Brandon Rohrer

Anthropic's technical blog is a great resource for production-ready AI systems. I went through some of their best articles lately (imo) -- They're really good at explaining the thought process behind anticipating challenges in a prod scenario and designing robust systems. I used Miskies AI to create visual and hands-on explanations for all articles. - Equipping agents for the real world with Agent Skills Claude is powerful, but real work requires procedural knowledge and organizational context. Introducing Agent Skills, a new way to build specialized agents using files and folders. https://lnkd.in/gCrFPFFV - Effective context engineering for AI agents Context is a critical but finite resource for AI agents. In this post, they explore strategies for effectively curating and managing the context that powers them. https://lnkd.in/gbetNUVW - Writing effective tools for agents — with agents Agents are only as effective as the tools we give them. They share how to write high-quality tools and evaluations, and how you can boost performance by using Claude to optimize its tools for itself. https://lnkd.in/gGzpctYF - How we built our multi-agent research system Research feature uses multiple Claude agents to explore complex topics more effectively. They share the engineering challenges and the lessons learned from building this system. https://lnkd.in/gw4C4r-9 Links to the original articles are also in the comments. ♻️ Share with someone building production-ready AI systems :) #AI #LLMs #GenAI

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✅ Day 75 of #100DaysOfDSA — Topological Sort (DFS Approach) 🔍 Problem: Given a Directed Acyclic Graph (DAG), return a linear ordering of vertices such that for every directed edge u → v, u comes before v in the ordering. ✅ Approach: Depth-First Search (DFS) Build the adjacency list from the edge list. Use a visited map to track explored nodes. Perform DFS from every unvisited node and push each node to a stack after exploring all its neighbors. Pop elements from the stack to get the topological order. ⏱ Time Complexity: O(V + E) One DFS traversal over all nodes and edges. 💾 Space Complexity: O(V + E) For adjacency list, visited map, and recursion stack.

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When considering integration involving large language models and external tools (via function calls), a few things you should consider. 1. If the tool doesn't feed back into the LLM in an iterative loop, why aren't you using a batch file instead of the LLM? I say this, because the goal of using external tools is to benefit from them right? Therefore, that information needs to feed back into the context window for the model to consider the gained information and continue on with the task it has been configured for. 2. How much data will the tool produce VS how much context window do you have. Now, with Llama and their Llama 4 model, the game has changed regarding standalone AI. When you can have a context window of 10 Million tokens, this becomes less of a concern. Unlike when you were limited to 4096 or 2048. 3. Hardware - Make sure you have adequate resources to load the model with as much context window as you need. 4. System Prompt: Don't just stick with "be a helpful AI", be creative and experiment. Welcome to Agentic AI. https://lnkd.in/enmTFz7J

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Some recent, exciting news on compressed tokenizers and how individual tokens modify images in encoder-decoder models. I hope this opens up a new avenue of research with smaller models across frameworks (perhaps JEPA?). https://lnkd.in/evFw8WBu

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Vector search is not a silver bullet for RAG. If your agent relies on similarity alone, you're leaving massive gaps in your retrieval. Vector search is brilliant for semantic queries. But entire categories of questions need exact match, structure, aggregation, graphs, and multimodal retrieval. In my latest video, I walk through 9 real-world cases, showing where vector search breaks and the retrieval engineering patterns that make agents reliable in production with n8n. We also share the hybrid retrieval n8n workflows we use. What you will learn: 🔹 Why similarity is not the same as relevance, and how to detect it 🔹 When to use keyword and pattern matching for IDs and error codes 🔹 Structured lookups and SQL for tabular questions 🔹 Reranking and metadata filtering for recency and specificity 🔹 Aggregation by counting and computing across records 🔹 GraphRAG for global patterns and multi-hop reasoning 🔹 Multimodal RAG to retrieve images and diagrams 🔹 Post-processing with tools for calculations and trend analysis 🔹 Handling false premise questions with verification and evals using DeepEval Generic chat with your data is easy. Production retrieval demands engineering. What retrieval challenges are breaking your RAG agents? Link to the full video in the comments 👇

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