Put OpenCV 4 to work on five vision applications. Use Python for projects covering face and eye detection, emotion recognition and fast QR code detection. trk.udemy.com/c/6457882/3227… #AD #OpenCV #ComputerVision

Match AI video tools to the shot you need. Work through Runway Gen-3 Alpha video-to-video workflows, Sora multi-scene narratives and Kling camera controls. trk.udemy.com/c/6457882/3227… #AD #AIVideo #Sora

Test AI beyond a single correct answer. Design API automation tests for ML endpoints and test prompt stability in GenAI systems. Demos cover RAG systems, bias checks and drift detection. trk.udemy.com/c/6457882/3227… #AD #SoftwareTesting #GenAI

Fuck it, still early but here goes ... We've just released Monty v1 - a Python sandbox that starts in 1 millisecond, not 1.5 seconds. I just ran 10k sandboxed scripts in 674ms, something that would take a cloud sandbox > 3 hours. This removes the biggest drawback of letting agents write code. The future is fast. Even better, it's open source, you can install it from PyPI, npm or Crates now. Serviced platform coming soon. Please get in touch if you want to be a design partner! Who should try it? ⚡ if you care about startup time, use Monty ⚡ if you care about long-lived sessions, use Monty - Monty can be dumped and resumed at any external function call ⚡ if you care about accessing functions in the agent/host, use Monty - Monty makes it trivial to expose local functions into the sandbox ⚡ if you care about scale, use Monty - Monty workers use as little as 2MB of memory, meaning you can run thousands of concurrent sandboxes on a single machine ⚡ if you care about security, use Monty - we've run 3 rounds of bounty program and thousands of researchers have tried to break into our sandbox, meaning it should be secure to run untrusted code Who should avoid it? 🚫 if you like to take a coffee break while waiting for sandboxes to start, DO NOT use Monty 🚫 if you enjoy the challenge of routing API requests from sandboxes through your corporate network to access state in your agent without exposing secrets to the sandbox, DO NOT use Monty 🚫 if your agent really needs to install packages from PyPI, Monty won't help you yet (spoiler: it probably doesn't) pydantic.dev/docs/monty/get-…

The motivation behind the new Copilot: intelligence is accelerating fast. Now we need to diffuse it everywhere.

this is f*cking gold How to build your first Jev AI agent (Full guide) it will probably make you a lot of money in the right hands, this changes everything:

Turn any codebase into an interactive knowledge graph! Understand-Anything is a Claude Code plugin that builds an explorable map of your project. It analyzes every file, function, class, and dependency, then gives you an interactive dashboard to explore it all visually. Run the analysis and five agents execute in parallel. They scan your project, extract structure, identify architectural layers, and build a knowledge graph. The output is an interactive React Flow visualization. The dashboard shows your codebase as a graph. Nodes are files, functions, and classes. Edges show dependencies. Everything is color-coded by layer (API, Service, Data, UI, Utility). Click any node to see its code, relationships, and a plain-English LLM explanation. Key features: • Visual exploration with searchable, interactive graph • Plain-English summaries for every component • Guided architecture tours ordered by dependency • Semantic search - find by meaning, not just name • Diff impact analysis - see what your changes affect It works across multiple AI coding agents - Claude Code, Codex, OpenCode, OpenClaw, Cursor, Antigravity It's 100% Open source I've shared the link in the comments!

Stanford just explained the exact algorithm that beat the world Go champion, in one lecture, for free - and it's the same math that now teaches robots by trial and error: 23:18 the one line of math that turns "copy the human" into "learn by trial and error" 42:54 the simple trick that decides whether a robot learns at all or never converges 1:18:55 why AlphaGo's human training data turned out to hurt its final performance, and self-play alone did better after watching I realized - the scary part of AI isn't how complex it is, it's that 80 minutes of free Stanford lecture is enough to read the AlphaGo paper and actually understand it. the article below is the full 6-month path through Stanford's robotics lectures, for free.

Pay attention to this new wave of System One models if you are building custom harnesses. First Jev. Now, Contrastive Language Model (CLM). CLM is 9x faster than Jev. CLM seems to be a better verifier than Jev, particularly at long-horizon tasks. How do Jev and CLM differ? CLM is contrastive, and Jev is trained with Reinforcement Learning for Calibrated Decisions (RLCD). Jev receives a situation plus predefined questions, and returns typed decisions with probabilities. CLM embeds the situation and candidate actions, compares their similarity, then ranks or selects the best match. The point is that there are several ways to attack this problem, which is exciting. You can see my recent guide on combining System One and System Two models for building custom harnesses. academy.dair.ai/resources/je…

Today at @WeAreDevs, Docker and the @linuxfoundation are announcing a collaboration around the Docker Sandbox Kit Specification: an open standard for declaring what an agent may do, where it may reach, and what it may touch. Open source under Apache 2.0. Read the deep dive: bit.ly/4rrcjql

JevBench v1.4.2 is live - and we have a new #1🏆 1⃣ decider-4b v2👑 2⃣ Jev 1.13.0 ‼️ 3⃣ JevK5 4⃣ Cygnet 5⃣ Hopper A 4B open model beats Jev on the benchmark. BUT IMPORTANT: Jev still smarter though, check explanation below. After you check the page: benchmarkheaven.com/jev-mode…

NeoHorse-Jev-4B is now open—a compact model built to turn application states directly into structured decisions and probabilities. 🤖 modelscope.ai/collections/To… 🏆 Scores 77.70 across six text decision benchmark groups, ranking first among the four open-weight models with complete results in the comparison. ⚡ Prefill-only inference avoids autoregressive answer generation and supports three decision primitives: Choice for action selection, Noul for yes/no judgment, and Score for ordered ratings. 🎮 The same interface can power request routing, tool selection, workflow control, games, robot manipulation, and autonomous-driving simulations. 🖼️ Supports text inputs or a single image combined with text, returning normalized probabilities that applications can use directly. 🛠️ Deploy locally through vLLM, SGLang, Python, CLI, or HTTP. Apache 2.0.

How to Build an AI Second Brain With Claude and Obsidian That Gets Smarter Every Day (Full Guide) article summary in 4k light & dark

We’re building Copilot as a new OS for work that spans every model, every form factor, and every task. Today, we’re announcing our biggest update to Copilot to date, bringing four things together: · Autopilot: proactive and long-running agent built for the enterprise · Code: build apps with Copilot, hosted inside your company’s tenant · Home: Chat + Cowork together · Office: now fully embedded in Copilot (and Copilot embedded in Office, of course!) Plus, you can invoke Copilot in Teams, and we’re introducing Today, a proactive experience that surfaces the most important information from across M365 without needing to ask for it. The way we work is changing and so are our workflows. This update brings AI into that flow, from answering a question, to building an app, to getting work done on your behalf.

NVIDIA IS GIVING 4 STRONG AI MODELS FOR FREE 🤯 no credit card required you can use: • DeepSeek V4.1 Flash • GLM 5.3 • GLM 5.3 Flash • Kimi K3 get access: build.nvidia.com base URL: integrate.api.nvidia.com/v1 hope this helps

this is unreal f*cking gold for Jev builders 20 repos people are building on Jev right now. browser agents, context tools, trading bots, even a drone 1. JEV-Ultrafast - a fast browser agent ↳ github.com/browser-use/jev-u… 2. Fast-JEV-Compaction - context compression ↳ github.com/tamaratran/fast-j… 3. JSON-Render - generative UI ↳ github.com/vercel-labs/json-… 4. Typesafe-MCP - use Jev with any client ↳ github.com/itsmostafa/typesa… 5. JEV-MCP - a judgment toolkit ↳ github.com/burnigtm/jev-mcp 6. Semdecide - a classifier that lives in your CLI ↳ github.com/sharziki/semdecid… 7. JEV-Codex-Router - routes each task to the right model ↳ github.com/0xNatoshi/jev-cod… 8. Winnow - garbage collection for your context ↳ github.com/GhalebDweikat/win… 9. JEV-Review - code review triage ↳ github.com/devagrawal09/jev-… 10. Blink - a repo navigator ↳ github.com/ellipsis-dev/blin… 11. Agent-Desktop - desktop automation ↳ github.com/lahfir/agent-desk… 12. Typesafe-Mario - an agent that plays Super Mario ↳ github.com/fhshaik/typesafe-… 13. JEV-Drone - drone control ↳ github.com/RomanSlack/jev-dr… 14. OneVOneJev - a browser FPS ↳ github.com/emrickgarrett/One… 15. JEV-Trader - HFT market making ↳ github.com/buberlo/jev-trade… 16. Prism - liquidity signal detection ↳ github.com/irfndi/prism-liqu… 17. Neo4Jev - knowledge graph traversal ↳ github.com/jexp/neo4jev 18. JEV-Curate - training data screening ↳ github.com/AkashPriyadarshii… 19. Canny - checks whether a task was actually completed ↳ github.com/qkal/Canny 20. KillMyIdea - scores startup ideas before you build them ↳ github.com/monteduro/killmyi… pick by what you do: > coding -> JEV-Review, Blink, Canny, JEV-Codex-Router > context -> Fast-JEV-Compaction, Winnow > automation -> JEV-Ultrafast, Agent-Desktop > clients and tools -> Typesafe-MCP, JEV-MCP, Semdecide > UI -> JSON-Render > trading -> JEV-Trader, Prism > data -> Neo4Jev, JEV-Curate > founders -> KillMyIdea > just for fun -> Typesafe-Mario, OneVOneJev, JEV-Drone grab the one closest to your job and ship something on top of it this week

Jev Engineering turns a static agent workflow into a graph that can rewrite itself while running. the video is basically the problem at scale: hundreds of routes → thousands of crossings → different agents → different tools → different confidence levels Jev Engineering doesn’t control every step. it controls the crossings. when two paths compete: → score both → kill the weak route → reroute the task so instead of one fixed chain, you get a live braid: state → decision → parallel paths → crossings → Jev → next state that’s the point of Jev Engineering: more parallel execution without letting the system lose the objective. the agents create the paths. Jev decides which paths survive. full breakdown below ↓

Jev has been exploding in popularity recently. If you already have access to the Jev API but aren't sure how to start experimenting with it, just copy this checklist: 1. jev-ultrafast Browser Use's fastest agent. Jev decides the next action and which element to click, and a language model is only called when text has to be typed. github.com/browser-use/jev-u… 2. typesafe-mario Jev plays Super Mario Bros. from structured emulator state, choosing every action from features pulled out of the game. github.com/fhshaik/typesafe-… 3. jev-plays-pokemon Reads Pokémon Red's game state as text, answers typed questions each turn, and lets plain code turn the answers into moves. github.com/milanboers/jev-pl… 4. jev-drone A camera-only autonomous drone in MuJoCo with a Jev judgment model sitting in the control loop at 2.5 Hz. github.com/RomanSlack/jev-dr… 5. robo-harness A real SO-101 robot arm workbench where Jev picks bounded joint steps from typed candidate actions under a spend budget. github.com/grmkris/robo-harn… 6. fast-jev-compaction Claude Code plugin that replaces the compaction summary with Jev decisions, scoring every tool call for whether it is still needed. github.com/tamaratran/fast-j… 7. jev-claude Routes Claude Code's own judgment calls through Jev: typed choices with probabilities at plan approval, on questions, and before risky commands. github.com/Panebianco00/jev-… 8. is-malicious Supply-chain check before you run anything: Jev Noul checks over source and build files, returning the implicated files and lines. github.com/luantak/is-malici… 9. sqlite-jev Jev inside SQL. Noul, Choice and Score judgments exposed as SQLite functions, with confidence on every row. github.com/mgaitan/sqlite-je… 10. jevinci Paints images by having Jev predict every pixel's colour in parallel, with confidence deciding how wide each stroke is drawn. github.com/achimala/jevinci Copy these complete Jev blueprints - then read full Jev setup below ↓ ↓