
454 Pull Requests in Twelve Days
A month of unattended building on a live product: 454 pull requests in twelve days, six production releases and no reverts, and the pieces that made it safe.
Long-form write-ups of how I build with agents, with the numbers. Memory, model routing, review gates and the hardware underneath.

A month of unattended building on a live product: 454 pull requests in twelve days, six production releases and no reverts, and the pieces that made it safe.

The standard answer to “how do I stop my agent making things up” is retrieval. Give it a search tool over your documents and your code, and it will look…

How do you start using bigger models for the complex tasks without driving the cost through the roof? My last article described a delivery pipeline end to…

In my last article I described what a closed build loop needs – a foreman that owns a backlog, a build that runs a real correctness loop, independent review…

The most important button in my development process now does almost nothing. When a pull request lands for review on one of my products, I read the summary…

One evening in June I pointed an AI agent at a product backlog, gave it the rules of engagement, and went to bed.

Every serious agentic memory system you can read about today has converged on roughly the same whiteboard. Capture the turns. Summarise them.

Compaction wipes what an agent session has learned. Five tiers of session memory, from files Claude Code already writes to a shared team brain.

Three upgrades to my second brain: diagrams drawn as Mermaid, scraping that respects a site's terms, and research triage grounded in my own stack.

Four rounds of tuning gpt-oss-120b on one DGX Spark: a 4.3x single-stream gain from flags, an EAGLE3 dead end, a source build and a one-line CUDA graph fix.

Most developers now use AI tools, yet the measured gains disappoint. Why the paradox happens, and the team-context infrastructure that fixed it for me.

Benchmarking the NVIDIA DGX Spark (GB10, 128GB): 120x more throughput at concurrency 256 than single-stream reviews show, with the full vLLM config.
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