Category: Artificial Intelligence

    Agent Chat using LangChain Part 3 – Fun Examples

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    Now that we’ve covered the technical implementation (see Part 1 and Part 2), let’s see what this agent can actually do. Here are three examples that demonstrate how natural language interactions can replace tedious UI workflows—each one progressively more powerful. Example 1: Quick Lookups The simplest use case is finding information without navigating through menus….

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    Agent Chat using LangChain Part 2 – Token Streaming with WebSockets

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    We all dislike typing a message to an AI agent and then just staring at a spinning loader for ten seconds with zero feedback. It feels slow and lifeless. In part 2 of this series, I’m going to walk you through how I added proper token streaming over WebSockets so users see the agent’s response…

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    Agent Chat using LangChain, Part 1 – Tools and Memory

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    There’s something really satisfying about building an AI agent that can actually take action in your application. In this post, I’ll walk you through how I integrated LangChain tools into my NestJS backend to create an agent capable of searching contacts, creating events, sending messages, and handling a bunch of other tasks. The Tech Stack…

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    Combat Agents: Automating Volatility-Based Decomposition

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    At Don’t Panic Labs, we frequently discuss Volatility-Based Decomposition (VBD). It is an architecture practice that groups and boundaries systems by where change occurs. Instead of organizing purely by domains or technical layers, VBD identifies parts of the codebase that change together or change frequently. We use those patterns to inform seams, ownership, and stabilization…

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    Building Agents with LangChain Agents

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    In previous posts, we have explored various ways to interact with LLMs. Today, I want to walk through creating agent_demo.py, a small but capable multi-agent application. Why is this important? Most people stop at simple prompts. But real power comes when we chain these interactions together to solve complex workflows. We aren’t just asking a…

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    The Price Per Token Is Dropping. Will It Stay That Way?

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    LLMs are at the heart of the current AI movement. An LLM’s ability to respond to prompts with human-like responses is astonishing. All of our first interactions with ChatGPT in 2022 were amazing. The first time I used GitHub Copilot to generate a basic database access method, it was clear that something significant was about…

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    From Guesswork to Structured Context: How Figma MCP Changed My Dev Workflow

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    Highlight: I used to rely on screenshots of Figma mockups. Now I pull structured layout and style metadata straight from the design files. When our design team shares a new mockup, they send the Figma file and link — no PNGs, no screenshots. But early on, that still meant a lot of guesswork for me….

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    Building a RAG Model

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    It feels like everyone wants to sprinkle a little AI on every software project right now. Sometimes for good reason, and sometimes just because they want a little AI. But AI, much like a seasoning for our food, might not be an improvement. We need to know what tool to use and when. One of…

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    Putting GitHub’s New AI Agent to the Test

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    GitHub has been on a tear recently, adding a lot of new features in a short amount of time. It’s exciting to see the platform evolve so quickly, especially with its deeper integration of AI. One of the newer features that caught my eye is Agent tasks. The core idea is that these tasks can…

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    Harnessing the Hype: A Pragmatic Guide to Using AI in Development

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    Artificial Intelligence is no longer a futuristic concept; it’s another part of our development toolbox. And like any powerful tool, it can be used to build incredible things with great efficiency, or it can create a mess that is difficult to untangle. The key isn’t just using AI; it’s using it with discipline and intent….

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