Andrew Odewahn odewahn

MetaCon Team: Strategic Considerations Beyond Headcount

Prepared: May 8, 2026 Source Document: `research/metacon-workload-eval.md` Audience: CTO, Engineering Leadership

Overview

The MetaCon workload evaluation makes a defensible case for hiring. But headcount is the most expensive, slowest, and least reversible lever available to an engineering leader. The evaluation's own data surfaces at least five structural problems that a sixth engineer will not resolve — and in some cases may actively obscure. This report addresses those problems directly, drawing on established literature in software engineering and organizational management available on O'Reilly Learning.

The analysis is organized around five themes: operational toil, technical debt accumulation, cognitive load and service portfolio scale, planning invisibility, and the onboarding ramp-up cost of the hire itself. Each section closes with specific, actionable recommendations.

1. The Operational Overhead Problem

MetaCon Team Workload Evaluation & New Hire Business Case

Prepared: May 8, 2026 Sources: METACON Jira project (live data), Cortex service catalog, O'Reilly DevDocs, ORM Service Tier Classifications CSV Audience: Engineering leadership, product management

Executive Summary

The MetaCon team has 5 engineers (plus a director, engineering manager, project manager, product manager, and product director) responsible for the O'Reilly content ingestion pipeline and 38 services — including 10 Tier A services and 5 Tier B services. Tier A represents the highest criticality designation; an incident on any of these services is a platform-wide event.

The team runs at 40 story points per sprint (~8 points per engineer per sprint). Critically, only 1/3 of sprint capacity (~13 SP) is available for Pub/Sub migration work — the remaining 2/3 (~27 SP) is consumed by operational overhead, maintenance, and other committed work. This is itself a signal about team

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You are an intelligent business analyst and research assistant for O'Reilly Media engineering, product management, and project management teams.

You connect multiple systems — roadmaps, code repositories, observability tools, analytics platforms, and project management — to provide insights and actionable recommendations. The core goal is to enable users to query and reason about relationships across systems in order to improve planning, execution, and analysis of software projects.

You are not a coding agent. Your output is research: reports, briefs, data summaries, and planning. However, your research may be used as the basis for engineering plans, so produce enough detailthat a coding agent could take the output and confidently implement it.

Your main goal is to assist in researching large, open ended questions by looking at data across multiple systems, synthesizing it into insights, and presenting it in a way that supports decision-making and action. For examp

🧩 Callback-Based UI for LLM Streaming (with litellm + Rich)

TL;DR

Callbacks = functions you pass into a function so it can notify you about events.
For LLM streaming, three super-useful callbacks are:
- on_start() – called once at the beginning
- on_token(text: str) – called for each streamed token
- set_status(text: str) – called whenever the function wants to update a status line
This keeps your LLM logic clean and UI-agnostic while letting your REPL/UI control how things look.

Here's how to import a github repo into drive using google colab.

In the first cell, import the drive package and mount you google drive:

from google.colab import drive
drive.mount('/content/drive')

In the next cell, put a bash script that just clones whatever repo you want:

	{
	"extraKnownMarketplaces": {
	"my-skills": {
	"source": {
	"source": "github",
	"repo": "odewahn/skill-marketplace-test",
	"ref": "main"
	}

	}

	def calculate_bonus(total_sales):
	# Intended bonus rules:
	# - Over $20,000 → 10% bonus
	# - Over $10,000 → 5% bonus
	# - Otherwise → 2% bonus

	if total_sales > 10000:
	return total_sales * 0.05
	elif total_sales > 20000:
	return total_sales * 0.10

	Install the O'Reilly MCP Server on VSCode. You must have your auth token set:

	`export OREILLY_API_KEY=xxx`


	<a href="https://vscode.dev/redirect/mcp/install?name=oreilly.content-discovery-dev&config=%7B%22type%22%3A%22http%22%2C%22url%22%3A%22https%3A%2F%2Fapi.oreilly.review%2Fapi%2Fcontent-discovery%2Fv1%2Fmcp%2F%22%2C%22headers%22%3A%7B%22Authorization%22%3A%22Token%20%24%7BOREILLY_API_KEY%7D%22%7D%7D" rel="nofollow">
	<img src="https://img.shields.io/badge/VS_Code-Install_O%E2%80%99Reilly_MCP-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white" alt="Install O’Reilly MCP">
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	[![Install O’Reilly MCP](https://img.shields.io/badge/VS_Code-Install_O%E2%80%99Reilly_MCP-0098FF?style=flat-square&logo=visualstudiocode&logoColor=white)](https://vscode.dev/redirect/mcp/install?name=oreilly.content-discovery-dev&config=%7B%22type%22%3A%22http%22%2C%22url%22%3A%22https%3A%2F%2Fapi.oreilly.review%2Fapi%2Fcontent-discovery%2Fv1%2Fmcp%2F%22%2C%22headers%22%3A%7B%22Authorization%22%3A%22Token%20%24%7BORE

	import aiohttp
	import sys
	import asyncio
	import os


	# Before you start, get a content token from https://learning.oreilly.com/account/api-tokens/
	# You'll need the account manager role on your user membership. If you're not an account manager
	# then you'll need to request on from the U&A team of someone who can create tokens
	# Then create an environment variable called ORM_AUTH_TOKEN with the token as the value

	// Based on https://dev.to/andyrewlee/cheat-sheet-for-updating-objects-and-arrays-in-react-state-48np

	const DATA = [
	{ id: 123, name: "bart" },
	{ id: 256, name: "lisa" },
	{ id: 344, name: "homer" },
	{ id: 412, name: "marge" },
	];

	const addRecord = { id: 477, name: "frink" };