As LLMs are increasingly deployed within agentic systems, their capabilities depend not only on the model weights but also on the harness: the prompts, tools, control flow, memory, and orchestration code surrounding t... Context & related coverage →
Unpaired image-to-image translation must decide, per image, what to change and what to preserve without paired supervision. Many diffusion-based unpaired translators control preservation through a single global noise... Context & related coverage →
Commercial vision-language models are reshaping computer vision, with visual priors broad enough to rival task-specific systems. This raises a natural question: do they reduce the need for classic, physics-informed lo... Context & related coverage →
Microsoft released a dedicated AI Gateway tier of Azure API Management in public preview, with a control plane built around models, MCP servers and tools rather than APIs. It fronts Foundry, Bedrock, Vertex AI and Ope... Context & related coverage →
Learning from demonstration (LfD) provides a developmental framework through which robots can develop motor skills by observing and imitating human dynamics, reducing reliance on explicit programming to teach a skill... Context & related coverage →
Added self-hosted environments: claude self-hosted-runner turns your own machines or containers into a place Claude Code web, mobile, and desktop sessions ca... · Added archive plugin source: install plugins from a zi... Context & related coverage →
Deep Agents, LangChain, and LangGraph each offer distinct approaches to building agents. In this post, we cover the key distinctions between our open source frameworks and when you should reach for each one. Context & related coverage →
Learn how LangChain built an autonomous SRE agent for Kubernetes deployments with Deep Agents, human approval for changes, LangSmith tracing, and evals. Context & related coverage →