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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
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Vlad retweeted
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!
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 :)
Vlad retweeted
Horrified by what happened to our dear friend Heidi Levine while she was on assignment today.
“They just kept kicking me and kicking me,” she said.
washingtonpost.com/world/202…
Vlad retweeted
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…
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! 👇
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
POV: it's 2026 and you want to make a reservation at your local restaurant that has 3.2 stars on Google Maps















