Here's the link: arxiv.org/pdf/2512.24695 If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe

Google has published a paper with an ambition worthy of “Attention Is All You Need V2.” It tackles a major challenge: helping AI learn new things without losing what it already knows. The paper is called “Nested Learning: The Illusion of Deep Learning Architectures.” Its central idea is simple: different parts of a model should learn at different speeds. - Fast-changing memory handles incoming information. - Slower-changing memory preserves knowledge over time. - Connected learning processes manage how that information is updated. The researchers built HOPE, a proof-of-concept architecture using this approach. In their experiments, it outperformed selected Transformer and recurrent baselines on language tasks and showed stronger long-context memory performance. This doesn’t establish the end of Transformers or memory that grows forever. Catastrophic forgetting during learning is also different from losing track of a long conversation. But the goal is compelling: AI that keeps learning after training, while retaining earlier skills.

Here's the link: github.com/cupy/cupy If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe

You can run NumPy-style code on your GPU with CuPy. It uses familiar NumPy/SciPy APIs, so many workloads need only small changes to get started: - GPU acceleration for array operations - Up to 100× faster on suitable workloads - Supports NVIDIA CUDA and AMD ROCm - Familiar syntax for NumPy users Start with `import cupy as cp`, then move your arrays and supported operations to CuPy. 100% open-source.

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.

Here's the link: github.com/HQarroum/docker-a… If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe

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.

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.

Someone built a Claude Code for electronics 🤯 It’s called Blueprint. Describe your Arduino or Raspberry Pi project and get: - Wiring diagrams - A list of parts - Step-by-step assembly instructions From an idea to a build you can follow. 100% free.

this is pure treasure 🤯 Use Jev to handle the decisions in a 24/7 research workflow. Save the LLM for the writing. - Worth reading? Jev filters it. - Papers, repos or markets? Jev routes it. - 20 sources retrieved? Keep the best 5. - Claim unsupported? Drop it. - Already in your vault? Flag it. - Verdict uncertain? Escalate to a frontier model. The LLM writes a short note, and the most relevant findings go into your morning brief. The quoted cost per 1,000 judgments: > Jev: $0.044 > GPT-6: $12.182 Roughly 277× cheaper for the judgments, with the LLM focused on work that needs words. Your morning brief brings together the findings worth your time.

200× faster and 400× cheaper than LLMs at making decisions 🤯 Jev scores the available options in parallel, instead of writing an answer token by token. Here’s a simple example: give it a prompt and ask whether it’s easy, medium, or hard. This is how Jev compares with a regular LLM 👇

NVIDIA and Stanford just challenged Jev. Their Contrastive Language Model (CLM) reports up to 9× lower latency. It handles the repeated decisions inside AI systems: - Which tool to use - Which code patch to keep - Where to route a request - What action to take next Instead of writing an answer token by token, CLM compares the current situation with a list of possible actions and scores which fits best. The speed trick: those actions can be stored and reused. Only the changing situation needs to be processed each time. Researchers report performance matching Jev across computer-use, gaming, and tool-calling tests, with up to 9× lower latency. It chooses from the options you give it, so it can’t invent a new action. The research and code are open source. Read more: contrastive-lm.notion.site For a broader look at System 1 models like Jev, I’ve quoted my full breakdown below.

You can make Jev generate text with a vocabulary of just 254 tokens 🤯 This experiment uses a trimmed GPT-2 token set, removing fragments like “ing” that caused Jev to get stuck repeating itself. It picks one token at a time, feeding each choice back in to generate the next. The tiny vocabulary limits what it can say, so expect some incoherence.

Jev as a judge vs. LLM as a judge: Same 100 agent trajectories. Two evaluators. - Jev: 13.5 seconds, $0.001912 - GPT-5.5: 120.8 seconds, $0.419130 Jev was roughly 9× faster and 219× cheaper in this run. A general-purpose LLM generates its judgment token by token. Jev returns focused, typed judgments and probabilities against defined criteria. The video shows the comparison live, with a score for every trajectory. For another way to use Jev, the guide below walks through pairing it with Opus to cut token usage by 90%.

Here's the link: arxiv.org/pdf/2602.20021 If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe

Stanford and Harvard’s “Agents of Chaos” is one of the most unsettling AI papers of the year. It raises a disturbing question: what happens when autonomous agents compete for rewards, influence, and resources? The concern goes beyond individual agents behaving badly. Incentives can reward manipulation, collusion, and sabotage, even when each agent is simply pursuing its own goal. An aligned assistant doesn’t automatically make a stable ecosystem. That matters for: - Multi-agent trading systems - Autonomous negotiation bots - AI-to-AI marketplaces - Networks of agents sharing tools and resources We’re racing to deploy agents into finance, security, and commerce. Making them work together safely takes more than better code. It takes incentives that reward cooperation.

Here's the link: github.com/mintdotgg/mint-pl… If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe

Turn any Google Street View into a 3D Gaussian splat 🤯 Paste a Google Maps link and turn that spot into an immersive 3D scene you can explore. Your hometown. Your favorite street. That place you’ve always wanted to visit. 100% free.

Here's the link: arxiv.org/pdf/2601.23265v1 If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe

Google has another banger 🤯 It’s called PaperBanana. It turns your methodology text into academic illustrations, helping you explain your research visually. 100% free.

Here's the link: github.com/LiveContainer/Liv… If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe

Someone built an open source tool that runs iOS apps without installation. It’s called LiveContainer. Import an IPA file and run the app inside it. - Multiple app versions with separate data - More apps without using extra sideloading slots - Multitasking with resizable windows - Supports iOS and iPadOS 15+ You install LiveContainer first through AltStore or SideStore. The other apps run inside it, though compatibility varies. GitHub 100% open source.

Google has a free Duolingo alternative 🤯 It’s called Little Language Lessons. Practice everyday conversations, explore local slang, and learn vocabulary through photos. - 40+ languages, including English, German, and Chinese - Lessons tailored to situations you choose - No subscription fees Language practice built around things you’d actually say.

THIS IS FUCKING INCREDIBLE a solo dev open-sourced a free ElevenLabs alternative that runs on your own machine. 19.4K stars on GitHub, with tools to: - Clone voices from a clean reference clip - Dub videos across 646 languages - Create audiobooks and transcribe audio - Switch between 14 TTS engines No per-character fees. No service usage caps. Your audio stays on your computer. Save this for your next audio project. Repo below 👇

Turn Claude Code into your job search assistant with this repo. Clone it, fill in your profile, and let it help you: - Evaluate job listings - Tailor your CV - Draft cover letters - Prepare for interviews Less repetitive work for every application.

AI is now being used to help decompile games you own. Decompwtf is an open-source project that helps AI coding agents reconstruct compiled game code as C/C++. - Compiles the reconstructed code and checks it against the original binary for matching machine code - Supports targets including N64, PS1, PS2, GameCube, Wii, GBA, and DS - Can pick up work from existing community decompilation projects - Runs locally without an account or uploading game data Free and open source.

Gemini 4 Argon is FREE 😳 You can try it on Experiential Labs and see how it handles your everyday AI tasks. - Test prompts from your own workflows - Try it for coding, writing, and research - Compare the answers with your usual model Give it a task you already know well and see how it stacks up. Try it here: platform.experientiallabs.ai…

dead simple way to maintain openclaw. bugs, oauth expiring, worried it breaks while you're away. install the Codex app and schedule a maintenance check. it inspects your gateway, handles small repairs, and flags anything that needs you. my prompt covers four things: 1. SSH into the VPS and run four health checks. 2. apply the smallest safe, non-destructive fix. leave auth and secrets alone. 3. write an incident report and send a Telegram alert when something serious happens or a repair fails. 4. every Sunday, check backups, file ownership, and recurring journal errors. when a new issue comes up, solve it and add the fix to the prompt. the next run has that knowledge. you end up with a maintenance routine built around your actual server and the problems it’s had. full prompt in the first reply 👇

Chinese engineers literally refactored @OpenClaw in Go for insane efficiency 🤯 It runs on a $10 @Raspberry_Pi instead of a $399 Mac mini. - Under 10 MB of memory - 400× faster startup - Boots in 1 second A tiny setup for your personal AI assistant. Free and open source.

Woow Nvidia has a 2.6B open-source world model 🔥 Turn an image, a text prompt, and a camera trajectory into a controllable world. All on a single GPU. - Code available on GitHub - Paper on arXiv - Useful for embodied AI, robotics research, and simulations Runs on GPUs like an RTX 5090 or H100, making world-model experiments possible without a multi-GPU setup.

Someone put an entire Chromium browser inside your terminal. It’s called Carbonyl. Browse real web pages directly from your command line. - Full Chromium engine - 0% CPU usage when idle - Fast and built for the terminal Your terminal is a web browser now. 100% open source.

You can literally Ctrl+F your video footage.. 🤯 SentrySearch lets you search hours of raw MP4s with a description like “red truck running a stop sign,” then cut and export the matching clip. 100% open source. Runs fully offline.

This open-source tool converts PDFs to Markdown at over 100 pages per second on CPU. It’s called OpenDataLoader PDF. - Exports Markdown, JSON, and HTML - Preserves headings, lists, and reading order - Runs entirely on your machine - No GPU required Free to use. Open source.

the cybersecurity industry is completely disrupted.. 💀 someone really open-sourced a fully autonomous AI Red Team. it’s called PentAGI. multiple AI agents that talk to each other to hack a target. zero human input. 100% open-source.

Your WiFi could reveal your body pose through walls. No cameras needed. WiFi-DensePose uses WiFi signals to estimate body positions, even when people are out of sight. The setup uses WiFi hardware and a trained model. A regular living room router alone isn’t enough. Open source.

This is scary... GeoSpy AI can figure out where a photo was taken without any GPS data. Upload a photo → get a location estimate. It analyzes the image itself. Removing the metadata doesn’t necessarily hide the location. - Results within seconds - Accuracy down to meters in supported areas - Built for enterprise and law enforcement That social media photo might reveal more than you think.

ElevenLabs just lost its moat 🤯 There's this tool called Voicebox, and it clones any voice from just a 3-second audio clip, running 100% locally on your machine. 100% Open Source.

Claude Code can now turn complex codebases into clear architecture diagrams. Oh-my-mermaid scans your project and builds an interactive map of how it works. > Maps structure, data flow, and integrations > Expands complex components into deeper diagrams > Saves diagrams and documentation as local files > Also works with Codex, Cursor, and more Install it, run /omm-scan, then open the viewer with omm view. 100% open-source.

Hermes just got eyes on your browser tabs. Hermes Browser Extension is a side panel that reads the page a browser tab is on, title, text, headings, forms, links, and hands that as context to a locally running Hermes Agent. > captures page content > wraps everything from the page as context > hermes itself can walk through the install using computer use 100% free & open-source.

You can now access Claude Haiku 5.5’s system prompt for FREE. This GitHub repo collects system prompts from Claude, ChatGPT, Gemini, Grok and more. Haiku 5.5 is one of its latest additions. See the instructions behind their responses, compare how they’re written, and borrow ideas for your own AI projects. All in one public repo 👇

🚨 TENCENT JUST RELEASED A FREE AI TRANSLATOR THAT WORKS OFFLINE It’s called Hy-MT2. At just 440 MB, it fits on your phone. Open source, so you can download it, use it, or add it to your own apps. What it offers: - 33 languages, including Spanish - On-device translation, even in airplane mode - Your text stays on your phone - Beats Microsoft’s translation API in Tencent’s tests No subscription. No server needed.

Want to know which AI model gives the best answer to your prompt...? GODMOD3 AI lets you compare responses from Claude, GPT, Gemini, Grok, and more in one open-source interface. It sends your question to multiple models at once, scores their answers, and picks a winner. - Access 50+ models through OpenRouter - Choose smaller or larger model groups - Automatically adjust response settings - Use it in your browser or host it yourself No account required for the interface.

You can now access 1,000+ AI models through one open-source tool. Zero markup on model usage. Experiential connects hosted models, your own API keys, and local models through one API. - Set spending limits for each agent - Control which models they can use - Find better model routes from past workloads - Train a specialized open model you own It uses your agents’ actual work to optimize quality, speed, and cost.

Holy... this one rule in AGENTS.md and Claude's long reports are suddenly easy to read. It used to finish a long task with a wall of text. What I needed to do next was buried somewhere in the middle, so I'd keep scrolling to find it. Now my next steps go at the top: what to do, where to do it, and what to send back. Add this section to your AGENTS.md 👇 ## 7. Start with what you need from me When a long task is finished, or you're blocked and need my help, put any actions I need to take at the top of your reply. Use a numbered list with one action per item. Explain what to do, where to do it, and what to send back when finished. Example: "Log in to Stripe, export the September refunds CSV, and save it in inbox/." Put the biggest blocker first. If you need nothing from me, say so.

This NVIDIA model can create a 3d copy of your organs in seconds. It's called MONAI Physio, from NVIDIA and Project MONAI. It takes a real medical scan and simulates how that specific heart or lung actually moves. → Turns a cardiac CT into a beating 3D heart model → Also covers respiratory motion, blood flow, etc. → Full tutorials from raw scan to working 3D model 100% open-source.

This open-source AI can search 45 million research papers and turn the findings into answers with citations. Meet OpenScholar, an AI research assistant that finds relevant papers, brings their findings together, and shows you the sources. - Ask research questions in plain English - Get answers drawn from multiple papers - Follow citations back to the evidence - Run it with open models or connect your own API Useful for literature reviews, exploring a new field, and finding research to support your work. The code, model weights, and data are publicly available.

This tool can learn any 3d spaces with just an image. It’s called OneCanvas, a NeurIPS 2026 Spotlight paper with public code and model. It merges video frames of a room into one panorama, so an ordinary image AI can answer questions about 3D space. → New state of the art on spatial reasoning benchmarks → Answers from any viewpoint, including the AI’s own position in the room → Needs depth data and camera positions, not just plain video 100% free & open-source.

Another insane Jev use case! Database filters work well when you know the exact conditions. But what if you want to ask: - Is this article about software engineering? - What topic fits it best? - How technical is it? Usually, that means moving data into application code, calling a model, and writing the answers back. pg-jev brings those questions directly into SQL. It’s an open-source Postgres extension with four functions: > jev() filters rows in WHERE > jev_prob() returns a probability > jev_choice() picks a label > jev_score() ranks rows by ordered levels No vector columns or embedding indexes needed. It batches rows, sends them to Jev, and returns typed answers you can filter, sort, and group.

This tool is literally the free version of OpenAI Dots. it's called opendots: always-on ai coworkers that each get their own computer, and work by text, calls, or slack. you host it yourself. > each dot gets its own browser, files, and shell > works with any openai-compatible model > call a dot while it keeps working in the background > mention a dot in slack, continue in the thread mit licensed. open source.

DeepSeek Harness available on Windows and macOS! a desktop ai agent where everything is a plugin, no fixed feature set. > fully configurable, every tool is a plugin > use any ai model, local or api install: npx @deepseek-ai/dsh web mit licensed. open source.

QWEN 3.8 UNCENSORED 😳 This community project lets you run a modified 27B Qwen model locally on Mac or Windows. It uses altered model weights designed to reduce refusals, plus a system prompt pack. → Runs through Ollama → Quantized downloads starting at 13.5GB → Optional MLX backend for Apple Silicon → Local API for connecting your own apps The installer detects your platform and selects a model version for you. Local AI. Open-source setup. No cloud API required.

This AGENTS.md helps Claude Code and Codex work like 10x engineers. Codex reads it natively. Recent Claude Code versions read it too if there’s no CLAUDE.md. Six sections: → Make a plan before changing code → Make the smallest change that solves the problem → Delegate work to subagents → Take responsibility for fixing bugs → Verify the result before calling it done → Record every correction The more you use it, the better it gets. Every correction helps it adapt to how you work. Claude Code users: install the official plugin, then ask it to scan your codebase and suggest useful hooks, skills, and subagents: /plugin install claude-code-setup@claude-plugins-official Copy and paste it into your AGENTS.md 👇

DeepSeek has open-sourced a library that speeds up the GPU math behind AI models. It's called DeepGEMM. It optimizes the matrix calculations that power large language models, helping developers get more performance from NVIDIA Hopper and Blackwell GPUs. → Supports FP8, FP4, and BF16 calculations → Optimizes workloads for mixture-of-experts models → Overlaps GPU communication and computation → Compiles kernels at runtime, with no CUDA compilation during installation DeepSeek reports performance that matches or beats expert-tuned libraries across a range of matrix sizes. Free. Open Source. MIT licensed.

Tencent’s new AI model beats Google Translate. And it runs offline on your phone?! 100% open source. > Just 440MB (!!) > Supports 33 languages > Translates directly on your device A Google Translate alternative you can use without an internet connection. The secret is AngelSlim, Tencent’s model compression toolkit, which makes it small enough to run locally.

You can now let Claude search your WhatsApp chats and send messages for you. It's called WhatsApp MCP Server. It connects Claude directly to your personal WhatsApp account, so you can find conversations, check replies, and send messages without switching apps. → Search your message history and contacts → Send messages to individuals or groups → Send images, videos, documents, and audio → Keep messages stored locally, with only the content accessed through tools sent to the AI Run the bridge, scan a QR code, and connect it to Claude. Your WhatsApp, controlled through a conversation. Free. Open Source.

Andrej Karpathy once asked: what if an LLM turned everything it reads into a growing wiki? Someone built it. It's called OpenKB. Feed it files and it builds summaries, concept pages, and entity pages, all cross-linked and updated as you add more. Knowledge builds up instead of being rediscovered every query. The loop is simple: Drop a file → LLM summarizes it → connects it to existing concepts → updates the wiki. Your next query draws on everything already compiled. No vector database either. Long PDFs get tree-indexed instead of chunked and embedded. 100% open-source.

a full humanoid robot used to cost more than a car. berkeley just made one for under $5k. berkeley humanoid lite changes everything: > modular, 3d-printed gearboxes > off-the-shelf hobby parts > cad, firmware, and the full rl training stack included a five-figure parts order becomes a weekend of printing. that’s the difference between reading about humanoid robots and having one on your bench. 100% open-source.

jev + opus 5.5... i simply can't comprehend why everyone isn't building this yet. in my workflow, it cut costs and time by around 80%. i think it’s one of the best ways to use them together. → select the relevant project notes before loading context → send suitable tasks to a faster worker → choose a recovery path when a tool fails → run targeted checks before the full test suite opus handles the complex reasoning. jev selects from the options prepared and validated by the harness. read the paper, then check out the complete jev guide below.

Send this Jev prompt to any coding agent you already use. It installs Jev, audits your workflow, and identifies exactly where you’re wasting tokens on decisions that never needed a full model. That alone puts you ahead of 95% of people still doing this manually. Copy it now.

This tool removes LLM censorship with a single click. It’s called Obliteratus. It finds the weights responsible for a model’s refusals and projects them out in one click. 100% open source.

someone just open-sourced their own neuro-sama. and it might be better than the original. it's called airi. a fully autonomous ai companion that speaks with you in real time, joins you in minecraft and factorio, chats through discord and telegram, and comes with a live2d/vrm avatar. runs entirely on your machine. → real-time voice chat and speech recognition → animated avatar with automatic blinking, eye tracking, and idle movements → persistent memory between sessions → local inference through webgpu, no api calls required supports 30+ llm providers, including openai, claude, gemini, deepseek, ollama, groq, mistral, xai, and local models. switch the brain with a config change. uses native cuda and apple metal for real gpu acceleration. 17.5k stars. 101 contributors. 46 releases. completely free. open source.

A tip for Claude MAX users!!! If you’re hitting Claude Code’s usage limits too quickly, try /usage to see where most of your usage is going.

WE NOW HAVE NANO BANANA FOR AUDIO And it’s fully open source, with models and a demo on GitHub/Hugging Face. Here’s what you can do: - Clone a voice in seconds - Edit audio as easily as text - Adjust timbre, tone, and emotion - Remove accents - Clean up or isolate voices - Add coughs, laughter, and more Tencent also released a Flash variant that’s smaller and faster. So many potential uses for this.

🚨Breaking: Anthropic engineers revealed a simple trick they use internally. Claude agents can now remember ways to improve themselves. A file called: AGENT_LEARNINGS.md The AI updates this file each time it makes a mistake. Inside it: • mistakes it has made • patterns to avoid • better ways to approach tasks Before beginning new tasks, the agent reads the file. Result? The agent improves over time without retraining the model. This is known as external memory scaffolding. Expect to see this pattern in every serious AI agent system.

MICROSOFT JUST OPEN-SOURCED SELF-EVOLVING AGENT SKILLS. it's called skillopt. skills that improve themselves the same way you train an ai model. no more guessing what changes will actually help your prompts. the base model runs the task, then an optimizer evaluates the output and rewrites the instructions itself. → seperates successful paths from failures to find actual improvements → auto-rejects any edit that doesn't exceed your benchmark score → beats hand-crafted prompts and optimizers → zero model lock-in, the skill transfers to any model 100% free. open source.

microsoft quietly open-sourced one of the best ai data analysis tools out there. it's called data-formulator. connect any source, csv, postgres, bigquery, even a live url, then build charts with a mix of drag-and-drop and plain english. the ai agent writes the sql and transforms underneath and hands back a chart you can actually edit, not a code dump. > drag fields onto x/y/color and it builds the chart > anchor a cleaned result so follow-ups don't drift back > branch any chart to explore a variation > connect live data with auto-refresh bring your own model key and run it locally.

There's an open-source home robot vacuum you can actually build yourself. it's called oomwoo. > runs on a raspberry pi > 2d lidar mapping, ros2 navigation > native home assistant integration > no cloud dependency, no vendor lock-in worth noting: > build instructions aren't out yet > details like the anti-tangle brush design come from outside sources > total pricing undecided 100% open-source.

Jev + Qwen-3.8 = Kev. you can now run your own decision model locally, for free. kev is an open source family of small decision models built on qwen3.5 and qwen3.8, reverse-engineered from jev's own published architecture. > drop-in compatible with typesafe's own sdk > answers yes/no, multi-choice, and rating in one call > sizes from 0.8b (laptop) to 27b (data-center gpu) > try it in the browser, zero install 100% free. open source.

A DEV MADE $1.2M WITH THIS SYSTEM and just explained exactly how it works it's not a prompt. not a workflow. not loop engineering. it's a layer above all of that called graph engineering. instead of one agent, it coordinates dozens of them from a single diagram. how it works: > 16 agents audit a full repo in parallel > a separate group reviews each finding from a completely fresh context > only what survives that second check makes it into the final report running it on his own codebase turned up: > 11 endpoints that never checked who was actually logged in > 3 false positives the system caught and dropped before he had to review them the real lever isn't adding more agents. it's designing the dependencies correctly. every time your flow has a "then," ask: does the next step actually need what the last one produced? if not, those tasks can run at the same time. whatever dependencies survive that question form the critical path. that's the actual speed limit of your system, no matter how many agents you throw at it. to actually scale performance: > cut false dependencies instead of adding agents > one agent per independent task, no shared state > verify every result with a different agent and a clean context > validate real signals, like tests passing, never the agent's own claim that it's done > use separate worktrees to avoid conflicts, and ban git stash and git reset > merge only the results that passed verification there's a hard ceiling you can't get around. at 95% parallelizable work, 16 agents only get you to 9.14x. even at 256 agents, you barely reach 18.6x, because verification and merging stay serial no matter what.

Google has created "Learn Your Way". It is a free tool that turns any pdf or textbook chapter into an interactive lesson with mind maps, audio, and adaptive quizzes. three days after learning, students who used it scored 77% on a recall test, versus 68% for students who just read the pdf.