All tags
Topic: "context-engineering"
Context Engineering: Much More than Prompts
gemini-code openai langchain cognition google-deepmind vercel cloudflare openrouter context-engineering retrieval-augmented-generation tools state-management history-management prompt-engineering software-layer chatgpt-connectors api-integration karpathy walden_yan tobi_lutke hwchase17 rlancemartin kwindla dex_horthy
Context Engineering emerges as a significant trend in AI, highlighted by experts like Andrej Karpathy, Walden Yan from Cognition, and Tobi Lutke. It involves managing an LLM's context window with the right mix of prompts, retrieval, tools, and state to optimize performance, going beyond traditional prompt engineering. LangChain and its tool LangGraph are noted for advancing this approach. Additionally, OpenAI has launched ChatGPT connectors for platforms like Google Drive, Dropbox, SharePoint, and Box, enhancing context integration for Pro users. Other notable news includes the launch of Vercel Sandbox, Cloudflare Containers, the leak and release of Gemini Code by Google DeepMind, and fundraising efforts by OpenRouter.
Cognition vs Anthropic: Don't Build Multi-Agents/How to Build Multi-Agents
claude cognition anthropic langchain huggingface microsoft llamaindex linkedin blackrock multi-agent-systems context-engineering agent-memory model-elicitation ai-evaluation deep-research-workflows framework-migration pydantic-schema walden_yan hwchase17 assaf_elovic sh_reya hamelhusain omarsar0 clefourrier jerryjliu0 akbirkhan
Within the last 24 hours, Cognition's Walden Yan advised "Don't Build Multi-Agents," while Anthropic shared their approach to building multi-agent systems with Claude's multi-agent research architecture. LangChain highlighted advances in context engineering and production AI agents used by LinkedIn and BlackRock. The community is engaging in a debate on multi-agent AI development. Additionally, Hugging Face announced deprecating TensorFlow and Flax support in favor of PyTorch. Research on agent memory and model elicitation techniques from LlamaIndex and Anthropic were also discussed.
not much happened today
seedance-1.0 codex claude-code kling-2.1 veo-3 bytedance morph-labs huggingface deeplearning.ai figure-ai langchain sakana-ai video-generation autoformalization ai-assisted-coding api-design context-engineering reinforcement-learning ai-evals hypernetworks model-fine-tuning foundation-models andrew_ng hwchase17 adcock_brett clementdelangue akhaliq jxmnop hamelhusain sh_reya
Bytedance showcased an impressive state-of-the-art video generation model called Seedance 1.0 without releasing it, while Morph Labs announced Trinity, an autoformalization system for Lean. Huggingface Transformers deprecated Tensorflow/JAX support. Andrew Ng of DeepLearning.AI highlighted the rise of the GenAI Application Engineer role emphasizing skills in AI building blocks and AI-assisted coding tools like Codex and Claude Code. Engineering teams are increasingly testing API designs against LLMs for usability. Figure AI's CEO stressed speed as a key competitive advantage, and LangChain introduced the concept of Context Engineering for AI agents. Reinforcement learning on LLMs shows transformative potential, and the community values AI evals and data work. Sakana AI released Text-to-LoRA, a hypernetwork method for generating task-specific LoRA adapters from natural language, enabling efficient model customization. The video generation race heats up with Bytedance's Seed-based model praised for quality, challenging American labs, alongside models like Kling 2.1 and Veo 3.