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Kevin Gu (@kevingu)

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Nav Toor (@heynavtoor)

🚨 Someone just open sourced a fully autonomous AI hacker and it's terrifying. It's called Shannon. Point it at your web app, and it doesn't just scan for vulnerabilities. It actually exploits them. Real injections. Real auth bypasses. Real database exfiltrations. Not alerts. Not warnings. Actual working exploits with copy-paste proof-of-concepts. Here's what this thing does autonomously: → Reads your entire source code to plan its attack → Maps every endpoint, API route, and auth mechanism → Runs Nmap, Subfinder, and WhatWeb for deep recon → Hunts for Injection, XSS, SSRF, and broken auth in parallel → Launches real browser-based exploits to prove each vulnerability → Generates a pentester-grade report with reproducible PoCs Here's the wildest part: It follows a strict "No Exploit, No Report" policy. If it can't actually break it, it doesn't report it. Zero false positives. It pointed at OWASP Juice Shop and found 20+ critical vulnerabilities in a single run including complete auth bypass and full database exfiltration. On the XBOW Benchmark (hint-free, source-aware), it scored 96.15%. Your team ships code daily with Claude Code and Cursor. Your pentest happens once a year. That's 364 days of shipping blind. Shannon closes that gap. One command. Fully autonomous. The Red Team to your vibe-coding Blue team. Every Claude coder deserves their Shannon. 10.6K GitHub stars. 1.3K forks. Already trending. 100% Open Source. AGPL-3.0 License.

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Brian Roemmele (@BrianRoemmele)

BOOM! Apple’s Neural Engine Was Just Cracked Open, The Future of AI Training Just Change And Zero-Human Company Is Already Testing It! In a jaw-dropping open-source breakthrough, a lone developer has done what Apple said was impossible: full neural network training– including backpropagation – directly on the Apple Neural Engine (ANE). No CoreML, no Metal, no GPU. Pure, blazing ANE silicon. The project (http://github.com/maderix/ANE) delivers a single transformer layer (dim=768, seq=512) in just 9.3 ms per step at 1.78 TFLOPS sustained with only 11.2% ANE utilization on an M4 chip. That’s the same idle chip sitting in millions of Mac minis, MacBooks, and iMacs right now. Translation? Your desktop just became a hyper-efficient AI supercomputer. The numbers are insane: M4 ANE hits roughly 6.6 TFLOPS per watt – 80 times more efficient than an NVIDIA A100. Real-world throughput crushes Apple’s own “38 TOPS” marketing claims. And because it sips power like a phone, you can train 24/7 without melting your electricity bill or the planet. At The Zero-Human Company, we’re not waiting. We are testing this right now on real ZHC workloads. This is the missing piece we’ve been chasing for our Zero Human Company vision: reviving archived data into fully autonomous AI systems with zero human overhead. This is world-changing. For the first time, anyone with a Mac can fine-tune, train, or iterate massive models locally, privately, and at a fraction of the cost of cloud GPUs. No more renting $40,000 A100 clusters. No more waiting in queues. No more massive carbon footprints. Training costs that used to run into the tens or hundreds of thousands of dollars? Plummeting toward pennies on the dollar – mostly just the electricity your Mac was already using while it sat idle. The AI revolution just moved from billion-dollar data centers to your desk. WE WILL HAVE A NEW ZERO-HUMAN COMPANY @ HOME wage for equipped Macs that will be up to 100x more income for the owner! We’re only at the beginning (single-layer today, full models tomorrow), but the door is wide open. Ultra-cheap, on-device training is here. The future isn’t coming. It’s already running on your Mac. Welcome to the Zero-Human Company era.

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Aakash Gupta (@aakashgupta)

The math on this project should mass-humble every AI lab on the planet. 1 cubic millimeter. One-millionth of a human brain. Harvard and Google spent 10 years mapping it. The imaging alone took 326 days. They sliced the tissue into 5,000 wafers each 30 nanometers thick, ran them through a $6 million electron microscope, then needed Google’s ML models to stitch the 3D reconstruction because no human team could process the output. The result: 57,000 cells, 150 million synapses, 230 millimeters of blood vessels, compressed into 1.4 petabytes of raw data. For context, 1.4 petabytes is roughly 1.4 million gigabytes. From a speck smaller than a grain of rice. Now scale that. The full human brain is one million times larger. Mapping the whole thing at this resolution would produce approximately 1.4 zettabytes of data. That’s roughly equal to all the data generated on Earth in a single year. The storage alone would cost an estimated $50 billion and require a 140-acre data center, which would make it the largest on the planet. And they found things textbooks don’t contain. One neuron had over 5,000 connection points. Some axons had coiled themselves into tight whorls for completely unknown reasons. Pairs of cell clusters grew in mirror images of each other. Jeff Lichtman, the Harvard lead, said there’s “a chasm between what we already know and what we need to know.” This is why the next step isn’t a human brain. It’s a mouse hippocampus, 10 cubic millimeters, over the next five years. Because even a mouse brain is 1,000x larger than what they just mapped, and the full mouse connectome is the proof of concept before anyone attempts the human one. We’re building AI systems that loosely mimic neural networks while still unable to fully read the wiring diagram of a single cubic millimeter of the thing we’re trying to imitate. The original is 1.4 petabytes per millionth of its volume. Every AI model on Earth fits in a fraction of that. The brain runs on 20 watts and fits in your skull. The data center required to merely describe one-millionth of it would span 140 acres.

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𝕱𝖔𝖗𝕷𝖔𝖔𝖕 (@forloopcodes)

someone just made a tool that indexes code by meaning, not string matching this project is the first time i have seen someone treat code like a knowledge graph. sometimes i feel they are the only ones actually pushing cs most rag pipelines for agents are embarrassing and we should be chunking by semantic scope, imagine your agent's accuracy if this was used instead of simple grep google spent millions on kythe to index code and devs just did it better as a weekend hobby project. never bet against a functional programmer with a strict type system. https://haskellforall.com/2026/02/browse-code-by-meaning

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Ray Fernando (@RayFernando1337)

Bookmark this if you're building an iOS app. Nobody warns you how brutal the App Store submission process is. Rudrank built a free CLI that automates all of it!! TestFlight, submissions, signing, screenshots. one install, done.

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Ryan Carson (@ryancarson)