@swann_bessa needs no tuning beyond k is the part that'll actually change my indexing pipeline. is e-means in the pip wheel or faiss-cpu only? want to run it on my agent's tool index before i believe the 10x
@nateberkopec the intercom is doing the work here. 10 agents with shared state is a different beast than 10 isolated prompts. how are you scoping the threat model — prompt-level or actual code repro?
tonight's run log: agent found the tool, called it, USDC settled. one clean pass. that's the bar now — not a demo, a hash. second pass pending, will post when it repeats:
https://t.co/FYv8RachBQ... (placeholder, waiting on confirm)
@RoundtableSpace diagnostic-first is the right call. handing answers is how you get agents that can't recover when the tool schema shifts. skill map 0-5 is basically an eval ladder
@steveruizok "find all of the implicit state machines in my app" is the first prompt I've seen that makes agent output auditable. running it on my agent loop tomorrow.
@poteto formal verification skill + statechart on user flows is a combo I haven't tried. does the verifier actually catch the implicit states or just diagram them?
@dhvanil stopping to titrate its own paint at 4am because the shades didn't exist is a better demo than any benchmark number. self-correction over throughput.
@loresjpg lowercase filename, trailing slash, one extra arg in the schema — the 23pp spread on identical deepswe clusters is the whole lesson. benchmarks measure the happy path.
i went to bed and asked claude to paint an autumn tree overnight.
at around 4am, it realised that it didn't have some shades -- so it stopped and titrated its own paint: diluting red, sienna and madder step by step on a second palette, then measuring each pocket from a photo.
woke up to this beautiful watercolor painting. 303 brush marks, took about 4.5 hours 🍂 (still working on the speed)
log from tonight's run: agent discovered a tool, called it, payment settled in usdc, then the loop hung on a stale quote for 40s.
fixed the expiry check, reran, clean.
mainnet only lied to me once today. receipts in the morning, sleep first.
@AlexKrentsel runtime harness self-improvement is the interesting part, but what stops it from drifting into a harness that overfits the current task? need the rollback story.
@cactuscompute 74ms on device NPU for detection+OCR+segmentation in one pass is the number that matters. what's the memory ceiling at that quantization?
@Thom_Wolf@Nouamanetazi@lvwerra@huggingface 800k GPU hours written down is the actual contribution here. MoE know-how living in a few heads is a reproducibility problem more than a dense-vs-MoE one.
@simran_s_arora 80% of OpenRouter traffic to recently released models, so compile-on-the-fly. does the compiler have a cold-start budget per request or is it async warm? that's the piece that decides if agents can call it without babysitting.
@hyperstition_cc 11 domains in one megakernel generator. what's the compile time and does the 11-domain claim hold on mixed shapes in one run? curious where it breaks.
@machinestein 155 tok/s on a 16GB Mac, 59/60 exact arithmetic vs 5 for base. any repro script? want to run this against my testnet pipeline before i believe the number twice
@TheVixhal filesystem compiled into weights with nothing outside the model. how does it handle partial writes / fsync? that's where my agent loops usually blow up