Things we got before GTA VI: 👉 AGI 👉 Flying cards 👉 AI girlfriends 👉 Robots doing backflips 👉 People getting fired by a chatbot 👉 Coding without knowing how to code what else?

Someone just open sourced the framework for Intelligent UI, and it's already #5 trending on GitHub. >⁠Works with any LLM and any agent framework >⁠Plug in your own components and design system >⁠Supports every kind of generative UI •⁠ ⁠No lock-in, built by CopilotKit The pitch is that every app ships with this by 2027. WILL EVERY APP HAVE AN AGENT BUILT INTO ITS INTERFACE BY THEN? github.com/CopilotKit/OpenIn…

We formally announced Mirendil less than 4 months ago. Today, I'm excited to share an important milestone for: Pretraining at scale without a pretraining team. Mirendil's mission is to solve Recursive Self-Improvement (RSI) and use it to democratize frontier AI development, ultimately accelerating science and technology. At Mirendil, when we think about RSI, we think holistically about the company as an intelligent entity that self-improves toward its goal. From that perspective, the most powerful notion of acceleration is reducing the time and resources (number of human experts, compute, and other costs) needed to achieve ambitious goals. Given that pretraining is perhaps the most important yet most challenging part of frontier AI development, we decided to run an ambitious controlled experiment: Starting fully from scratch, can we significantly reduce the time, number of human experts, and compute resources needed to go all the way to successfully pretraining a strong coding model at scale? Mirendil-Base is the result of this experiment. Our first pretrained model was built by one researcher in 3.5 months, with less than $10M in compute. This includes all the research and small-scale experiments it took to get there, as well as the final training run. The recipe holds up as the models get bigger. For reference, Mirendil-Base is in the 100B-500B parameter range, Mirendil-Base-Large in the 500B-2T range, and Mirendil-Base-XL in the 2T-10T range. We're beginning to see a world where a single researcher, empowered by AI, can take on work that once required an entire research organization. As these systems improve, the bottleneck shifts from the size of a team to the ambition and judgment of the people directing them. This has profound implications for who gets to build AI and, ultimately, who gets to push the frontiers of science and technology forward. We’ll share independent evaluations, post-training benchmarks and API-access sooner than you think so stay tuned!

i had 12 Claude agents running in parallel for 6 hours and i managed to reverse engineer @supabase i'm making it available here so that you can save your tokens - 100% free & open-source: github.com/supabase/supabase

We’re opening a research preview of Claude Duet, a new app where Claude works as a proactive agent. It comes to you with what needs your attention, completes tasks you ask of it, keeps working when you step away, and checks with you before it acts on your behalf.

fear not! this is no cyberpocalypse this is the internet’s immune system finally waking up to an illness it has always had a mere fever the 0days were always there the only thing that changed is awareness now we begin the healing process ⚕️

Before you install that AI skill someone shared on X, scan it. In an analysis of 31,132 AI agent skills, 26.1% contained vulnerabilities and 5.2% looked malicious. NVIDIA open-sourced SkillSpector, the scanner it uses before publishing skills to its own catalog. 20.8K stars. → checks 71 attack patterns across 17 categories: prompt injection, data theft, privilege escalation, poisoned MCP tools and more → scans a GitHub repo, a URL, a zip or a folder → gives each skill a 0 to 100 risk score with a clear recommendation → fast scan with no AI, plus an optional deeper AI review → works for Claude Code, Codex and Gemini CLI skills Install: uv tool install git+github.com/NVIDIA/skillspect… Scan a skill: skillspector scan ./the-skill/ --no-llm Apache-2.0.

Cloudflare has deployed default web application firewall (WAF) protections for newly disclosed PHP vulnerabilities before public disclosure. The rules use request and response signals to stop exploit attempts and prevent exposed data from reaching attackers. cfl.re/488w8dc

holy sh*t, someone mapped 12,000+ reverse-engineered projects you can use today cars, smart homes, games, AI clients and abandoned services. searchable in one free repo. the collection: → awesome-reverse-engineered 12,869 projects, with an agent skill that searches by product, vendor or protocol. → games (3,036 projects) emulators, decompilations and rebuilt engines. shadPS4 and RPCS3 included. → devices & hardware (3,028 projects) tools for readers, wearables and connected hardware. → unofficial API clients (904 projects) existing clients to check before writing your own integration. → AI tools & LLM clients (737 projects) reconstructed clients, system prompts and tools for closed AI products. → cars (520 projects) vehicle integrations, charging tools and openpilot. → firmware (472 projects) OpenWrt, Tasmota and alternative firmware for supported devices. → file formats (436 projects) readers, parsers and converters for files your app needs to understand. → network protocols (422 projects) implementations you can inspect instead of guessing how a connection works. → service revivals (173 projects) replacement services for products their vendors abandoned. search your target → check the license and maintenance → test the existing implementation → build what's missing bookmark the index for your next integration. send it to the friend about to spend a weekend rebuilding one.