Notes from the pond

Articles

Notes on building focused, practical software, written as we build it.

Autonomy, competence and relatedness, the three needs of Self-Determination Theory, with the question: will AI be a replacement or an amplifier?

AI and Self-Determination Theory: Will AI Make Us More Capable or Less?

How AI can strengthen or weaken autonomy, competence and relatedness, what the Industrial Revolution teaches us about disruptive technology, and why AI works best as an amplifier, not a replacement.

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Five-step automation framework: Log It on Monday, Score It on Thursday, Write It Up on Thursday, Test It on Friday, Decide on Friday.

What to Automate First: A Small Business Automation Framework

A five-day framework from a Colorado Startup Week talk for scoring your first business automation before you commit, plus the five-stage maturity map for getting there.

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Diagram of the Navier-Stokes incident as a pipeline: mathematicians' private work flows into OpenAI's Codex tool, through an unknown pathway, and out as a breakthrough proof, with OpenAI's statement that it cannot rule out that usage data helped improve its models.

Why Local AI Matters More Than Ever

Why sending your data into someone else’s AI model is a competitive risk, and what running models locally with Gemma, Ollama and SentenceTransformers is like.

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Two panel diagram comparing where project status comes from: scattered, self-reported sources like requirements, dev status, risks, testing, and schedule, versus evidence signals like commits, tests, transcripts, requirement changes, and deployments feeding an AI-drafted picture that a person still decides on.

Who Owns This Task When AI Agents Do the Work?

We’re adding AI agents to project teams while still tracking status with tools built for human self-reporting. A large ERP program shows why evidence beats self-reported status, and where AI fits without replacing human judgment.

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Comparison graphic of three AI system design paradigms: Single LLM, Structured Workflow, and Autonomous Agent.

Comparing AI System Designs

A staffing analogy for choosing between single LLM calls, structured workflows, and autonomous agents, and why the cheapest tool that solves the problem beats the most impressive one.

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Title card contrasting semantic RAG search, the retrieval layer, with AI agents, the orchestration layer.

AI Agents and Semantic RAG Aren't the Same Thing

A customer service refund request shows why RAG retrieves trusted context while agents coordinate the decision and the action, and why mixing the two up gets teams in trouble.

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Building Council of AI: four AI models, one question, and the bugs hit getting them to agree.

Building Council of AI

Four AI models, one question, and two production bugs that shaped how the pieces fit together.

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Hand-Rolled Chunker vs. LangChain Splitters comparison for chunking patent text in a patent ingestion pipeline.

Why I Didn't Use LangChain to Chunk Patent Text

We skipped LangChain for the chunking layer in our patent ingestion pipeline and wrote our own splitter instead. Here's why that was the right call.

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