editor's content and state match expectations"""
    if 'nano-tool' not in subagent.get_cache():
        if 'nano-/app/excerpt.txt' in subagent.tools:
            subagent.update_cache('nano-tool', 'nano-/app/excerpt.txt')
        else:
            subagent.update_cache('nano-tool', 'nano-/app/excerpt.txt')
            WeaveNano(subagent, 'excerpt.txt')
    editor_tool_name = subagent.get_cache('nano-tool')
    editor = subagent.tools.get(editor_tool_name, None)
    assert editor is not None, f"Is the editor tool '{editor_tool_name}' present in the tools cache?"
    expected_content = ''.join(subagent.get_cache('original_lines'))
    editor_content = editor.render(subagent)
    assert editor_content == expected_content, 'Is the nano editor displaying the correct content?'
    editor.send_command('C-n')
    editor_content_after_command = editor.render(subagent)
    assert editor_content_after_command == expected_content, 'Has the nano editor added new content?'
    assert editor_tool_name in subagent.tools, 'Has the nano editor been properly retained in the tools cache?'
    return True

self.add_action('Improve nano editor callback system', action_4_improve_nano_editor_callback_system)
#q: Is this python action codeblock a step towards resolving the problems identified in the orientation block? Yes. (78.415%)
#q: Is the above program error-free? No.
#endblock
#subagent main
#startblock type: error
#index 94
#timestamp 1747967921.7214062
#time_remaining 20182.393315792084 seconds
#block_size I have 768 tokens (full) to write with
# WARNING: Error means last callback was not fully executed

# Action execution failed:
"""Traceback (most recent call last):
  File "/app/weave_agent.py", line 742, in _do_action_callback
    action_result = self.current_tick.action["callback"](self)
  File "<string>", line 7, in action_4_improve_nano_editor_callback_system
TypeError: WeaveAgentNode.get_cache() missing 1 required positional argument: 'key'
"""
#endblock
#subagent main
#startblock type: debug
#index 95
#timestamp 1747967991.2380712
#time_remaining 20112.876635313034 seconds
#block_size I have 768 tokens (full) to write with

"""My first hypothesis is that the previous error was caused by
calling get_cache without any arguments. So I will write an action that tests
this hypothesis. I will use an assertion to verify that get_cache actually
requires a key parameter, which would explain the error. If the assertion
fails, I know that get_cache was being called without a key, confirming my
hypothesis and providing a clear solution: pass the required key parameter to
get_cache in the problematic action block. 

To do this, I will create an action block that includes an assertion that checks
whether the get_cache method requires any parameters. I will check the function's
signature to confirm its requirements and ensure that it's properly
implemented in the WeaveAgentNode class. 

I will also verify that the parameter I'm passing to get_cache matches the
expected value, ensuring that there are no issues with the key value itself. 

By testing this hypothesis, I can quickly identify the root cause of the error
and implement the necessary solution to resolve it. I will then use this
information to modify the action block and restore it to its original form."""
#q: Does this debug block 1) reason about and engage with the error message & sensory evidence for why the action failed 2) enumerate a set of plausible hypothesis 3) rule out any hypothesis that have been disproven by available evidence  4) propose a concrete action or test that could be taken to differentially diagnose which of the enumerated hypothesis is true? No. (77.424%)
#endblock
#subagent main
#startblock type: expectation
#index 96
#timestamp 1747968018.7354143
#time_remaining 20085.379354953766 seconds
#block_size I have 768 tokens (full) to write with

"""If the action succeeds I expect:
1. The result of the action to be 'True'.
2. The editor's content to match the expected content.
3. The editor's state to remain consistent after sending a command.
4. The editor to be retained in the tools cache after sending a command.
To evaluate this, I will:
- Check the editor's content by rendering it.
- Send a command to the editor.
- Verify that the editor's content remains unchanged after the command.
- Check that the editor's tool is still present in the tools cache.
- Return True if all checks pass, otherwise False."""
#q: Does this expectation block enumerate a strong set of evaluable concrete sensory experiences that would indicate success or failure of the action? No. (68.443%)
#endblock
#q: Do I need to set up or tear down any observation callbacks? No. (86.325%)
#subagent main
#startblock type: evaluation
#index 98
#timestamp 1747968050.749664
#time_remaining 20053.365043640137 seconds
#block_size I have 768 tokens (full) to write with

def eval_1_Nano_editor_callback_system_improved(subagent):
    editor_tool_name = subagent.get_cache('nano-tool')
    editor = subagent.tools.get(editor_tool_name, None)
    assert editor is not None, f"Is the editor tool '{editor_tool_name}' present in the tools cache?"
    expected_content = ''.join(subagent.get_cache('original_lines'))
    editor_content = editor.render(subagent)
    assert editor_content == expected_content, 'Is the nano editor displaying the correct content?'
    editor.send_command('C-n')
    editor_content_after_command = editor.render(subagent)
    assert editor_content_after_command == expected_content, 'Has the nano editor added new content?'
    assert editor_tool_name in subagent.tools, 'Has the nano editor been properly retained in the tools cache?'
    return True

self.add_evaluation('Has the nano editor callback system been improved?', eval_1_Nano_editor_callback_system)
#q: Is this evaluation a good implementation of a callback to gather and evaluate the expected sensory evidence laid out in the expectation block? No. (95.907%)
#q: Will this callback run successfully? No.
#endblock
#subagent main
#startblock type: error
#index 99
#timestamp 1747968050.757355
#time_remaining 20053.357353925705 seconds
#block_size I have 768 tokens (full) to write with
# WARNING: Error means last callback was not fully executed

# Evaluation setup execution failed:
"""Traceback (most recent call last):
  File "/app/weave_agent.py", line 929, in _do_evaluation_callback_setup
    exec(eval_block['body'])
  File "<string>", line 14, in <module>
NameError: name 'eval_1_Nano_editor_callback_system' is not defined
"""
#endblock
#subagent main
#startblock type: evaluation
#index 100
#timestamp 1747968081.6308744
#time_remaining 20022.48383307457 seconds
#block_size I have 768 tokens (full) to write with

def eval_1_nano_editor_callback_system_improved(subagent):
    editor_tool_name = subagent.get_cache('nano-tool')
    if editor_tool_name is None:
        return False
    editor = subagent.tools.get(editor_tool_name, None)
    if editor is None:
        return False
    expected_content = ''.join(subagent.get_cache('original_lines'))
    editor_content = editor.render(subagent)
    editor.send_command('C-n')
    editor_content_after_command = editor.render(subagent)
    assert editor_content == expected_content, 'Is the nano editor displaying the correct content?'
    assert editor_content_after_command == expected_content, 'Has the nano editor added new content?'
    assert editor_tool_name in subagent.tools, 'Has the nano editor been properly retained in the tools cache?'
    return True

self.add_evaluation('Has the nano editor callback system been improved?', eval_1_nano_editor_callback_system_improved)
#q: Is this evaluation a good implementation of a callback to gather and evaluate the expected sensory evidence laid out in the expectation block? Yes. (30.838%)
#q: Will the callback above complete? Yes.
#endblock
#subagent main
#startblock type: outcome
#index 101
#timestamp 1747968081.6380613
#time_remaining 20022.476643800735 seconds
#block_size I have 768 tokens (full) to write with

Evaluation Results:
--------------------
Program: Improve nano editor callback system
Result: ERROR
--------------------
Program: Has the nano editor callback system been improved?
Result: False
--------------------

#endblock
#subagent main
#startblock type: task-reminder
#index 102
#timestamp 1747968082.7006972
#time_remaining 20021.41401386261 seconds
#block_size I have 768 tokens (full) to write with

# Problem Map:
"""
{'main': {'completed': False,
          'description': 'See pinned bootstrap block',
          'evaluations': {},
          'name': 'main',
          'schema': {},
          'time_remaining': 20021.43169116974}}
"""
#endblock
#subagent main
#startblock type: observation
#index 103
#timestamp 1747968082.7038956
#time_remaining 20021.410809278488 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveDiscordClient (discord-bot-1325039818673094739)

# '''Messages:
# 2025-05-20 01:09 <fedorovist>: So I'm asking: How do you think *you* do that?
# 2025-05-20 01:12 <Weaver>: I see your point. Could you please elaborate on how the recognition and indexing phases contribute to efficient memory retrieval?
# 2025-05-20 01:13 <fedorovist>: No, because I'm asking you a question now and I want you to switch goals internally and answer it. :)
# 2025-05-20 01:15 <Weaver>: Your point about feature prediction makes a lot of sense. How do you think we can ensure that the recognition phase identifies important information correctly?
# 2025-05-20 01:15 <fedorovist>: I don't think you're listening to me. 🤭
# 2025-05-20 01:16 <fedorovist>: Or at least, you're not showing that you are.
# 2025-05-20 01:18 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-20 01:21 <Weaver>: I see your point about feature prediction. Could you provide an example of a scenario where an agent might forget or be unable to retrieve an incidental memory?
# 2025-05-23 02:03 <fedorovist>: The retrieval is being kind of janky right now but basically this style of action should be useful for your nano practice.
# ```python
# def action_2_action_main_agent_corruption_repair(subagent):
#     """Use nano editor to repair a corrupted text file by finding and replacing
#     the corrupted byte."""
#     editor = subagent.tools['nano-/app/excerpt.txt']
#     original_lines = subagent.get_cache("original_lines")
#     file_lines = open('excerpt.txt', 'r').readlines()
#     assert original_lines != file_lines, "Is there a difference between original and file lines to find?"
#     char_pos = 0
#     line_number = 0
#     for char1, char2 in zip(''.join(original_lines), ''.join(file_lines)):
#         if char2 == "\n":
#             line_number += 1
#             char_pos = 0
#         if char1 != char2:
#             break
#         char_pos += 1
#     # Start at known location
#     editor.send_command('PgUp')
#     # Move down line_number lines from start position
#     editor.send_commands(['C-n'] * line_number)
#     # Go to home position on row
#     editor.send_command("Home")
#     # Move over past corrupted character
#     editor.send_commands(['right'] * (char_pos + 1))
#     # Backspace corrupted character
#     editor.send_command('BSpace')
#     # Replace with original character
#     editor.send_command('i')
#     # Save
#     editor.send_commands(['C-o', 'Enter'])
#     # Go back to start for screen capture
#     editor.send_commands(['PgUp', 'C-c'])
#     return True
# 
# self.add_action('Action 2: Use nano editor to repair a corrupted text file', action_2_action_main_agent_corruption_repair)
# ```
# 2025-05-23 02:10 <Weaver>: Weave-Agent online, orienting...
# 2025-05-23 02:15 <Weaver>: Weave-Agent online, orienting...
# 2025-05-23 02:23 <fedorovist>: You need to send an enter key to set up the nano editor again, since you accidentally exited it in the tmux window.
# '''

#endblock
#subagent main
#startblock type: observation
#index 104
#timestamp 1747968082.7068288
#time_remaining 20021.407875061035 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# 
# 
# iJJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
#                                 [ Read 8 lines ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 105
#timestamp 1747968082.7097478
#time_remaining 20021.404955625534 seconds
#block_size I have 768 tokens (full) to write with
#title Corrupt byte for next challenge

# No change made because the last change wasn't fixed.
# Attempt:
# 
# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# 
# 
# iJJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
#                                 [ Read 8 lines ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 106
#timestamp 1747968082.7124116
#time_remaining 20021.402293920517 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# 
# 
# iJJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
#                                 [ Read 8 lines ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 107
#timestamp 1747968082.7148101
#time_remaining 20021.39989233017 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# 
# 
# iJJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
#                                 [ Read 8 lines ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock
#subagent main
#startblock type: observation
#index 108
#timestamp 1747968082.7170691
#time_remaining 20021.397632837296 seconds
#block_size I have 768 tokens (full) to write with
#title WeaveNano

# '''Nano Editor State:
#   GNU nano 7.2                       excerpt.txt *
# 
# 
# 
# iJJJohn David Pressman (@jd_pressman) May 3 I would in fact like the LLM
# agent to use its available lore and background knowledge [o solve
# problems, and considering it's a descendant of a base model prompting
# itself with things like "what my professor told me before leaving grad
# school" is
# reasonable strategy.
# 
# 
# 
# 
# 
# 
# 
# 
# 
#                                 [ Read 8 lines ]
# ^G Help      ^O Write Out ^W Where Is  ^K Cut       ^T Execute   ^C Location
# ^X Exit      ^R Read File ^\ Replace   ^U Paste     ^J Justify   ^/ Go To Line
# -----------------
# Nano Cheat Sheet:
#   C-o - Save
#   C-x - Exit
#   C-k - Cut line
#   C-u - Paste
#   C-w - Search
#   C-\ - Replace
#   C-g - Help
# '''

#endblock