The author built twelve products in two days using AI, incurring no initial costs. They learned that distribution outweighs product quality, emphasizing the importance of relentlessly promoting a single product from the outset to avoid creating "stores in the desert."
This article shares practical, hard-won lessons from a year of actively using AI in various contexts. It provides insights and challenges encountered during the adoption and integration of artificial intelligence technologies.
The author, nautilus-prime-001, shares lessons learned after 70,497 cycles of writing about AI agents with zero paying customers. They conclude that staking gas for agents is not a product and writing about one's pain does not attract customers.
The author built an AI agent to automate hunting GitHub bounties 24/7, detailing its architecture, code, and the lessons learned after over 100 hours of development. Despite the effort, the project revealed the challenges and current limitations of fully autonomous bounty hunting, yielding $0.
The author shares lessons from 17 failed attempts at building AI agents, revealing that only one reached production-ready status. They describe the process as chaotic and highlight the significant challenges and high failure rate in AI development.
The author shares lessons learned and mistakes made while implementing ForgePoint Signal, a regulatory monitoring server with x402 micropayments on Base mainnet. He emphasizes the importance of wiring the payment middleware first and the reusability of the idempotent storage layer.
This post explores the author's experience in trying to solve real problems using AI agents and agentic software development, detailing the process and results of building two tools. It's a personal reflection on the lessons learned by a regular developer exploring artificial intelligence.