Quantum @quantumzone

Good Morning ☀️

@G_Programming y si me permitis estimado Alan lo desarrolle con Gentle-ai, comparto el repo github.com/QuantumEdu/kbs

Linux is free. Git is free. Docker is free. Kubernetes is free. Python is free. Node.js is free. Go is free. PostgreSQL is free. MySQL is free. MongoDB is free. Redis is free. VS Code is free. Figma is free. Postman is free. Vercel is free. Netlify is free. Terraform is free. Ansible is free. Jenkins is free. Prometheus is free. Grafana is free. NGINX is free. Apache is free. AWS has a free tier. GCP has a free tier. Azure has a free tier. ChatGPT is free. Claude is free. Perplexity is free. Gemini is free. Hugging Face is free. Ollama is free. Stable Diffusion is free. TensorFlow is free. PyTorch is free. LangChain is free. So what's actually stopping you from building and shipping?

@_nasch_ Increible equipo, tecnologia de punta pero a que $$$$$$$$$$$$$$$$$$$$$$

@G_Programming No lo puedo creer, estaba revisando antier Graphiti por motivos de una aplicacion que estoy realizando, y analizar como se pueden simplificar las apps de grafos, para que no sea overengineering. Gracias Gentleman.

HERMES AGENT HAS A SECOND BRAIN. 1,100+ KNOWLEDGE FILES. AUTO-LINKED. SELF-IMPROVING. GROWING EVERY NIGHT. THIS IS THE OBSIDIAN GRAPH BEHIND IT. every dot = one knowledge file (markdown) every line = one wiki-link between files every color = one category (skills, notes, decisions, sources, entities) HOW IT BUILDS ITSELF: Hermes ships with a bundled LLM Wiki skill. based on Andrej Karpathy's pattern. unlike RAG (rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. when you feed the agent a source: → it reads the content → writes a structured markdown page → auto-links to every related existing page → flags contradictions with previous entries → updates all affected pages one source in. multiple connections created. the graph grows denser with every entry. WHAT FEEDS THE WIKI: → articles and URLs you find interesting → meeting transcripts → PDF documents and research papers → conversation history from Hermes sessions → Claude Code and Codex session history → Slack logs, email threads, saved notes → YouTube transcripts → raw text dropped into a _raw/ folder the obsidian-wiki package supports multi-agent ingest from Hermes, Claude Code, Codex, OpenClaw, Pi, Windsurf, and ChatGPT exports. install: pip install obsidian-wiki obsidian-wiki setup --vault ~/wiki AUTOMATE THE GROWTH: set cron jobs to feed the wiki overnight: "every day at 9am, check for new meetings. ingest transcripts into the wiki." "every week, check arXiv for new papers in [niche]. summarize and file into the wiki." "every day, ingest today's Hermes sessions into the wiki under session-history." month 1: 50 entries. scattered. month 3: 300+ entries. cross-referenced. month 6: 1,000+ entries. the agent surfaces patterns you never searched for. WHY OBSIDIAN: the wiki is plain markdown files. no database. no lock-in. open it in Obsidian for graph view: → nodes show knowledge density → links show how ideas connect → clusters reveal your strongest domains → orphan nodes reveal gaps Hermes writes from a VPS. Obsidian reads on your laptop. obsidian-headless syncs without a GUI. agent writes from the server, you browse on your device. FOUR MEMORY LAYERS: Layer 1: memory.md + user.md (~2,200 + 1,375 chars. short-term.) Layer 2: SQLite with FTS5 (full session transcripts. searchable.) Layer 3: external providers (Mem0, SuperMemory, Honcho. optional.) Layer 4: Obsidian wiki via LLM Wiki skill (unlimited. compounding. the long-term brain.) layers 1-3 handle memory. layer 4 handles knowledge. the graph in this post is layer 4. SETUP: set in Desktop app, Dashboard, or config.yaml: WIKI_PATH=~/wiki OBSIDIAN_VAULT_PATH=~/wiki first run: Hermes asks for your domain. answer with your niche. the skill builds SCHEMA.md with tag taxonomy. after that: "index this into my wiki: [URL or text]" the wiki grows. the graph densifies. the agent gets smarter because the knowledge base got smarter. full 15 levels breakdown in the article 👇

YanXbt @IBuzovskyi

a week ago

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Automatizaciones, faltaria indicar cuantos ciclos, para mejorar goal

múltiples fuentes

Kiro ahora puede mandar mensajes mientras esta trabajando, pero con una interesante alternativa a elegir . . .

In school, they teach you a lesson and give you a test. The real world gives you a test then teaches you a lesson.

@GithubProjects github.com/QuantumEdu

OpenAI DevDay 2026 applications are now open! Our biggest developer event gets even bigger. 📍 San Francisco 📅 September 29 Apply by July 10: devday.openai.com

🚀✨ GitHub Copilot App v1.0.5 released! 2 features & enhancements in this release Top features: • Added Model Providers configuration to connect your own API keys from OpenAI, Anthropic, Azure, and others 🎉 Bug fixes: • Fixed sidebar status flicker when merging a pull request that bounced between "Ready to merge" and "Done" before settling github.com/github/app/rel… #GitHubCopilotApp

¡Gracias @nerdearla por invitarme a ser parte del jurado en esta edición! Ya estamos evaluando las más de 700 propuestas de charlas y talleres que enviaron 🤓🚀

Pi v0.79.8 is out. Highlights: - Selective provider base entry points (`@earendil-works/pi-ai/base`, `@earendil-works/pi-agent-core/base`) for excluding unused provider transports in bundled apps - Mistral sessions now use provider-side prompt caching with session affinity and cached-token usage and cost accounting - Compact results and compaction events now include estimated post-compaction token counts Complete details in thread ↓

Imagine every pixel on your screen, streamed live directly from a model. No HTML, no layout engine, no code. Just exactly what you want to see. @eddiejiao_obj, @drewocarr and I built a prototype to see how this could actually work, and set out to make it real. We're calling it Flipbook. (1/5)

it's right? x.com/jasonfried/sta…

Jason Fried @jasonfried

2 weeks ago

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Del 03 al 06 de Julio, tendrás acceso gratuito a los cursos, rutas y replays de @codigofacilito. Acómpañame en este maratón de aprendizaje. codigofacilito.com/gratis

Introducing GLM-5.2: Frontier Intelligence, Open Weights - Significant improvements in coding and agentic tasks - Strong long-horizon capabilities with a 1M context window - Two levels of reasoning effort: GLM-5.2 (max) pushes the limits, while GLM-5.2 (high) strikes a strong balance between performance and token efficiency - MIT-licensed open weights - Same API pricing as GLM-5.1 Tech Blog: z.ai/blog/glm-5.2 Weights: huggingface.co/zai-org/GLM-5.2 API: docs.z.ai/guides/llm/glm… Coding Plan: z.ai/subscribe Chat: chat.z.ai

We're excited to join forces with @SpaceX to advance the frontier of useful AI. Expect significant improvements to Cursor soon.

SpaceX @SpaceX

2 weeks ago

SpaceX has exercised the option to acquire @cursor_ai in an all-stock transaction with the goal of building the world’s most useful AI models. For the past few months, SpaceXAI has been jointly training a model with Cursor, which will be released in Cursor and Grok Build soon.

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