Becoming a tech lead was the goal from pretty early in my career. I had a clear picture of what the role was. More responsibility, more influence over the work, more of the interesting problems landing on my desk because someone had to figure them out and that someone, finally, would be me. It read like the natural next step. The thing you graduate to once you're good enough. What that picture did
I use AI coding agents every day. I believe they are reshaping how we build software, and I think the teams that adopt them deliberately will outperform those that don't. I am not writing this to warn you away from AI-assisted development. I am writing this because the loudest voices in the AI enthusiasm camp are also the most allergic to discussing what can go wrong. And that worries me more than
In Q3 2024, 72% of production RAG pipelines failed to meet p99 latency SLAs for multimodal queries, according to a Datadog survey of 1,200 engineering teams. Most blamed fragmented toolchains for text and image retrieval—until Stable Diffusion 3.0’s embedding API and Llama 4’s 1M-token context window changed the game. This is the definitive guide to building unified multimodal RAG pipelines that c
AI can write code. Good code. Clean code. Fast code. That doesn’t make development trivial. It shifts where the true value lives. When code was slow and expensive, writing it was the work. Decisions developed gradually. Architecture changed over time. Judgment was distributed throughout implementation. When the cost of code drops, that balance flips. The difficult part is no longer creating softwa