Practical articles on AI, DevOps, Cloud, Linux, and infrastructure engineering.
Our early postmortems quietly assigned blame and taught people to hide mistakes. Here's the template and the facilitation rules that finally made them honest and useful.
We used to ship code and turn it on in the same breath, so every deploy was a bet. Feature flags split those two events apart and made rollbacks a config toggle.
When our single LLM provider had a 40-minute outage, every AI feature went dark. A gateway with routing and fallback fixed that, and cut spend 30% as a bonus.
A p99 that jumped to 3.4 seconds during traffic ramps turned out to be cold starts. Here's how we measured them properly and cut the tail, with real init timings.
Most SLI dashboards track things nobody notices. Here's how we picked the handful of signals that map to real user pain, and dropped the vanity metrics.
Reliability arguments used to be shouting matches between SRE and product. An error budget turned them into arithmetic. Here's how we made the number drive the roadmap.
A 180k-token context window is not a license to stuff everything in. Here's how we cut prompt size 60% without hurting answer quality, and what to trim first.
Users hit stop, but our server kept paying for tokens for another 40 seconds. Here's how we wired real cancellation and backpressure into an SSE streaming endpoint.
We moved 40 TB of user media off S3 and cut the bill by 70 percent, mostly by killing egress fees. Here's where R2 won and where we kept S3 anyway.
We had the same 180-line build workflow copy-pasted into 60 repos. Fixing one bug meant 60 PRs. Here's the reusable-workflow setup that made it one.
Our failover config looked perfect in the console and did nothing during a real outage. Here's the health-check design that actually flipped regions when it mattered.
Most agents that "need memory" actually need a smaller context window and a database. Here's how we cut a support agent's token bill by 60 percent by deleting memory.
