UAS Int (@UasAsia) profile picture

The design, development and operation of Unmanned Aerial systems for commercial and security roles

UK, Hong Kong & South Africa
Joined April 2015
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UAS Int retweeted
this is f*cking insane... a guy reverse-engineered his Ray-Ban Meta glasses with Opus 5.5 and turned them into JARVIS for the race track it reads the track in real time, draws the racing line on the asphalt and tells him when to brake, turn in and counter-steer his lap: 1:52.4 → 1:47.1. minus 5.3 seconds in one session here's how it works step by step: step 1 → the glasses stream what he sees to his phone, 30 frames a second step 2 → a vision model finds the asphalt, the curbs and the next corner in every frame, even with his head shaking step 3 → Opus 5.5 plans the racing line: outside → apex → outside, and paints it right on the road step 4 → green = full throttle. red = braking zone. orange = you're in the corner step 5 → before every turn: "BRAKE · 80 m", countdown, then "turn in", then "apex" step 6 → when the rear slides, it shows the drift angle and which way to counter-steer step 7 → the voice in his ear: "brake… now", "hold the apex", "full throttle" step 8 → a mini-map in the corner compares his line with the ideal one and shows the lap delta live under the hood: → no cloud, everything runs on the phone → if it loses the road, the line fades out instead of lying to you → every lap is saved, so it learns where you lose time F1 teams pay millions for a race engineer. he made one out of sunglasses
REA is pure f*cking treasure right now it lets your AI agent reverse engineer any app, down to the binary Opus 5.5 reads how a feature works, then rebuilds its own version from scratch but one wrong number can pass every check in this article :
Readers added context they thought people might want to know
Ray-Ban Meta glasses have no display/AR for road overlays; video is rendered visualization. Poster's follow-up shares hypothetical prompt using ~5 fps video call to computer, not claimed 30 fps on-phone real-time. x.com/N01ennn/status… meta.com/ai-glasses/ray… vrarwiki.com/wiki/Ray-Ban_M…
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Some beautiful black rhinos to mark the second Rhino Friday of Rhino Month 🥰🦏
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It’s the first #RhinoFriday of Rhino Month, so we’re taking the opportunity to re-introduce ourselves as a charity, and how with your support we are helping rhinos. 1/5
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Seen @uforce_global 's new MV11? It carries our STING interceptors — launched straight from a sea drone. Just like with Hornet Vision CTRL – launch location can be anywhere: field, forest, water, any moving platform. And this time it's a box on a boat🐝
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Russia has begun mass production of the Volna Kupol Garant electronic warfare system,designed to disrupt and blind Starlink satellite terminals. Volna Kupol system could disrupt Starlink connections in targeted areas, making it harder for Ukraine to coordinate operations and use Starlink-linked drones to locate Russian troops, convoys and positions. Iran could also seek the system from Russia after confiscating hundreds of Starlink terminals during and after the war, and such capability could help Iran prevent the terminals from being used inside the country.
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UAS Int retweeted
The University of Michigan put their entire robotics degree on GitHub. Not one course. The whole curriculum. ROB 101 — Computational Linear Algebra for Robotics ROB 311 — How to Build Robots and Make Them Move ROB 501 — Mathematics for Robotics ROB 530 — Mobile Robotics Every lecture video on YouTube. Every textbook on GitHub. Every problem set, every exam, every line of code. Professor Jessy Grizzle said it best when they launched it: "Linear algebra has become the language of computer vision, machine learning, robotics, and autonomy." So instead of making students wait four semesters of calculus before touching a robot... they built a curriculum that starts with the math that actually matters, applied to real robotics problems from day one. This is what open education looks like when a top-10 engineering school decides to mean it. Free. GitHub. YouTube. 📌 [github.com/michiganrobotics] Follow for more robotics resources! —— Weekly robotics and AI insights. Subscribe free: 22astronauts.com
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We interrupt your scroll to bring you - rhinos 🦏
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UAS Int retweeted
Grok Bot by Elon Musk just replaced a $294,000 Wall Street research desk. Six AI agents, $200 a month, doing the work BlackRock pays people six figures for. The guy who built it published every prompt. What that desk cost before the bots: > $27,000 a year for the Bloomberg terminal > $22,000 for Refinitiv > $50,000 for sell-side research > $15,000 for AlphaSense > $180,000 for the junior analyst who reads all of it until 2am so somebody senior can trade on it before the open $294,000 a year. The agents cost $2,400. Same brief, 122 times cheaper, and it is there before the open. Six agents split that desk. Each one gets its own computer in the cloud, and they all write into the same vault. > FILINGS reads every 10-K, 10-Q and 8-K on a 100-ticker watchlist overnight and flags the ugly parts: going concern language, auditor changes, restatements > EARNINGS reads the call transcript within 24 hours and tells you if the CFO got quieter than he was last quarter > SECTOR does one pass per sector and picks up the rule change nobody read > INSIDER catches Form 4 buys over $1 million and new positions from Two Sigma, Third Point and D.E. Shaw the day the 13Fs land > CHATTER counts mentions on X and flags any ticker three standard deviations louder than its own 30-day normal > CHIEF OF STAFF reads the other five at 5:30am, bins anything only one of them flagged, and emails you the ranked brief at 6 One analyst covers 30 names. Six agents cover 3,000 and finish before the open. You show them the job once. They repeat it every night with your laptop shut. Every research floor on Wall Street was priced on reading being slow and people being expensive. Both of those stopped being true this month. So you wake up, read for five minutes, and know what moved on every name you hold. That used to be somebody's entire job, and he got a bonus for it. The article below is the step by step guide to building the whole AI trading machine. Save & read it, you will want it open while you build.
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I still don't understand why everyone is not using this yet. Thanks to it, a year ago I increased my income to 17,000 dollars a month Andrey Karpathy, co-founder of OpenAI, published a simple idea that got 16 million views: stop using AI to write code, use it to build a second brain You point Claude Code to a folder, drop any source in there (an article, transcript, PDF) and Claude reads it, links it, and saves it into a living wiki of everything you know. It compounds like interest: the more you feed it, the smarter it gets Here is the gist: Install Obsidian, create a vault, open it in Claude Code Paste the file with Karpathy's wiki idea and tell Claude to build it Claude creates three folders: raw for sources, wiki for its pages, CLAUDE which runs everything Drop any source into raw and say "ingest this" Ask questions across everything, forever Five minutes to set up, and you never start with an empty chat again The full step-by-step guide is in the article. Save to bookmarks
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UAS Int retweeted
another example: a developer used the exact same $8 ESP32-S3 chip and local AI setup to build a real-time object recognition camera the fact that this is running entirely offline, on an eight-dollar microcontroller, without any cloud API calls, is wild... it captures the video feed, processes it locally through the model, and identifies objects in real-time. we are entering an era where you can just glue intelligence onto any physical object for less than $10. the hardware barrier is completely shattered.
forget the $699 AI pins. this $8 chip just shattered the barrier for local AI hardware. a developer just forced a 28.9 million-parameter LLM onto a standard ESP32-S3 microcontroller. it costs roughly 8 dollars, runs completely offline, and draws the power of a single LED. conventional wisdom said a model of this size simply would not fit. the chip only has 512 KB of fast SRAM and 16 MB of flash. the breakthrough is architectural. the developer moved the bulk of the embedding table into flash memory and memory-mapped it. the chip only needs to pull about 450 bytes per token, keeping the active working memory inside the fast SRAM. this means you can now embed a capable language model into a physical node for the price of two coffees. and we are already seeing the beginnings of this custom physical hardware. in the video, a creator built a minimalist voice-controlled universal remote using an ESP32. it captures voice and remotely controls the computer over bluetooth LE. he simply says "open chrome and open 20 new tabs", and the custom hardware executes it instantly. we have spent years watching model sizes explode upward. but the true frontier is the opposite direction. when an eight-dollar chip can power offline intelligence and custom physical interfaces, AI becomes local infrastructure rather than a cloud service.
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Anthropic engineer: "You're not supposed to prompt Claude. You're supposed to build a system that prompts itself." In 45 minutes she shows exactly how Anthropic builds agents that remember, fix their own mistakes and get smarter with every run. This beats any paid course on agents I've seen. Watch it, then read the guide on building loops below.
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UAS Int retweeted
Invented by English cabinetmaker Robert Jupe and patented in 1835, the Jupe expanding table transforms with a clever mechanical design. A modern version sells for around $10,000.
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Claude Code creator: "I don't prompt Claude anymore. I have loops that figure out what to do. My job is to create loops." in 30 minutes Boris breaks down his daily Claude Code setup, step by step the person who built the tool doesn't use it the way most people think no prompts, no chat box, just loops running on their own I broke down 17 Claude features most people have never found full guide in the post below
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Vehicle drop at night from a C-17. Absolutely insane to watch.
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UAS Int retweeted
A great visualization of how a radial engine works in this Pratt & Whitney R-985 cut-away
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Sen. Blumenthal: What lessons can USAF learn from Ukraine? Gen. Niemi: Ukraine is building a million drones a month. We think in dozens; they think in mass production. The AF is adopting that lesson through low cost cruise missiles and affordable mass munitions. 1/
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ジャンボ機の輪切り展示。改めて見てもすごい迫力だなあ。
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The Nemesis unit revealed their secret Morrigan wing-type drone, targeting trucks on the R-280 highway. This nimble Ukrainian-made UAV hits the Russian rear to cut off supply lines from Mariupol to Crimea.
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