@everythingmeta

All opinions somebody else's Computers and stuff MTS at Parallel Web Systems. Previously Head of Forward Deployed Engineering at Baseten.

Location, Location
Joined January 2011
built an app to watch the full competitive landscape of clinical trials inside key disease areas every piece of data comes from @p0 > Task API for clinical trial news, sentiment & deal size > Data Connectors (PubMed, NPI Registry & clinicaltrials .gov for) for direct data on registered trials > Monitor API for daily updates to the database link + repo in comments!
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Waking up to see that everything has been compromised
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is training a model the new buying a .com domain?
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$1B used to mean something
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20% of YC startups whenever a tweet like this drops
Inference is a beautiful business. deepseek reports 155T tokens/day from one inference cluster at $38/gpu-hr. I estimate their cluster at ~5 MW, implying ~$160M/MW/year in API revenue and 84–92% margins after $3–6/GPU-hour rent working assumptions: 96% input-cache hits, ~107 input token/output token ratio -> ~$14.35 revenue per billion tokens at off-peak prices. working backwards from 155T tok/day, 60,000 uncached input tok/sec/gpu prefill + 14,000 output tok/sec/gpu decode, each two-GPU pair handles approximately 1.5m input + 14k output tokens/sec -> 2400 gpus. on blackwell hardware I assume 2 kW/gpu including external networking, cache storage, etc. No wonder everyone wants be part of the token flow / bid up GPUs :)
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we used Parallel to build Parallel’s first brand campaign. here's a look at the research, connections, and creative decisions behind the artwork.
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Typesafe's CEO @CompleteSkeptic does not trust public benchmarks. Neither do I, Diogo. So @p0 and I wrote a blog evaluating 8 decision models on real company training datasets we have personally audited and iterated on. For what it's worth though, Jev is still on top 👑 parallel.ai/blog/are-all-sys…
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can't next-token predict any of this. pure chaos.
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parallel.ai/blog/are-all-sys… We spent two weeks extensively testing decision models on real production use cases. It's been super fun, we learned a lot, and we are sharing our findings! 👇
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3. We got big lifts applying DSPy-like iteration to the prompts. One of the many benefits of having a universal classifier is that you can n-shot prompt it rather than fine-tuning a classifier
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4. At real scale, we still get better performance from our own tuned and trained models (much smaller models) but there is no compelling reason to start this way until you know what you are optimizing for
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POV: it's 2026 and you want to make a reservation at your local restaurant that has 3.2 stars on Google Maps
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trying to imagine explaining to myself 10 years ago that we made a Markov chain so long that the pope is mad about it
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The problem with a lot of AI assistant use cases is that specifying the work to be done in sufficient detail actually IS the work.
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