EtoDermerzel

🚀 Official launch! 🎭 AI with Personality – Volume I 💼 PACO (logistics) 👑 FELIPE (monarch) 🧠 RAÚL (strategist) 🖥️ Runs 100% locally with llama.cpp 💸 Only €10 → etodemerzel.gumroad.com #LocalAI #CustomPrompts #IndieDev #TerminalPower

🛒 Buy/Compra 今すぐゲット パック第1-2-3巻 on @Gumroad → etodemerzel.gumroad.com/l/seisaku-writ… ✨ Super oferta/super deal 超お得 ¡solo por tiempo limitado!

@gvanrossum You’re a living legend! 6 months ago I couldn’t write a single line of code. After diving into Bash, I’m now learning Python and exploring CS on my own. Asimov opened my mind to machines, and now I hope to return the favor: github.com/BiblioGalactic

GLADIA was the one who, among so many machines, showed that art depends on humans. Here are the tools that would let her dream with lights, colors, space, and time 👉 github.com/BiblioGalactic… #Bash #BiblioGalactic #Local #CreativeCoding #GenerativeArt #DigitalArt #OpenSource

If you're running solo and want AI everywhere—local, private, and integrated into your actual workflow—I built exactly what we need. No cloud dependencies. No API costs piling up. Just practical implementations you can deploy today. → github.com/BiblioGalactic

Delightfully amusing how RAG is suddenly the discourse du jour. Meanwhile, BiblioGalactic has been sitting here for a month, quietly doing the damn thing while everyone was still theorizing. github.com/BiblioGalactic But sure, welcome to the party. Fashionably late, as always.

Explore my GitHub — not code for programmers, but tools for thinkers. 玩一玩，让它成为你的：思想者的工具。 自立するためのコードを覗いてみてください。 💻🤖🌸🌎🌍🌏 👉 github.com/BiblioGalactic Ni los juegos de los 90 eran tan entretenidos. #bash #IA #NewEra #Oldvibes

@maripydev github.com/BiblioGalactic

El primer "Hola mundo" es engañoso, te hace creer que programar es fácil 🙃😂..

@SalzmannOnX github.com/BiblioGalactic

Vibe coding is coding

@helloiamleonie github.com/BiblioGalactic

Wait,... am I getting this right? Short-term memory is non-persistent storage Working memory -> store in list Long-term memory is persistent storage Procedural memory -> store in .md file Episodic memory -> store in database Semantic memory -> store in database

@livingdevops github.com/BiblioGalactic

- Linux is free. - Docker is free. - Kubernetes is free. - Git and Github are free. - GitHub Actions is free. - Python is free. - AWS, GCP, Azure are free (limited use). - Terraform is free. - ArgoCD and Flux are free. - Prometheus and Grafana are free. Your laptop and internet connection: That’s all you need to start. No expensive bootcamps. No fancy certifications. Just you, your determination, and these free tools. While others are complaining about job markets and layoffs, DevOps engineers are getting multiple offers. Companies are desperate for people who can automate, deploy, and manage infrastructure. The same skills that took me from X to 3X salary, All learned using free tools. Every successful DevOps engineer started exactly where you are right now. Zero experience. Zero connections. Just curiosity. They didn’t wait for the “perfect” time. They didn’t make excuses about not having money for courses. They downloaded Docker, broke things, fixed them, and repeated. Start today. Your future self will thank you.

@PythonPr github.com/BiblioGalactic

Types of Databases!!!

@alexfinn github.com/BiblioGalactic

If you've never coded in your life you can download Claude Code right now and by the end of the day have a fully working app 99% of people have have no idea You can't imagine the gold rush that's about to happen when people finally wake up

@midudev github.com/BiblioGalactic

Este repo es una joya: tutoriales crear agentes de IA. Listos para producción y con casos de uso reales. Todo el código disponible y explica cómo desplegarlos: github.com/NirDiamant/age…

Look for them in the hidden back shelves of libraries—the ones nobody checks anymore. Those old code manuals gathering dust? They're worth more than gold now. Written by humans. With humans. For humans to control their machines.

@lochan_twt Yo, if you dig it, don't be shy—use it and make it yours! Just remember to toss a GitHub follow or star if you liked it! github.com/BiblioGalactic

ai intern interview question

@Python_Dv Yo, if you dig it, don't be shy—use it and make it yours! Just remember to toss a GitHub follow or star if you liked it! github.com/BiblioGalactic

A simple breakdown of Agentic AI - layer by layer Before we dive in - we’re offering a Free AI Agentic Program for anyone who wants to go deeper into these concepts. Let’s walk through how it all fits together. 1️⃣ LLMs: the foundation This is where it all starts. Models like GPT or DeepSeek power the system. Main ideas: ✅ Tokenization & inference: how text gets processed and generated ✅ Prompt engineering: crafting better inputs for smarter outputs ✅ LLM APIs: ways to connect and use these models in apps Think of this layer as the engine behind everything else. 2️⃣ AI Agents — built on top of LLMs Agents give LLMs the ability to act, not just respond. They handle: ✅ Tool use & function calling: linking models to APIs or external tools ✅ Reasoning: using methods like ReAct or Chain-of-Thought ✅ Task planning: breaking large goals into smaller steps 3️⃣ Agentic Systems — multiple agents working together When several agents coordinate, you get a full system that can collaborate and adapt. Core features: ✅ Inter-agent communication: talking through protocols like A2A ✅ Routing & scheduling: assigning tasks to the right agent ✅ State coordination: keeping shared progress consistent ✅ Multi-agent RAG: retrieving and combining knowledge across agents 4️⃣ Agentic Infrastructure — the foundation for scale and safety This layer makes everything reliable, secure, and ready for production. It includes: ✅ Monitoring: tracking performance with tools like Opik ✅ Error handling: recovering gracefully when things fail ✅ Security & access control: defining what agents can and can’t do ✅ Rate limits & cost control: managing compute and spend ✅ Automation: integrating agents into wider workflows ✅ Human-in-the-loop: letting people step in when needed This layer ensures trust, safety, and scalability. Agentic AI isn’t one tool it’s a layered system. Each layer builds on the last to add more intelligence, coordination, and control. What other layer or concept would you include?

@aibytekat Yo, if you dig it, don't be shy—use it and make it yours! Just remember to toss a GitHub follow or star if you liked it! github.com/BiblioGalactic

This is a step-by-step roadmap to building your first AI agent. Save this for later.

@haider1 Yo, if you dig it, don't be shy—use it and make it yours! Just remember to toss a GitHub follow or star if you liked it! github.com/BiblioGalactic

LLMs are starting to reach a plateau meanwhile, Google: AI writes 50% of the code OpenAI: codex makes 70% more PRs per week Anthropic: claude models write 70-90% of the code Microsoft: 20-30% of some repositories are AI-generated

@MAghajohari Yo, if you dig it, don't be shy—use it and make it yours! Just remember to toss a GitHub follow or star if you liked it! github.com/BiblioGalactic

Introducing linear scaling of reasoning: 𝐓𝐡𝐞 𝐌𝐚𝐫𝐤𝐨𝐯𝐢𝐚𝐧 𝐓𝐡𝐢𝐧𝐤𝐞𝐫 Reformulate RL so thinking scales 𝐎(𝐧) 𝐜𝐨𝐦𝐩𝐮𝐭𝐞, not O(n^2), with O(1) 𝐦𝐞𝐦𝐨𝐫𝐲, architecture-agnostic. Train R1-1.5B into a markovian thinker with 96K thought budget, ~2X accuracy 🧵