All postsApril 2025 to September 2026
Thoughts on engineering,
AI, and building products.
2026
One question a week
In 2014 my capstone project sent my family one question a week for about eighteen months, and then we stopped. The answers never went anywhere. Today the same application is an iPhone app.
An Agent Reads My Email
Every morning at five, a local model reads yesterday's mail and writes me a briefing. How it works, what 217 days of numbers say, and what happens when spam is written for the model instead of for me.
The Processing Layer Is Not a Moat
LLMs are the refinery. Everyone has access to the refinery now. The only question is: who owns the crude?
Be Your Own Historian
One conviction, held since 2012, and what it predicts. In a world where intelligence is cheap and getting cheaper, the only thing that stays scarce is the record of a particular life.
Notes from the AI Gatherings, Vol. 1
First in a recurring series - what we compared notes on this week at the Friday morning AI gathering: cleaning data 700 calls at a time, the two economies of tokens, and building the tool before the feature.
The Machine I Can't Turn Off
A four-year-old Mac Studio runs a night shift of local agents - launchd as the runtime, open-weight models as the brains, git as the memory. What I've learned building it, and the economics thesis I'm testing.
Whispering to the Machine: Take Three
Five months after Take Two, the third snapshot. The agents work nights now, the world pushed its way into the workflow, and the most durable thing on my machine is the record.
The architecture of advice
Once you have a month of AI session data, you stop asking 'how much?' and start asking 'what kind?' Mapping the hidden patterns that define how AI actually influences a multi-repo technical portfolio.
30 days of AI collaboration
A data-driven retrospective of one month of high-intensity AI-assisted engineering: 466 sessions, 55,224 turns, 1,772 decisions extracted from raw chat logs into a durable knowledge graph. Built in early April, a month before Anthropic announced 'dreaming' as a feature.
Getting good output from Lens
If your Lens recaps come out cluttered with reasoning preambles or hallucinated detail, the fix is almost always upstream of Lens. A field guide to picking a model and configuring LM Studio for clean daily snapshots.
Four frontier models, one year of office sensor data, four very different dashboards
I gave a year of Adafruit Clue environmental data to Gemini, GPT-5.4, GPT-5.5, and Claude Opus 4.7. Same prompt, same dataset, four very different answers, and only one of them asked for more data.
The move was upstream
I spent most of a day in a stalled debug loop with one AI model before opening a fresh session with another. The lesson wasn't about which model is smarter. It was about going upstream.
Why personal AI belongs on hardware you already own
Apple just put two hardware engineers at the top of the company. The cloud labs are losing money on their best customers. From inside a hybrid practice, here's what the local share looks like and why it's growing.
A general-purpose MoE multimodal beat every dedicated vision model on my father's handwriting
I assumed a specialized vision model would win. I was wrong. A head-to-head on a hard handwriting corpus ended with the general-purpose MoE on top.
Running real models locally on a Mac Studio that isn't new anymore
How I run a multimodal LLM on four-year-old hardware to read a family archive without sending anything to the cloud.
What family archives are for, now that the AI can read them
I have three collections of family letters spanning a century. Until recently, reading them properly would have taken years. Now it takes an afternoon.
The Memex Has Been Waiting 80 Years for This Moment
Vannevar Bush described the memex in July 1945. It took 80 years to become buildable, because it required two things that didn't coexist until now: cheap private AI, and the conviction that data ownership is the last real moat.
From Tools to Framework
Frameworks are inevitable. They emerged for Ruby, Python, and PHP. Now they're emerging for agentic development. Here's the one that evolved on my workbench, and what it taught me about working with AI.
Context Architecture
AI agents are only as good as the information they can find. Context architecture is the skill of building structured environments where agents reliably retrieve exactly what they need.
Specification Precision
The highest-demand AI skill in 2026 isn't coding. It's writing instructions so precise that a machine can't misinterpret them.
The System That Built Itself
Six months of surviving as a one-person engineering team produced something I didn't expect: a layered AI operating system that grew organically from daily necessity.
Eliminating Waste in the SDLC
When AI can use the same tools you use, Jira and GitHub and Sentry and GCloud, everything about the software development lifecycle changes. Not because AI writes the code. Because it eliminates the waste around the code.
Whispering to the Machine: Take Two
Eight months after writing about AI-powered development, the ground has moved so far that the original piece reads like a dispatch from another era. Here's what the work actually looks like now.
The One-Person Engineering Team
The role of the software engineer is changing. What happens when one person with AI agents can cover the ground that used to require a full team?
2025
Whispering to the Machine
What collaborating with AI in software development actually looked like in mid-2025. The workflow, the lessons, and the shift from writing code to orchestrating it.
Building an environmental monitor with the Adafruit Clue
A CircuitPython and Python environmental data logger on the Adafruit Clue. BLE ambitions, a USB Serial pivot, and a pywebview gateway with live charts. Originally posted on officeofadamthede.com.