Normal Computing 🧠🌡️
@NormalComputing
We build AI systems that natively reason, so they can partner with us on our most important problems. Join us https://bit.ly/normal-jobs.
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We’re excited to announce our preprint "Solving the compute crisis with physics-based ASICs"! Our team at @NormalComputing, together with collaborators at @ARIA_research, @ucsantabarbara, @Penn, @sfiscience, @Cornell, @ARPAE, and @Yale , has posted a new preprint: "Solving the…
@gavincrooks from @NormalComputing taking the #FPI stage at #Neurips2025. ⚛️💻 Why spend massive energy simulating probabilistic sampling on deterministic GPUs when we can build hardware that is natively probabilistic?
We are pleased to announce that our CEO, @FarisSbahi, will be presenting on behalf of @NormalComputing at the @ARIA_research stand at #NeurIPS2025. He will be speaking today at 19:00 PM at booth 1343. Faris will share our latest R&D work with ARIA, including how our CN101…
Inbound to Neurips @ San Deigo. @FarisSbahi will be speaking about @NormalComputing at the ARIA/UK DBT stand at the expo, Tuesday at 19:00. (We're hiring!) I'm speaking on Sunday at 13:15, Frontiers in Probabilistic Inference workshop. Let's do this thing.
Found a bug with this tool just recently, can recommend. The ability to drill down in your LLM traces in an easy fashion is quite useful. When you treat traces like training data, you may need to view it in a different way.
AI for hardware goes *much* deeper than AI for software.
OK super narrow hiring post for a high-prio role (actually, there may not be anyone in the world who fits this) but if you: - have some familiarity with both RL+agents, and have gone deep on at least one - have experience with hardware engineering, device verification in…
at Normal we recently build an agent that runs for 21 days autonomously we probably need to optimize it...
Claude Sonnet 4.5 runs autonomously for 30+ hours of coding?! The record for GPT-5-Codex was just 7 hours. What’s Anthropic’s secret sauce?
Everyone knows RLHF and RLVR, but do you know the 17 other RLXX methods published in papers and blog posts? Here's a condensed list:
Diffusion for everything! We share a recipe to start from a pretrained autoregressive VLM and, with very little training compute and some nice annealing tricks, turn it into a SOTA diffusion VLM. Research in diffusion for language is progressing very quickly and in my mind,…
Today we're sharing our first research work exploring diffusion for language models: Autoregressive-to-Diffusion Vision Language Models We develop a state-of-the-art diffusion vision language model, Autoregressive-to-Diffusion (A2D), by adapting an existing autoregressive vision…
More than $5 million in new funding from several private companies—including @Fidelity and @NormalComputing—will expand Maryland’s Quantum-Thermodynamics Hub, co-led by Nicole Yunger Halpern (@nicoleyh11), and support it for three more years. Read more: go.umd.edu/227t
Live from #AIInfraSummit: Maxim Khomiakov (@maximkhv) is on the Demo Stage presenting “Normal EDA: AI-Native Verification Without the Rework.” Mission-critical design verification is fragmented and manual. Humans (and LLMs) are struggling to tame the mathematical complexity of…
It’s been an incredible couple of days at #AIInfraSummit so far. If you haven’t yet, swing by before the end of the summit and chat with our team on how AI is transforming chip design and verification. See the demo today at 1:15–1:30pm PDT on the Demo Stage.
The Normal Computing team is ready for #AIInfraSummit! The Normal Computing team is setting up at Booth 725. Come meet with the team this week to learn how we’re rethinking chip verification with AI-native tools.
On Sept 11, 1:15–1:30pm PDT, don’t miss Maxim Khomiakov (@maximkhv) on the Demo Stage at #AIInfraSummit presenting “Normal EDA: AI-Native Verification Without the Rework." If you care about chip verification speed, accuracy, and AI’s role in silicon, this is the session for you!
Heading to Santa Clara next week for #AIInfraSummit? Stop by Booth 725 to meet with our team and see Normal EDA in action. Teams use Normal EDA to generate full, production-grade collateral from specs, accelerating signoff, reducing engineering effort, and surfacing edge cases…
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Martin Larocca
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