LogicLock: Validate Rule Definitions Against Code with Python CLI

Laying the foundation for FlowState API: Multi-tenant Workspace & Project models • UUID primary keys for security & horizontal scaling • RESTRICT cascades to protect owner-data integrity • Composite unique constraints for workspace-scoped slugs • Explicit db_table & index tuning for predictable DB performance • Admin optimized with select_related to kill N+1 queries Trade-off: Keeping tenant scoping explicit at the API layer instead of implicit model managers. Background workers, tests, and Django admin stay predictable while DRF handles request isolation. #Django #Python #BackendEngineering #SystemDesign #DevOps #OpenSource

I replaced 2,000 lines of Python automation code with 12 n8n workflows. They run better. Three months ago, my automation stack was 8 Python scripts: content pipelines, lead enrichment, infrastructure monitoring, invoice processing. 2,000+ lines of code, 5 cron jobs, and a prayer that nothing breaks overnight. The problems were predictable. A failure at step 7 of 12 meant reading logs, finding the exact line, re-running the entire chain. No visibility into what ran or failed. Cron jobs competing for resources. Every change required SSH and a service restart. n8n replaced all of it. Open-source, self-hosted, 400+ integrations, and — the part that surprised me — native AI agent nodes. Three workflows that changed my stack: 1. Content trend pipeline: Fetches HackerNews, ArXiv, GitHub trending. An AI agent node ranks topics for my LinkedIn audience. Runs in 45 seconds vs 3 minutes with Python. Never fails silently. 2. Lead enrichment: Typeform webhook to Clearbit enrichment to AI scoring to auto-routing. Response time to high-value leads: 3 minutes (was 2-4 hours manual). 3. Infrastructure monitor: Checks 12 endpoints every 5 min. AI node analyzes error patterns when degradation is detected. Caught a DB connection leak 40 minutes before downtime. The n8n vs Make vs Zapier question has a clear answer by use case. n8n wins for engineers who want code access, self-hosting, and AI capabilities. Make wins for marketing teams. Zapier wins for non-technical users. Self-hosted setup: Docker Compose, 10 minutes. Unlimited executions. Zero per-task pricing. Your data stays on your server. Honest limitation: fewer integrations than Zapier (400 vs 6,000), learning curve is real for non-coders, and the AI nodes are new with thin documentation. Debugging time dropped 80%. Visibility went from zero to complete. Full comparison table, setup guide, and 3 workflow templates in first comment. Save this for your next automation project. #WorkflowAutomation #n8n #AIAutomation #NoCode #DevTools

Why solving LeetCode problems is more than just "coding"—it’s about mapping real-world logic! 🧩 Today, I tackled LeetCode 1391 (Check if There is a Valid Path in a Grid). At first, it looked like a simple path-finding problem, but the real challenge was in the rules of connection. The Problem: In a grid where every cell is a "street" (Horizontal, Vertical, Curves), you have to find if a path exists from start to end. But there's a catch: two streets only connect if they "handshake" correctly. My Key Learnings: 1️⃣ Bidirectional Validation: It’s not enough to know where Cell A can go. You must also check if Cell B is "open" to receive the connection from Cell A. In coding, this means checking if (Cell_A -> Cell_B) AND if (Cell_B -> Cell_A). 2️⃣ Mapping Complex Rules: Instead of writing 50 if-else statements, I used a Direction Map. Mapping each street type (1 to 6) to its possible directions made the code clean and scalable. 3️⃣ BFS for Shortest/Valid Path: I used Breadth-First Search (BFS) with a visited array. This ensures we explore the grid efficiently without getting stuck in infinite loops. Why this matters? Whether you are building a GPS system, a game, or a complex microservice architecture, the logic remains the same: Do your components talk to each other correctly? My Tech Stack for DSA: 🚀 Language: C++ 🛠️ Tool: BFS Algorithm 🧠 Concept: Coordinate Geometry & Direction Mapping Solving these "Medium" challenges is building the "Architect Mindset" I need for bigger systems! What’s the most interesting grid problem you’ve solved? Let’s connect and discuss! 👇 #LeetCode #DataStructures #Algorithms #ProblemSolving #CPlusPlus #CodingJourney #SoftwareDeveloper #BFS #GridLogic

𝗠𝗼𝘀𝘁 𝗣𝗲𝗼𝗽𝗹𝗲 𝗨𝘀𝗲 𝗔𝗜. 𝗙𝗲𝘄 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗧𝗵𝗲 𝗖𝗼𝗱𝗲 𝗔𝗜 𝗰𝗮𝗻 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗰𝗼𝗱𝗲. 𝗕𝘂𝘁 𝗶𝗳 𝘆𝗼𝘂 𝗱𝗼𝗻’𝘁 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗣𝘆𝘁𝗵𝗼𝗻 𝗱𝗲𝗲𝗽𝗹𝘆, 𝘆𝗼𝘂 𝘄𝗼𝗻’𝘁 𝗸𝗻𝗼𝘄 𝘄𝗵𝗮𝘁 𝗶𝘁’𝘀 𝗱𝗼𝗶𝗻𝗴, 𝘄𝗵𝘆 𝗶𝘁 𝗯𝗿𝗲𝗮𝗸𝘀, 𝗼𝗿 𝗵𝗼𝘄 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝗶𝘁. 𝗧𝗵𝗮𝘁’𝘀 𝘄𝗵𝗲𝗿𝗲 𝗿𝗲𝗮𝗹 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘀𝘁𝗮𝗿𝘁𝘀. 𝗕𝗲𝗹𝗼𝘄 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗰𝗼𝗿𝗲 𝘁𝗼𝗽𝗶𝗰𝘀 𝘆𝗼𝘂 𝘀𝗵𝗼𝘂𝗹𝗱 𝗸𝗻𝗼𝘄: 1. Object-Oriented Programming (OOP) 2. Decorators 3. Generators & Iterators 4. Context Managers 5. Async Programming (async/await) 6. Multithreading & Multiprocessing 7. WebSockets 8. Data Structures & Algorithms 9. Memory Management & Garbage Collection 10. File Handling & Serialization 11. List/Dict/Set Comprehensions 12. Exception Handling (advanced patterns) 13. Functional Programming (map, filter, lambda) 14. Modules & Packaging 15. Virtual Environments & Dependency Management 16. Type Hinting & Static Typing 17. Testing (unit tests, mocking, pytest) 18. Logging & Debugging 19. API Development (FastAPI/Flask) 20. Database Handling (SQL/ORMs) Master these, and you’re not just using AI you’re actually building with it. #Python #AI #SoftwareEngineering #Developers #Coding #MachineLearning #Programming #Tech #LearnToCode

I started this on a Sunday afternoon while playing with my daughter — fired up the PC, set Claude Code loose on an investigation, and went back to playing. The finding: the BC compiler's libraries expose a public API to transpile AL directly into C#. No service tier needed. That turned into a question: what if we fix the loose ends of that C# output and just execute it? A few weeks later, AL Runner exists. 🔧 What it does: transpiles your AL source to C#, rewrites BC runtime types to in-memory mocks with Roslyn, and executes your test codeunits — no container, no Docker, no SQL Server required. ⚡ How fast: unit tests run in milliseconds. Full transpile + compile + execute in seconds. Full BC pipeline overhead alone is 15+ minutes. ✅ How trustworthy: the repo ships with 3,400 [Test] procedures across 266+ AL scenarios as living proof of correctness. Run them yourself — they finish in ~30 seconds. 🤖 Why AI agents love it: it's a cross-platform CLI tool. Agents can write a test, run it, verify it fails for the right reason, implement the fix, and verify it passes — all in their own loop, without waiting on a container. And a shoutout to the people who made this much further along than I could have alone: Steve Endow, Brad Prendergast, Jeremy Vyska — who donated tokens and ran up to 9 parallel AI implementation agents to close gaps — plus Torben Løkke Leth (who also builds ALchemist on top of it), Flemming Bakkensen, and Volodymyr Dvernytskyi Full post with install instructions, dependency model, and the pipeline strategy: 👉 https://lnkd.in/dbXJm3En #BusinessCentral #ALDevelopment #DevOps #UnitTesting #OpenSource

This is going to be a huge step forward to both human and agentic development because fast tests mean fast feedback and faster fixes. 🥳🎉

3 days ago I posted about AL Runner. Since then, a lot happened. 91 issues closed. 612 new test procedures added — the suite now stands at 4,025 [Test] cases across 614 AL scenarios. The full matrix runs in about 8 seconds on my machine. Most of those fixes came from telemetry. When AL Runner hits an unhandled pattern, it asks whether you want to report it. If you say yes, a GitHub issue is created automatically. When enough of those pile up, I kick off a batch of AI agents to work through them — they write failing tests, implement the fixes, and open PRs for me to review. A few things fixed this week that matter for real-world projects: - FieldRef.Validate(Variant) — NullReferenceException gone - Format(Option) — member names now render correctly - TestPage.SetValue — now persists to the underlying record - Auto-stubbed codeunits with Enum overloads — no more NavOption→NavCode cast errors - Mark(bool) in if-expressions, Evaluate(Char, Text), SourceTableTemporary page isolation, and more Writing AL tests is nothing new. Running them without a service tier, Docker, or SQL Server — that's what AL Runner changes. Don't have AL tests yet? Still try it. Point it at your project. If something doesn't compile or a mock is missing, AL Runner will ask if you want to report it. Say yes — a GitHub issue gets created and I'll fix the gap. dotnet tool install -g MSDyn365BC.AL.Runner 👉 https://lnkd.in/d8dQ-CNN #BusinessCentral #ALDevelopment #OpenSource #UnitTesting #DevOps

You've nodded along when someone said "that lives on the heap," but never quite understood why memory is split between Stack and Heap? I just published a deep dive that breaks down: ✴️ Why StackOverflowError actually happens ✴️ How Stack frames get pushed and popped ✴️ When value types escape to the Heap ✴️ Why closures force data out of the Stack This isn't theory, it's the foundation behind every memory leak you've debugged and every recursion limit you've hit. #SoftwareEngineering #MemoryManagement #Programming #DeveloperCommunity

🚀 Solved LeetCode 2515 – Shortest Distance to Target String in a Circular Array Today I tackled an interesting problem that highlights the importance of handling circular data structures efficiently. 🔍 Problem Insight: Given a circular array of words, a target string, and a starting index, the goal is to find the minimum number of steps required to reach the target by moving either left or right. 💡 Key Learnings: Circular arrays require thinking beyond linear traversal Always consider both directions (forward & backward) Optimizing distance using min(distance, n - distance) is the key trick Simple logic + correct observation = optimal solution ⚙️ Approach Used: Traverse the array to find all occurrences of the target Calculate distance from the starting index Take the minimum considering circular movement 📈 Complexity: Efficient O(n) solution with constant space 🔥 Takeaway: This problem reinforced how small tweaks (like circular behavior) can change the entire approach. A great example of combining logic + observation for clean and optimal solutions. #LeetCode #Algorithms #ProblemSolving #CodingJourney #Java #DataStructures #CompetitiveProgramming

𝐌𝐲 𝐅𝐚𝐯𝐨𝐫𝐢𝐭𝐞 𝐓𝐨𝐨𝐥 𝐢𝐧 𝐒𝐓𝐋: 𝐦𝐚𝐩 When it comes to solving problems efficiently, one thing I keep coming back to is map in C++ STL. What is map? 𝐦𝐚𝐩 𝐢𝐬 𝐚𝐧 𝐚𝐬𝐬𝐨𝐜𝐢𝐚𝐭𝐢𝐯𝐞 𝐜𝐨𝐧𝐭𝐚𝐢𝐧𝐞𝐫 𝐭𝐡𝐚𝐭 𝐬𝐭𝐨𝐫𝐞𝐬 𝐝𝐚𝐭𝐚 𝐢𝐧 𝐤𝐞𝐲-𝐯𝐚𝐥𝐮𝐞 𝐩𝐚𝐢𝐫𝐬 𝐚𝐧𝐝 𝐤𝐞𝐞𝐩𝐬 𝐤𝐞𝐲𝐬 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜𝐚𝐥𝐥𝐲 𝐬𝐨𝐫𝐭𝐞𝐝. Example: {key → value} like {1 → 3} means number 1 appears 3 times — here 𝐢𝐭.𝐟𝐢𝐫𝐬𝐭 𝐠𝐢𝐯𝐞𝐬 1 (𝐤𝐞𝐲), 𝐢𝐭.𝐬𝐞𝐜𝐨𝐧𝐝 𝐠𝐢𝐯𝐞𝐬 3 (𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲), and it is the iterator. Why? Because it makes life so much easier • 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐬𝐨𝐫𝐭𝐢𝐧𝐠 𝐨𝐟 𝐤𝐞𝐲𝐬 • 𝐄𝐚𝐬𝐲 𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 𝐜𝐨𝐮𝐧𝐭𝐢𝐧𝐠 • 𝐂𝐥𝐞𝐚𝐧 𝐚𝐧𝐝 𝐫𝐞𝐚𝐝𝐚𝐛𝐥𝐞 𝐜𝐨𝐝𝐞 • 𝐑𝐞𝐝𝐮𝐜𝐞𝐬 𝐜𝐨𝐦𝐩𝐥𝐞𝐱 𝐥𝐨𝐠𝐢𝐜 𝐢𝐧𝐭𝐨 𝐬𝐢𝐦𝐩𝐥𝐞 𝐥𝐢𝐧𝐞𝐬 The best part? Using map, we can solve multiple questions on LeetCode with the same pattern — especially problems based on: • 𝐅𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐲 𝐜𝐨𝐮𝐧𝐭𝐢𝐧𝐠 • 𝐃𝐮𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐬 / 𝐩𝐚𝐢𝐫𝐬 • 𝐇𝐚𝐬𝐡𝐢𝐧𝐠 𝐜𝐨𝐧𝐜𝐞𝐩𝐭𝐬 Recently, while solving a problem on counting “good pairs”, map helped me: • 𝐒𝐭𝐨𝐫𝐞 𝐟𝐫𝐞𝐪𝐮𝐞𝐧𝐜𝐢𝐞𝐬 𝐞𝐟𝐟𝐨𝐫𝐭𝐥𝐞𝐬𝐬𝐥𝐲 • 𝐀𝐩𝐩𝐥𝐲 𝐭𝐡𝐞 𝐬𝐢𝐦𝐩𝐥𝐞 𝐟𝐨𝐫𝐦𝐮𝐥𝐚: 𝐟 * (𝐟 - 1) / 2 • 𝐂𝐨𝐧𝐯𝐞𝐫𝐭 𝐚 𝐛𝐫𝐮𝐭𝐞-𝐟𝐨𝐫𝐜𝐞 𝐎(𝐧²) 𝐬𝐨𝐥𝐮𝐭𝐢𝐨𝐧 𝐢𝐧𝐭𝐨 𝐎(𝐧 𝐥𝐨𝐠 𝐧) Sometimes, it's not about writing complex code… it's about choosing the right data structure. Once you master map, you unlock solutions to many problems with the same approach What’s your go-to STL container? #cpp #stl #coding #datastructures #leetcode #programming #developers