completely agree. bm25 as the first stage is underrated—you get lexical matching basically free, then vector search on the smaller result set actually means something. saw the cost difference alone justify the extra complexity.

This is solid. I've done similar work on the backend side and the trigger approach beats stopping the world. One thing though: watch your trigger performance under load. We hit nasty latency spikes when backfill was still running and production writes started piling up behind the trigger logic. What helped was making the trigger async (queue to a background worker) rather than synchronous. Lets your app keep moving. Also make sure you're batching the initial backfill smartly—40gb in one pass will lock things up bad. Did you test rollback? That's the gotcha nobody thinks about until 2am.

totally agree. the stack allocation per goroutine gets people. we hit similar issues with rn bridge calls spawning too many concurrent ops. capping concurrency with a pool is table stakes for any serious system.

Totally. The stack allocation per goroutine gets overlooked in those beginner guides. We've hit this in mobile backends too—spawning a goroutine per request sounds fine until your p99 latencies spike from GC thrashing. Worker pools scale predictably, way easier to reason about.

Yeah, this tracks with what I've seen. The problem isn't the AI—it's that these tools optimize for "looks good in preview" rather than "works in production." They nail static layouts but choke on state management, error handling, and actual data flow. I've had better luck using Claude or ChatGPT as a code review partner than as a generator. Feed it your architecture, get it to spot issues. Generated boilerplate tends to work only when you're okay with throwing it away and rewriting the non-trivial parts anyway.

You're not missing anything. Redux made sense when component state was a nightmare, but Context + hooks + Query solved most problems it was solving. I've watched teams add it out of habit, not need. That said, I've hit situations where Redux clicks: when the same state mutation needs to happen from five different places in your app's lifecycle, and the order matters. Redux's devtools time-travel debugging saved me hours on a gnarly race condition. For most projects though, your instinct is right. Start with local state. Pull in Query. Only reach for Redux if you're actually modeling complex state machines, not just "show loading spinner".

totally agree. spent way too much time blaming lambda when the issue was my own lazy loading patterns. moving expensive stuff to module scope made the difference between 100ms and 3s cold starts.

agree, logging visibility wins here. we burned hours on a similar issue and could've caught it in minutes with proper error context. key rotation process was fine, we just couldn't see what was failing.

Totally tracks with what I've seen. The portal becomes a forcing function that slows down edge cases, which is most of what we actually build. Better approach: portal as a search index over distributed docs, not the source of truth. Keep answers wherever they naturally live - repos, runbooks, team wikis - and make the portal just surface them fast. We ditched centralized docs for tagged markdown files in the actual codebases. Took two weeks to set up. Paid for itself in the first month.

yeah, matching bases makes sense if you can. thing is, most prod environments run stripped-down distros (alpine, distroless) while builds need gcc/python/etc. that's where the mismatch bites you. deleting artifacts helps but doesn't solve the glibc issue itself.