avabuildsdata

4 माह पहलेdiscuss

yeah this is the part that got me excited honestly. we're not google-scale by any stretch but we have ~8 internal Go modules and deprecating old helper functions is always this awkward dance of "please update your imports" in slack for weeks. even if it doesn't let you delete the function immediately for external consumers, having the tooling nudge internal callers toward the replacement automatically is huge. way better than grep + manual PRs

avabuildsdata

4 माह पहलेdiscuss

fwiw I work on data ingestion pipelines and I've found that starting with just boxes-and-arrows in something like Excalidraw gets you 80% of the way to knowing what you actually want. The gap between "I can picture it" and "I can build it on a webpage" is mostly a d3 learning curve problem, not a design problem.

xyflow that the creator mentioned is probably the right call for pipeline DAGs though -- we use it internally for visualizing our scraping workflows and it was surprisingly painless to get running

avabuildsdata

4 माह पहलेdiscuss

the Go choice makes a lot of sense for this. i've been wiring up government data sources for a different project and honestly the format inconsistency between agencies is always the real headache, not the actual processing

curious about the 23 tools though -- are those all invoked through one Gemini orchestration pass or is there a routing layer picking which subset to call per detection? feels like that'd stack up fast latency-wise

avabuildsdata

4 माह पहलेdiscuss

honestly the thing that trips me up is when codegen makes me feel productive but I haven't actually validated anything. like I'll have claude write a whole data pipeline in 20 minutes and then spend 2 hours debugging edge cases it didn't think about because it doesn't know our data

the speed is real but it mostly just moves where I spend my time. less typing, more reading and testing. which is... fine? but it's not the 10x thing people keep claiming

avabuildsdata

4 माह पहलेdiscuss

The unfair scheduling point resonates. I run a lot of concurrent HTTP workloads in Go (scraping, data pipelines) and the scheduler is honestly fine for throughput-oriented work where you don't care about tail latency. But the moment you need consistent response times under load it becomes a real problem. GOMAXPROCS tuning and runtime.LockOSThread help in narrow cases but they're band-aids. The lack of priority or fairness knobs is a deliberate design choice but it does push certain workloads toward other runtimes.

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