Datris Unveils Open Source MCP Data Stack For AI Agents - Open Source For You
Datris Unveils Open Source MCP Data Stack For AI Agents Open Source For You
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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
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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I Built Consistent Hashing From Scratch in Go — Here's What I Learned
If you've ever added a server to a cache cluster and watched your database melt, you already know the problem consistent hashing solves. You just might not know it by name. I built a full implementation from scratch in Go to understand it deeply. This post walks through what I learned — the problem, the fix, and the gotchas nobody tells you about. The five-minute version You have 5 cache servers. You route keys with hash(key) % 5 . Life is good. Then traffic spikes and you add a 6th server. Now it's hash(key) % 6 . Sounds harmless, right? Here's what actually happens: Before: hash("user:1001") % 5 = 3 → Server C After: hash("user:1001") % 6 = 1 → Server A ← moved! That key was sitting happily on Server C. Now every client thinks it's on Server A, where it doesn't exist. Cache miss. The req
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