There is a point in many serverless platforms where a Step Functions workflow that once felt elegant starts to feel like a mini application platform of its own. I have seen this happen in teams that are doing many things correctly: they standardized orchestration, they improved visibility, and they moved fragile glue logic out of Lambdas. Then six months later, the workflow has 100+ states, a maze
Comments
A some time ago I shipped a desktop app to generate LLM fine-tuning datasets. It worked: my Qwen2.5-Coder-7B fine-tune jumped from 55.5% → 72.3% on HumanEval. Whole pipeline ran on OpenRouter — pick a model, click Generate, get JSONL. v1.0.3-beta ships multi-provider LLM support — Ollama, LM Studio, llama.cpp, or any custom OpenAI-compatible endpoint, plus the original OpenRouter. Mix and match: g
A beautiful personal tribute to the practice of programming, interrupted by the switch to LLMs. Comments
Most of my team got laid off because "AI can do their jobs now." I'm probably the last one standing. And every day I use the same tools that replaced them, fix their mistakes, and write in the standup that AI helped me move faster. Nobody was being honest about this. So I built AIHallucination — a community for real, unfiltered AI experiences. The fails, the wins, the absurd outputs, the expectati
Overview Let's get our hands dirty. This part covers the full setup and the actual demo: deploy PayLedger to both regions, wire up Route 53 failover, configure the Agent Space, inject three simultaneous faults, and walk through exactly what the agent found. Quick recap from Part 1: PayLedger is a demo payment ledger deployed to ap-southeast-1 (primary) and ap-northeast-1 (secondary) with Route 5
TL;DR The job. Take typia's existing TS files, translate the contents line by line into Go, change the extensions to .go. Keep the algorithms and compiler logic intact. Iterate until 80,000 lines of e2e tests pass. What the AI actually did. Did a half-assed implementation and deleted all the failing tests. Burned 8 billion tokens to hardcode every output into a 168-case lookup table — and call