Erfan Rostami
230 posts
Samir Menon @blintzbase and I are thrilled to announce Sail @sailresearchco ! We build infrastructure for long-horizon agents: inference served at unbeatable prices-per-token for open models, plus sandboxes designed to run for days, weeks, or longer.
We've raised $80M, w/ our seed led by @Sequoia
and series A led by @KleinerPerkins. We're using this capital to build the most efficient infrastructure for long-horizon agents.
What makes agents so different? Unlike a human waiting at a keyboard (top priority: speed), agents need scale, reliability, and sustainable cost. Sail finds this efficiency everywhere in the stack: we carefully choose our chips, write custom inference engines, and run a global controller that fully utilizes every computer in our fleet. Tight integration from silicon to API lets Sail open up the cost / latency frontier to our customers - the most patient agents can now access 10x more intelligence per dollar.
We're excited to be working with great companies like
@parallelweb, @detaildotdev,@Jackandjillai, and @quadrillion_ai to deploy long-horizon agents with trillions of tokens.
Our team is thoughtful in our engineering craft and relentlessly ambitious in our pursuit of peak performance. We previously trained at companies like NVIDIA, OpenAI, Google, and so many trading firms. Now we're ready to do the work that will define our careers, in the most compute intensive market of all time.
Welcome to the era of abundant intelligence. We can't wait to build with you!
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@w1tness1ngh ok but if Wasabi goes down, do the workloads start crying?
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next up: we've designed and built our first jalapeno cluster. calling it wasabi - for only ur spiciest workloads.
OpenAI@OpenAI
We’ve designed and built our first AI chip: Jalapeño. Designed from the ground up by OpenAI and brought to production with @Broadcom, Jalapeño is purpose-built for the LLM workloads powering ChatGPT, Codex, the API, and future agentic products. Chips are foundational to the AI economy. Building our own expands our full-stack platform from products to models to infrastructure, and will help us scale intelligence, serve more people, and expand access to AI.
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Today, we are formally announcing @mirendil. Mirendil exists to be a straight shot at solving bottlenecks to step-change acceleration across all areas of science and technology. If you are excited about this mission, feel free to reach out.
Behnam Neyshabur@bneyshabur
Today, I’m excited to formally announce @mirendil with my amazing co-founders Harsh Mehta, Shayan Salehian, and Tara Rezaei! We’re fortunate to work with @a16z and @kleinerperkins, who led our seed round of $200M, followed by a major investment from NVIDIA, among others. Mirendil exists to accelerate science and technology, and through them, to help solve humanity's most pressing problems. Self-accelerating AI R&D is the most direct path to delivering on AI's broader promise, which is why we believe the most important application of AI is AI itself. Get this loop right, and it compounds. It fundamentally changes the rate of progress itself across all domains. We believe this capability should be democratized. It should be used to power all scientific efforts trying to innovate at the frontier. There are far more important problems—and broader ones—than any single lab can take on, so more groups should be able to pursue them. This pulls concentration of power away from a few labs: businesses and science labs can own their AI and infrastructure, keep their margins, and control their own destiny instead of ceding it all to a single AI lab. We’re a small team with a singular focus. Our founding team consists of 20 researchers and engineers from frontier institutions including Anthropic, xAI, Google DeepMind, and OpenAI, united by a passion for science and a drive to build the technologies that move it faster. If you want to build the system that builds systems, join us! @HarshMeh1a, @shayan_, @tararezaeikh
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Today, I’m excited to formally announce @mirendil with my amazing co-founders Harsh Mehta, Shayan Salehian, and Tara Rezaei!
We’re fortunate to work with @a16z and @kleinerperkins, who led our seed round of $200M, followed by a major investment from NVIDIA, among others.
Mirendil exists to accelerate science and technology, and through them, to help solve humanity's most pressing problems.
Self-accelerating AI R&D is the most direct path to delivering on AI's broader promise, which is why we believe the most important application of AI is AI itself. Get this loop right, and it compounds. It fundamentally changes the rate of progress itself across all domains.
We believe this capability should be democratized. It should be used to power all scientific efforts trying to innovate at the frontier. There are far more important problems—and broader ones—than any single lab can take on, so more groups should be able to pursue them.
This pulls concentration of power away from a few labs: businesses and science labs can own their AI and infrastructure, keep their margins, and control their own destiny instead of ceding it all to a single AI lab.
We’re a small team with a singular focus. Our founding team consists of 20 researchers and engineers from frontier institutions including Anthropic, xAI, Google DeepMind, and OpenAI, united by a passion for science and a drive to build the technologies that move it faster. If you want to build the system that builds systems, join us!
@HarshMeh1a, @shayan_, @tararezaeikh
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We closed our Series F today at a $13B valuation.
Our inference business grew 20x in the last year. I want to explain why:
The growth comes from a shift I think is permanent: companies want to own their intelligence layer. Instead of relying exclusively on closed models, teams are post-training open models for their specific use cases. Customers like Abridge, Cursor, Decagon, Harvey, HubSpot, Lovable, Notion, OpenEvidence, and Parallel are building this way.
But post-training is still more of an art than a science. That’s why we’ve been working hands-on with customers to build specialized models that match or exceed closed models on the tasks they care about. We provide not just the weights, but also the training recipes and tooling so that they're in charge of the continual learning process.
I think more companies, both AI-natives and enterprises, will own their intelligence layer. And I’m excited to help build that future.
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VOLTAI chartered a yacht for their happy hour at Reindustrialize. This is how you do it.
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Marvell spent $36 billion over 10 years to own every connectivity distance in a data center.
$22.5B in acquisitions. $18B in organic R&D. Cavium for compute and networking. Inphi for $10B for data center interconnect. Celestial AI for photonic fabric. Xcon for scale-up switching.
And one technical bet that could have destroyed the company: skipping the 7nm process node entirely, jumping from 14nm straight to 5nm.
Murphy on stage: "Nobody does this. Nobody takes that kind of risk. But we did and it worked - flawlessly."
That node-skip is what got Marvell into the hyperscaler qualification cycles driving 75%+ of revenue today.
The full picture on what $36B built and whether it is defensible:
podcastalpha.substack.com/p/episode-summ…
Source: COMPUTEX 2026 Keynote - youtube.com/watch?v=DSnugw…
YouTube
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tasteful team. join us
Thais Castello Branco@thaiscbranco_
We’re excited to introduce Taste Labs. Our mission is to end AI slop. We’re building the data and infrastructure layer to give AI models and agents taste. And today we’re coming out of stealth, announcing our $18.5M seed funding, co-led by @CRV and @AmplifyPartners AI has nailed objective domains and made it easy to generate anything. But it still feels off. Now, the challenge is judgement. What fits, what feels like you, what’s GREAT. This requires turning a fuzzy, subjective domain into something we can measure and codify. We’re starting with design. There are two sides to cracking this, the foundation model layer and the agent layer: - We’ve already been working with the top frontier labs to evaluate and improve their models, crafting the right post-training data and RL environments. - We’ve also been working with app-layer companies to build the context and verification tools for their agents to produce better, more on-brand, more creative outputs. We want a future where AI feels right. If you’re passionate about this mission, join us!
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We’re excited to introduce Taste Labs.
Our mission is to end AI slop. We’re building the data and infrastructure layer to give AI models and agents taste.
And today we’re coming out of stealth, announcing our $18.5M seed funding, co-led by @CRV and @AmplifyPartners
AI has nailed objective domains and made it easy to generate anything. But it still feels off. Now, the challenge is judgement. What fits, what feels like you, what’s GREAT. This requires turning a fuzzy, subjective domain into something we can measure and codify. We’re starting with design.
There are two sides to cracking this, the foundation model layer and the agent layer:
- We’ve already been working with the top frontier labs to evaluate and improve their models, crafting the right post-training data and RL environments.
- We’ve also been working with app-layer companies to build the context and verification tools for their agents to produce better, more on-brand, more creative outputs.
We want a future where AI feels right.
If you’re passionate about this mission, join us!
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@SKundojjala or more simply companies/competitors rebuild EDA stack with more powerful models
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EDA is currently 11-12% of R&D budgets at many fabless companies. Cadence claims more headroom for this to go up as companies spend up to a third of R&D on AI and significant of that can be tapped by Cadence. The math comes from Jensen's statement of his willingness to spend 50% of human engineer cost on tokens. Engineer comp is roughly two-thirds of R&D, so AI spend could reach one-third of R&D and within that Cadence sees an opportunity for it. But a bear case is also seems to be there as customers up their AI budget there is a risk of value migration from EDA to models.
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thats pretty insulting to al qaeda
RJC@RJCcapital
semianalysis is worse than al qaeda
Humble, TX 🇺🇸 English
we got our client +250k views in less than 24 hours (100% organic) on their launch
the hardest part was making the character interaction feels real. we did it by:
- natural sounding script
- rehearsing on-camera chemistry
- precise character art direction
- and a ton of compositing
lmk if you want a more in-depth breakdown
girish@googrish
“don’t train your own model” is common ai advice. it's wrong. your token bill's the proof. today, we’re excited to launch castform into open preview. castform is the easiest way for you to train your own model, on your own data. open-weights models are performant and much cheaper. when trained on your task & proprietary data, they beat closed models. the thing standing between you and that was weeks of plumbing & years of ml expertise. with castform, model training is as simple as prompt engineering. @castformai bring your agent traces or raw corpora. castform turns it into training data, picks the right algorithmic recipes, manages gpus, and gives you an ide to watch and chat with your model as it learns. see what you can build with castform👇
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trying to get a box of GPU today and cant! so makes sense
Negligible Capital@negligible_cap
*ASTERA LABS, COREWEAVE, NEBIUS, ROCKET LAB TO JOIN NASDAQ-100 $ALAB, $CRWV, $NBIS, $RKLB and $TER are joining the Nasdaq 100 They’re replacing $CHTR, $CTSH, $INSM, $VRSK, and $ZS
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Pretraining fundamentally does not make sense anymore for anyone other than frontier labs. Although there are a lot of people at enterprises & startups who have "Pretrainitis" to show “impact” and get promotions, fundamentally, it doesn’t make sense.
There is probably higher ROI in partnering with a frontier lab to do prompt engineering, although it isn’t as “sexy” as pretraining.
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bullish on the team
girish@googrish
“don’t train your own model” is common ai advice. it's wrong. your token bill's the proof. today, we’re excited to launch castform into open preview. castform is the easiest way for you to train your own model, on your own data. open-weights models are performant and much cheaper. when trained on your task & proprietary data, they beat closed models. the thing standing between you and that was weeks of plumbing & years of ml expertise. with castform, model training is as simple as prompt engineering. @castformai bring your agent traces or raw corpora. castform turns it into training data, picks the right algorithmic recipes, manages gpus, and gives you an ide to watch and chat with your model as it learns. see what you can build with castform👇
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Formal verification meta upcoming. AI both pulling it fwd (secure software) and enabling it (FV harnesses)
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Erfan Rostami がリツイート
Jensen Huang is a titan and a teacher. He recently sat down with me to explain his vision of the future of technology and humanity. He’s calm, clear, and very funny. We touched on many topics, ranging from the $20T or more AI economy in five layers to changes in labor for the AI age. Here are some of my main takeaways:
1) The world is moving from retrieval to generation
2) Generation offers intelligence customized to the individual
3) Nvidia is making the "generators" of intelligence
4) We've seen this kind of revolution at least three times before with energy (Generators), Telecommunications (vacuum tubes / transistors?), and now intelligence (GPUs)
5) There is a five-layer cake of participation in this many-many trillion dollar revolution: Energy, Chips, Infra, Models, and Applications.
6) There are many ways to participate in this revolution, and everyone has a role
7) We'll be pushed to dream up new problems to solve with this unprecedented intelligence
8) In this new future, it's not just having the answer, it's having the right questions
8) The right questions will drive us toward our individual and collective human purpose
9) We move from the carpenters to the architects
I believe this is the realistic future. Thanks to Jensen and the entire @nvidia team for the conversation and for letting us share!
00:00 Introduction
00:42 From Chatbots to Generative AI
03:35 Agentic AI That Does Work
05:26 Downstream Industry Impact
06:25 Computing Shifts From Retrieval to Generation
11:26 A Planet Cocooned by Intelligence
14:27 Inside the NVIDIA AI Factory
20:48 AI Five Layer Cake
21:58 Beyond Chatbots to Biology
23:54 Tokens and World Models
24:53 Trillions in Applications
27:13 Ditch the AI Doom
31:32 Jobs Tasks vs Purpose
38:40 Closing the Tech Divide
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Introducing @PoeticHQ: a new AI system that executes complex multi-hour tasks with 99%+ accuracy and 10x fewer tokens than agents.
We raised $50M at $500M from Kleiner Perkins, Founders Fund, First Harmonic, and Genius Ventures to build AI that does complex work inside Fortune 500 companies without hallucination.
While code is too brittle, agents are too unpredictable. The work that runs the global economy - anti-money laundering, fraud investigations, underwriting - needs extreme accuracy.
So we built a new kind of software that pairs the flexibility of AI with the predictability of code.
When the world stays the same, Poetic runs fixed code: fast, cheap, identical every time. When the world changes, Poetic uses AI to regenerate its approach and find its way back to the objective.
In one year, we went from zero to an eight-figure run rate as a team of four.
Since then, we’ve scaled the team and executed the highest-stakes processes at AIG, SoFi, and Chime. At SoFi, a large US bank, Poetic reached 99%+ quality on fraud investigations in five weeks.
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