ProbablyMarkets @ProbablyMarkets

Stop renting a chatbot and calling it your company’s brain. Utopia is the first open-source enterprise world model I’ve seen that actually treats knowledge like a system, not a vibe. It's a Self-hosted, open source and built around the one question every other knowledge base ignores: when. Every fact in it carries a start date and an end date. Nothing gets overwritten. When something changes, the correction closes the old version and opens a new one. The history stays intact. So the graph isn't a snapshot anymore. It's a recording you can rewind. You drag a timeline on the screen and watch the whole thing redraw itself to show what was true on that date. Who owned what. What the policy said before it changed. All of it, replayable. And every fact points back to the exact sentence it came from. Nothing gets in without a receipt. Here's the part that got me. The whole thing is one binary and one Postgres database. That's it. No Elasticsearch, no separate vector service and no message queue. Most RAG stacks are six services duct-taped together. This is two. It even runs fully offline on an air-gapped network with any model you want. github.com/deeplethe/utop…

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@aleabitoreddit What are your picks apart from SIVE?

Every. single. enterprise company I talk to is building (or already has) one of these right now

Cerebras @cerebras

3 months ago

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Bofff con lo que costaba encontrarla en buena calidad

comunismo pelis @comunismopelis

3 months ago

Shin-chan: en busca de las bolas perdidas - Película Completa

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UN INGENIERO SENIOR DE ANTHROPIC ACABA DE PUBLICAR UN PDF DE 12 PÁGINAS SOBRE "GRAPH ENGINEERING" PARA SISTEMAS MULTIAGENTE El cambio de mentalidad: la memoria de tus agentes muere con su ventana de contexto. Un knowledge graph la hace permanente. Extraer → Resolver → Ensamblar → Consultar → Repetir Las 5 fases de todo agentic graph: → Extraer: Haiku saca entidades y triples sujeto-predicado-objeto. Una llamada por documento. El esquema de Pydantic es el único training data. → Resolver: Sonnet agrupa "Edwin Aldrin" con "Buzz Aldrin" sin ningún solape de texto, usando las descripciones como contexto. → Ensamblar: nodos canónicos, edges tipados y provenance en cada triple. Un único grafo conectado. → Consultar: serializas un subgrafo, Sonnet razona sobre los triples y cada respuesta cita un edge concreto. → Repetir: cada documento nuevo vuelve a entrar por el mismo pipeline y se fusiona con los nodos que ya existen. El graph no se reconstruye, crece. Conéctalo a tus sistemas multiagente como memoria compartida. Los workers escriben en él, los evaluadores contrastan contra él y los loops siguen vivos de un día para otro. Este PDF de 12 páginas ha cambiado cómo estoy construyendo sistemas multiagente. Léelo y luego pásate por el artículo de abajo👇

angel @angeldot_

3 months ago

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GOOGLE ACABA DE MATAR LA EXCUSA DE “NO ENTIENDO ESTE REPO” pegas cualquier repositorio y codewiki genera automáticamente: → documentación interactiva → diagramas de arquitectura → explicaciones del código → tutoriales paso a paso → un chatbot que entiende todo el proyecto todo generado por gemini, siempre actualizado te dejo la repo abajo 👇🏻

Quieren ahorrar tokens? Prueben graphify. Básicamente agarrás cualquier carpeta (código, SQL, docs, hasta imágenes) y te arma un grafo de conocimiento que podés consultar. 76k estrellas en GitHub +

I turned Claude into an entire company This time, I built the infrastructure Everything below is real and installable Skills 1,000+ agent skills → github.com/VoltAgent/awes… 345 Claude Code skills → github.com/alirezarezvani… Anthropic’s official Agent Skills → github.com/anthropics/ski… Memory Claude Memory Compiler → github.com/coleam00/claud… Agent Memory → github.com/rohitg00/agent… Planning With Files → github.com/OthmanAdi/plan… Agents 100+ specialized Claude Code subagents → github.com/0xfurai/claude… Subagent Manager → github.com/webdevtodayjas… Hooks Claude Code Hooks Mastery → github.com/disler/claude-… Workflows Claude Code workflow recipes → github.com/ithiria894/awe… Agent Harness Skills, memory, security, and research-first workflows → github.com/affaan-m/every… Plugins Anthropic’s official Claude Code plugins → github.com/anthropics/cla… Official Claude plugin directory → github.com/anthropics/cla… Marketplace 199 agents 162 skills 106 commands 92 plugins → github.com/wshobson/agents Not prompts Not wrappers The infrastructure layer around Claude Same model Different system Bookmark this and build your own AI company

Lunar @LunarResearcher

4 months ago

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El Segundo Cerebro Definitivo El método Karpathy + Claude Code leyendo todo tu baúl de Obsidian es el segundo cerebro más inteligente del planeta. El método es simple y brutal. Si no puedes construir algo desde cero, no lo sabes. Los tutoriales son un aprendizaje falso y tu cerebro los borra en 3 días. La mayoría de la gente ignora esto. Construyen un segundo cerebro que simplemente se queda ahí, carpetas de notas que nadie vuelve a abrir, texto muerto. Apunta Claude Code hacia el baúl y este despierta. 5000 notas, una sola mente. Lo lee todo y responde con tus propias palabras y tus propias demostraciones, no con las suposiciones de un modelo. Entonces el ciclo se cierra. ¿Quieres entender las redes neuronales? Sáltate el video de 3 horas y pídele a Claude Code que construya una pequeña. 200 líneas desde cero. Observa cómo se entrena, rompe una capa, mira cómo falla, arréglala. Lo entiendes en 20 minutos en lugar de 3 semanas. En el instante en que lo captas, la nota queda escrita. Una idea por archivo, enlazada a otras 10, depositada en el baúl mientras el conocimiento aún está fresco. Ahora empieza a acumularse: Mes 1: 60 notas. Mes 6: 900 notas. Cada nota nueva atrae a las antiguas, así que preguntas cualquier cosa y la respuesta viene de tu cerebro, no de internet. El Contraste Antes: 40 pestañas, 6 PDF leídos a medias, 0 retenido. Después: Constrúyelo una vez, hazlo tuyo de por vida. La configuración toma 4 minutos. Texto plano, sin ataduras a ninguna plataforma. Un segundo cerebro que nadie lee es un cementerio. El tuyo acaba de empezar a pensar.

Chesny @chesny

3 months ago

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LE PIDES A TU IA UNA API Y TE DEVUELVE ALGO SIN TESTS. El jefe de ingeniería de Chrome en Google lo arregló. 76.000 estrellas. Se llama Agent Skills. Le enseña a tu IA a trabajar como un ingeniero senior, paso a paso. → Escribe el plan antes de tocar código → Escribe los tests antes de dar por hecho algo → Revisa su propio código antes de entregarlo → No dice "listo" hasta que todo funciona de verdad Se instala en Claude Code, Cursor o Copilot con un solo comando. Repo abajo.

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Claude just dropped 13 FREE AI courses (with certificates). No $500 course needed. No “guru” required. Just real skills — straight from Anthropic. Here’s the full list: 👇 1. Claude 101   lnkd.in/gCPUQsRg 2. AI Fluency: Frameworks & Foundations   lnkd.in/gS6ceZ_M 3. Introduction to Agent Skills   lnkd.in/g_wWNiEb 4. Building with the Claude API   lnkd.in/gDr5K_B4 5. Claude Code in Action   lnkd.in/g9wWZbK9 6. Introduction to Model Context Protocol   lnkd.in/gAj5HqMY 7. MCP: Advanced Topics   lnkd.in/g3eDwBFY 8. AI Fluency for Students   lnkd.in/gKKujHGG 9. AI Fluency for Educators   lnkd.in/gVcKnuhA 10. Teaching AI Fluency   lnkd.in/g9P4gJFM 11. AI Fluency for Nonprofits   lnkd.in/gpsm_BVf 12. Claude with Amazon Bedrock   lnkd.in/gbfPjSFt 13. Claude with Google Vertex AI   lnkd.in/gvVgB4Ub — If you go through even HALF of these… You’ll be ahead of 95% of people using AI. Most people won’t. Because they’re still: • Watching random YouTube videos • Buying overpriced courses • “Learning AI” without actually building Don’t be that person. Do this instead: 1. Save this post (you’ll come back to it) @AiwithDharmik 2. Pick 1 course → start today 3. Share it with someone who needs this Free. Practical. No excuses.

Learn Coding by playing games 1. Kubernetes k8sgames.com 2. DevOps devops.games 3. Linux overthewire.org 4. Git ohmygit.org 5. Python codecombat.com 6. CSS & HTML codepip.com 7. Cybersecurity picoctf.org 8. Mobile Coding (like Duolingo) sololearn.com 9. For Complete Beginners scratch.mit.edu 10. 25+ Programming Languages codingame.com Follow @ai_explorer25 for more such insightful content

AI_Explorer @ai_explorer25

4 months ago

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Andrej Karpathy (ex-OpenAI co-founder, now Anthropic) revealed his full AI workflow. 1 hour of pure examples: • 00:32 - why some models hallucinate math and others solve it perfectly • 04:37 - turning ChatGPT into your personal data analyst • 09:05 - Claude Artifacts: full apps built without a single line of code • 14:51 - Cursor Composer: how he actually writes code now • 23:25 - true audio vs fake audio: the difference nobody explains • 40:00 - what AI models actually see when you show them an image • 53:10 - memory: how ChatGPT silently learns who you are 1 hour 10 minutes. zero filler. just him showing his actual screen bookmark & watch now ↓

chuplung @choopyplug1

4 months ago

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aprender System Design es una de las mejores inversiones que podés hacer. pero, ¿a qué llamo system design? es aprender a diseñar sistemas que sean escalables, confiables y fáciles de mantener. esto incluye entender cómo se comunican los servicios, cómo elegir bases de datos, cuándo usar cachés o colas, y cómo diseñar sistemas resilientes con reintentos, timeouts y observabilidad. más que memorizar tecnologías, se trata de entender por qué un sistema funciona de una manera y no de otra. ¿y por qué hoy gana todavía más importancia? porque la IA no reemplaza la necesidad de construir buenos sistemas. solo acelera cómo los desarrollamos. además, los agentes también tienen que resolver muchos de estos problemas: llaman APIs, usan tools, manejan estado, coordinan múltiples pasos y tienen que recuperarse cuando algo falla. cuanto mejor entiendas system design, mejores decisiones de ingeniería vas a tomar, tanto al construir software tradicional como aplicaciones con IA.

Best 10 AI projects that will get you hired in 30 days: 1. RAG App with Real Evaluations Build a real-world RAG system that’s ready for production, with testing, monitoring, and ways to collect feedback and keep improving it. Stack: OpenAI or Claude + vector DB + evals Guide: pinecone.io/learn/retrieva… 2. Autonomous Research Agent An agent that can plan what to do, search for information, cite its sources, and write reports. Stack: LangGraph + tools + citations Example: github.com/langchain-ai/l… 3. AI Customer Support Copilot Human-in-the-loop support assistant with guardrails and analytics. Stack: OpenAI + Zendesk or Intercom + moderation platform.openai.com/docs/guides/as… 4. Voice AI Agent (Calls + Scheduling) Handles real phone calls, booking, and follow-ups. Stack: Twilio + Whisper + LLM Starter: twilio.com/docs/voice 5. AgentOps Dashboard Track agent reliability, cost, failures, and regressions. Stack: OpenTelemetry + evals Inspo: github.com/open-telemetry… 6. AI Data Extraction Pipeline Extract structured data from PDFs, emails, and images at scale. Stack: Vision models + schema validation Example: github.com/Unstructured-I… 7. Code Review AI for GitHub Automated PR summaries, risk flags, and suggestions. Stack: GitHub Actions + LLM Docs: docs.github.com/en/actions 8. Multi-Agent Planner for Workflows Planner and executor agents that can fall back to a reliable, predictable option when needed. Stack: LangGraph or CrewAI CrewAI: github.com/joaomdmoura/cr… 9. AI Search Engine with Fresh Data Search the web, forums, and social media in one place, with citations you can trust. Stack: Search APIs + rerankers Ref: meilisearch.com/docs 10. Enterprise Prompt Security Tool Find prompt injection, data leaks, and unsafe answers before they cause problems. Stack: policy checks + red teaming Guide: github.com/openai/openai-…

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How to become AI engineer in next 6 months: By the end, you want to be able to: - build LLM apps end-to-end - use APIs from OpenAI / Anthropic / open-source stacks - design prompts and context properly - add tool calling and structured outputs - deploy real projects So, let’s discuss your roadmap month by month Month 1: Get solid enough in coding and fundamentals What to learn: - Python really well - Git + GitHub - CLI / terminal basics - JSON, APIs, HTTP, async basics - basic SQL - basic data handling with pandas - virtual environments, package management, error handling - FastAPI or Flask Month 2: Master LLM app development What to learn: - prompting fundamentals - system vs user instructions - structured outputs / JSON schemas - function/tool calling - streaming responses - conversation state - cost / latency / token basics - failure handling - prompt injection awareness Month 3: Learn RAG properly What to learn: - embeddings - chunking - vector databases - metadata filtering - reranking - retrieval quality issues - hallucination reduction - citations and grounding Month 4: Agents, tools, workflows, evals - agent loops - tool selection - state management - retries - when NOT to use agents - multi-step workflows - evaluation harnesses - task success metrics Month 5: Deployment, product thinking, and reliability What to learn: - FastAPI production patterns - Docker - background jobs - queues - auth + API key security - logging - observability - prompt/version management - eval dashboards - cost monitoring - rate limits - caching Month 6: Specialize and become hireable these knowledge and skills you gained can be applied in three directions you need to choose one of them and focus on practice although everything mentioned above is also best learned purely through practice Direction 1: AI product engineer Best if you want startup jobs fast Focus on: - LLM apps - RAG - agents - deployment - product UX Direction 2: Applied ML / LLM engineer Focus on: - fine-tuning - when to fine-tune vs prompt - evaluation - inference optimization - open-source models - training pipelines Direction 3: AI automation engineer Focus on: - workflow orchestration - business process automation - multi-tool systems - CRM, docs, email, support, ops use cases This roadmap will help you go through a practical path, and the key is to study each of these points and then test them in real work By month six, you will already have several built products or examples of completed tasks And it will be much easier to get a job as an AI engineer Save it so you don't lose it and can return to study later

Rahul @sairahul1

4 months ago

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Claude users after reading this:

The AI Colony @TheAIColony

4 months ago

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