I just sequenced a human genome to 30× coverage entirely at home. As far as I know, this is the first time this has been done. I didn’t step foot in a lab once. Every step - from saliva collection, to running the sequencer - took place in a single room with a dining table kitchenette. Six weeks ago, I had never done wet lab biology before. I used an Oxford Nanopore P2 Solo - the only commercially available sequencing device portable enough to do 30x human genome sequencing at home. Biggest takeaway - I could build something that combined software, hardware, and molecular biology far faster than I thought was possible. I can name >100 specific instances where AI helped me solve a technical problem that would previously have blocked me because I lacked access to a domain expert. For example: how do I save my sequencing run when my DNA extraction yield is 4x lower than I need it to be, and I have this limited set of reagents to hand? To make this work, I had to navigate multiple disciplines: - writing software to monitor sequencing runs and orchestrate remote GPU infra for basecalling - learning executing 5 hour long molecular biology protocols - building a hardware device to quantify DNA concentration Apologies for the hyperbole, but I feel super lucky to be living in 2026. A few weeks ago I decided to sequence a human genome to 30x at home. Then I actually did it. And I did it really quickly.

We need heat shields to protect us, since we use the air to slow us down as we return to Earth. From orbital speed, it gets to 1650°C / 3000°F. From the Moon: 2750°C / 5000°F. For yesterday's Starship suborbital test flight, peak was 1450°C / 2600°F. Great to see the @SpaceX progress over the last 3 flights. Making them truly reusable is complex and necessary for permanent, cheap space access. image compilation: @niccruzpatane

Today marks a pivotal moment for Isomorphic Labs. We have secured $2.1 Billion in our second external funding round, led by Thrive Capital. They are joined at the table by Alphabet, GV and new investors MGX, Temasek, CapitalG and the UK Sovereign AI Fund. This milestone accelerates our ability to build the pioneering novel AI models that power our AI drug design engine (IsoDDE) and deploy them at scale: delivering scientific breakthroughs with a precision previously thought impossible, accelerating and expanding our pipeline of therapeutic programs toward the clinic. All with the ultimate goal of delivering life-changing new medicines to patients. Moving forward, we will scale our drug candidate pipelines across multiple therapeutic areas, expand our global footprint, and push the boundaries of frontier AI research to power our drug design engine. Deeply grateful to everyone sharing our vision to solve all disease with AI. Let’s build the future of medicine. Read the full announcement here: bit.ly/4v2OI03

Microsoft just solved the context window problem. Right now, every AI suffers from a fatal flaw: the "context window problem." When an AI reasons through a complex problem, it generates a massive chain-of-thought. But there is a catch. It has to keep every single token of that thought in its active memory. The technical term is the "KV Cache." The longer the AI thinks, the heavier it gets. It slows down. It gets expensive. Eventually, it runs out of space. We thought the only fix was renting bigger, more expensive cloud GPUs to hold all that context. Microsoft just proved us wrong. They published a paper called "MEMENTO." Instead of giving the AI a bigger memory, they taught it how to forget. Here is how it works: Instead of generating one endless stream of consciousness, a Memento-trained model breaks its reasoning into small blocks. After it finishes a block, it writes a dense, highly compressed summary of its own logic—a "memento." Then, it does something unprecedented. It physically deletes the entire previous reasoning block from its memory cache. It only carries the memento forward. The model reasons, extracts the core logic, and instantly drops the dead weight. The results rewrite the economics of running AI. • Context length compressed by 6x. • Active memory usage (KV cache) reduced by 2.5x. • Zero loss in math, science, or coding accuracy. And here is the real implication. Big tech has been charging you by the token for massive context windows you don't actually need. With this architecture, small businesses and solo operators can run complex, multi-step autonomous agents entirely locally. You don't need an enterprise cloud setup. A standard machine running an open-source model can now reason indefinitely without overflowing its memory. No API fees. Complete privacy. We spent the last two years trying to give AI an infinite memory. It turns out, the secret to smarter AI isn't remembering everything. It's knowing exactly what to forget.