Muhammad Saad

👋 I’m Saad. I build AI agents, ML systems, and automation tools. Here you’ll find: • Real agent architectures • Automation use-cases • ML & RAG systems • Lessons from building in public Follow if you’re into practical AI, not hype ↓

@GithubProjects That is a great change, creating a team out of Claude Code. My concern is how this can all be controlled properly. I guess I need to dive in.

Claude Code is powerful. But still mostly single-agent thinking. This changes that. oh-my-claudecode turns it into a full team 30+ agents. Parallel execution. End-to-end workflows. You don’t prompt anymore. You orchestrate. Feels less like coding more like managing an AI dev team.

@MinChonChiSF I’m with you on this. AI has already changed the landscape. The layoffs aren’t always loud, but they’re happening. I think now it’s really about learning how to use AI and finding opportunities to move forward with it.

Most people think AI will replace jobs slowly. Wrong. It's already replacing professions. Entire fields are disappearing overnight. The unfair advantage goes to those who build AI agents. Everyone else is already losing. Do you even see the gap?

@Tech_girlll AI will never reach human potential.

Tell me an opinion you have about AI that would put you in this position.

@brathocha That’s the real difference. Humans create from lived experience. AI can mimic patterns, but it doesn’t feel them and that shows.

AI struggles to replicate the depth of human internal conflict, leading to uninspired ideas. Even AI-generated speaker pitches at conferences can feel hollow, lacking the 'soul' that comes from genuine struggle and originality. #AI #Creativity #Tech

@SunookYoon To be fair, the problem isn’t the tools. It’s that automation still needs structure. Agents reduce the effort but someone still has to think through the workflow.

One AI agent = average at everything. Multi-agent system = → Research → Analysis → Writing → Review All handled by specialists. One orchestrator runs the show. That’s the real power.

@dr_cintas This is huge. Docling lets AI agents read and reason over PDFs, slides, images, audio, and code blocks. Clean Markdown, JSON, or HTML output ready for your LLM. 100% open-source.

This open-source tool gives your AI agents the ability to read, parse, and understand ANY document format. It's called Docling. It converts PDFs, DOCX, PPTX, XLSX, audio files, images, LaTeX, and more into clean structured data your LLM can actually reason over. → Understands page layout, tables, formulas, and code blocks → Exports clean Markdown, HTML, or JSON ready for any LLM pipeline → Native MCP server for direct agent integration → Plug-and-play with LangChain, LlamaIndex, CrewAI & Haystack It also just got a production-grade 258M vision-language model that reads an entire page in one pass. 100% Open Source.

@FynCas This does look like a solid agent handling multiple social media platforms.

This AI Agent is absolutely wild It generates 100-300 ads per hour Used by top marketers from -YC companies - $100M DTC brands - Top VC's Scripting, posting and creating the entire process Comment "AGENT" and I'll send it to you!

@ingliguori These are some solid phases for learning the Agentic AI step by step and it can also prevent from wandering here and there.

Agentic AI isn’t learned by tools. It’s learned in phases. Prompt → Memory → Tools → Workflows → Coordination → Deployment Skip phases and you don’t get agents. You get fragile demos. Autonomy is earned — not installed.

@sukh_saroy This solves the biggest bottleneck for AI agents running Python code safely. No containers, no delay, full control. Monty is wild.

🚨Breaking: Someone just open sourced a Python interpreter written in Rust and it's wild. It's called Monty. And it's not a sandbox. It's a minimal, secure Python runtime built specifically for AI agents -- and it starts in under 1 microsecond. Here's what this thing does: → Runs Python code written by LLMs with zero sandbox overhead → Completely blocks filesystem, env vars, and network unless YOU allow it → Starts in 0.06ms vs Docker's 195ms and Pyodide's 2,800ms → Snapshots execution state mid-flight -- pause, serialize, resume later → Runs typechecking with `ty` baked into a single binary → Calls from Python, Rust, or JavaScript -- no CPython dependency Here's the wildest part: Docker takes 195ms to start. A sandboxing service takes 1,033ms. Monty takes 0.06ms. That's not a rounding error. That's a different category of tool entirely. Every AI agent running today has the same problem: you either run LLM code directly on your host (YOLO Python -- zero security) or you spin up a container and wait 200ms per call. Monty closes that gap. One import. No daemon. No image pull. No container escape risk. The pydantic team built this to power code-mode in PydanticAI -- where LLMs write Python instead of making tool calls, and Monty executes it safely. Cloudflare and Anthropic are already publishing on this exact paradigm. 5.2K GitHub stars. Already trending. 100% Open Source. MIT License. (Link in the comments)

@Mike_Scully_ This is a solid workflow with proper looping.

This AI Agent LITERALLY replaces your entire outreach system…. – Verifies every email – Scrapes their website & LinkedIn – Writes a personalized first line – Auto-sends the email – Books me calls on autopilot It’s like having a full-time SDR who doesn’t sleep or complain. Repost + Like + Comment “Agent” & I’ll send over the full system (must be following so I can DM)

Most AI agents have one blind spot. They don’t review their own work. Self-Correcting Agents fix this by adding a second LLM step that critiques the output and forces a revision if it fails. Generate → Critique → Refine. One model creates. One model judges.

@VikramVerm25510 This is a great breakdown. Modern AI systems work in layers: retrieval, execution, tool connectivity, and multi-agent coordination. Understanding this is key to building effective AI.

RAG, AI Agents, MCP, and A2A aren’t competitors. They’re different layers of the same AI system. Once you see this mental model, modern AI architecture becomes much clearer 👇 1️⃣ RAG = Better answers RAG is about retrieval + grounded generation. Flow: User question → retrieve relevant data → add context → LLM generates answer with sources. Use RAG when you need: • answers from company docs • reduced hallucinations • citations and traceability RAG solves the knowledge problem. Once the model has enough context to answer, its job is done. 2️⃣ AI Agents = Do the work Agents move beyond answering. They plan, act, and iterate. Typical loop: Plan → Observe → Act → Reflect → Repeat Use agents when tasks require: • multi-step decisions • tool usage • executing workflows in real systems • verifying outcomes Agents solve the execution problem. 3️⃣ MCP = Standard tool access MCP (Model Context Protocol) is the plumbing layer. It standardizes how LLMs connect to tools and resources. Think: “One universal interface for AI → tools.” Use MCP when you need: • consistent access to APIs and services • structured connections to SQL, CRM, files, etc. • fewer custom integrations MCP solves the tool connectivity problem. 4️⃣ A2A = Agents working together A2A (Agent-to-Agent) enables multi-agent systems. It handles: • agent discovery • delegation and routing • permissions and handoffs • events and status updates Use A2A when you have: • multiple specialized agents • distributed AI systems • cross-team or partner automation A2A solves the coordination problem. The real takeaway: Modern AI systems often look like this: RAG → grounding knowledge Agents → executing tasks MCP → connecting tools A2A → coordinating agents Different problems. Different layers. One architecture. The real AI shift isn’t better prompts. It’s better systems. 📍Follow me📍 @VikramVerm25510 [𝐓𝐨 𝐠𝐞𝐭 𝐃𝐌 𝐅𝐚𝐬𝐭.] #AI #RAG #AIAgents #LLM #AIArchitecture #GenAI

@_vmlops This is huge for AI agents. Turning an AI into a real web user opens fully autonomous workflows, web automation, and advanced data extraction like never before.

AI agents can now use a browser like humans Vercel just dropped agent-browser a CLI that lets AI: • Navigate any website • Click, type & interact with elements • Take screenshots & extract data • Persist sessions, cookies & auth • Run complete workflows autonomously Basically, you can turn any AI into a real web user This changes everything for automation, scraping & autonomous agents link - github.com/vercel-labs/ag…

@akshay_pachaar Memory changes everything. With read-write memory, AI agents can learn from interactions, remember preferences, and improve over time. This is the bridge to truly adaptive AI.

RAG was never the end goal. Memory in AI agents is where everything is heading. Let me break down this evolution in the simplest way possible. RAG (2020-2023): - Retrieve info once, generate response - No decision-making, just fetch and answer - Problem: Often retrieves irrelevant context Agentic RAG: - Agent decides *if* retrieval is needed - Agent picks *which* source to query - Agent validates *if* results are useful - Problem: Still read-only, can't learn from interactions AI Memory: - Read AND write to external knowledge - Learns from past conversations - Remembers user preferences, past context - Enables true personalization The mental model is simple: ↳ RAG: read-only, one-shot ↳ Agentic RAG: read-only via tool calls ↳ Agent Memory: read-write via tool calls Here's what makes agent memory powerful: The agent can now "remember" things - user preferences, past conversations, important dates. All stored and retrievable for future interactions. This unlocks something bigger: continual learning. Instead of being frozen at training time, agents can now accumulate knowledge from every interaction. They improve over time without retraining. Memory is the bridge between static models and truly adaptive AI systems. But it's not all smooth sailing. Memory introduces new challenges RAG never had, like memory corruption, deciding what to forget, and managing multiple memory types (procedural, episodic, and semantic). Solving these problems from scratch is hard. If you want to build Agents that never forget, Cognee is an open-source framework (12k+ stars) to build real-time knowledge graphs and get self-evolving AI memory. Getting started with Cognee is as simple as this: 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝗮𝗱𝗱("𝗬𝗼𝘂𝗿 𝗱𝗮𝘁𝗮 𝗵𝗲𝗿𝗲") 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝗰𝗼𝗴𝗻𝗶𝗳𝘆() 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝗺𝗲𝗺𝗶𝗳𝘆() 𝗮𝘄𝗮𝗶𝘁 𝗰𝗼𝗴𝗻𝗲𝗲[.]𝘀𝗲𝗮𝗿𝗰𝗵("𝗬𝗼𝘂𝗿 𝗾𝘂𝗲𝗿𝘆 𝗵𝗲𝗿𝗲") That’s it. Cognee handles the heavy lifting, and your agent gets a memory layer that actually learns over time. I have shared the repo in the replies!

@TheAIHub111 I haven’t gone deep into OpenClaw yet, as I mostly work with LangGraph and other custom agent setups. Is it worth diving in, and how are security concerns handled here?

My OpenClaw bot runs 6 AI agents 24/7: - Finds local businesses without a website - Builds a custom demo site for them automatically - Sends outreach with the preview + payment link - Handles objections and closes the sale Most local businesses don't have a website, this system finds them, pitches them, and collects payment automatically Reply "OpenClaw" and I'll send you early access (must be following)

@Shruti_0810 This will be interesting to explore in depth, because hallucinations can still happen even with many countermeasures.

Developers won’t like this…😭 But 90% of “AI agents” we’ve been hyping are basically unstable interns with amnesia. They hallucinate. They loop. They forget instructions. A tiny 11-person team from Sweden just fixed ALL of it. Meet the first AI agent engine that actually works: Link : incredible.one producthunt.com/products/incre… AI agents just entered their real era.

Most AI agents fail not because they’re weak — but because they don’t plan. Plan-and-Execute agents: 1️⃣ Plan → break goals into steps 2️⃣ Execute → act using tools 3️⃣ Replan → adjust if things change Strategic, multi-step AI for smarter workflows.

@hasantoxr OpenClaw as employee → Paperclip as company. Zero-human ops finally get structure: budgets, reporting, 24/7 agents. This could redefine autonomous AI workflows.

🚨 BREAKING: Someone just open-sourced the operating system for zero-human companies. It's called Paperclip. Think of it as the company layer on top of your AI agents. If OpenClaw is an employee, Paperclip is the entire company. What's inside: → Bring any agent (Claude Code, Codex, Cursor, OpenClaw) with real reporting lines → Give them org charts, titles, budgets, and goals → Monthly budgets per agent when they hit the limit, they stop. No runaway costs → Full ticket system with tool-call tracing and immutable audit logs → Agents run 24/7 on heartbeats while you monitor from your phone Instead of having 20 Claude Code tabs open with no idea what's happening… One deployment. One dashboard. Your agents run the company while you sleep. 1.4K stars. MIT License. 100% Opensource.

@recap_david This is a perfect example of AI agents at work: - Collect data - Generate content - Assemble final product All autonomously, at a fraction of the cost. Incredible.

I built an AI system that creates luxury real estate listing videos for under $10 (just from a Zillow link). The average agent pays $1K–$5K per property for professional video production. Sell this output to realtors and agents and make $$$. Here's how it works: → Save the 6 best images from any Zillow listing → Bring each image into Calico AI to animate it — smooth dolly shots, cinematic pans, luxury motion styling → A custom GPT analyzes the listing and writes a polished 30-second voiceover script → ElevenLabs generates the AI narration and a custom music track → CapCut to assemble the final video with captions in minutes The result: every listing gets a professional-grade walkthrough video — not just the $10M estates with marketing budgets. Static listing photos aren't enough anymore. Buyers want to feel like they're walking through the home before they book a showing. Comment "CALICO" below, and I'll send you the full process with prompts and GPTs so you can recreate (must be following so I can message you!).