We built a new kind of robot for the underdogs. Today, robotics is stuck between ~$100k systems and low-cost robots not meant to survive deployment. We think builders deserve better. Our mission is to develop the best hardware platform for others to deploy on. Meet Feather, an affordable general-purpose robot designed for real work: • 1m reach • Human strength • 10-hour battery • Shipping for the past year + available today Founded by @whoishoa and Parsa Bakhtiari, Feather is coming out of stealth with $7.6m in pre-seed funding led by @GradientVC, with participation from @BuilderVC, @geometryvc, @SEEDInnov, and Virgo VC. We’re hiring across engineering, ops, and go-to-market. If you’re an absolute maverick, join our cause today –

Jev Founder, Diogo Almeida, just released a 12-page PDF on how to use Jev with LLMs It is more useful than most paid AI courses: this is a 10-step blueprint on how to build a faster, cheaper and more controllable AI system around Claude, Codex, Grok or any other LLM: step 1 → split the responsibilities: the LLM generates, Jev makes bounded semantic decisions and deterministic code keeps authority step 2 → build the state: give Jev the current request, relevant evidence, policy and proposed action instead of sending the entire conversation step 3 → choose the right primitive: Choice selects a route, Score evaluates an ordered rubric and Noul returns the probability that a statement is true step 4 → replace giant evaluation prompts with atomic questions: intent, urgency, evidence, risk and scope become separate typed decisions step 5 → put Jev before the LLM: select the context, tools, provider and workflow before paying for an expensive generative call step 6 → give the LLM a bounded job: once Jev selects the route, the model receives only the instructions, files and tools required for that branch step 7 → put Jev after the LLM: check whether the result answers the request, uses sufficient evidence and stays inside the permitted scope step 8 → route by confidence: high-confidence low-risk cases proceed automatically, uncertain cases request more context and consequential actions go to review step 9 → batch independent decisions: ask multiple Choice, Score and Noul questions over one shared state instead of creating another LLM call for every judgment step 10 → record the complete decision receipt: state version, question, probabilities, selected route, model, latency, outcome and human override most AI courses teach you how to write a bigger prompt this 12-page guide teaches you how to build the control system around every prompt the result: smaller contexts, fewer unnecessary LLM calls, safer tool execution and decisions you can actually inspect, test and improve Send this PDF and the original Jev article to Claude Code or Codex and start rebuilding one expensive LLM decision at a time ↓

Portable Computer for Windows is now available on @AMD Ryzen AI Max Series processors. It makes it easy to run local AI agents that work with your connected apps and local files. Kick off tasks or schedule recurring work that runs entirely on your device.

Faceless YouTubers shouldn't need a 5-person production team anymore. Claude Opus 5.5 powered this video. 🔥 We handled the rest. So, I partnered with @FxChaos and we built Scale With Brew around one idea: Don't give creators more AI tools. Do the work for them. No team to manage. No VAs to train. No tools to learn. Scale With Brew's AI handles the production. Real humans review, revise + QC everything before delivery. You get the finished, upload ready video and thumbnail.

Today, we are launching Agent Detection-1, the first system-one model built to classify visitors on your web platform. Agents are already on your web platform. Muse, Instinct, Claude Code, Codex, and thousands more are booking, buying, and filling out forms. Behind each agent could be a real customer. Cloudflare, Akamai, and other bot tools are built to block agents. Analytics like Amplitude, Datadog, and Google Analytics can't tell them apart from people. We want to detect agents, figure out which ones are useful, and redirect them to where they can actually get things done. Agent Detection-1 tells you who's at the door, the moment they arrive: > Person or agent, and which agent > Send useful agents to your MCP server, llms.txt, or agent card > Stop the bad ones, like scrapers and credential stuffers > Every visit gets logged on your Resemble dashboard Just add a one-line script and find out who's visiting your website. Starting at just $1 per 1,000 visitors.

$META LAUNCHES AI GAME-BUILDING TOOLS Meta is launching Horizon Create and Horizon Studio, letting users build complete 2D or 3D games from AI prompts on mobile or in a browser. Games can then be published across Facebook and Instagram, where users can jump from a clip directly into a multiplayer session without downloading a separate app. Meta says the tools can generate gameplay systems, difficulty, art direction and multiplayer features, while still letting creators manually refine the experience. The rollout is part of Meta’s broader shift to make Horizon a mobile-first platform rather than primarily a VR product.

Introducing Fast Search in the Perplexity Search API. Fast Search runs on Photon, our new Rust-based retrieval and ranking service that we built with a small team of engineers and hundreds of agents. It returns 95% of search results in 230 ms or less.

In 12 weeks, we built a research facility that is run entirely by AI. AI designs, executes, and observes experiments end-to-end across biology, chemistry, and materials science. We’re introducing SciUniverse: a benchmark that measures AI’s ability to do real-world scientific research.

Introducing Alexandria in Claude. Access over 100 data providers and 113M+ indexed sources easily through Firecrawl. Agents using Alexandria score 21% higher on answer quality than those using built-in web tools. Get started using the Firecrawl connector today!

Today at ZapConnect, we debut Next Gen Zaps. One of the most important product moments in @Zapier's history. With Next Gen Zaps, customers can deploy agent-built Zaps in seconds. Simply describe your problem to your agent and it will build you a Zap that runs automatically, even when your laptop is closed. Use it from Claude, ChatGPT, etc. to migrate existing Zaps, create new ones, or bring external automations onto Zapier. It's one line of text away. My two favorite parts: 1. Hardening: Zaps will run the deterministic parts with code not AI. That means more reliability and lower cost. 2. Healing: When a Zap breaks, it falls back to agentic mode and can figure out how to recover without human intervention. The early data shows how much we still have to learn. But we think it's good enough to share now. One early access customer told us, "This is what Zaps were always supposed to be: easy." Now in beta. Let us know what you think: