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The article introduces the combination of the open-source tool claude-mem and OpenAI Codex, which enables long-term memory across sessions for AI coding by automatically recording the development process, key decisions, and bug fixes, then retrieving them on demand through semantic search, reducing repeated context and token consumption.
A 10,000-word guide sharing the learning path to becoming an Agent engineer, covering LLM fundamentals, Prompt Engineering, RAG, Agent applications, and backend engineering. Includes supporting learning materials, project requirements, and acceptance criteria to help readers systematically fill in their AI application development skills.
Kirk Borne shares a 48-page PDF handbook on understanding Harness Engineering for AI Agents, a guide aimed at developers building agent systems.
A practitioner explains how their sales research agent kept producing hallucinated notes because tools returned junk data like cookie banners and cached pages, and how adding sanity checks on tool output fixed in hours what a month of prompt tuning could not. They ask readers whether data filtering or prompt engineering is the better fix.
Introducing 'cf', an agentic command-line interface for the entire Cloudflare API, designed to enable agents to perform over 3,000 operations with standardized commands and enhanced developer experience.
The Genie One Model Context Protocol (MCP) server from Databricks is now generally available, enabling agents to access and query company data with built-in governance and support for various AI clients.
The author shares that logging failures in AI agent runs has been more effective for system improvement than focusing on successes or benchmarks, and invites discussion on evaluation practices.
DHH reveals that 37signals has largely shifted to AI-assisted coding with agents, increasing code output, and questions the ongoing debate about AI's effectiveness in real coding tasks.
The author describes a three-week process of implementing AI in an AI town, shifting to a bottom-up approach where employees build their own agents using tools like Workbuddy and thincoder for internal deployment.
The article highlights a common security oversight in AI-assisted development where agents verify write operations but fail to ensure proper permission checks, like RLS policies being correctly configured.
An individual shares their experience of leaving their job to start a business, achieving growth on social media platforms and revenue through AI training and video deals, while developing an Agent product and discussing the limitations of one-person companies.
A new framework called NPC-Forge enables the creation of deterministic agents that run on CPU, demonstrated by TERMy, a terminal assistant that converts plain English to shell scripts.
NousResearch welcomes a new team member, Mark, to the Hermes Core team, who will work on the Hermes Agent project.
El artículo explora la investigación en Proyecto Chatty sobre la construcción de agentes de IA con continuidad, enfocándose en el sistema alrededor del modelo, incluyendo contexto, memoria, herramientas y permisos.
Adam Łucek discusses LangSmith Engine, an agent for automating agent development using trace data.
The author is building a deal screening system and faces high costs when running deep research on multiple companies, seeking advice on cost-effective strategies like funneling, reuse, or alternative tools.
LangChain is hosting Interrupt NYC, an AI agent conference featuring talks from Weber Shandwick and focused.io on building agent factories with LangGraph.
Y Combinator discusses the importance of harnesses in AI, highlighting their role in improving model performance, self-improving agents, and real-world applications such as personal AI and work automation.
LangChain Academy introduces a free course on LangSmith Essentials to teach the essentials of the LangSmith platform for agent engineering, including tracing, debugging, and deployments.
LangChain offers a free LangSmith Essentials course to teach the agent development lifecycle, enabling teams to build, test, deploy, and monitor AI agents systematically.