[brief update] we have a new set of funds for @haystackvc announcing haystack 8 plus more, but it's really about my teammates @aashaysanghvi_ & @DivyaDhulipala thank you to all of our frequent ecosystem co-conspirators, our LPs, our co-investors, our founders. i'm very grateful 🙏 and this is the most fun role in the world! semilshah.com/2026/04/26/new…

October is Cybersecurity Awareness Month

1/ Today we're launching @different_ai , my third company, with $48M in seed funding led by @htaneja at @generalcatalyst, with participation from @ravi_lsvp at @lightspeedvp. Different is an AI marketing system that runs demand generation end-to-end for lean B2B marketing teams.

Introducing Exa 3.0: our largest ever improvement to search quality. Exa is now #1 on every benchmark we tested, from public evals to verticals like coding and GTM intelligence. It's the result of multiple research breakthroughs 🧵

Should hardware engineers trust AI?

🔀 Dynamic Model Escalation For Efficient Evals 💰 Most eval pipelines pick one judge model and run it on everything. If it’s a big model, it’s too slow and too expensive to score production traffic, so teams sample a few percent and hope. If it’s a small model, it’s cheap enough to score every trace, but nobody knows which of its scores to trust. The question isn’t which judge model to use. It’s when the judge should admit it doesn’t know. Here is what we have built at @fiddler_ai as part of our Centor Evaluation Runtime. Two models function with two distinct jobs. A decisioning model sits in the hot path and scores every span/trace in under 100ms. A reasoning model sits behind it and only ever sees what the decisioning model sends it. The interesting part is the handoff. The decisioning model doesn’t just emit a verdict. It emits a verdict plus an uncertainty score about how much that verdict should be trusted, derived from the trace itself and from where this verdict sits relative to everything the model has scored before. That signal is what routes. Traces the decisioning model is sure about keep their score, and that’s the majority of traffic. Traces where the signal says the model is operating outside its competence go to the reasoning model, which re-scores them with the time and context a fast model can’t afford. So the split isn’t fast versus accurate. It acutally works as decide JIT versus deliberate post-hoc analysis. The decisioning model’s job is to be right quickly and to know when it isn’t. The reasoning model’s job is to be right on the cases that need thought. One eval score comes out the other end, and the user never needs to know which model produced it. Ofcourse we keep an audit of all of this if user wants to actually know the details. This is very similar to how humans perform a good on-call rotation. The pager doesn’t wake the senior most engineer (large reasoning model) for every alert. It wakes them for the ones that the on-call engineer (fast decision model) using the the runbook can’t resolve. So, what are the main advantages of this: The reasoning model’s bill is a function of how unusual your traffic is, not how much of it there is, which is the only way scoring every production trace stays affordable. And the escalation queue becomes the most useful dataset you have: a running list of where the decisioning model is weak and where your traffic is drifting. When you label those, feed them back into the next version of the decisioning model, and the escalation rate drops over time. The eval system tunes itself on its own misses. Welcome to the world of self-tuning AI Observability! Eval Models: fiddler.ai/centor-models

Q: Recommendations for a wireless connected printer copier etc that's actually easy to set up, kids can also print easily from their iPads etc? I am of the mind this doesn't exist. (thank you in advance!)

Seasonal neighborhood decorations:

Excited to introduce Onyx and announce our $8M seed round led by Dimension to build a virtual immune system! Onyx has always been a story of connection. The first connection was to my cofounder @amlakhan, a deeply curious technologist who I’ve been fortunate to call a friend for 19 years now. I've always said that working together was a cornerstone of starting Onyx, and it truly has been one of the best decisions I've ever made. Connection is also at the heart of what Onyx does. We believe AI is key to making biological complexity tractable, ultimately transforming life sciences into an engineering discipline. But that requires the right data. AI performance compounds with context, and richer biological context comes from more connected data. In biology, such context is often missing: molecular data, clinical history, and change over time are typically captured in isolated fragments. Onyx provides the substrate that connects them, pairing genomics, transcriptomics, proteomics, and metabolomics with rich clinical histories. By studying these layers together in people over time, our data platform traces how real human biology changes in response to infection, inflammation, and treatment. Seeing how these trajectories differ across people brings us closer to understanding what drives disease—and to developing precision medicines that work better for real people. Yet connecting data is only possible because of the people who have connected with our mission. Nearly a thousand members have already joined our research studies. They are more than research participants: they are difference-makers transforming how we understand some of humanity’s most complex and chronic diseases. I always say that our work is for people, but the truth is that people make our work possible. That connection comes full circle, and at Onyx, people will always come first. Finally, as we raised our oversubscribed round, we sought more than just business partners. We wanted investors we felt a true connection to—not merely sources of capital, but co-conspirators in creating meaningful change. To that end, we feel incredibly fortunate to have joined forces with @_DimensionCap, @haystackvc, @refactor, @standard_vc, @robotventures, and many more. Now back to building!

"Will AI cause Armageddon?" is the wrong question. The discussion about the threats that AI might pose has taken on an apocalyptic tone: will it extinguish life on Earth? Will it destroy humanity? It won’t, but it poses some far more real dangers to us all—right now, not in some imagined future. In the next couple of years, we are likely to face a new wave of rampant, pervasive cyberattacks and cybercrime. As the recent series of attacks by AI agents show, we no longer need to speculate about whether AI can conduct cyberattacks. It can. The important question now is what happens when attacks that once required skilled human beings can be conducted by machines, at machine speed and machine scale. For most of cybersecurity’s history, we have built defenses around an unstated assumption: the attacker is human. A person—or a team—has to inspect a system, find a weakness, exploit it, see what happens and decide what to do next. That takes time. Detection therefore buys time. Defenders spot an intrusion and respond before the attacker gets much further. Frontier AI attacks that assumption. An agent does not need to sleep. It can inspect code, probe a network, hit a barrier, devise another approach and try again. Make a thousand copies and they can work against a thousand targets at once. Allow agents to share what they learn and hacking begins to change from a human-scale activity into a machine-scale one. At Rubrik, we are seeing part of this transformation firsthand. Through our participation in Anthropic’s Project Glasswing, one lesson became clear. AI is not necessarily creating new vulnerabilities. It is collapsing the cost and time required to discover them. That changes the economics of cybersecurity. Modern software may contain thousands of small imperfections. Most lead nowhere. A few, combined in the right sequence, open a path through a supposedly secure system. Historically, finding those chains required talented people, lots of time and some luck. Frontier models can increasingly do the searching themselves, discovering paths through complex systems that humans might never find. This is what makes complacency about the new threat so dangerous. In 1998 a group of hackers appeared before Congress and warned that the rapidly expanding internet contained vulnerabilities capable of producing enormous disruption. There was no single cyber Armageddon. Instead North Korea crippled Sony Pictures. WannaCry raced through more than 200,000 computers in roughly 150 countries, disrupting hospitals and factories. NotPetya began as a Russian attack on Ukraine and caused about $10 billion in damage worldwide. Ransomware shut down Colonial Pipeline and disrupted fuel supplies across the American east coast. It would be strange to look at that history and conclude that the warnings were overblown. The catastrophe did not arrive as one spectacular event. Cyber insecurity became a permanent condition. Now imagine that condition at machine speed. That is why “Will AI cause Armageddon?” is the wrong question. A hospital whose systems are encrypted, a bank that loses control of its identities, or a company that cannot recover its data doesn’t care much whether civilization remains intact. The question is whether AI can make attacks faster, cheaper, more sophisticated and vastly easier to replicate. We are beginning to get the answer. Cyber-defense therefore has to change as well. For years the industry has concentrated on prevention and detection: keep the attacker out and, failing that, spot him quickly. Both remain essential. But when attacks operate at machine speed, detection may no longer buy enough time. Intrusion, exploitation and damage can begin to collapse toward the same instant. Companies must assume that some attacks will succeed. They need to be able to recover just as fast. The answer is not less AI. It is more AI on the defensive side: continuously hunting vulnerabilities, watching identities and data, detecting abnormal behavior, patching weaknesses and helping restore compromised systems. Machine-speed attacks require machine-speed defense. There has always been an asymmetry in cybersecurity. An attacker has to be right once. A defender has to be right every time. AI makes that imbalance more dangerous because attackers do not need permission, compliance reviews or perfect systems. They need only find one opening—and now they can search for it continuously, cheaply and at an enormous scale. Criminal groups and hostile governments will use this technology. They would be foolish not to. The first generation of cybersecurity was built to stop human beings sitting at keyboards. That world has ended. The next attack may be launched by thousands of agents, moving at a speed no human team can match. If the hackers are becoming machines, our defenses must become machines too.

Personal AI is going to be hugely important, and it works best when it knows everything about you. That's exactly what you shouldn't hand to a platform whose business depends on it. Wrote up why the version that wins is one you own.

One of the most exciting spaces to invest in. Congrats Irvin and team!

Introducing @Supertake, a new platform that transforms your unique takes on the world into real, shareable investment portfolios using frontier AI and trading agents. Supertake makes it easy for anyone to invest in their ideas, even if they know nothing about investing. 1/

Buying and deploying proprietary data is critical for the next frontier. But when it comes to buying real data from real people, trust is the first step in making sure the largest supply of real-world data can move into training as efficiently as possible. @integralprivacy ensures that your data will be monetized the right way. useintegral.com/get-paid

We built a time machine for the web. Introducing Exa Snapshot: an index of 400 billion historical snapshots of webpages that lets you search as if it's the past. Snapshot is already being used for backtesting prediction models, RL at labs, exploring the pre-AI web, and more.

Today we’re announcing we’ve raised $20M in seed financing led by @QuietCapital , with participation from @GradientVC , @haystackvc and @CompoundVC and other amazing folks to build the verification layer for AI-driven biology. We are also delighted to be welcoming @vqctran who is joining us from @GoogleDeepMind as a co-founder. AI is not going to cure all diseases without a verification substrate - a scalable environment where models can test predictions and learn against living human biology. AI is becoming increasingly capable of generating biological hypotheses, therapeutic candidates and experimental designs. But generating ideas is advancing much faster than our ability to test what those ideas will actually do in human biology. Existing systems force a tradeoff: the most biologically relevant approaches are slow and expensive, while faster in vitro and computational systems often lack the fidelity needed to capture human response. Polyphron’s bet is that tissue is the fastest, cheapest, most parallelizable verification substrate that still contains the biology that matters and we believe the path to biological superintelligence runs through closed-loop interaction with these living and simulated human systems. Thank you to @axios for covering the announcement. Link to our new website in the thread.

Excited to share the @Nominal_io x @aerovironment partnership - one of our biggest yet. Driving insights across the entire hardware development lifecycle with an intelligent platform. All systems Nominal! 🚀

exciting!

Beautiful, precise engineering.

Private school education if you’re having kids soon will cost roughly $1.5-2M all in in the Bay Area, $2-2.5M if you include college.

Think big. That’s always been a driving motivator for us, and now we have a partner to match our ambition. Today, we’re proud to announce Applied Intuition’s strategic collaboration with HUMAIN to deploy physical AI across Saudi Arabia, starting with autonomous trucking. This is a major step toward our goal of making a billion machines intelligent. Together, we will deploy thousands of autonomous trucks throughout key Saudi logistics corridors by 2030. This will make it the largest autonomous trucking network in the world, and our Self-Driving System and Vehicle OS technology will be the centerpiece of this collaboration with HUMAIN. This is physical AI at national scale. appliedintuition.com/blog/ap…