agents

New paper proposes self-compacting language model agents that can decide when to clean up their own traces of reasoning and tool calls to avoid accumulating mistakes and stale information.

A 29-year-old Chinese salesperson built an ETH price simulation engine using Claude and 6 AI agents, earning $306,000 last month, showcasing the potential of AI in quantitative trading.

Almost all AI model and agent progress depends on evaluations (evals). Understanding workflows and agent performance through evals will become a core enterprise competency for driving automation.

Stripe launched Stripe.Directory, a new way for users and agents to search for businesses on Stripe.

Mindstone Rebel is an AI workspace with agents that understand your work and ask before acting.

This article explains the concept of loop engineering in AI agents, emphasizing that the core loop is trivial but the critical work lies in the harness around the model, including knowing when to stop and preventing context rot.

Otto (MIT) is an open-source browser extension that turns a real tab into a controllable node via CLI or agent, solving the 'agent needs a browser' problem without headless farms or expensive cloud services.

TMax presents a straightforward method for building AI agents that operate in terminal environments, combining practical design principles for effective command-line automation.

Oak is an open-source version control system designed for AI agents, offering branch-per-session workflows, content-addressed lazy mounts, and faster performance than git. It is in public beta and available as a CLI tool and reusable Rust library.

Explains how intent-based lead generation agents are built in n8n, focusing on architecture that filters signal from noise.

Cloudflare introduces temporary accounts that let agents deploy before completing signup, streamlining the onboarding process.

This article discusses recommendation systems powered by AI agents.

Box CEO Aaron Levie argues that AI agents will use software 100X more than people, requiring guardrails, authoritative data sources, logging, and collaboration features; platforms enabling headless interactions will be best positioned.

A curated roundup of 10 open-source tools for training AI agents using reinforcement learning, covering frameworks like OpenPipe ART, verl-agent, Agent Lightning, and Unsloth, with details on their use cases and strengths.

The Agent Reinforcement Trainer (ART) is an open-source framework that plugs GRPO-based RL into any Python app, enabling agents to learn from environment interaction via trajectory scoring and LoRA updates, with claims of outperforming OpenAI's o3 on email retrieval using a Qwen 2.5 14B model.

A 58-page paper from Google DeepMind on building agents specialized in game theory, highlighting key insights from the research.

The article details a setup running six AI agents 24/7 on a Minisforum MS-S1 Max mini workstation with AMD Ryzen AI Max+ 395 chip, costing $11/month in electricity. It highlights the shift from cloud API costs to local inference, enabling always-on agents for tasks like email sorting, research monitoring, and document processing.

A tweet highlights Q from Korea achieving a session with nearly 300 subagents running for over a day using the Codex desktop app and lazycodex, showcasing frontier agentic engineering.

Perplexity Brain is a memory system that builds a persistent context graph across tasks, projects, decisions, files, and sources, enabling agents to start with relevant context instead of from scratch, improving answer correctness and reducing task costs.

A Hacker News user asks if anyone is using Google's A2A agent-to-agent protocol, noting confusion six months ago and the rise of MCP, but now seeing potential for agent interaction.