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So Something

Joined August 2023
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funny detail in google's new tpu announcement: agents pause so often on a db lookup, a tool call or a human that they built software to suspend idle sessions and move the memory off chip so the fancy accelerator has the same problem as a backend worker. mostly waiting on io
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webhook receiver checklist: 1. verify the signature on the raw body, before json parsing 2. return 2xx fast, push the work to a queue 3. dedupe on the event id, delivery is at least once 4. don't trust arrival order, refetch the object if state matters
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redis goes down. do you fail open or fail closed? i lean fail open for cache (just hit the db) and fail closed for rate limits. otherwise a redis blip means no throttling and a db stampede. what's your default?
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anthropic on agent memory: the agent wrote a wrong location note, trusted it next session, and looped on a mountain an offline 'dreaming' pass rechecked the note against the transcript and fixed it would you let your agent rewrite its own long-term memory while idle?
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hot take: most list endpoints don't need a total count postgres keeps no cached row count, so a filtered COUNT(*) visits every matching row. on big tables it can cost more than the page itself fetch limit+1 rows and return has_more instead
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delete /items/123. first call 204. client retries, item's already gone. 204 again or 404? i lean 204 so a flaky network doesn't turn a success into an error for the client. what's your default?
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openai's agent pipeline found 948 working linux root exploits at roughly $1,279 each finding bugs just got cheap. reviewing 948 patches didn't kernel maintainers are about to have a very long inbox
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pgbouncer in transaction mode quietly breaks anything that leans on session state. SET without LOCAL, session advisory locks, LISTEN, temp tables. prepared statements too unless you're on 1.21+ with max_prepared_statements set which one got you first?
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anthropic open sourced flashprove, zk proofs that a reply came from the model your provider claims. weights stay hidden. catch: proving costs ~70 to 95x the gpu time of running it on vllm. honest q: would you pay more per call to know your api isn't quietly swapping models?
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celery on a redis broker, things that bite: 1. acks_late + non idempotent task = a worker crash runs it twice 2. acks_late task running past visibility_timeout (1h default) gets redelivered while still running 3. eta/countdown longer than that timeout gets redelivered too
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hot take: the best part of openai's pando git writeup isn't the nearly 5x faster pushes it's that they gave up repo-wide consistency. each branch updates on its own, so two branches can look out of sync once agents push in parallel, the global lock is the first thing to go
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Casually updating CRED and checked “What’s new” out of curiosity. Expected bug fixes, got Alan Turing and a philosophy lesson instead. 💀😂 Bro, I just wanted to know what changed! Whoever writes @CRED_club release notes deserves a raise. 😂
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user requests /orders/123 but it belongs to someone else. do you return 403 or 404? 403 is more honest, 404 doesn't leak that the order exists. i lean 404 for anything with guessable ids. what's your default?
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meituan's longcat-2.5 writeup has my favorite training detail: they wipe git history and cached files from the task repos so the model can't just dig the fix out of git log honestly that's the first thing i'd try too do your agent eval sandboxes strip that stuff?
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classic django mistake: looping over orders and reading order.customer.name in the template. 1 query for the list, then 1 more for every row. fix: select_related("customer") for foreign keys, prefetch_related("items") for reverse fks and many to many.
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deepseek open sourced a research coding agent that watches a repo and team chat and takes work when someone allowed asks best line in its docs: an agent acting for a whole team can waste a whole team's attention so it only talks when someone must act. more agents need that
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rate limiting a public api, token bucket or sliding window? token bucket allows short bursts, which most clients actually need. sliding window log is stricter but stores every request timestamp in redis. also curious what you key it on. api key, user id, or ip
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computer use agents: screenshots and guessing pixels, or reading the os accessibility tree like vercel's new agent-desktop does? stable refs and retry-safe clicks sound boring but that's what makes it usable in a real workflow which one would you trust in prod?
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every fastapi codebase has that one async def endpoint calling requests.get() and everyone wonders why p99 goes up when one upstream is slow. plain def would've been fine, fastapi runs it in a threadpool
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mistral large 4 weights are finally out. 1T params, open, but even the nvfp4 build wants 4 gb300s and the api is $0.70 in, $2.10 out per M honest question for builders: does open weights at this size change anything for you, or do you just call the api anyway?
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