Anthropic Races to Contain Leak of Code Behind Claude AI Agent - WSJ
Anthropic Races to Contain Leak of Code Behind Claude AI Agent WSJ
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I built a Python pipeline that auto-generates digital products using Claude API — here's the architecture
I built a machine that makes digital products. It runs 24/7 on a $600 Mac mini in my home office. Here's the honest story: 119 pipeline runs, 57 products shipped, $0 in revenue so far — and why I'm publishing this anyway. The concept The idea is embarrassingly simple: scan the internet for pain points → rank which ones make viable products → auto-generate the product with Claude → publish it to a static site and Gumroad → repeat weekly. No human writes the content. No human formats the pages. I only touch two things: approving or rejecting ideas (via Telegram inline buttons on my phone) and occasionally debugging Python. The pipeline trend_scan.py → scrapes Reddit for questions and complaints → synthesizes pain points into product ideas idea_rank.py → scores each idea: audience size, searc

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Multi-Model AI Orchestration for Software Development: How I Ship 10x Faster with Claude, Codex, and Gemini
I shipped 19 tools across 2 npm packages, got them reviewed, fixed 10 bugs, and published, all in one evening. I did not do it by typing faster. I did it by orchestrating multiple AI models the same way I would coordinate a small development team. That shift changed how I use AI for software work. Instead of asking one model to do everything, I assign roles: one model plans, another researches, another writes code, another reviews, and another handles large-scale analysis when the codebase is too broad for everyone else. The Problem Most developers start with a simple pattern: open one chat, paste some code, and keep asking the same model to help with everything. That works for small tasks. It breaks down on real projects. The first problem is context pressure. As the conversation grows, t
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Multi-Stage Continuous Delivery - Speaker Deck speakerdeck.com El problema con los pipelines tradicionales El concepto de Multi-Stage CD es sencillo: llevas código a prod en varias iteraciones y a través de diferentes ambientes — dev, staging, prod — con fases bien definidas: build, prepare, deploy, test, notify, rollback. Suena limpio. Y en papel, lo es. El problema es la realidad. Según el State of DevOps Report 2020, el 95% del tiempo se va en mantenimiento de pipelines, el 80% en tareas manuales, y el 90% en remediación también manual. Nadie escribe esas métricas en su README, pero todos las vivimos. Los retos concretos son tres y son los de siempre: la disponibilidad de ambientes (el clásico "no le muevan a dev que estoy probando algo" ), satisfacer dependencias externas correctamente

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