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Simplifying AI
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Simplifying AI
@simplifyinAI
Helping you master AI daily with step-by-step AI guides, latest news & practical tools • DM for Collabs
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GitHub: github.com/microsoft/mark…
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Microsoft just changed the game 🤯
They open-sourced a tool that converts literally any file into clean markdown for LLMs in under 60 seconds.
- Converts 10+ file formats out of the box.
- Run via command line, Python API, or Docker.
- Built-in MCP server for direct Claude Desktop integration.
100% open source.

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Simplifying AI がリツイート

You can now generate fully explorable 3D worlds from a single text prompt 🤯
OpenArt just integrated Marble, a spatial intelligence platform by World Lab that doesn't just make basic 3D models; it generates entire environments you can actually walk through and edit.
• Text, image, or video to a persistent 3D world
• Open a 3D World Cam to take and auto-enhance pictures inside the scene
• Cast your existing characters directly into the environment
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Link: github.com/steipete/Codex…
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You can now run ElevenLabs-level voice cloning completely offline 🤯
LuxTTS is a local TTS model that clones voices from 3 seconds of audio at insane speeds. It runs at 150x real-time without you ever having to pay a subscription.
- Works perfectly on both CPU and GPU
- Takes up just 1GB of VRAM
- Outputs crisp 48kHz audio instead of standard 24kHz
100% Open Source.

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GitHub: github.com/ysharma3501/Lu…
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Link: github.com/czlonkowski/n8…
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🚨 BREAKING: China just fixed a 10-year-old flaw hidden inside every major language model.
Every AI you use today (ChatGPT, Claude, Gemini) is built on a massive flaw. It’s called the residual connection.
Here’s the problem: every layer inside an AI blindly stacks its output on top of the last one. There is no filtering, no judgment, just blind accumulation.
Imagine a meeting where every person shouts their ideas at full volume, forever. The early ideas (the fundamental patterns) get drowned out by the newer, louder layers piled on top.
The technical term is “prenorm dilution,” but in practice, it means your AI forgets its own most important work as it gets deeper.
We’ve been building models like this since 2017.
Now, Moonshot AI (Kimi) just dropped a paper that completely replaces this broken system.
They call it “attention residuals.”
Instead of blindly accumulating everything, each layer now “votes” on which previous layers actually matter using softmax attention over depth. The network learns to remember what’s important and ignore what isn’t.
The results are absolutely insane:
- It matches the performance of models that used 25% more compute to train
- Tested on a 48-billion-parameter model with massive gains in math, code, and reasoning
- Inference slowdown is less than 2%
- It’s a direct drop-in replacement for existing systems
The Transformer replaced recurrence with attention across words in 2017. This paper is doing the exact same thing across layers of depth.
This is the same class of idea. The entire architecture of AI is about to change.

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Paper: arxiv.org/abs/2603.15031
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You can now give Claude Code a visual map of your entire codebase 🤯
Someone just open-sourced a tool that builds a local graph of your entire project so Claude instantly knows how every file is wired together.
- Runs 100% locally on your machine.
- Maps out your entire repository structure automatically.
- Gives Claude Code the exact context it needs to build without hallucinating.
100% open source.

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Repo: github.com/sgoudelis/grou…
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Project: github.com/tirth8205/code…
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