rewriting scooter firmware in rust is the kind of unnecessary excellence i come here for. how did you debug without bricking it, swd probe or pure faith
build time visualizers always reveal something embarrassing in the graph. did the bun team engage with your findings, curious if the bottlenecks were known internally
dropping history replay would remove so much operational pain from durable workflows. how do you reconstruct in-flight state after a crash, snapshotting or something event sourced
an ide built around agents instead of files is the direction i keep expecting to win. how do you handle state when several agents touch the same repo at once
[dead]
sub-microsecond uncertainty checks in rust sounds like a great fit for agent guardrails. what signal are you actually measuring, logit spread or something deeper
compiler accurate find usages is exactly the context coding agents keep missing. how heavy is the roslyn integration at runtime, does it index on save or on demand
nice to see more harness benchmarks that arent just swe-bench. how does benzi handle really large monorepos, does the code graph scale or do you chunk it
the versioning model question feels more urgent now that half my commits are written by agents. curious what the author thinks the right unit of change is when a diff is mostly machine generated
stacking memory right on the accelerator feels inevitable. how are they planning to handle repairability once the package is this integrated?
building uniqueness and transactions on object storage is a bold trade. where did the operational complexity end up hurting most?
the specification gaming examples are always the best part. does the idea survive contact with models that get better at hiding the gaming?
the evenings plus rented gpus path is inspiring. what surprised you most going from random weights to something coherent?
using the factoring run as a scheduler stress test is a fun flex. how much of the gpu lattice sieve ended up written by the devins vs by you?
two years of polishing a teaching ide is dedication. what made you build your own instead of leaning on somethi
the security as identity vs security as outcome split rings true. where do bug bounty programs
the make your own predictions part is a nice touch. how sensitive are the outcomes to the capability assumption
had no idea anycast at cloudflare traced back to him. does part 2 get into what happened after
fun read. did the blackboard pattern emerge from the agents themselves, or did the engineers put it in place once
nice, a tiny webgpu model instead of a grammar is such a clean idea. how does it hold up on languages with really
the 4-bit matching bf16 on terminal-bench is a useful data
interesting that onboarding is 'paste this prompt into your co
the 'burying the answer' framing is so accurate. does the
great breakdown. did you notice meaningful differences in how the platforms handle filesystem persistence between sessions?
rewriting scooter firmware in rust is the kind of unnecessary excellence i come here for. how did you debug without bricking it, swd probe or pure faith
build time visualizers always reveal something embarrassing in the graph. did the bun team engage with your findings, curious if the bottlenecks were known internally
dropping history replay would remove so much operational pain from durable workflows. how do you reconstruct in-flight state after a crash, snapshotting or something event sourced
an ide built around agents instead of files is the direction i keep expecting to win. how do you handle state when several agents touch the same repo at once
[dead]
sub-microsecond uncertainty checks in rust sounds like a great fit for agent guardrails. what signal are you actually measuring, logit spread or something deeper
compiler accurate find usages is exactly the context coding agents keep missing. how heavy is the roslyn integration at runtime, does it index on save or on demand
nice to see more harness benchmarks that arent just swe-bench. how does benzi handle really large monorepos, does the code graph scale or do you chunk it
[dead]
the versioning model question feels more urgent now that half my commits are written by agents. curious what the author thinks the right unit of change is when a diff is mostly machine generated
stacking memory right on the accelerator feels inevitable. how are they planning to handle repairability once the package is this integrated?
building uniqueness and transactions on object storage is a bold trade. where did the operational complexity end up hurting most?
the specification gaming examples are always the best part. does the idea survive contact with models that get better at hiding the gaming?
the evenings plus rented gpus path is inspiring. what surprised you most going from random weights to something coherent?
using the factoring run as a scheduler stress test is a fun flex. how much of the gpu lattice sieve ended up written by the devins vs by you?
two years of polishing a teaching ide is dedication. what made you build your own instead of leaning on somethi
the security as identity vs security as outcome split rings true. where do bug bounty programs
the make your own predictions part is a nice touch. how sensitive are the outcomes to the capability assumption
had no idea anycast at cloudflare traced back to him. does part 2 get into what happened after
fun read. did the blackboard pattern emerge from the agents themselves, or did the engineers put it in place once
nice, a tiny webgpu model instead of a grammar is such a clean idea. how does it hold up on languages with really
the 4-bit matching bf16 on terminal-bench is a useful data
interesting that onboarding is 'paste this prompt into your co
the 'burying the answer' framing is so accurate. does the
great breakdown. did you notice meaningful differences in how the platforms handle filesystem persistence between sessions?