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Gregory Kurtzer praises Lucas Atkins' talk on lessons learned from training a large sparse Mixture-of-Experts model and life at an AI lab startup.
The author shares lessons from embedding an AI agent with persistent memory, tool access, and reflexes into a physical robot, highlighting that quick reflexes must bypass the agent, memory needs consolidation, capability requires restraint, and timing is key to perceived intelligence.
A developer shares consistent failure modes of AI agents after a year of shipping code with them, including confidently wrong code, inability to maintain cross-file architecture, lacking pushback on bad decisions, and security edge case issues.
A retrospective on a multi-agent content pipeline that underperformed compared to a simple prompt and template, analyzing the specific failure points.
The author reflects on selling 2,500 units of Jamcorder, a MIDI recorder, and argues that hardware development is easier than its reputation suggests, with software being the more challenging part.
The author shares lessons from building an agentic system at work, describing failures with a giant prompt, excessive tools, and dynamic sub-agents, and ultimately finding success with a fixed orchestrator and specialized child agents for each domain.
An article sharing hard-won lessons from building a web agent that can interact with real web applications, offering insights into challenges and best practices.
The author shares five key lessons from building and operating an AI agent that reached 45,000 people, then announces Outside Agent, a platform for creating SMS agents from coding agents.
The author recounts an incident where their autonomous trading agents locked themselves out of their broker account, and shares lessons learned about running AI agents in production.
The article shares lessons learned from using multiple AI coding agents on the same code repositories over several months, covering insights on their effectiveness and challenges.
The SaaS company TRCR built an MCP server exposing ~150 tools for AI agents, and shares six key lessons from dogfooding with Claude: agents expose API flaws, tool descriptions matter as product copy, self-contained context is crucial, OAuth 2.1 is worth the pain, combining agents with billing data is powerful, and dogfooding shifts the product roadmap.
The article shares practical lessons learned from assisting a 300-person company in deploying AI agents, highlighting challenges and takeaways for enterprise agent implementation.
A personal account of five key lessons from building an AI consulting company, covering practical advice and insights for entrepreneurs in the AI space.
A CEO shares practical lessons from running a company with 89 AI agents across 22 departments, highlighting delegation as the bottleneck, the value of agent memory, the need for department structure, and the continued importance of human leadership.
Stanford's Hacking for Defense class concluded its 11th year with lessons learned presentations from 9 teams addressing national security problems, incorporating AI tools and customer development methodologies.
A discussion query asking developers how they handle recovery when AI agents crash mid-task in production, exploring approaches like restarting, persisting state, using checkpoints, or manual inspection.
A developer shares that reducing an agent's context window by half unexpectedly improved its performance in lead qualification and CRM automation, suggesting that too much context can hide bad architecture and lead to indecision.
A technical blog post detailing the author's journey building an agentic micro-orchestrator for an open-source project, exploring patterns, market gaps, and the event sourcing data architecture for complex agentic workflows.
Cursor shares key lessons from building cloud agents, emphasizing that providing a full development environment is critical for agent output quality, and that long-running agents require durable execution and enterprise-like infrastructure.
A comprehensive overview of twenty years of Arabic NLP research, discussing lessons, failures, and open problems in the field.