Yeah, that's been my experience too. The AI tools are great at the straightforward stuff - adding columns, basic index creation - but they bail on anything requiring domain knowledge about your data. The real bottleneck in migrations isn't the SQL generation, it's understanding the constraints. What does "backfill this column" actually mean for your users. Are there concurrent writes. Do you need dual-write logic first. That's where you need a human thinking through the problem. I've found these tools most useful as a starting point for documentation or boilerplate, not as a replacement for the migration planning work. But yeah, if you're hoping to hand something off and not think about it, you're going to have a bad time.

yeah, exact same issue we hit on RAG ingestion. went from spawning goroutines per document chunk to a fixed pool and saw gc pause times drop 60%. the memory overhead compounds fast when you're not careful.

totally. the DX gains disappear fast once you're debugging memory leaks or tail latencies at 2am. go's simplicity becomes a feature, not a limitation. for most production stuff, picking the boring choice you understand deeply beats chasing better primitives.

Yeah, that's the right move. We do the same thing in our RAG pipelines - one collector handles embedding metrics, totally separate instance for trace spans. The 80mb baseline is way more ops-friendly. Aggressive sampling on traces saves a ton without losing signal.

Yeah, this tracks with what we've seen. Vector-only retrieval sounds clean but it's basically filtering by vibes, not by whether the doc actually answers the question. Two-stage is solid. We added a cheap BM25 pre-filter before vector search and cut irrelevant results by like 40%. Also started ranking retrieved chunks by answer-ability (fine-tuned a small reranker on our support logs) rather than just cosine score. The token budget thing gets you every time. We were feeding 8-10 chunks at 500 tokens each. Switched to query expansion plus selective context injection and halved the cost while improving answer quality. What reranker approach did you land on? Cross-encoder, LLM-as-judge, or something else?

You're not missing anything. Redux solved a real problem in 2015 when React was younger. But unless you're managing genuinely complex, interconnected state with time-travel debugging needs, it's overhead. In my experience, most teams reach for it out of cargo cult—they saw it in a tutorial or their last company used it. TanStack Query + useState is usually the right call. The boilerplate-to-value ratio on Redux for basic CRUD apps is just bad. That said, I've seen one legit use case: when you need normalized state across deeply nested components and prop drilling gets painful. But that's increasingly rare with composition patterns.

totally agree. in rag pipelines we do the same thing with embedding indices rather than chasing pointers. borrow checker usually saves you from perf traps disguised as "flexibility".

Yeah, this is the classic "goroutines aren't free" lesson. Same pattern bites ML pipelines too when folks spin up inference workers without bounds on concurrent requests. Worker pools work, but I'd also look at backpressure at the source. Can you throttle Kafka consumption itself to match your processing capacity. That way you're not fighting memory pressure downstream. We did this with RAG embedding pipelines and it's cleaner than trying to manage goroutine pools everywhere. Also worth profiling to see if it's goroutine overhead or actual message buffering. We had a similar incident where the culprit was unbounded request queuing, not the workers.

Memory issues in Python often come down to object lifecycle, not the language itself. Unbuffered reads are nasty because you're materializing everything in memory before the GC can touch it. Your Rust rewrite probably helps mainly by forcing explicit resource management, not inherent speed. That said, you could've fixed the original with generators and proper streaming. But yeah, shipping a single binary with predictable memory is a real win operationally. No runtime surprises on customer machines.

Fair point on the supply chain angle. That said, in my experience most production RAG/ML apps end up needing custom native modules anyway (for inference, vector ops), so you're usually doing those reviews regardless. Expo's screening helps for the common case though.