#Ai Agent Developers

Comment “AI” and I’ll DM you the link. Molehub provides direct visibility into AI agent internals, exposing raw tensors, attention maps, gradients, and low-level computation usually hidden behind APIs. It offers a transparent look at how autonomous agents process data and execute tasks without human supervision. #ai #machinelearning #aidevelopment #opensource #technology

These 4 resources will level up your AI knowledge and career 1) Multi AI Agent Systems with CrewAI - fundamentals of what agents are, tools, memory, and guides you on building your first AI agent 2) Function calling is all you need (YouTube) - this video opened my eyes to how simple the underlying concepts can get and really changed my view on building AI agents 3) Deep Dive into LLMs like ChatGPT (YouTube) - from one of the most famous people in the AI space now, this video is a a treasure trove of knowledge and goes deep 4) AI Fundamentals by IBM - this playlist is continuously updated with the latest and greatest on AI. Everything from deep dives into RAG to specific concepts like prompt caching. Comment LEARN and I’ll send you all the links #learnai #ai #learntocode #aicourse #techcareer

Comment "AI" to get this GitHub repo with 88K+ stars packed with real, production-ready LLM apps. All free. All open source. Most AI repos explain ideas. This one shows you how real startups actually build AI products. These are working apps. Not tutorials. Not toy demos. Inside the repo, you’ll find: * RAG apps that actually work in real projects * AI agents with real memory, not gimmicks * Multi-agent systems that coordinate tasks * Chatbots built like real SaaS products * AI tools for productivity * Full end-to-end LLM apps, from frontend to backend The real power here is visibility. You can see: * How prompts are structured * How tools and memory are wired * How agents talk to each other * How everything connects in a real AI product It feels like opening the blueprint of modern AI startups. If you want to build real AI apps instead of just watching tutorials, this repo is pure gold. Comment "AI" and I’ll send you the link. #llm #opensourceai #aiapps #aidevelopment #rag #aiagents #genai #aitools #ainews #aicommunity #chatgpt #github #startups #aiadventureyt

Google is also using it as an “experimental feature”! When a company launches something so useful, even competitors adopt it. #aiml #techinfo #softwareengineers #anthropic

If you’re serious about getting an AI engineer role and willing to work hard, then this 6 month roadmap is for you! . . . #AI #AIengineer #AgenticAI

🚀 Big news: We just powered up your AI agents with 12 models from OpenAI, Anthropic, and Google. Intelligence for any task: · Claude Opus 4.5 · Claude Sonnet 4.5 · Claude Haiku 4.5 · GPT-5.2 · GPT-5.1 Codex Max · GPT-4.1 · GPT-5 Nano · Gemini 3 Pro · Gemini 2.5 Flash · and more... Three thinking modes to match your work: ⚡ Standard: Fast responses for everyday tasks 🧠 Thinking: Deep analysis and research 🎯 Reasoning: Complex problem-solving and logic One drop-down gives you access to everything. Your agents, connected to your Workspace DNA. 🧬 Start building → taskade.com/genesis 🤖 Explore agent templates → taskade.com/agents

How to build AI agents ? From prompt engineering → tool calling → memory → multi-agent workflows — AI Agents are the future of intelligent systems. Start simple. Add tools. Give memory. Design reasoning loops. Deploy with confidence. 🚀 If you’re a developer, this is your unfair advantage in 2026. #AIAgents #GenerativeAI #LLM #AIEngineering #MachineLearning SmolAgents LangGraph LlamaIndex TechInnovation

This GitHub repo shows what REAL AI agents look like in the wild 🤖 Not polished demos. Not hype threads. Actual open-source agents you can run, test, and break. From research agents to coding agents to browser automation, this is where you truly understand how agentic systems work. Comment “GIT” and I’ll share link to the repo. —— If you want to actually build AI agents step-by-step (not just watch trends), I teach this in my AI trainings sessions.

Train a production-ready AI model in 5 minutes, no PhD, no code, no data science degree. Most people think building AI requires months of work, a team of ML engineers, and complex infrastructure. Here’s the micro-hack that changes everything 👇 Step 1 (2 min) : Prototype with Teachable Machine (Free) Upload your data, train instantly in the browser. No setup. No code. Step 2 (3 min) : Scale with Vertex AI (Pro) Google’s AutoML builds your production model, scalable, enterprise-ready, deployment-ready. Result? 92% accuracy. Millions of predictions. Zero DevOps. ✅ Stop waiting 6 months for ML models. Stop paying data science teams to do what a 5-minute workflow can. Go from prototype to enterprise, with zero infrastructure nightmares. This is the ML stack nobody talks about. Would you use this to build your first AI model? Comment YES 👇 . . . . . [ ai, aiautomation, aiforbusiness, aiforbeginners, techhacks, artificalintelligence, freetool, aiagency, aiml ] #MachineLearning #NoCodeAI #TeachableMachine #VertexAI #AIForBeginners

Most people think AI agents are just chat models. They’re not. Inside n8n, every real AI agent has 4 core components: 1️⃣ Brain – the LLM you choose (OpenAI, Mistral, DeepSeek, Anthropic). This powers reasoning and responses. 2️⃣ Prompts – define the role, tone, and behavior of your agent. 3️⃣ Memory – stores past conversations so your agent has context. 4️⃣ Tools – give your agent real-world abilities like Gmail, HTTP requests, or even other workflows. Brain thinks. Prompts guide. Memory remembers. Tools act.

Most ML projects don’t get interviews. These do. Came across a GitHub repo with 50+ solid AI / ML project ideas across agents, RAG, voice AI, multimodal systems. If you’re preparing for AI roles, this is worth checking. Follow and comment ML and I’ll send the repo link in DM. #MLProjects #GenerativeAI #DataScience #AIProjects #TechCareers [ machine learning projects, AI engineer projects, ML projects for resume, generative ai projects, RAG projects, AI agents, voice AI projects, AI portfolio ideas ]

In 2023 I first found out about Make.com. You can pair it with ChatGPT and suddenly you could automate simple flows, personalize emails, and ship “good enough” systems without a dev team. 2024 was the turning point: agents. Instead of automating steps, you could have agents decide and use tools you assign to it. 2025 blew the doors open with coding agents. It lowered the barrier completely. If you can explain what you want in plain language, you can build. Apps, tools, internal systems, prototypes, workflows, fast. Now we’re barely into 2026 and we’re already seeing autonomous systems of AI agents that operate, build, run, communicate, iterate on their own. Moltbot is just one example. We’re stepping into the AGI era. And if the pattern holds, what's coming next won't be linear... It'll be exponential.