Gesture Control Open-Source Project Released

I’ve tried 6+ vibe coding tools over the last year. Here’s my honest breakdown - from being a complete beginner to connecting MCP servers and shipping real projects. 🧵 Phase 1 → Cursor Started with Python automation. The free tier was surprisingly generous, and the VS Code-like experience made onboarding effortless. 👉 Perfect starting point if you’re new to coding with AI. Phase 2 → Claude Code (VS Code) Built my entire personal portfolio using this. Also connected MCP servers here — this is where things started getting serious. Claude is easily the best all-rounder… but you’ll hit limits faster than expected on the Starter plan. 💡 What I learned the hard way: Disable unused MCPs Start a new chat per task Don’t fight limits — design your workflow around them Phase 3 → Ollama + Kimi K2.5 (Local) Tried going fully local. It was a great experiment… but broke down after 3–4 prompts. Real-world development needs consistent performance → which means cloud APIs. 👉 Lesson: Local ≠ scalable (yet) Phase 4 → Antigravity (Game Changer 🚀) This completely changed how I build. It actually controls a browser using Playwright and tests your UI before you even see it. ✔ Live UI previews ✔ AI-generated test data ✔ Edge case validation ✔ Multi-model support (Claude, Gemini 2.5 Pro, etc.) If you’re building frontend apps — you NEED to try this. Phase 5 → NVIDIA Free API + Cline in VS Code This is where things started feeling like a real dev setup. Access models like: Kimi K2.5 MiniMax M2.5 Nemotron 3 Super All for free. Plug into Cline, connect MCP servers — and you’ve got a powerful, cost-efficient stack. Phase 6 → OpenCode (Current) Runs on MiniMax M2.5 under the hood. Honestly? Surprisingly powerful for a free tool. 👉 If you’re just starting out - start HERE before spending a rupee. The real lesson? The tool matters less than the workflow. Start free. Build real projects. Then level up your stack. Which vibe coding tool are you using right now? 👇 #VibeCoding #AITools #ClaudeAI #BuildInPublic #Automation #OpenSource #DeveloperTools

In my last post, I shared how we tame the "invisible math" of complex projects to stop your databases from freezing. But optimizing the backend servers is only half the battle. What happens when your browser tries to do the heavy lifting? 💻⚙️ Let's say you are running a Critical Chain analysis for a project with over 100 interconnected tasks. Finding the absolute most efficient path through that maze is a mathematical nightmare (in computer science, we call this "NP-Hard"). The number of possible scenarios skyrockets, and the practical complexity goes off the charts. If you try to force a standard web browser (running on everyday JavaScript) to compute that on the fly, your laptop will likely freeze, the fan will start spinning, and the tab will eventually crash. :) That is why we are taking APUtime’s architecture to the next level. 🚀 We are currently rewriting our entire Process Management core engine into a powerhouse programming language called Rust. Why should a non-tech manager care about what language our engineers use? Because of what it unlocks for your daily operations: ⚡ Lightning-Fast Browser Speeds: We are porting this new engine directly into the browser (using technologies like WebAssembly and WebWorkers). This bypasses traditional browser limits, giving your team "native" desktop-app speed. Massive project graphs can now be recalculated directly on your laptop in milliseconds, without freezing your screen. 🧩 A Single Source of Truth: We are using this exact same Rust engine across all our systems (plugging it into our Python and PHP backends). This guarantees 100% predictable behavior. Whether a schedule is calculated on our servers or right there on your screen, the rules and results are absolutely identical. No discrepancies, no syncing errors. Upgrading our core isn't just a tech flex. It is about empowering operations teams to manipulate massive, 100+ task schedules on the fly, instantly, without ever waiting for a loading bar. We handle the impossible math under the hood so your team can just focus on execution. Have you ever had a software tool crash or freeze on you when you needed it to calculate a complex scenario? 👇 #ProjectManagement #SoftwareArchitecture #RustLang #Operations #TechLeadership #APUtime #Innovation #ProcessOptimization #BusinessScaling

What makes lsdefine/GenericAgent interesting is how directly it improves LLM and agent workflows. By tackling the challenge of making agent behavior more reliable, not just more powerful, this project is addressing a key pain point for developers working with LLMs and agents. At its core, GenericAgent is a self-evolving agent that grows in capabilities with each use, forming a personal skill tree. This approach is in stark contrast to traditional methods that rely on pre-trained models and language tags, making it difficult to steer and reuse these tools in real projects. Here are some key highlights of GenericAgent: - Self-Evolving: Automatically crystallizes each task into an skill, allowing capabilities to grow with every use. - Minimal Architecture: 3K lines of core code, with an Agent Loop of just 100 lines, and no complex dependencies or deployment overhead. - Clearly positioned around agentic workflows, making it easy to integrate into existing projects. - Built with Python, making it accessible to a wide range of developers. The traction makes sense: a repository sitting at #5 with around 3,512 new stars in the current trending window is usually solving a problem people can feel immediately. With its strong execution, high compatibility, and token efficiency, GenericAgent is a game-changer for developers working with LLMs and agents. Repo: https://lnkd.in/gwqFex5k #GitHub #OpenSource #GitHubTrending #LinkedInForDevelopers #Python #GenericAgent #AiAgent #Automation

I saved almost an hour of manual work… with a simple script. At work, we had a process to request secure access to multiple hosts. It was repetitive, slow, and honestly… easy to mess up. Doing it manually could take close to an hour. So I asked myself: 👉 Why am I doing this by hand? I built a script using Python and pexpect to automate the entire workflow. Now? ⏱️ Active manual work dropped to ~10–15 minutes ⚡ The process runs in the background 🧠 We can focus on real issues instead of repetitive tasks But the biggest takeaway wasn’t speed. It was this: 👉 Automation isn’t about replacing work — it’s about freeing your attention for what actually matters. This small change allowed us to: Respond to incidents faster Reduce human error Improve consistency across the process Lately, I’ve been thinking more about automation, reliability, and how systems behave in real-world conditions — and this was a small but meaningful step in that direction. If you’re doing something repetitive… it might be worth asking: 👉 Should this even be manual? #Automation #Python #Backend #SoftwareEngineering #DevLife #ProblemSolving #Tech

You’ve learned the patterns, But can you actually apply them when it matters? That’s where most developers get stuck. Turning DSA patterns into real problem-solving A lot of people stop at: “I understand Two Pointers” “I understand Sliding Window” But when they see a real question… They freeze. Because knowing a concept is not the same as knowing when to use it. I stopped asking “What’s the solution?” And started asking “What pattern fits this problem?” In this carousel, I broke down real examples: → Two Sum → HashMap for instant lookup → Valid Palindrome → Two Pointers → Longest Substring → Sliding Window → Buy & Sell Stock → Track min/max → Merge Sorted Array → Two Pointers (from the back) Notice the difference? Each problem is just a variation of a pattern. Nothing random. Nothing magical. This is how strong developers think: They don’t memorize answers. They recognize patterns and apply them with confidence. And this is what really matters in real-world engineering: - Writing efficient code - Making performance decisions - Solving problems under pressure If you’ve followed this series from the begining You already have a stronger foundation than most beginners. Now I’m curious 👇 Which of these do you still find confusing? 1. Knowing the pattern 2. Applying the pattern 3. Recognizing the pattern Drop your answer, let’s break it down together. #DataStructures #Algorithms #CodingInterview #ProblemSolving #SoftwareEngineering #TechGrowth #web3 #Pointers

After watching Iron Man, every kid wanted their own JARVIS. In 2008, that was just a dream. In 2026, you can actually build it. I spent the last few weeks building Jarvis v1 from scratch :- a production-grade AI agent with: → A LangGraph state machine at the core → 6 tools: web search, calculator, file I/O, code execution, and memory → 3 layers of memory: user profile, project context, and chat history → Smart guardrails: loop detection, code security review, human-in-the-loop → Parallel tool execution, independent tasks run simultaneously, not one by one No magic abstractions. No copy-pasting tutorials. Every component built to be understood, not just shipped. And this is just v1. v2 brings multi-agent architecture (specialist agents for research, coding, writing), a React web UI with streaming responses, RAG memory, Docker sandboxed code execution, and a self-improving agent that writes its own tools. The best part? It's fully open source. Clone it, customise it, make it yours. Swipe to see how it works 👉 GitHub: https://lnkd.in/gigEC6GR #AIAgents #LangChain #LangGraph #Python #BuildInPublic #OpenSource

Prompts are code. If you're not versioning, testing, and deploying them like code, you're one "quick fix" away from breaking production. I work on classification pipelines where the prompt is the core business logic. The specific instructions determine whether an input gets routed correctly or silently miscategorized. Yet the default approach everywhere is the same: prompts as string literals, edited inline, tested by eyeballing outputs, deployed with whatever commit happens to include them. Here's how I treat prompts as a first-class engineering artifact. Separate files, not inline strings. Each prompt is a versioned template with a changelog and metadata about which model and temperature it was tuned for. I can update a prompt without touching application code and roll back without a full redeploy. Eval set before every merge. I maintain golden input-output pairs for each prompt. A change that improves new cases but regresses existing ones gets caught before it ships. The eval runs in the pipeline, not in my head. Structured templates, not f-strings. Named slots with type constraints instead of variables jammed into strings. Makes it trivial to log exactly what the model saw, reproduce any call, and catch bugs where the wrong data type slipped in. Metrics on every version. Accuracy, confidence, token count, latency, cost per call — all tracked per prompt version. When I switch versions, I can see whether the tradeoff was worth it. This has stopped me from "optimizing" a prompt that saved tokens but quietly dropped accuracy. Rollback is a config change. Because prompts are versioned and decoupled from app code, switching back takes one line. No hotfix. No revert commit. The AI community talks endlessly about prompt engineering but treats the engineering part as optional. Versioning, testing, observability, rollback — that's what turns a fragile string into a reliable system. #AIEngineering #PromptEngineering #Python #Backend #SystemDesign #LLM #AppliedAI #SoftwareEngineering #BuildInPublic

𝗧𝗵𝗶𝘀 𝗧𝗿𝗲𝗲 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗟𝗼𝗼𝗸𝘀 𝗟𝗶𝗸𝗲 𝗗𝗙𝗦… 𝗨𝗻𝘁𝗶𝗹 𝗡𝘂𝗺𝗯𝗲𝗿 𝗧𝗵𝗲𝗼𝗿𝘆 𝗕𝗿𝗲𝗮𝗸𝘀 𝗜𝘁 𝗢𝗽𝗲𝗻 🌳 Today’s problem looked like a simple tree traversal - until a quiet condition appeared: Count ancestors where nums[i] * nums[ancestor] is 𝗮 𝗽𝗲𝗿𝗳𝗲𝗰𝘁 𝘀𝗾𝘂𝗮𝗿𝗲. Brute force (walking up ancestors for every node) is too slow. The real win comes from 𝗰𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘁𝗵𝗲 𝗽𝗲𝗿𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲. 💡 𝗧𝗵𝗲 𝗞𝗲𝘆 𝗜𝗻𝘀𝗶𝗴𝗵𝘁 A product is a perfect square iff every prime factor appears an even number of times. So reduce every number to its square-free form: keep only primes with odd exponent. Examples: 12 = 2^2 × 3 -> 3 18 = 2 × 3^2 -> 2 Now the condition becomes: Two numbers form a perfect square product iff their square-free forms are equal. 🚀 𝗪𝗵𝗮𝘁 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝗯𝗲𝗰𝗼𝗺𝗲𝘀 For each node, count how many ancestors have the same square-free value. That’s just DFS + frequency map on the path. 🛠️ 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 1. Build adjacency list 2. Precompute square-free value for each node 3. DFS from root while maintaining a hashmap of frequencies 4. At each node, add map.get(k[node]) to answer 5. Backtrack (remove from map) No ancestor traversal. No pair checks. ✨ 𝗞𝗲𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀 1. Perfect square checks → parity of prime exponents 2. Convert math conditions into hashable signatures 3. DFS + hashmap is powerful for ancestor path problems 4. Optimize by transforming the condition, not the traversal A beautiful mix of Trees + Number Theory + Hashing. #algorithms #datastructures #dfs #numbertheory #trees #hashmap #graph #primefactorization #coding #programming #leetcode #codinginterview #interviewprep #problemSolving #competitiveprogramming #developer #devlife #engineering #tech #learning #growth #100DaysOfCode #career #faangprep

I vibe-coded an open source Vibe System Design platform called DeSysFlow in just 48 hours 💙. System design always felt slow and overcomplicated. So I tried something different. Just vibe it. DeSysFlow turns your existing codebase into structured system design docs. What it does 🤖 • Converts codebase into HLD and LLD • Generates Mermaid diagrams • Creates non technical reports for founders and stakeholders • Supports CLI and Web App • Works with OpenAI, Claude, and Ollama You can configure 📂 • Programming language • Role • Cloud • Web search • LLM provider • Writing style Run it 🔥 • CLI: /letsvibedesign cli • Web: /letsvibedesign ui It includes session management, chat history, parallel agents, agent in loop for refinement, and versioned documentation. Built in 48 hours. Fully open source. GitHub 🔗 https://lnkd.in/dycitKVW Would love feedback and contributors 💙 #opensource #ai #systemdesign #buildinpublic #vibecoding

Ever feel like your business operations are held together by digital duct tape? 🛠️ We’ve all been there. You start with a basic template or a generic bot to save time. But as you scale, those "simple" solutions start to crack. They don't understand your specific business logic, they don't talk to your tech stack properly, and they certainly don't grow with you. At Autom8tion Lab, we see this breaking point every day. Companies outgrow generic tools and need systems engineered for their actual reality, not a one-size-fits-all template. We move you beyond basic low-code shortcuts. By combining the flexibility of n8n with the raw power of custom Python, we build automation that actually fits your unique workflows. No workarounds. No compromises. Just high-performance systems that deliver measurable results: typically a 10x productivity boost within 90 days. 🚀 Stop patching holes and start building a foundation. Ready to trade the duct tape for custom engineering? Let’s build something that works. DM us or visit autom8ionlab.com to get started. #Automation #AI #BusinessOps #Python #n8n #DigitalTransformation #Efficiency #Autom8tionLab