| | | 🔗 Stories, Tutorials & Articles | | | | | | | The RAG Obituary: Killed by Agents, Buried by Context Windows | | | | | Agent-based setups are starting to edge out old-school RAG. As LLMs snag multi-million-token context windows and better task chops, the need for chunking, embeddings, and reranking starts to fade. Claude Code, for example, skips all that - with direct file access and smart navigation instead. Retrieval isn't dead, but it's morphing into something far more agentic.
Bigger picture: Bigger windows and sharper attention mean LLMs can now process whole documents and run tasks directly - no more stitching together fragments just to get work done. |
| | | | | | | | | | Serverless RL: Faster, Cheaper and More Flexible RL Training | | | | | | New product, Serverless RL, available through collaboration between CoreWeave, Weights & Biases, and OpenPipe. Offers fast training, lower costs, and simple model deployment. Saves time with no infra setup, faster feedback loops, and easier entry into RL training. |
| | | | | | | | | | Write Deep Learning Code Locally and Run on GPUs Instantly | | | | | | Modal cuts the drama out of deep learning ops. Devs write Python like usual, then fire off training, eval, and serving scripts to serverless GPUs - zero cluster wrangling. It handles data blobs, image builds, and orchestration. You focus on tuning with libraries like Unsloth, or serving via vLLM. |
| | | | | | | | | | Technical Tuesday: 10 best practices for building reliable AI agents in 2025 | | | | | UiPath just dropped Agent Builder in Studio - a legit development environment for AI agents that can actually handle enterprise chaos. Think production-grade: modular builds, traceable steps, and failure handling that doesn’t flake under pressure.
It’s wired for schema-driven prompts, tool versioning, and DeepRAG to lock in relevant context. Model-agnostic deployment? Yep. Plus sharp evals and trace logs to keep things safe, sane, and audit-friendly. |
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