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| 🔗 Stories, Tutorials & Articles |
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| What if you don't need MCP at all? |
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Most MCP servers stuffed into LLM agents are overcomplicated, slow to adapt, and hog context. The post calls them out for what they are: a mess.
The alternative? Scrap the kitchen sink. Use Bash, lean Node.js/Puppeteer scripts, and a self-bootstrapping README. That’s it. Agents read the file, spin up their own tools, and get moving. |
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| How to write a great agents.md: Lessons from over 2,500 repositories |
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A GitHub Copilot feature allows for custom agents defined in agents.md files. These agents act as specialists within a team, each with a specific role. The success of an agents.md file lies in providing a clear persona, executable commands, defined boundaries, specific examples, and detailed information about the tech stack.
Why it matters: More devs are pulling agentic tools into daily workflows to squash issues faster - with context baked in. |
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| Hacking Gemini: A Multi-Layered Approach |
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A researcher found a multi-layer sanitization gap in Google Gemini. It let attackers pull off indirect prompt injections to leak Workspace data - think Gmail, Drive, Calendar - using Markdown image renders across Gemini and Colab export chains.
The trick? Sneaking through cracks between HTML and Markdown parsing, plus some wild URI linkification edge cases that Gemini’s Markdown sanitizer missed. |
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| Practical LLM Security Advice from the NVIDIA AI Red Team |
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NVIDIA’s AI Red Team nailed three security sinkholes in LLMs: reckless use of exec/eval, RAG pipelines that grab too much data, and markdown that doesn't get cleaned. These cracks open doors to remote code execution, sneaky prompt injection, and link-based data leaks.
The fix-it trend: App security’s leaning hard into sandboxed runtimes, tighter data perms, and markdown that can’t stab you. |
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| Code execution with MCP: building more efficient AI agents |
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Code is taking over MCP workflows - and fast. With the Model Context Protocol, agents don’t just call tools. They load them on demand. Filter data. Track state like any decent program would.
That shift slashes context bloat - up to 98% fewer tokens. It also trims latency and scales cleaner across thousands of tools. Bonus: better security. |
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| 20x Faster TRL Fine-tuning with RapidFire AI |
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RapidFire AI just dropped a scheduling engine built for chaos - and control. It shards datasets on the fly, reallocates as needed, and runs multiple TRL fine-tuning configs at once, even on a single GPU. No magic, just clever orchestration.
It plugs into TRL with drop-in wrappers, spreads training across GPUs, and lets you stop, clone, or tweak runs live from a slick MLflow-based dashboard.
System shift: Fine-tuning moves from slow and linear to hyper-parallel. Think: up to 24× faster config sweeps. Less waiting, more iterating. |
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