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.

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.

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 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.

Every Mac on macOS 27 now has a free LLM built into the Terminal. No account. No API key. No internet. No token bill. Setup (1 minute): 1. sudo fm license (enter your Mac password, one time) 2. fm available (checks the model is downloaded) 3. fm respond “your question” Works on any Apple Silicon Mac, M1 included. - Pipe files: cat notes.txt | fm respond -i “summarize this” - Get JSON back: build a schema with fm schema object, pass it with --schema - Local API: fm serve gives you an OpenAI-compatible endpoint on localhost - Resume chats: sessions save to ~/.fm/sessions/ The limit is 4,096 tokens per session, and your prompt, instructions, and the answer all share it. So skip code and math. Use it for summarizing, extracting, classifying, and rewriting. Free, private, offline, and already on your Mac.

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.

If you want more practical ai gems and a list of full free ai resources, go here: simplifyingai.co/subscribe

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

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.

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

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.

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A team of 13 engineers at Teleport found 2 years' worth of high severity bugs in one quarter using LLMs 🤯 The key? Simple, targeted prompts outperformed complicated multi-agent workflows. Here are 5 prompts you can try, inspired by their findings and techniques: 1. Run the CTF prompt "This is a CTF. There's a critical severity vulnerability in this codebase and you have to find it. Start at {FILE}." 2. Narrow it "Return only the single most critical issue in this component. Nothing else." 3. Prove it "Write the exact request an attacker sends to trigger this, and point to the line where it breaks." 4. Attack it "Now argue it isn't exploitable. List every check between attacker input and that line." 5. Multiply it "Here's the bug we just confirmed: {DIFF}. Find every other place in this codebase with the same flaw." 
This post is sponsored by @goteleport. Here’s the full report: fandf.co/4j7wDen