Targeted how-tos: prompt caching, bugfix loops, QA, tracing, setup drift.
*A five-part series tracing Anthropic's public thinking through Dario Amodei's writing and the company's model spec — one foundational document per entry, ea…
*Part 2 of 5 — tracing Anthropic's public thinking with FRE|Nxt Labs production commentary.*
*Part 3 of 5 — tracing Anthropic's public thinking with FRE|Nxt Labs production commentary.*
*Part 4 of 5 — tracing Anthropic's public thinking with FRE|Nxt Labs production commentary.*
*Final entry in the Amodei Playbook — from the safety floor of 2023 to the endgame framing of 2026.*
*A five-part series tracing Harrison Chase's public thinking on production AI agents — from LangChain as a weekend project to ambient agents — with FRE|Nxt L…
*Part 2 of 5 — tracing Chase's production agent thinking with FRE|Nxt Labs commentary.*
*Part 3 of 5 — tracing Chase's production agent thinking with FRE|Nxt Labs commentary.*
*Part 4 of 5 — tracing Chase's production agent thinking with FRE|Nxt Labs commentary.*
*Final entry in the Chase Playbook — from an 800-line weekend project to the UX that replaces chat.*
*A four-part series breaking down the Claude Code creator's actual workflow — parallel agents, CLAUDE.md as postmortem, the "model wants tools" design philos…
*Part 2 of 4 — breaking down the Claude Code creator's actual workflow with FRE|Nxt Labs production commentary.*
*Part 3 of 4 — breaking down the Claude Code creator's actual workflow with FRE|Nxt Labs production commentary.*
*Final entry in the Cherny Playbook — from origin story to design philosophy.*
Cursor without rules is a capable assistant that doesn't know your project. Cursor with well-written rules is an assistant that already knows your stack, con…
The problem with AI-assisted teams is not that the tools are inconsistent — it's that each developer's tool has a different idea of what "correct" looks like…
Every session without a `GEMINI.md` starts from zero. You re-explain your stack. You re-explain your conventions. You catch mistakes that your context would…
The first time we ran Gemini CLI we expected it to behave like a browser-based chat. It doesn't. It's a terminal agent that reads your project, runs commands…
When one engineer's Gemini CLI session produces different code shape than another's, the root cause is almost always context — not the tool. One person has a…
*A six-part series tracing Andrej Karpathy's public thinking from 2017 to 2025 — one foundational post per entry, each with FRE|Nxt Labs live commentary on h…
*Part 2 of 6 — from Software 2.0 to the March of Nines, tracing Karpathy's public thinking with FRE|Nxt Labs production commentary.*
*Part 3 of 6 — tracing Karpathy's public thinking with FRE|Nxt Labs production commentary.*
*Part 4 of 6 — tracing Karpathy's public thinking with FRE|Nxt Labs production commentary.*
*Part 5 of 6 — tracing Karpathy's public thinking with FRE|Nxt Labs production commentary.*
*Final entry in the Karpathy Playbook — from 2017's Software 2.0 to 2025's reality check.*
Before contributing, understand what runs when you type `python cli.py run --suite smoke`.
QA Agent is open source. Contributions that add integrations, improve the report format, or extend the agent's tool set are all welcome.
A good test case reads like a conversation between a QA engineer and a developer — not like code.
A green result is only useful if you trust it. A red result is only useful if you can debug it fast.
From zero to running your first automated browser test in 15 minutes.
A flat folder of 30 test cases becomes unmanageable fast. Suites and personas are how you keep it structured.
Code changes land fast. Your QA process shouldn't be the bottleneck.
Publishing cables is not just writing markdown. The failures usually happen in validation, index generation, or submodule pointer updates.
This cable installs the actual stack artifacts from Ragav's `~/.claude*` and `~/.codex` setup so anyone can replicate it quickly.
Manual QA catches issues, but it does not scale when UI changes land every day.
We had tests. We also had regressions. The problem was coverage shape, not raw test count.
You cannot optimize what you do not measure. For agent systems, that means evaluating both quality and execution cost — and doing it per task class, not in a…
When an agent fails, raw logs usually tell us *what* happened, not *why* it happened in that step sequence.
Prompt caching is one of those optimizations we all think we enabled "well enough" until costs spike and response times drift.
The fastest way to ship bugs is to skip one of those three steps.