Huge, did NOT expect that release. Evals looks very solid, significant jump compared to composer 2! But: it’s 10x more efficient than the competition. Looks really exciting. Need to try it out
CODE
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GitHub’s Spec Kit tackles unreliable vibe coding
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GitHub acaba de solucionar el mayor problema del vibe coding.
— Nico (@nicos_ai) 18 mai 2026
Acaban de lanzar Spec Kit y en días ya tiene +95K estrellas.
¿La idea?
En vez de tirar prompts vagos y rezar para que el agente no rompa tu proyecto…
Spec Kit obliga a la IA a crear una especificación estructurada… pic.twitter.com/pLVkNzrxdUGitHub has just solved the biggest problem with vibe coding. They've just launched Spec Kit and in days it already has +95K stars. The idea? Instead of throwing vague prompts and praying the agent doesn't break your project… Spec Kit forces the AI to create a structured
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Complex AI agent workflows with ClaudeCode
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J’ai un Claude qui task des ClaudeCode avec des megabriefs de plus de 700 lignes parfois ça monte à 800 ou 900 lignes, il y a même des requêtes qui demandent des temps d’arrêts de 30 minutes pour laisser tourner afin de contrôler des comportements, les IA tournent des heures en
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Insforge: Backend Platform for AI-Native Developers on Y Combinator
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More here:
→ https://
ycombinator.com/launches/QP6-i
nsforge-the-backend-platform-for-ai-native-developers
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Optimizing Workflows with AI Coding Agents and Tools
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Working with Codex gives you access to tons of things like your entire local context (files, notes, repo) skills, MCPs and of course an overall really strong agent. Once Codex makes stuff, you can still use the MagicPath native agent to improve it to your liking. As I shown in
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How to use persistent objectives with Codex AI agents
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how to use /goal in codex — keep Codex working on a persistent objective until it's solved:
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Mental Models for Guiding AI Coding Agents
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A mental model for working with coding agents is that they're blind squirrels running into a maze and bumping into walls. You must place the walls (verifiable constraints) strategically so that they end up in the general region you want them in.
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A Closer Look Into The Math Behind Neural Networks
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A Closer Look Into The Math Behind Neural Networks! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #GoLang #CloudComputing #Serverless #DataScientist #Linux #Programming #Coding #100DaysofCode https://
geni.us/Math-Neural-Ne
tworks
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MiniMax Releases M2.7 Open-Weight LLM with Self-Evolution Capabilities
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💡 @MiniMax_AI M2.7 is an open-weight LLM built for serious dev work.
— SambaNova (@SambaNovaAI) 18 mai 2026
It’s the first in MiniMax’s M-series to “self-evolve” via its own training + eval loop (agent harness optimization). Designed for complex coding, multi-agent systems, and pro-grade workflows.
Learn more:… pic.twitter.com/BZJp8zgCLg@MiniMax_AI M2.7 is an open-weight LLM built for serious dev work. It’s the first in MiniMax’s M-series to “self-evolve” via its own training + eval loop (agent harness optimization). Designed for complex coding, multi-agent systems, and pro-grade workflows. Learn more:
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LLMs and production code debugging limitations
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pure llms aren’t what’s debugging production code