Agentic Coding Is Not a Trap: I Answered the Viral HN Post With My Own Production Logs I made the exact mistake that viral post criticizes: I gave an agent an ambiguous task and went to make coffee. Came back 40 minutes later to 23 modified files, three broken tests, and a refactor nobody asked for. I'm not telling this to complain — I'm telling it because that day I started keeping logs of my a
DeepClaude: I Combined Claude Code with DeepSeek V4 Pro in My Agent Loop and the Numbers Threw Me Off DeepSeek V4 Pro correctly solves 94% of deep reasoning tasks in my loop… but the latency cost makes it unusable for 60% of my agent cases. Yeah, you read that right. And that completely blows up the narrative of "combining models is always better." Tuesday night I watched the DeepClaude post cli
Specsmaxxing: I Wrote YAML Specs for My AI Agents — Here's What Changed (and What Didn't) A YAML spec for an AI agent is basically the blueprint you leave for the contractor when you can't be on-site. If the blueprint is solid, they build exactly what you want. If there's one ambiguous detail — "wall at the back" with no measurements — they make a call, and when you show up, the wall is in the w
Barman Replacing pgbackrest: I Migrated My Postgres Backups in Production and Here's What I Found The weekend I migrated from Vercel to Railway — the same one I mentioned when I talked about cold starts — I spent nearly twelve hours reading Postgres logs I'd never had to read that seriously before. It wasn't a tutorial. It was real production, real data, and the underlying question was always th
Kimi K2.6 vs Claude vs GPT-5.5: I ran it against my real coding cases and the numbers surprised me I was looking at a PR I'd asked Claude Sonnet 3.7 to refactor — a TypeScript data ingestion service with three layers of badly chained async — when I saw the Hacker News thread about Kimi K2.6. The claim was straightforward: Kimi K2.6 beats Claude and GPT-5.5 on coding benchmarks. LiveCodeBench, SW
When you have 5 unrelated questions, should you pack them into one message to the LLM, or send 5 requests simultaneously? Which is faster? Splitting into multiple independent parallel requests is almost always faster. This isn't a gut feeling — it's determined by the underlying inference mechanism of LLMs. Let's walk through the reasoning from first principles. To understand this problem, you firs