Just finished building a custom Retrieval-Augmented Generation (RAG) Chatbot from scratch. Over the past few days, I have been diving deep into Generative AI architecture and successfully built a conversational AI engine that goes beyond the basic LLM cutoff limits by proactively reading and learning from my own offline knowledge base. Technical Stack & Implementation Details: • Python & LlamaIndex for core logic and RAG orchestration • Groq integrated to power ultra-fast inference leveraging the Llama 3 (70B) model • HuggingFace's sentence-transformers (`all-MiniLM-L6-v2`) used locally to map large document bodies into embedded semantic vectors. Key Milestones: • Vector Store Indexing: Implemented a process to automatically ingest raw textual data, logically split it, mathematically embed it, and securely cache the mathematical vectors in local storage. • Semantic Retrieval: Designed the query engine to capture conversational inputs, perform mathematical similarity searches against the cache, and inject the `Top_K` most relevant context chunks directly into the model's awareness. • Context Management: Bypassed token degradation and rate limits in long conversations by utilizing LlamaIndex's `ChatSummaryMemoryBuffer` to condense older history chunks without losing context. I am proud of the clean, modular "Separation of Concerns" codebase that powers this engine efficiently. Check out the code and architectural setup on my GitHub: https://lnkd.in/dFcYyZM2 #AI #GenerativeAI #MachineLearning #Python #LlamaIndex #Groq #RAG #SoftwareEngineering #DataScience

The leap from "Chatbot" to "Agent" starts with a single primitive: Tool Calling. Nyalalabs is excited to host a technical workshop featuring Abel Chin, an indie AI engineer and ex-ASEAN scholar. Abel’s journey is proof that it’s never too late to start building—having truly kicked off his builder journey in mid-2025, he’s now deep in the trenches of Agentic AI. In this session, we aren’t just talking about theory. We are writing code. 💻 Key Takeaways: Master the definition of strict JSON schemas to ensure model reliability. Learn how to bridge the gap between LLM reasoning and API execution. Walk away with a functional, runnable Python script that actually does things. If you’re ready to break LLMs out of their text-only boxes, this is for you. 📅 When: April 17th, 8:00 PM - 10:00 PM 🔗 Register via Luma: https://luma.com/2z5ak2pj #ArtificialIntelligence #LLMOps #Python #AgenticAI #TechCommunity #SingaporeAI #SoftwareDevelopment

🧠 This is how AI actually works (no code, just thinking) - 🙃 See the Visual : mini demo - more insights. ◆ Python - Loop #ThinkFirst_8 Most people think AI is magic… 👉 It’s not. It’s just data + structure + iteration 📊 Look at the flow carefully 👇 • Data comes in (raw, messy) • Organized using structures (List / Dictionary) • Processed step-by-step (Loops) • Filtered → Patterns → Insights 💡 Pause & think: If you had this data… 👉 How would YOU find: • Total value? • Important signals? • Patterns? 🔁 That thinking = AI logic Not models. Not tools. 👉 Just structured problem solving at scale 🚀 Mini challenge: Can you map this flow to a real use case? (sales, users, logs…) Next: We’ll turn this thinking into actual code #FamAI #LearnFirst_BuildSmart #Python

We all know git blame — but it was missing one thing: a sense of humor. Introducing gitblame-ai 🔥 It scans your repo, identifies the most "interesting" (read: suspicious) lines of code using custom heuristics, and sends them to Claude AI for a brutally honest critique. 🚢 Features: Roast Mode: Savage senior engineer critiques. Corporate Mode: Passive-aggressive "circling back" on your variables. Pirate Mode: "Arrr, this indentation be shallower than a coral reef!" Smarter Scanning: Automatically respects your .gitignore. Perfect for team standups, Friday afternoon vibes, or just holding your friends accountable in the funniest way possible. 🚀 Try it now: pip install gitblame-ai  ⭐ Star it on GitHub: https://lnkd.in/dtww5zyG #Python #OpenSource #AI #Git #ClaudeAI #DeveloperTools #BuildInPublic

𝐈 𝐭𝐡𝐨𝐮𝐠𝐡𝐭 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐚 𝐑𝐀𝐆 𝐜𝐡𝐚𝐭𝐛𝐨𝐭 𝐰𝐨𝐮𝐥𝐝 𝐛𝐞 𝐬𝐭𝐫𝐚𝐢𝐠𝐡𝐭𝐟𝐨𝐫𝐰𝐚𝐫𝐝. It wasn’t. But it’s easily one of the most interesting challenges I’ve tackled lately. I started with a Node.js version to get the gears turning quickly. It’s a great way to grasp the fundamentals of how retrieval and LLMs handshake. But as the complexity grew, I rebuilt the stack in Python to lean into the broader ecosystem and refine the data flow. Here is the reality of building RAG: ✨ 𝐂𝐨𝐧𝐭𝐞𝐱𝐭 𝐢𝐬 𝐊𝐢𝐧𝐠: Retrieval is everything. ✨ 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 𝐌𝐚𝐭𝐭𝐞𝐫𝐬: Chunking strategy is the difference between a helpful bot and a hallucinating one. ✨ 𝐏𝐫𝐨𝐦𝐩𝐭𝐬 𝐚𝐫𝐞𝐧'𝐭 𝐁𝐚𝐧𝐝-𝐀𝐢𝐝𝐬: You can't fix a broken retrieval pipeline with a "better" prompt. ✨ 𝐈𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 > 𝐏𝐞𝐫𝐟𝐞𝐜𝐭𝐢𝐨𝐧: Each version taught me something new about how these non-deterministic systems actually behave. Current focus: 🎯Improving retrieval accuracy 👥Handling multiple chat sessions properly 🎨Making the UI smoother and more intuitive Still learning, still iterating. If you’ve worked on RAG systems, I’d love to know—what was something unexpected you ran into? #AI #RAG #MachineLearning #LLM #Python #NodeJS

I’ve just published a deep‑dive on LangChain — one of the most exciting frameworks for building modular LLM applications. In this blog, I break down: 🔹 How prompts, chains, agents, and memory fit together 🔹 Why RunnableSequence is the new way to build workflows 🔹 How to use FakeListLLM + HuggingFace embeddings for offline demos 🔹 Real‑world use cases like customer support bots, research assistants, and workflow automation 💡 The blog includes runnable code snippets, a Jupyter Notebook template, and a GitHub‑ready README so you can try everything yourself. GitHub Link: https://lnkd.in/gGUD4f4K Medium Blog Link: https://lnkd.in/gjBXSexR LangChain isn’t just about chaining prompts — it’s about designing intelligent systems that combine reasoning, context, and external tools. #LangChain #LLM #AI #GenAI #Python #InnomaticsResearchLabs #LearningByDoing

AgentPrime has received another meaningful upgrade today. A new feature has been added: a real in-app toggle for the optional Python Brain backend. Users can now turn advanced memory and orchestration features on or off directly from Settings, eliminating the need to rely on environment variables. What's improved: - Python Brain is optional by default - A proper Settings toggle to enable/disable it - The preference is persisted - The UI clearly indicates when a restart is required - Desktop-only mode remains clean for users who prefer the core IDE experience This enhancement builds on the broader “Trusted Agent Loop v1” work, which includes: - Plan explanation before applying changes - Visible review checkpoints - Verification report UI - One-click revert for the last applied agent session - In-app runtime/doctor status surface The goal with AgentPrime is to make AI coding workflows feel powerful without compromising product stability, clarity, or trust. I am focusing on efficiency while keeping the budget capped at 300. The project is nearing completion. Check it out here: https://lnkd.in/evSr9RwG #IDE #Ai #Solo Cursor

What if you could build an AI agent that automatically generates unit tests for your code, saving you hours of tedious work? This concept is called automated test generation, and it's a game-changer for developers. Imagine you're working on a complex project with thousands of lines of code. Writing unit tests for each function or method can be a daunting task. That's where AI comes in - by analyzing your code and generating tests based on its structure and behavior. For example, you can use a library like Pytest to write tests in Python. Here's an example of how you might use it: ```python def add_numbers(a, b): return a + b def test_add_numbers(): assert add_numbers(2, 3) == 5 ``` But what if you could automate the process of writing that test_add_numbers function? That's where automated test generation comes in. What potential applications or challenges do you see in automated test generation - can AI really replace human testers? 💬 Have questions or working on something similar? DM me — happy to help. #AutomatedTesting #AIforDev #TestGeneration #Pytest #SoftwareDevelopment #ArtificialIntelligence #DevTools #LLMUseCases #CodeQuality

LangChain Just Released Deep Agents — And It Changes How You Build AI Systems (April 2026) "There’s a pattern I’ve watched repeat itself across almost every team that gets serious about building agents. It’s not that LangGraph is bad. It’s extremely powerful. But it’s a runtime — a low-level primitive — and most people are using it as if it’s an application framework. LangChain noticed this, and deepagents is their answer deepagents is a standalone Python library — installable with pip install deepagents — that sits on top of LangChain and LangGraph. The LangChain docs describe it as an "agent harness": it provides the same core tool-calling loop as other frameworks, but with a set of built-in capabilities baked in so you don't have to reinvent them. The Five Capabilities That Make This Different 1. Built-in Planning with write_todos 2. A Virtual Filesystem 3. Subagent Spawning 4. Automatic Context Compression and Summarization 5. Long-term Memory Across Conversations" https://lnkd.in/eZfihAmm

Everyone is using LangChain, LangGraph, CrewAI... But do you actually know what happens under the hood? Today I built AlexIsAgent — a fully custom AI agent with tool calling, written from scratch in Python using the Groq API. No frameworks. No abstractions hiding the magic. Here's the 7-step flow that every AI agent framework secretly follows: 1 - User sends a query 2 - Agent forwards query + context to the LLM 3 - LLM decides to call a tool (returns tool name + args) 4 - Agent executes the tool (API call, DB query, etc.) 5 - Tool result is returned to the LLM 6 - LLM generates the final answer using the tool result 7 - Agent responds to the user That's it. That's the entire agent loop. Most developers jump straight into frameworks without understanding this foundation. I wanted to change that — so I implemented every single one of these steps manually: - Custom Agent class - Conversation memory - Tool schema definition - Automatic tool selection - Function execution from model response Building from scratch gave me clarity that no tutorial or framework ever could. If you're serious about AI engineering — don't just use the tools. Understand what they're doing for you. 🔗 https://lnkd.in/gEKxGp_N A big thank you to RoboSiddhi for giving me the time and space to build this — couldn't have done it without your support! #AI #AIAgents #Python #LLM #MachineLearning #BuildInPublic #GroqAPI #SoftwareEngineering