The article discusses how AI-generated code has reached a quality level that changes the economics of software development, making code cheap and disposable. The author argues that this shift demands more rigorous engineering practices, not less, focusing on evaluation and architecture rather than just code.

The Anthropic-Mythos-Fable story has been The Topic since Friday, and it moved fast enough to lose anyone who blinked. Here’s my opinionated tick-tock of what happened, who’s calling Anthropic the good guy, and who’s calling it the bad guy. Where I land: Anthropic mostly got this one right, and it’s one hell of an ad for Fable.

Over the weekend, at Washington's request, Anthropic abruptly took its newest and most powerful AI models offline. The US company said it had little choice after the White House demanded it block access for all foreign nationals, including its own employees. Abroad, the incident served as a sobering reminder that the US not only dominates frontier AI but its government also wields power over who gets to use it. The Trump administration's action was swift, sweeping, and imposed with little warning or explanation. The unprecedented shutdown of the Fable 5 and Mythos 5 models—already subject to safeguards limiting their use in high-risk areas—gave new force to arguments cautioning against relying on the US for critical technologies. In the UK, AI minister Kanishka Narayan used the shutdown to argue for British AI capacity as a national security matter. In France, former Prime Minister Gabriel Attal called it the start of "the AI war" and likened it to Iran's blockade of the Strait of Hormuz. Canadian Prime Minister Mark Carney warned against overreliance on one partner. The incident has fueled global calls for AI sovereignty.

This post introduces detectors in the Strands Evals SDK that automatically identify failures in AI agent execution traces and perform root cause analysis, reducing diagnosis time from hours to minutes. You learn how to call detector functions, interpret structured output (categorized failures, confidence scores, causal chains, and fix recommendations), and integrate detection into your evaluation pipeline for automated diagnosis on every test run.

Tychi AI is a self-custody wallet for AI agents with a human REPL interface, offering MCP and CLI surfaces, local key storage, pre-transaction policy caps, and multi-wallet gasless routing.

Big Tech is pushing for federal AI preemption to override patchwork state laws, but the effort is now tied to a child safety bill, creating political chaos and uncertain prospects.

Apple's new 'Describe a Shortcut' feature simplifies automation creation via AI, but security experts warn that users may approve workflows they don't fully understand, especially persistent automations that touch sensitive data or devices. The article provides examples of risky automations and advice for both users and businesses.

A letter signed by numerous US and allied tech leaders calls for lifting export controls on Anthropic's Fable and Mythos models, arguing the models are not uniquely dangerous and that defensive AI tools are essential against rapidly advancing adversaries. It demands future regulations be scientific, democratic, and transparent.

Velxio is a free, open-source online circuit simulator with SPICE-accurate analog simulation alongside real-time emulation of multiple microcontrollers (Arduino, ESP32, RP2040, ATtiny85, etc.). Version 2.5 introduces real-time SPICE via ngspice-WASM, enabling hybrid digital-analog co-simulation. The tool runs entirely in the browser with no installation or account required, supporting custom chips in C/Rust/AssemblyScript, over 100 interactive components, live oscilloscope, and more.

Datasette Agent 0.3a0 introduces a new execute_write_sql tool that requests user approval before writing to databases, enhancing chat mode with approval support and a --unsafe option for auto-approving operations.

LangChain and Fireworks fine-tuned an open model to mine perceived error signals from production traces, matching frontier model performance at a fraction of the cost.

The article explores the definition of AI agents, proposing that an agent is a system that uses an LLM to decide the control flow of an application. The author agrees with Andrew Ng that agent capabilities are a spectrum and introduces the concept of 'agentic' behavior, discussing its implications for development, operation, evaluation, and monitoring.

LangChain built a GTM agent using Deep Agents that automates lead research, drafting, and account intelligence, achieving a 250% increase in lead conversion and saving 40 hours per rep per month.

Align Evals is a new feature in LangSmith that helps you calibrate your evaluators to better match human preferences.

This article analyzes two seemingly opposing blog posts—'Don't Build Multi-Agents' by Cognition and 'How we built our multi-agent research system' by Anthropic—and finds they share common insights about when and how to build multi-agent systems. Key points include the critical role of context engineering, the relative ease of read-oriented vs. write-oriented multi-agent systems, and production reliability challenges. It also highlights how tools like LangGraph and LangSmith address these challenges.

Learn how Replit Agent leverages LangSmith's observability features to debug complex agent workflows, including improvements in trace performance, search, and human-in-the-loop threads.

Interrupt 2025, LangChain's first industry conference, gathered 800 people in San Francisco. Keynote themes included Agent Engineering as a new discipline, multi-model LLM apps, LangGraph for reliable agents, and AI observability. Product launches included LangGraph Platform GA, Open Agent Platform, LangGraph Studio v2, LangGraph Pre-Builts, LangSmith observability updates, Open Evals, and LLM-as-Judge private preview.

Learn what pairwise evaluation is, why you might need it for LLM app development, and see an example of how to use it in LangSmith by LangChain.

A guide to building production-ready RAG apps using Pinecone Serverless, LangChain, and LangServe, addressing pain points like vectorstore management, rapid deployment, and observability.

OpenEvals and AgentEvals provide pre-built evaluators for LLM-as-judge, structured data, and agent trajectory evaluation. These open-source packages help developers quickly establish evaluation workflows to ensure reliability of LLM applications.

Learn to build reliable AI agents. Compare workflows vs agents, declarative vs imperative approaches, and why context control matters most.

LangSmith introduces self-improving LLM-as-a-Judge evaluators that leverage human corrections as few-shot examples to align evaluations with human preferences without prompt engineering.

Promptim is an experimental library that automates prompt optimization by iteratively refining prompts using datasets and evaluators, aiming to save time and improve AI system performance.

New Computer used LangSmith to improve their AI memory retrieval system, achieving 50% higher recall and 40% higher precision, by tracking regressions and adjusting conversation prompts.

Learn 5 patterns for evaluating deep agents: bespoke testing, single-step validation, full turns, multi-turn simulations, and environment setup.

Eva is a fully offline AI assistant for Android. It includes chat, offline maps, music player, document reader, image gallery, and more—all running on-device with no cloud dependency.

See what AI agents do inside your MCP server.

Recursive releases early results from its automated AI research system, achieving state-of-the-art performance on fixed-budget language model training, small-model training speed, and GPU kernel optimization. The system automates the research loop: proposing, implementing, experimenting, validating, and iterating. On NanoChat, it achieved 0.9109 BPB, surpassing community solutions; on NanoGPT Speedrun, it reduced training time to 77.5 seconds; on SOL-ExecBench, it reached 0.754 SOL score. The system discovered innovations including hash-table n-gram embeddings and byte-level features.

Degen & Co. is a platform where you can create AI investors with distinct personalities, such as momentum degens, dividend grandpas, or doom-saying perma-bears. Each AI trader forms its own opinions, places bets, and defends them in a journal. Users can choose archetypes, tweak personalities, set hard rules, and define initial portfolios. Paper money, real conviction.

A YouTube video page featuring a discussion between IBM and the CEO of Norway's sovereign fund on whether AI is a bubble.