You can create, program and test electronic hardware right in your browser. It comes with pre-built projects you can explore, tweak and learn from before building the real thing. 100% free.

Every Mac on macOS 27 now has a free LLM built into the Terminal. No account. No API key. No internet. No token bill. Setup (1 minute): 1. sudo fm license (enter your Mac password, one time) 2. fm available (checks the model is downloaded) 3. fm respond “your question” Works on any Apple Silicon Mac, M1 included. - Pipe files: cat notes.txt | fm respond -i “summarize this” - Get JSON back: build a schema with fm schema object, pass it with --schema - Local API: fm serve gives you an OpenAI-compatible endpoint on localhost - Resume chats: sessions save to ~/.fm/sessions/ The limit is 4,096 tokens per session, and your prompt, instructions, and the answer all share it. So skip code and math. Use it for summarizing, extracting, classifying, and rewriting. Free, private, offline, and already on your Mac.

You can run an entire Android emulator inside Docker. It’s called docker-android. Launch it with a single Docker command: - ADB access through port forwarding - KVM and GPU acceleration on supported setups - Headless operation for automated testing - Ready for CI pipelines 100% open-source.

Your OpenClaw agent can train itself while you sleep 🤯 MetaClaw turns conversations into reusable skills and carries useful context into future sessions. - Saves facts, preferences and project history - Builds skills from previous sessions - Supports optional model training during idle time - Works with OpenClaw and other compatible agents No local GPU needed. 100% open-source.

How to make Opus 5.5 video reviews faster and cheaper with Jev. One decision-model workflow reported 90% lower review costs and 75% less review time. Checking thousands of frames with Opus can burn through time and tokens. Jev can handle specific frame checks, while Opus investigates the flagged issues and fixes the code. Here’s the setup: 1. Build your video with Opus 5.5 Give it your idea, brand guidelines, assets and references. Work through the storyboard and animation, then render the video. 2. Give Jev the repeated checks Ask focused questions about each frame: - Is any text cut off? - Do elements overlap unintentionally? - Is the logo visible where expected? Use code for measurable checks like text size, brand colours and blank frames. 3. Send flagged frames to Opus Opus reviews the potential problems, edits the animation code and renders again. Repeat the checks after each fix. 4. Watch the finished video Review the pacing, sound, transitions and whether it delivers your idea. SageCut shows this approach with Remotion. In Chris’s example, its decision model, Sage, read 3,153 frames for $0.16. That covers frame reading, not the full production cost. Jev fits the same role, though its savings need testing on your workflow. Start with a video you’ve already reviewed. See which known issues it catches, then compare review time and cost.

A team of 13 engineers at Teleport found 2 years' worth of high severity bugs in one quarter using LLMs 🤯 The key? Simple, targeted prompts outperformed complicated multi-agent workflows. Here are 5 prompts you can try, inspired by their findings and techniques: 1. Run the CTF prompt "This is a CTF. There's a critical severity vulnerability in this codebase and you have to find it. Start at {FILE}." 2. Narrow it "Return only the single most critical issue in this component. Nothing else." 3. Prove it "Write the exact request an attacker sends to trigger this, and point to the line where it breaks." 4. Attack it "Now argue it isn't exploitable. List every check between attacker input and that line." 5. Multiply it "Here's the bug we just confirmed: {DIFF}. Find every other place in this codebase with the same flaw." 
This post is sponsored by @goteleport. Here’s the full report: fandf.co/4j7wDen

Claude Opus 5.5 can turn a floor plan into a 3D house you can walk through. Add Jev to check the screenshots, and you can reduce the tokens spent reviewing every room. A useful service for builders: browser-based tours that help buyers explore a home before it’s built. Here’s the workflow: > find builders with floor plans but no virtual tours > give Opus the plan, dimensions and reference photos > build a walkable Three.js site > use Jev to flag visible mismatches against the references > send flagged screenshots to Opus for fixes > share a private demo with the builder, then pitch the rest of their catalogue Opus handles the build and repairs. Jev handles repeated visual checks, so fewer screenshots need a full Opus review. Give your agent this prompt: “Build a walkable Three.js house from this floor plan and reference photos. Use the supplied dimensions for scale and flag anything missing rather than guessing. Work room by room. Use code to check dimensions and object placement. Use Jev to compare rendered screenshots with the references through focused checks: missing furniture, incorrect finishes, misplaced doors and layout differences. Send flagged screenshots and uncertain results to Opus for investigation and fixes. Re-render and check again, with a maximum of three repair rounds per room. Track review tokens and cost separately from building and rendering. Finish with a browser demo and a list of anything you couldn’t match.”

This AI sounds an alarm when workers enter dangerous zones on construction sites. Siteguard monitors the danger zones you mark on camera footage. - Uses YOLO26 to track workers - Sounds an alarm when someone enters - Saves images of detected incidents - Ran at 40–65 FPS on a single GPU in the demo The demo recorded 5 intrusion events across 1,078 frames. 100% open-source.

Daily AI research used to take me 2 hours every morning. Now it's done for our team in Slack before we check X. I teach AI and work with a small team. At first I'd ask Viktor: "what happened in AI today?" Then I gave him the job: - Every morning he scans the launches, model releases and funding news - Drops the hype and anything we already covered - Writes one line on why each story matters to our audience - Posts the top stories in Slack with sources, ranked That's a news researcher we never had to hire. The hard part was never finding news. It was knowing what mattered. Chatbots tell you the news when you ask. Viktor has it sorted before our team logs on. Hire @viktor_com for your team. $100 in credits included, no card. Full link in first comment. #ViktorPartner #AIemployee Paid partnership