thomasdavis

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thomasdavis

How SSHomarchy renders Omarchy inside an SSH terminal

Try the real thing:

ssh -p 2222 sshomarchy.com

SSHomarchy is not a TUI painted to resemble a desktop. One real Omarchy 4.0.2 desktop runs Hyprland and Quickshell on a 960x540 virtual display. The gateway

thomasdavis

# SSHomarchy Product Requirements Document

**Version:** 1.0  
**Status:** Build-ready product and technical specification  
**Date:** 4 September 2026  
**Product owner:** Thomas Davis  
**Working domain:** `sshomarchy.com`  
**Codename:** **THE ONE BOX**

> **Build directive**

thomasdavis

    const result = await generateText({
      model: openai.chat(OMEGA_MODEL), // Use centralized model config, force Chat Completions API
      output: Output.object({ schema: DecisionSchema }),
      prompt: `You are analyzing whether Omega (an AI Discord bot) should respond to this message.

Think of this like teaching a child when to join a conversation: clear rules, observable behaviors, respect for boundaries.

## DECISION FRAMEWORK (Hierarchical - check in order)

### LEVEL 1: EXPLICIT REJECTION (Overrides everything)

thomasdavis

Dynamic Tool Selection for AI Agents

A guide to implementing per-step tool filtering using ensemble scoring. This approach solves the problem of LLMs having too many tools to choose from effectively.

The Problem

When you give an LLM agent 40+ tools, several issues emerge:

1. Context bloat: Tool definitions consume tokens, leaving less room for actual conversation
2. Selection confusion: LLMs struggle to pick the right tool when faced with many similar options

thomasdavis

{
  "basics": {
    "name": "Thomasaasd asda",
    "label": "Invenasdtbosar andasd Businessman",
    "picture": "https://example.com/photo.jpg",
    "email": "thomas.edison@example.com",
    "phone": "(123) 456-7890",
    "website": "https://thomassdedison.com",
    "summary": "asdasdsasdsdsProlific inventor and businessasdasdman known for developing many deviasdasdsces that greatly infasasluenced life around the world, including the phonograph, the motion picture camera, and the electric light bulb.",
    "location": {

thomasdavis

Thanks! I’ll explore potential bottlenecks and failure modes of your current AI summary tooltip design when handling large AG Grid datasets (10,000 to 1 million+ rows), and then provide a range of strategies to make it scale effectively without sacrificing UX or LLM performance. I’ll also consider architectural and LLM prompt/streaming optimizations tailored for your Vercel + OpenAI setup.

I’ll report back shortly with practical options.

Scaling LLM-Based Summaries for Large Datasets

Introduction: Implementing an AI summary tooltip in a data grid provides users with quick insights, but scaling this to massive datasets (tens of thousands to millions of rows) introduces significant challenges. The current approach – sending all active column values to an OpenAI model via the Vercel AI SDK – works for small tables, but breaks down as data grows. We need to analyze where this design fails at scale, and explore both frontend and backend strategies to make the summary feature robust for any dataset si

thomasdavis

Thanks! I’ll design a full, modular pipeline that builds a Deep Research system like OpenAI’s but customized to your analyzer stack. It will:

• Use your analyzer output to deeply understand the user's query and tone
• Decompose complex or multi-part prompts into subquestions using QDMR-style logic
• Auto-generate research plans with prioritized search queries, tailored to the topic
• Use Firecrawl/Perplexity APIs to retrieve high-signal results, emphasizing academic sources
• Loop over search + synthesis, revising the plan if the evidence doesn’t satisfy the question
• Generate sectioned Markdown reports with structured citations, tables, graphs where needed
• Embed inline source links with strategies to future-proof them (e.g., reverse RAG or citation mirrors)

thomasdavis

Great, I’ll look into academic and practical frameworks for formally breaking down user questions and intent, drawing from linguistics, epistemology, ontology, and NLP. This will include theories of meaning, discourse analysis, question decomposition, and related computational approaches.

I’ll let you know as soon as I have a structured summary of the best-supported methodologies and tools.

Formal Frameworks for Analyzing User Intent in Natural Language

Introduction

Understanding a user's intent from a natural language query often requires decomposing the utterance into formal components of meaning. Consider the example: "Tell me if the US elections affected the Canadian ones?" – This question contains an imperative request ("Tell me...") and an embedded yes/no query about causality between two events. To analyze such an utterance, one must identify the speech act (a request for information), the semantic content (whether U.S. elections had an effect on Canadian elections), and the impli

thomasdavis

Below is a practical recipe + Node .js scaffolding for turning your 250-page Markdown dump into three linguistics-aware assets:

1. CLDF StructureTable – grammar-feature spreadsheet
2. XIGT (JSON) – interlinear examples
3. OntoLex-Lemon (JSON-LD) – lexicon entries

The same pattern works for any other schema; just swap the “output-format” system message.

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