How to Use AI Models Better Than 99% of Data Scientists in 2026
You open your laptop on a random Tuesday in April 2026.You’ve got GPT-5.4 open in one tab, Claude Opus 4.6 in another, Gemini 3.1 Pro in a… Continue reading on AI & Analytics Diaries »
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Resolve.ai Alternative: Open Source AI for Incident Investigation
Key Takeaway: Resolve.ai is a $1B-valued AI SRE platform used by Coinbase, DoorDash, and Salesforce — but pricing requires contacting sales with no public pricing page. Aurora is an open source (Apache 2.0) alternative that delivers autonomous AI investigation with sandboxed cloud execution, infrastructure graphs, and knowledge base search — completely free and self-hosted. What is Resolve.ai? Resolve.ai is an AI-powered autonomous SRE platform founded in 2024 by Spiros Xanthos (former SVP at Splunk, co-creator of OpenTelemetry ) and Mayank Agarwal. It raised $125M in Series A at a reported $1 billion valuation , backed by Lightspeed and Greylock with angels including Fei-Fei Li and Jeff Dean. Resolve.ai positions as "machines on call for humans" — a multi-agent AI system that autonomously
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
Multi-Stage Continuous Delivery
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Multi-Stage Continuous Delivery
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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