Most bad AI-generated code isn't the model's fault. It's the prompt's fault. "Build me a login feature" gets you something back. Then an hour of fixing things the AI was never told about. Wrong folder structure. No tests. A random new dependency nobody approved. So we started using a fixed template for anything beyond a quick script. Follow: The Image Attached The "out of scope" and "constraints" sections do the most work. Without them, the model quietly makes decisions for you. You find out three files later. None of this is exotic. It's the same brief you'd hand a new developer joining the team. What would you add to this?

Throwback to the time my friend and I joined an "AI Hackathon" specifically knowing we could use AI compete and make up for our lack of knowledge 😅 The official rules said AI was allowed. The invigilators, however, said the opposite. We were in our 3rd semester, which meant our entire technical stack consisted of C++ and a prayer 🤧 . When the invigilator announced that AI tools were strictly banned and we had to "build the model from scratch," we... basically just looked at each other (CLUELESS). For the next four hours, we weren't just developers; we were the human equivalents of a massive "Switch" statement. We manually hardcoded every "AI decision" into a C++ file that was 20% logic and 80% sheer panic (Wrote like 20 if-conditions worth 500 lines :D) By the time the judges arrived, our "AI" was basically just a very aggressive chatbot that could only answer like three things. Then came the plot twist. [8] The judge looked at our 500 lines of hardcoded C++ and asked: "Why didn't you just use the built-in AI features in Jupyter?" Us: "Uhhhhh, Sir the invigilators said it was against the rules." He just gave us a long, confused stare that lasted exactly four seconds too long and we were as confused as well. It turned out majority of the teams were using AI, and we were too scared to touch it 😭. Though, it were probably the best 4 hours I had ever spent at a competition.

The thing about AI that no one is talking about: As a developer, I want to go back and select regions of time, annotate them, and merge them back into the current region of space. We run through all these experiments and some stuff is in the git reflog but other stuff is just caught in the Claude cache and being able to minority report out the best regions is all we really want as people that author via review now.

Most spatial projects stall between a good idea and a pipeline that runs.  This challenge closes that gap and takes you from idea to a running spatial pipeline in three days. Bring a use case and leave with a scheduled job producing real output. → Day 1: Validate the idea in Claude chat over the Wherobots MCP against live data. → Day 2: Scaffold the project in Claude Code with the Spatial AI Coding Assistant and ingest a real dataset. → Day 3: Build the final table with bronze, silver, and gold layers and schedule it as a recurring job. The AI dev tools handle the boilerplate so your time goes to the analysis, not the setup. Join the challenge with Matt Forrest: https://bit.ly/4bbPn7V

🚀 Just finished building a multi-agent AI system for financial competitor analysis — and learned more from debugging it than from the build itself. The idea: feed in a company's financials (CSV) + a list of competitors, and let an AI agent pipeline do the rest — gather the data, analyze it, research competitors on the web, compare performance, get self-critiqued, revise, and write a full report. All autonomously. Stack: 🔹 LangGraph for the agent orchestration (StateGraph, conditional edges, revision loops) 🔹 LangChain + GitHub Models API for the LLM layer 🔹 Tavily for live web research 🔹 SQLite checkpointing for state persistence 🔹 Streamlit for the UI Full pipeline now live, checkpointed, and honest about its own data gaps. 🎯 #AI #LangGraph #LangChain #GenAI #SoftwareEngineering #BuildInPublic #MachineLearning #Debugging

Stack Overflow Pivots to API-First Backend for AI Agents 📌 Stack Overflow is undergoing a radical transformation, pivoting from a traditional Q&A forum to an API-first backend designed specifically for AI agents. By providing a verified, live corpus of knowledge, the platform aims to solve the "intelligence gap" and prevent AI hallucinations. This strategic shift turns Stack Overflow into a critical ground-truth layer that ensures autonomous agents access high-quality, non-stale data for complex programming tasks. 🔗 Read more: https://lnkd.in/d-Fcc5HH #Stackoverflow #Aiagents #Backendasaservice #Apifirstdesign

Stop Building AI Demos—Here’s the 7‑Layer Stack That Actually Ships (2026 Guide) + Video Introduction: The gap between a functional AI prototype and a production‑ready AI application is often measured in layers, not lines of code. While a Jupyter notebook can demonstrate a model’s capability, a reliable product demands a resilient architecture that handles authentication, context retrieval, tool execution, and observability without crumbling under load. This article dissects the seven‑layer modern AI stack—from user interface to storage and monitoring—and provides battle‑tested commands, code snippets, and configuration examples to turn your demo into a deployable asset....

🤖 Built a RAG system that refuses to guess. Most "chat with your docs" demos look impressive until you ask something the corpus doesn't actually cover and they hallucinate an answer anyway. I built Support Knowledge Copilot to address that specific failure mode: a support knowledge assistant designed around retrieval quality and citation reliability, not just fluent output. Key design choices: 🔹 Hybrid retrieval dense (Qdrant) + BM25, fused via Reciprocal Rank Fusion. Embeddings handle paraphrase; BM25 catches exact tokens like error codes (e.g. AUTH-4291)🔹 Shared chunk IDs across both indexes, so fusion and citation checks operate on the same evidence unit 🔹 Grounded generation with inline [chunk_id] citations every claim is traceable to a source 🔹 A second-pass citation verifier that checks whether each cited chunk actually supports its claim 🔹 A no-answer path that refuses rather than fabricates when evidence is insufficient 📊 Results on a 55-question golden evaluation set (retrieval-only): → Hybrid: 100% correct-source@5, 98.5% recall@5 → Sparse-only (BM25): 88.2% the clearest illustration of why fusion matters 📈 On a full answer + citation evaluation: → 91.7% citation support rate → 100% refusal accuracy on genuinely unanswerable questions 🛠️ Stack: Google AI Studio (Gemini Flash + gemini-embedding-001), Qdrant, BM25, FastAPI, Streamlit. 🔗 Live demo: https://lnkd.in/gFk6jmbQ The broader goal was measuring failure modes as rigorously as building features retrieval quality, answer quality, citation validity, and refusal accuracy are tracked as separate metrics, since a system can perform well on one axis while quietly failing on another. Feedback and questions welcome. #RAG #LLMOps #AIEngineering #MachineLearning #GenAI #DevOps

Been levelling up my Generative AI journey by building with LangChain—working hands-on with models, prompts, chains, memory, embeddings, and vector stores to design structured LLM workflows. On top of that, I’ve implemented a Retrieval-Augmented Generation (RAG) pipeline that ingests domain data, embeds it, and uses a vector database to ground responses in the most relevant context. These building blocks are now powering my AI projects, including a CLIP-based meme search engine and other AI-native products I’m exploring as a full-stack developer. Repo links: https://lnkd.in/gqWScaPQ https://lnkd.in/gPrP2jTD Also,I recently shared a detailed post about my ContentForge project—an AI-powered content repurposing platform—but if you haven’t checked it out yet, here’s the Live Link as well: https://lnkd.in/gJPmFRt7

OpenCode became an inference provider for a boring reason: economies of scale. We don’t train models. We don’t run the infrastructure serving them. We sit between developers and the companies that do. As OpenCode’s usage grew, aggregating inference started to make economic sense. Instead of treating model access as someone else’s layer, we could offer it directly through OpenCode. That changed how I think about the AI stack. Model labs are moving upward into applications. Applications with enough usage are moving downward into model access, routing, and billing. Not everyone is trying to own the entire stack. They’re trying to own the layer where their scale gives them leverage. The models underneath can still be interchangeable. The relationship with the user cannot.