Counter-Terrorism OSINT: Turning Open Data into Actionable Intelligence
Open Source Intelligence, or OSINT, has become one of the most important tools in modern counter-terrorism. In a world where extremist… Continue reading on Medium »
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Your Go Tests Pass, But Do They Actually Test Anything? An Introduction to Mutation Testing
GitHub Copilot, Cursor, Claude Code — these tools can generate hundreds of lines of Go in seconds. But there's a problem that doesn't get enough attention: AI-generated code ships with AI-generated confidence, not correctness. Your test suite says "all green." Your coverage report says 85%. But how many of those tests actually catch real bugs? How many are just going through the motions — executing code paths without verifying behavior? This is where mutation testing comes in. And this is why I built mutest . The Hidden Cost of AI-Assisted Development AI coding assistants are good at producing code that looks correct. They generate functions with proper signatures, idiomatic error handling, and even test files. But there's a gap: AI tends to produce tests that cover code paths rather than
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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Arcee AI Releases Trinity Large Thinking: An Apache 2.0 Open Reasoning Model for Long-Horizon Agents and Tool Use
The landscape of open-source artificial intelligence has shifted from purely generative models toward systems capable of complex, multi-step reasoning. While proprietary reasoning models have dominated the conversation, Arcee AI has released Trinity Large Thinking. This release is an open-weight reasoning model distributed under the Apache 2.0 license, positioning it as a transparent alternative for developers [ ] The post Arcee AI Releases Trinity Large Thinking: An Apache 2.0 Open Reasoning Model for Long-Horizon Agents and Tool Use appeared first on MarkTechPost .
Microsoft and Google release new AI models on the same day: capabilities in speech, image, and local open-source solutions fully deployed. - 富途牛牛
Microsoft and Google release new AI models on the same day: capabilities in speech, image, and local open-source solutions fully deployed. 富途牛牛
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