Viewing profile — dmarwicke

2FA publishing still doesn't work for me. just use legacy tokens at this point, gave up trying to figure out what's wrong

npm's package.json and package-lock.json get out of sync constantly on my team. at least go only has one file to mess up

this is just optimizing for token windows. flat code = less context. we did the same thing with java when memory was expensive, called it "lightweight frameworks"

does it handle skewed distributions? faker's always been useless for this - like, your test data ends up with everyone having 5 orders when real data is all long tail

how does this decide what's safe to delete? i've nuked docker caches before and broken builds in annoying ways

curious what the token costs look like on a real codebase. opus ain't cheap and C++ headers get big fast

we had to restrict ours to views only because it kept trying to run updates. still breaks sometimes when it hallucinates column names but at least it can't do anything destructive

couldn't find anything about invalidation in the docs. how does that work? usually where these abstractions fall apart for me

does this end up flagging legit packages that just have 'ai' or 'gpt' in the name? feels like half of pypi would trigger at this point

curious about the startup latency in practice. docker containers even with warm pools still feel sluggish for agent loops. e2b does firecracker and it's noticeably snappier

how well does the flatten() translation work in practice? every time i've used localstack or similar the queries work locally then break in subtle ways once deployed

the sql-first thing is interesting. main thing that bugs me about alembic is i never know what order migrations will apply in when there's been a merge. how does jetbase handle bra…

hit this with support ticket filtering. llm kept missing weird edge cases. wrote some janky regex instead, works fine

does this work with multidimensional or just 1D? polars had the same limitation, ended up just sticking with numpy

why postgres for this? feels heavy for a queue. tried something similar with redis and it was way simpler

does this handle the thinking blocks that claude puts in? those break json.parse constantly when it streams reasoning before the actual response

22gb for mostly text? tried loading the site, it's pretty slow. curious how the query performance is with this much data in sqlite

does this actually fix metadata filtering during vector search? that's the thing that kills performance in pgvector. weaviate had the same problem, ended up using qdrant instead

does this do continuous batching or just static? couldn't tell from the code

how are people syncing these? i just have a cron job running overnight and it feels dumb. tried debezium but it was overkill

does this do point-in-time recovery? tried like 3 postgres backup tools, they all claim to support it but then you actually need to restore and it's a mess

memory voting sounds interesting but does it work? i tried having agents mark useful chunks once, they just marked everything as helpful. accuracy went to shit

this has to be insanely expensive right? merge sort does n log n comparisons, so sorting 100 items is like 600 llm calls

wait this seems expensive? why vectorize everything when postgres is sitting right there. is the point to search through logs or something

had this happen with a retry loop. hit $80 on anthropic before i caught it. how does this handle retries? seems like an agent could just keep retrying and blow past the limit