Carlo Cayos

A senior Google engineer just dropped a 424-page doc called Agentic Design Patterns. Every chapter is code-backed and covers the frontier of AI systems: → Prompt chaining, routing, memory → MCP & multi-agent coordination → Guardrails, reasoning, planning This isn’t a blog post. It’s a curriculum. And it’s free.

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Most .NET devs are overcomplicating GenAI integration. You don’t need to stand up your own model servers or wrestle with custom APIs. With Amazon Bedrock + Semantic Kernel, you can: - Plug directly into multiple LLMs (Anthropic, Mistral, Titan) - Stream responses inside your .NET apps - Keep your setup clean and production-ready I walk through the whole setup in my latest video: https://lnkd.in/eeT9xsUm

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Here are 600+ Claude Code SubAgents. Combine these subagents with Claude-Flow and watch the magic happen. Github Repo: https://lnkd.in/gBAyJXdP Example Prompt: create a diagrams directory and then i want you to create me the diagrams of the entire system. these are all planning documents. read through all the .md files and then I want you to create me html visualizations.  you need to identify all of the subagents that could be helpful for this and then figure out the best way to utilize them to create a full suite of visualizations. you can create all of these in html. I need to be able to  visually understand every aspect of this system that is being planned. also I need visualizations that show how to build it, in what order and then visualizations of what I would build first etc. break down all of the plans into  many many visualizations that would be useful to someone about to develop this project make sure its done in simple svg so its extremely explainable and understandable by a human being. that is your ultimate goal. 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐚𝐥𝐥 𝐨𝐟 𝐭𝐡𝐞 𝐬𝐮𝐛𝐚𝐠𝐞𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐜𝐨𝐮𝐥𝐝 𝐛𝐞 𝐮𝐬𝐞𝐟𝐮𝐥 𝐢𝐧 𝐚𝐧𝐲 𝐰𝐚𝐲 𝐟𝐨𝐫 𝐭𝐡𝐢𝐬 𝐭𝐚𝐬𝐤 𝐚𝐧𝐝 𝐭𝐡𝐞𝐧 𝐟𝐢𝐠𝐮𝐫𝐞 𝐨𝐮𝐭 𝐡𝐨𝐰 𝐭𝐨 𝐮𝐭𝐢𝐥𝐢𝐳𝐞 𝐭𝐡𝐞 𝐜𝐥𝐚𝐮𝐝𝐞-𝐟𝐥𝐨𝐰 𝐡𝐢𝐯𝐞𝐦𝐢𝐧𝐝 𝐭𝐨 𝐦𝐚𝐱𝐢𝐦𝐢𝐳𝐞 𝐲𝐨𝐮𝐫 𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐭𝐨 𝐚𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡 𝐭𝐡𝐞 𝐭𝐚𝐬𝐤. There is a list of example scenarios in the Repo's Readme. There are agents to help you with all aspects of your business and life. 500+ Non Coding Agents. Claude Code has access to the internet and any mcp tools you give it. Simply tell it what you want and then tell it to: 𝐈𝐝𝐞𝐧𝐭𝐢𝐟𝐲 𝐚𝐥𝐥 𝐨𝐟 𝐭𝐡𝐞 𝐬𝐮𝐛𝐚𝐠𝐞𝐧𝐭𝐬 𝐭𝐡𝐚𝐭 𝐜𝐨𝐮𝐥𝐝 𝐛𝐞 𝐮𝐬𝐞𝐟𝐮𝐥 𝐢𝐧 𝐚𝐧𝐲 𝐰𝐚𝐲 𝐟𝐨𝐫 𝐭𝐡𝐢𝐬 𝐭𝐚𝐬𝐤 𝐚𝐧𝐝 𝐭𝐡𝐞𝐧 𝐟𝐢𝐠𝐮𝐫𝐞 𝐨𝐮𝐭 𝐡𝐨𝐰 𝐭𝐨 𝐮𝐭𝐢𝐥𝐢𝐳𝐞 𝐭𝐡𝐞 𝐜𝐥𝐚𝐮𝐝𝐞-𝐟𝐥𝐨𝐰 𝐡𝐢𝐯𝐞𝐦𝐢𝐧𝐝 𝐭𝐨 𝐦𝐚𝐱𝐢𝐦𝐢𝐳𝐞 𝐲𝐨𝐮𝐫 𝐚𝐛𝐢𝐥𝐢𝐭𝐲 𝐭𝐨 𝐚𝐜𝐜𝐨𝐦𝐩𝐥𝐢𝐬𝐡 𝐭𝐡𝐞 𝐭𝐚𝐬𝐤.

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MIT is trying to beat the context-window hole. https://lnkd.in/dhywXFsF This paper describes how their model breaks a problem into subtasks, and solves them with different threads. For example, "write code" -> thread 1. "push to git" -> thread 2. This leads to small context windows, reducing memory and compute requirements. A topic worth exploring :)

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Where are all the agents? We were promised autonomous intelligence and we only got chatbots and glorified workflows sprinkled with LLMs. Where is the autonomy if I still have to account for all possible edge cases? Why do I still have to deal with APIs, spaghetti logic, and esoteric protocols? Why can’t I just describe the outcome I want in plain english and the computer will figure out all the details? These are the questions we’ve been asking ourselves at Latitude for the past few months. Today we’re introducing a new way to build automation software. Building for autonomy requires upgrading your mental model of what software is. All you need now are prompts and tools. Code is out, the LLM is the backend now. Latitude is the first full-stack agent engineering platform built around this idea. It is a code editor but you won’t see any code, only plain-english instructions. We have all the advanced tools you need to design flexible behavior: agent orchestration, tool calling, evaluations, and monitoring. And the best part is you don’t even have to worry about any of it. We’ve built an agent—Latte!—that takes care of everything for you. You don’t have to be an expert to program computers anymore. We’re bringing joy to building software, and this is just the first step.

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The plug-n-play framework to build MCP Agents! (fully open-source; works with any LLM) DeepMCPAgent is a framework for dynamic MCP tool discovery to build MCP-powered Agents built using LangChain/LangGraph. You can bring your own model and quickly build production-ready agents. Key features: - Works similarly to how you integrate MCP servers in Cursor/Claude, etc. - Supports HTTP/SSE MCP servers with headers & auth - Supports all chat models (OpenAI, Anthropic, Ollama, Groq, local, etc.) - Tools are typed via JSON-Schema → Pydantic → LangChain BaseTool. - Connects to multiple MCP servers simultaneously. The project also comes with CLI support. So you initiate an interactive agent chat directly from the CLI by just specifying the MCP server and the LLM name. The project is fully open-source. Find the GitHub repo in the comments! ____ Find me → Avi Chawla Every day, I share tutorials and insights on DS, ML, LLMs, and RAGs.

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1 PM to 0.5 engineers? That’s the new dynamic Andrew Ng pointed out in a recent talk: “Engineering velocity has skyrocketed… but product management hasn’t kept up.” He revealed something that’s already playing out across high-performing teams: 🧠 Engineers, supercharged by AI tools, are moving faster than ever. 🧩 The new bottleneck? Deciding what to build. In fact, some teams are proposing a reversal of the classic staffing ratio — not 1 PM to 6 engineers, but 2 PMs for every engineer. Let that sink in. Here’s why this matters for product leaders like you: ✅ Your engineers can now ship in hours what used to take weeks. ❌ But if your product discovery, decision-making, and customer validation cycles still run on the old cadence… 🚦 You’ve become the bottleneck — no matter how efficient your sprints look on paper. This shift puts massive pressure on VPs of Product to rewire how their orgs operate:   Faster feedback loops must become a core competency   AI fluency isn’t optional — it’s foundational   PMs need better instincts, better tools, and better systems for keeping up If your team still treats product management as a coordination function instead of a strategic velocity engine, you’ll fall behind — fast. 📌What to ask yourself this quarter:  Are your PMs equipped to make product decisions at AI-speed? Is your discovery process optimized for today’s engineering reality? How are you proactively reducing your own bottlenecks? Ng’s insight is a warning. And it’s one that strategic product leaders will heed before it costs them innovation cycles, market share, and talent. Curious to hear from others, are you seeing this shift on your team too?👇 #ProductManagement #ProductLeadership https://lnkd.in/ebE5ZGvW

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🦀 Rust is the best language for building agentic systems, full stop. It gives you near-native performance with memory safety, zero-cost abstractions, and deterministic behavior, all without a garbage collector. Compared to Go, Rust wins on predictable latency, embedded targets, and fine-grained control. Go’s goroutines are nice for services, but agentic systems need tighter loops, cache-aware design, and safety guarantees you can trust in production. Python? Great for prototypes, unusable at scale without gluing it to something faster. C# and Java? Both come with heavy runtimes, unpredictable GC pauses, and slower cold starts. JavaScript? Excellent for front-end glue, terrible for high-performance agent execution. In my own work, Rust powers some of my most ambitious projects. QuDAG is a quantum-resistant, DAG-based darknet infrastructure built for autonomous agent swarms, complete with secure rUv token transfers and ultra-low latency consensus. I also built DAA for adaptive governance and QVM for multi-job quantum scheduling. I have pushed into CUDA-accelerated WASM pipelines and created Fact Tools for structured truth verification. One of my wildest projects is geometric-langlands. It’s a powerful computing framework that tackles the Geometric Langlands Conjecture, blending traditional math logic with neural networks. It comes with simple command-line tools, can run in the browser through WASM, and can tap into GPU power with CUDA for serious speed. Rust’s ability to target lean binaries and WASM means I can npx my-agent and run the same system anywhere, whether that is on a microcontroller, inside a container, or in a browser, without extra dependencies. Memory is deterministic, threading is safe by design, and performance is consistent whether your agents are running locally or in a distributed swarm. For agentic systems where every cycle counts, Rust isn’t just better, it’s the language I trust when I refuse to compromise. Check out my Rust crates: crates.io/users/ruvnet

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