We build AI systems that natively reason, so they can partner with us on our most important problems. Join us https://xitter.cf/t.co/G90CO1W456
New York, NY
Joined June 2022
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Normal Computing 🧠🌡️ retweeted
Took a stab at the Zen of Agentic Software:
Explicit is better than virtual
The agent should see what you see
You should see what the agent sees
Build systems the agent can observe, test, and repair
Give the agent good tools
Prefer working with text
The best tool is often a CLI
A CLI is just a sub-agent
Tool output should be signal, not exhaust
Failure should be loud enough to debug
Success concrete enough to verify
Leave room for surprise, not for confusion
Rely on the model’s intelligence, not its knowledge
Make knowledge available. Retrieve just in time
Build memory so the agent does not relearn the world
Memory helps. Memory lies. Design for both
Garbage context in, garbage out
Intent is the highest-signal context
If a model can’t solve problem with perfect context, it can’t solve problem
A longer prompt is not a substitute for data
Prompting is always the first baseline
If one agent is too slow, consider parallelism
Verifiers are great, use more of them
Do not sneer at glue. Enough glue, arranged well, becomes product
Normal Computing 🧠🌡️ retweeted
"In the US we're expecting to have a 49 gigawatt shortfall around 2028...2030 on a global scale".
@NormalComputing Co-Founder @FarisSbahi on the future energy requirements for compute:
"In the next few years we're going to hit this really hard wall when it comes to the energy requirements of compute".
"We need to be thinking about next generation architecture from a somewhat radical point of view... exploring new kinds of architectures that would be considered unconventional".
"Designing this kind of silicon and optimising with the level of complexity that silicon has... [is] something very difficult to do with legacy software that hasn't really changed in the last 40 years".
Normal Computing 🧠🌡️ retweeted
At GTC, Nvidia CEO Jensen Huang highlighted a new equation: Revenue = Tokens per Watt × Total Available Gigawatts.
Normal Computing CEO @FarisSbahi says this explains the industry’s push for energy-efficient and workload-specific chips:
"Not that many years from now, rather than having a very small number of different chips running all of our workloads, we're going to have hundreds, if not thousands of different chips tailored to each workload and application in the data center."
Normal Computing 🧠🌡️ retweeted
Rethinking compute and rewiring plant immunity: Our March update is live🚀
Dive into this month’s updates, including:
- The latest from ARIA, including funding, events, and RFPs
- A deep-dive into @NormalComputing world-first thermodynamic computing chip
- A Q&A with Philip Carella at @JohnInnesCentre, who is leading an opportunity seed project within our Programmable Plants opportunity space, working to rewire plants’ immunity to better fight pathogens
- Recommended reads –from using AI systems to optimise protein production to social networks for AI Scientists
Read the full piece and subscribe to get our monthly updates directly to your inbox: ariaresearch.substack.com/pu…
Normal Computing 🧠🌡️ retweeted
thrilled to share that i joined @NormalComputing as an ai engineer in their copenhagen office
the company is not only building thermodynamic hardware with up to 1000x energy efficiency, but also an EDA product for building new hardware
this is a future that i'm excited for!
Normal Computing 🧠🌡️ retweeted
my results on AI for autonomous progress:
we ran codex+claude for 43 days straight to build a System Verilog compiler/simulator
normalcomputing.com/blog/bui…
Normal Computing 🧠🌡️ retweeted
Struggling with minibatch noise in Stochastic Gradient Bayesian Inference? Want your chains to naturally run on stochastic hardware?
Introducing — Stochastic Gradient Lattice Random Walk!
New work from @NormalComputing in collab with @zierhjmensch, @adnarim066, @wellingmax, and the dream team at normal computing, @Sam_Duffield, @MaxAifer and @ColesThermoAI.
Normal Computing 🧠🌡️ retweeted
New open source: cuthbert 🐛
State space models with all the hotness: (temporally) parallelisable, JAX, Kalman, SMC
Normal Computing 🧠🌡️ retweeted
@NormalComputing by Company Policy.
All AI brands look the same? Not this one. This is B2B with swagger. The name. The brash red. The sophisticated design system. The just-the-right-side-of-arrogant key line: “when the future of AI arrives, it will be Normal.”
Normal Computing 🧠🌡️ retweeted
@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?
Normal Computing 🧠🌡️ retweeted
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.
Normal Computing 🧠🌡️ retweeted
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.
Normal Computing 🧠🌡️ retweeted
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 particular
- are ready to dive into the insanely deep hole that is AI for hardware
- are already based in CPH or NY, or eager to move there asap
- can start a new FT role within 30 days
- are interested in going deep on application-specific agents
- lean extroverted, and are comfortable with public-facing comms
- are keen to join a fast-moving + fast-growing startup
DM me with “APPLICATION” as the first token, followed by evidence for the above
do NOT use that token for any other role inquiries (your DM will be deleted); apply through links, or DM others on the team. same goes for replies.
this is far from the only role open, but it’s the one i personally have the highest vested interest in finding a killer fit for it + will be prioritizing it when reviewing DM apps
it’s a pretty specific list, and i’m looking for high alignment on ~all of the above, but if your life journey has somehow led you to this specific set of skills, there likely isn't anywhere you can get more use of them
signal-boosts appreciated. it’s a very high-leverage position + will be compensated accordingly :)
Normal Computing 🧠🌡️ retweeted
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:
Normal Computing 🧠🌡️ retweeted
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, provides as promising of a path of unifying modalities as the 'omni' autoregressive models.
Amazing work led by @mariannearr @ServerProcessor over the summer.
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 language model for parallel diffusion decoding. Our approach makes it easy to unlock the speed-quality trade-off of diffusion language models without training from scratch, by leveraging existing pre-trained autoregressive models.
Normal Computing 🧠🌡️ retweeted
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
Normal Computing 🧠🌡️ retweeted
Read more at arxiv.org/abs/2508.20883
Including scaling LRW up to image generation with Stable Diffusion 3.5 🐱
Really fun work with @MaxAifer @blip_tm @ColesThermoAI
Normal Computing 🧠🌡️ retweeted
Thrilled to share that I’ll be at #AIInfraSummit 2025 in two weeks, representing the Normal Computing team!
On Sept 11 (1:15–1:30pm PDT), I’ll be on the Demo Stage presenting:
“Normal EDA: AI-Native Verification Without the Rework”
Verification teams use Normal EDA to generate full, production-grade collateral from specs, accelerating sign-off, reducing engineering effort, and surfacing edge cases that humans and general purpose LLMs miss - If you’re curious about where chips and AI meet, this session is for you.
We’ll also be on the floor all week at Booth 725 — come by to connect, see our platform in action, and chat about the future of AI-native verification.
Looking forward to catching up with old friends and making new ones in Santa Clara!
Normal Computing is heading to #AIInfraSummit as an official Event Partner, and we’re bringing our latest breakthroughs in AI-native chip verification with us.
Visit us at our booth for a live demo of our unified, physics-aware EDA platform to enable 2x faster time-to-market.
Catch Maxim Khomiakov’s (@maximkhv) talk “Normal EDA: AI-Native Verification Without the Rework”. He’ll unpack how Normal EDA accelerates complex verification with Normal EDA: drag-and-drop a spec, and our AI generates a zero-to-one test plan, SystemVerilog/UVM benches, and full traceability via NormML.
Register with code NORMALCOMPUTINGEVENT10 to save 10% on your pass.
This conference arrives on the heels of our second consecutive inclusion in EE Times’ Silicon 100: Startups to Watch, a nod to the growing importance of AI-centric EDA and our leadership in crafting the architectures that will unlock the next era of scalable, energy-efficient compute for the semiconductor industry.
See you in Santa Clara!
