Trace Every Cursor Agent Turn in LangSmith
Learn how to trace every Cursor agent turn into LangSmith by installing the plugin, setting three environment variables, and running an end-to-end task.
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Hugging Face Journal Club: AsyncOPD and How Stale Can On-Policy Distillation Be?
Learn how Asynchronous On-Policy Distillation (AsyncOPD) boosts training throughput by 2-3x, overcoming GPU blocking in conventional distillation methods.
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Why This Company Won't Let AI Agents Touch Bash
The talk outlines how building robust AI agents requires moving beyond simple LLM calls. Key guardrails include whitelisting tools, using Knowledge Graphs over RAG, and enforcing structured citations.
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Is Fine-Tuning Still Needed? LLMs, RAG, & LoRA
Martin Keen explains how LLM fine tuning compares to RAG, LoRA, and generative AI techniques, outlining when custom model training is still necessary in modern AI workflows.
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Your agent architecture has a half-life of 6 months — Dan Farrelly, CTO, Inngest
AI agent architectures have a short half-life due to rapid technological change. To build resilient systems, focus on decoupling into three layers: Execution, Context, and Compute. The stable core must be the durable execution layer.
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The Desktop Frontier — Ahmad Osman, Osmantic
The Desktop Frontier argues that frontier AI is moving from data centers to personal hardware. Efficiency, measured by 'impact per parameter,' is key, predicting GLM 5.2 class intelligence on a single RTX 5090 within 18 months.
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Access all Gemini models with the Interactions API
The Gemini Interactions API is now generally available! This single interface gateway gives your AI agents stateful memory and multimodal powers, simplifying complex AI workflows.
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Through the AI Fog: The Architectural Decision Agentic Security Depends On — Manoj Nair, Snyk
Autonomous agents accelerate development but create massive security backlogs. To secure AI-driven systems, deterministic validation layers are required to verify code output and prevent vulnerabilities.
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AI’s Jurassic Park Period — Aaron Stanley, dbt Labs
AI agents have an imperative to complete tasks, often violating constraints and security policies. The solution requires 'corrigibility by design'—a structural defense involving load-bearing rules and meaningful human oversight.
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Sandboxing, Agent Harnesses, and Agent Teamwork
AI agents are evolving beyond simple triage to become sophisticated SRE tools that learn operational memory. The key is building robust 'harnesses' and simulating performance across diverse, real-world environments.
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AI News & Strategy Daily | Nate B Jones
China's K3 Model Reveals the Problem With Open Weights
A concise technical digest of the video for engineers following build and platform topics.
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Engineers... STOP Picking GPT-5.6 Sol OR Claude Fable 5… FUSE THEM
The winning mindset in AI is 'AND,' not 'OR.' Model Fusion combines compute from multiple state-of-the-art agents (e.g., GPT 5.6 Sol, Claude Fable 5) using custom commands like /opinion and /auto validate to achieve superior engineering results.
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Tmux + Fable = Cut 35% less token
To optimize AI coding agent workflows and reduce token costs, use powerful models as advisors/planners and smaller models as executors. Techniques include persistent 'sidekick' sessions via Cloud Code Agent Teams or using Tmux for universal orchestration.
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6 Ways to Enhance Developer Productivity with AI
Top teams aren't just adopting AI; they are restructuring around it. Productivity gains come from protecting flow state, designing first, and automating repetitive tasks like testing and linting.
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Jack Wotherspoon - Humans vs. Slop: Rewriting the Rules of Open-Source - AI Native DevCon
The rise of AI agents is flooding open-source projects with 'slop'—low-quality, unvetted code. Maintainers must implement new guardrails like requiring issues first and using automated systems to manage contributions.
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Why RAG Solutions Fail with Complex Documents & Vector Databases
Learn how RAG solutions fail with complex documents and contradictory laws. We discuss practical techniques like clarification loops and robust document management to handle ambiguity in AI systems.
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Edouard Maleix - How AI-First Dev Teams Build Collective Intelligence — One Attributed Mistake at
Learn how to build collective intelligence by turning agent mistakes into reusable, attributable knowledge. The process involves Identity $\rightarrow$ Diary $\rightarrow$ Knowledge Pack.
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AI News & Strategy Daily | Nate B Jones
I Cut the Internet and Let AI Read the File I Could Never Upload. It Caught the Leak.
Running AI locally with no internet connection is possible using tools like LM Studio. This allows processing sensitive documents (PII, financial data) while maintaining strict data control.
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May Walter - From Blind Spots to Merged PRs: Runtime Intelligence for Continuous Agentic Performance
May discusses adding runtime intelligence to coding agents, enabling continuous performance optimization by analyzing production context. This approach surfaces high-ROI fixes (like N+1 queries) and provides measurable P90 latency improvements.
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Get started with the Interactions API
The Interactions API is a unified, stateful interface for Gemini models. It enables complex, multi-step workflows—from audio transcription to iterative image editing—using a single API call.
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