Introducing GovSignals and announcing our $5.5 million dollar raise from @Unusual_VC! Trillions of dollars every year are often going to large, non innovative incumbents.  Yet infrastructure projects are falling behind & our national defense is at risk from our adversaries. Where is all the money going, and how do we bring the best solutions to government? GovSignals is building to fix Government Contracting and bring the most innovative solutions back to America.

For some unknown reason, Thomas Jefferson is preoccupied this morning with recording the temperature. He writes that upon awaking at 6 AM, the temperature is a pleasant 68.0 degrees Fahrenheit, but that it is creeping up toward 70. On his way to Congress, Jefferson purchases a thermometer. It costs three pounds and 15 shillings. No-body knows why he is so concerned with the temperature today, especially, and for what reason he desires an accurate read.

Introducing Sätteri — High-performance Markdown & MDX processing for the JavaScript ecosystem. satteri.bruits.org/

Announcing a new division of Midjourney called "Midjourney Medical"

Get paid to wait The Claude Code spinner might be the most watched line on Earth. So I turned it into an ad marketplace. Advertisers bid on it. You keep 50% of the money. Install the extension → get cash from ads. Introducing Kickbacks

The darling baby gorilla of the Gladys Porter Zoo in Brownsville, Texas, celebrates his first birthday. The ape child is given the name “Harambe.”

Introducing MA-S2: Mission Assurance Security Standard for Software. AI is accelerating vulnerability discovery. Adversaries are moving faster than any human team can respond. Institutions need autonomous remediation, attack path modeling, and domain awareness. The bar has changed. MA-S2 is our proposed standard for software security — one we're actively working towards, and publishing as our responsibility to every enterprise that wishes to secure its own digital footprint and ecosystem. Learn more: ma-s2.com/

Ghostty is leaving GitHub. I'm GitHub user 1299, joined Feb 2008. I've visited GitHub almost every single day for over 18 years. It's never been a question for me where I'd put my projects: always GitHub. I'm super sad to say this, but its time to go. mitchellh.com/writing/ghostt…

Introducing Pods Hyperspace Pods lets a small group of people - a family, a startup, a few friends, to pool their laptops and desktops into one AI cluster. Everyone installs the CLI, someone creates a pod, shares an invite link, and the machines form a mesh. Models like Qwen 3.5 32B or GLM-5 Turbo that need more memory than any single laptop has get automatically sharded across the group's devices - layers split proportionally, inference pipelined through the ring. From the outside it looks like one OpenAI-compatible API endpoint with a pk_* key that drops straight into your AI tools and products. No configuration beyond pasting the key and changing the base URL. A team of five paying for cloud AI burns $500–2,000 a month on API calls. The same team's existing machines can serve Qwen 3.5 (competitive on SWE-bench) and GLM-5 Turbo (#1 on BrowseComp for tool-calling and web research) for free - the hardware is already on their desks. When a query genuinely needs a frontier model nobody has locally, the pod falls back to cloud at wholesale rates from a shared treasury. But for the daily work - code reviews, refactors, research, drafting - local models handle it and nobody gets billed. And when it is idle, you can rent out your pod on the compute marketplace, with fine-grained permissions for access management. There's no central server involved in inference. Prompts go from your machine to your pod members' machines and back: all of this enabled by the fully peer-to-peer Hyperspace network. Pod state - who's a member, which API keys are valid, how much treasury is left - is replicated across members with consensus, so the whole thing works on a local network. Members behind home routers don't need port forwarding either. The practical setup for most pods is three models covering different jobs: Qwen 3.5 32B for code and reasoning, GLM-5 Turbo for browsing and research, Gemma 4 for fast lightweight tasks. All running on hardware you already own. Pods ship today in Hyperspace v5.19. Model sharding, API keys, treasury, and Raft coordinator are all live. What Makes This Different - No middleman. Your prompts travel from your IDE to your pod members' hardware and back. There is no server in between reading your data. - No vendor lock-in. Pod membership, API keys, and treasury are replicated across your own machines using Raft consensus. If the internet goes down, your local network keeps working. There is no database in someone else's cloud that your pod depends on. - Automatic sharding. You don't configure layer ranges or calculate VRAM budgets. Tell the pod which model you want. It figures out how to split it across whatever hardware is online. - Real NAT traversal. Your friend behind a home router with a dynamic IP? Works. No VPN, no Tailscale, no port forwarding. The nodes handle it. - Free when local. This is the part that matters most. Cloud AI bills scale with usage. Pod inference on local hardware scales with nothing. The marginal cost of your 10,000th prompt is the electricity your laptop was already using. Coming soon: - Pod federation: pods form alliances with other pods. - Marketplace: pods with spare capacity can sell inference to other pods.

“Ghost Murmur” is wild. The CIA seems to imply they can detect magnetic signals from a beating heart at 40 miles range. All I can do is imagine all the other applications… many things that thought they were hiding may no longer be! So what I can glean from the article: they imply they are able to detect the magnetic signals (H-field) of a heart beat from 40 miles away. That’s something normal only doable across a few meters before the signal falls below the noise floor. Reading between the lines, it seems like they are probably using a distributed array of sensors, sensor fusion (h-field, e-field, motion, thermal, etc), and then add AI to infer the below-floor signal. H-field typically drops off very rapidly as it travels from the source, where E-field (typical RF energy) is what propagates very far. It’s a bit hard to imagine that even an array of sensors, sensor fusion, and signal inference is enough to pick up H-field at 40 miles without there also being some sort of physics breakthrough as well. Even in a barren desert that has the lowest noise floor you can find. But either way, the implications seem fun to consider. SCIFs rarely shield H-field emissions. Can their emissions now be picked up at significant range? Same for air-gapped systems. Did data exfil just get a lot easier? H-field propagates through all kinds of material, including dense earth. Do previously unknown underground facilities suddenly glow? All kinds of electronics that are viewed as passive because they do not transmit RF might be much easier to detect by simply being powered on. Maybe even passive detection of drones and aircraft? I have so many questions and so many ideas.

Say hello to agentOS (beta) A portable open-source OS built just for agents. Powered by WASM & V8 isolates. 🔗 Embedded in your backend ⚡ ~6ms coldstarts, 32x cheaper than sbxs 📁 Mount anything as a file system (S3, SQLite, …) 🥧 Use Pi, Claude Code/Codex/Amp/OpenCode soon