Low-code Bottleneck: nCode Transpilation Pipeline for n8n Workflows

Something as trivial as 'getOrDefault' cost me hours of debugging 🤦♂️ I wanted a simple thing - “use a default value if the key is missing.” AI generated the code using `getOrDefault`. It looked correct. I reviewed it. Didn’t think twice. But the issue wasn’t a missing key. The key existed… with a null value. And getOrDefault doesn’t apply the default in that case. So the code was “right”… but the behavior was wrong. What’s uncomfortable is: I knew that a key can have a null value. Still, I didn’t catch it during review. Not because the code was complex but because I didn’t fully account for this nuance. That’s the real risk 😅 It’s not about AI making mistakes. It’s about us assuming the code does what we intend. What you ask for, what gets generated, and how it actually behaves - can all be different. And these gaps are not easy to catch with just tests or lower environments. Fundamentals and language-specific nuances matter more than ever. Because in the end, correctness is still our responsibility. #SoftwareEngineering #CleanCode #Java #Coding

🚀 ShipIt Agent v1.0.2 — The most powerful open-source Python agent framework After weeks of deep engineering, I'm releasing SHIPIT Agent v1.0.2 — a complete agent framework that goes beyond others. What's new: 🎯 Deep Agents — GoalAgent decomposes objectives and tracks success criteria. ReflectiveAgent evaluates and revises its own output. Supervisor delegates to workers and reviews quality. AdaptiveAgent creates new tools at runtime. 📊 Structured Output — One parameter: agent.run(prompt, output_schema=MyPydanticModel). Returns typed, validated instances. No chain wrapping needed. 🔗 Pipeline Composition — Sequential, parallel, conditional routing. Cleaner than Other LCEL. Full streaming support. 🧠 Advanced Memory — Conversation memory (4 strategies), semantic search with embeddings, entity tracking. AgentMemory.default() for zero-config. 📡 Real-Time Streaming — Every deep agent, pipeline, and team supports .stream(). Watch goal decomposition, reflections, worker delegations, and quality scores in real time. The numbers: 285 tests, 12 examples, 8 notebooks, 13 doc pages, 10 LLM providers, 30+ tools. pip install shipit-agent GitHub: https://lnkd.in/dpUiYqzF Docs: https://lnkd.in/dTxQtvF7 #AI #Python #LLM #AgentFramework #OpenSource

Everyone is talking about "vibe coding" complex systems with AI. But what happens when that system needs to execute trades in nanoseconds? The vibes stop, and hardcore computer science begins. 📉⚡ I recently architected a hybrid High-Frequency Trading (HFT) engine. I used AI to accelerate the raw syntax generation, but the underlying system design required a deep dive into bare-metal performance optimization. The core challenge: How do you combine the rapid ML iteration of Python with the deterministic, nanosecond latency of C++? The solution: 🚀 C++ "Hot Path" for raw market data ingestion. 🧠 Python "Smart Path" for PyTorch-based regime detection. ⚡ POSIX Shared Memory (mmap) + Flatbuffers for true Zero-Copy Inter-Process Communication, bypassing the OS network stack and starving the Python Garbage Collector. 🛡️ Optimistic Shadow Books & ZMQ Watchdogs to ensure the AI doesn't bankrupt the account during a microsecond latency spike. AI is an incredible tool for writing code, but taking 100% intellectual ownership of the system architecture understanding how every byte moves through RAM is still the job of the engineer. I wrote a deep dive on the architecture and the tradeoffs made here: https://lnkd.in/gVDSQ_pR #SystemsEngineering #Cplusplus #Python #HFT #AlgorithmicTrading #MachineLearning #SoftwareArchitecture

Most people think debugging is about finding the bug. It's not. It's about building the system that finds the next one faster. At my current role, when I joined, critical backend issues took about 48 hours to resolve. Not because people were slow — because there was no structure around how issues got triaged, reproduced, and traced through the pipeline. I didn't write a magic tool. I just built a repeatable debugging workflow. Structured logging in the right places. Clear escalation steps. A habit of writing down what broke and why after every incident. Resolution time dropped to about 12 hours. The lesson I keep relearning: the highest-leverage engineering work is often not building new features. It's making the system easier to understand when something goes wrong at midnight. That applies to every backend I've worked on — Java microservices, Python pipelines, LLM-integrated workflows. The stack changes. The need for structured observability never does. #SoftwareEngineering #BackendEngineering #Debugging #Python #ProductionSystems #AIEngineering #BuildInPublic

AI Tools Every Developer Should Know From APIs to Production. Another interesting episode from Sateesh Tech Talk, breaking down the end-to-end AI stack for building real, production ready AI apps using familiar software engineering patterns. 🎥 https://lnkd.in/g-YqcHQj You’ll learn: • How requests flow through APIs, RAG, LLMs, tools, and production systems • Upper vs. lower layers of the AI stack • Implementing the same architecture in Java and Python • Why RAG, tools, and observability matter If you can build APIs or microservices, you already have the skills to build AI systems.

How PRFlow actually reads your codebase. Most tools send raw diffs to the AI. PRFlow uses a four-step pipeline. 1. File classification. Every changed file is categorised: source code, config, auto-generated, documentation. 2. Scope extraction. For Python, JavaScript, TypeScript, Go, Java, Rust, C#, and Ruby, PRFlow identifies the exact function or class that changed. 3. Cross-file dependency enrichment. If your changed function calls something in another file, PRFlow includes that too. 4. Token budget allocation. Context is distributed proportionally so nothing important gets cut off. The AI sees exactly what matters. Not a raw diff. Not an entire codebase. #PRReview #CodeReview #Developers

🧱 𝗜 𝗦𝗼𝗹𝘃𝗲𝗱 𝗮 “𝗩𝗶𝘀𝘂𝗮𝗹” 𝗣𝗿𝗼𝗯𝗹𝗲𝗺 𝗨𝘀𝗶𝗻𝗴 𝗢𝗻𝗹𝘆 𝗮 𝗦𝘁𝗮𝗰𝗸 — 𝗡𝗼 𝗚𝗲𝗼𝗺𝗲𝘁𝗿𝘆 𝗡𝗲𝗲𝗱𝗲𝗱 Today’s problem looked visual and innocent: 𝗚𝗶𝘃𝗲𝗻 𝗯𝗮𝗿 𝗵𝗲𝗶𝗴𝗵𝘁𝘀 𝗼𝗳 𝗮 𝗵𝗶𝘀𝘁𝗼𝗴𝗿𝗮𝗺 (𝘄𝗶𝗱𝘁𝗵 = 𝟭), 𝗳𝗶𝗻𝗱 𝘁𝗵𝗲 𝗹𝗮𝗿𝗴𝗲𝘀𝘁 𝗿𝗲𝗰𝘁𝗮𝗻𝗴𝗹𝗲 𝗮𝗿𝗲𝗮. This is the classic problem from 𝗟𝗲𝗲𝘁𝗖𝗼𝗱𝗲 — and it’s a beautiful lesson in how 𝗺𝗼𝗻𝗼𝘁𝗼𝗻𝗶𝗰 𝘀𝘁𝗮𝗰𝗸𝘀 𝗰𝗼𝗻𝘃𝗲𝗿𝘁 𝗴𝗲𝗼𝗺𝗲𝘁𝗿𝘆 𝗶𝗻𝘁𝗼 𝗶𝗻𝗱𝗶𝗰𝗲𝘀. 🧠 𝗧𝗵𝗲 𝗡𝗮𝗶𝘃𝗲 𝗧𝗵𝗼𝘂𝗴𝗵𝘁 (𝗮𝗻𝗱 𝘄𝗵𝘆 𝗶𝘁 𝗳𝗮𝗶𝗹𝘀) For every bar i, try expanding left and right until you hit a smaller bar. That’s O(n) per index -> O(n²). Too slow for n=10^5. So the real question becomes: For each bar, how far can it extend without hitting a smaller height? That is the entire problem. 💡 𝗞𝗲𝘆 𝗜𝗱𝗲𝗮 — 𝗡𝗲𝗮𝗿𝗲𝘀𝘁 𝗦𝗺𝗮𝗹𝗹𝗲𝗿 𝗘𝗹𝗲𝗺𝗲𝗻𝘁𝘀 For every index i: 1. Find the first smaller bar on the left 2. Find the first smaller bar on the right A monotonic increasing stack gives both in linear time. ▶️ 𝗟𝗲𝗳𝘁 𝗦𝗰𝗮𝗻 (𝗟 -> 𝗥) Pop until the top is strictly smaller. That index defines the left boundary. ◀️ 𝗥𝗶𝗴𝗵𝘁 𝗦𝗰𝗮𝗻 (𝗥 -> 𝗟) Same logic to get the right boundary. Now each bar knows the exact range where it is the minimum. ⏱️ 𝗖𝗼𝗺𝗽𝗹𝗲𝘅𝗶𝘁𝘆 Each index pushed/popped once → O(n) Space → O(n) ✨ 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀 1. “Nearest smaller element” is a powerful pattern 2. Monotonic stacks turn nested loops into linear scans 3. Think: If this bar is the minimum, how wide can the rectangle be? A visual problem, solved with clean stack discipline. #DataStructures #Algorithms #MonotonicStack #Java #ProblemSolving #CodingInterview #Stack #DSA #LeetCode #Coding #Programmer #SoftwareEngineer #InterviewPrep #CompetitiveProgramming #100DaysOfCode #Tech #Learning #ProblemSolvingSkills

Liter-llm v1.1.0 Launches Rust-Core LLM Client with 11 Native Bindings 📌 liter-llm v1.1.0 drops a Rust-core LLM client with 11 native bindings - slashing dependency risks and vendor lock-in. Built for DevOps teams managing multi-provider AI stacks, it compiles logic into a single binary, letting you swap models by changing just a string. Smaller than Python rivals, faster than Go tools - and ready to run anywhere. 🔗 Read more: https://lnkd.in/d6sT5KcA #LiterLlm #Rustcore #Llmclient #Openaiproxy #Vendorlockfree

𝐀𝐈 𝐜𝐨𝐝𝐢𝐧𝐠 𝐭𝐨𝐨𝐥𝐬 𝐚𝐫𝐞 𝐠𝐫𝐞𝐚𝐭 𝐚𝐭 𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐜𝐨𝐝𝐞.  𝐓𝐡𝐞𝐲'𝐫𝐞 𝐭𝐞𝐫𝐫𝐢𝐛𝐥𝐞 𝐚𝐭 𝐤𝐧𝐨𝐰𝐢𝐧𝐠 𝐲𝐨𝐮𝐫 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞. Copilot and Claude don't know you route everything through a service layer. They don't know which imports are banned. They don't know your layer boundaries. So they generate perfectly valid code that quietly breaks your rules. 𝐈 𝐛𝐮𝐢𝐥𝐭 𝐀𝐫𝐤𝐥𝐢𝐧𝐭 𝐭𝐨 𝐟𝐢𝐱 𝐭𝐡𝐢𝐬. You define your architectural rules in a plain YAML file. Arklint enforces them in pre-commit hooks and CI and exposes them to AI tools via MCP so they check your rules before writing code. 𝐓𝐨𝐝𝐚𝐲 it hit 𝐯1.0.0. Native packages for Python, Node.js, and .NET. But 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜 beyond that. If Python's on your machine, Arklint works on any codebase. 📦 𝐏𝐲𝐏𝐈 → 𝐩𝐢𝐩 𝐢𝐧𝐬𝐭𝐚𝐥𝐥 𝐚𝐫𝐤𝐥𝐢𝐧𝐭 📦 𝐧𝐩𝐦 → 𝐧𝐩𝐱 𝐚𝐫𝐤𝐥𝐢𝐧𝐭 📦 𝐍𝐮𝐆𝐞𝐭 → 𝐝𝐨𝐭𝐧𝐞𝐭 𝐭𝐨𝐨𝐥 𝐢𝐧𝐬𝐭𝐚𝐥𝐥 𝐚𝐫𝐤𝐥𝐢𝐧𝐭 🌐 𝐀𝐧𝐲 𝐨𝐭𝐡𝐞𝐫 𝐬𝐭𝐚𝐜𝐤 → 𝐣𝐮𝐬𝐭 𝐧𝐞𝐞𝐝𝐬 𝐏𝐲𝐭𝐡𝐨𝐧. "𝒑𝒊𝒑 𝒊𝒏𝒔𝒕𝒂𝒍𝒍 𝒂𝒓𝒌𝒍𝒊𝒏𝒕" 𝐚𝐧𝐝 𝐲𝐨𝐮'𝐫𝐞 𝐝𝐨𝐧𝐞. If this solves a problem you've had 𝐭𝐫𝐲 𝐢𝐭, 𝐬𝐭𝐚𝐫 𝐢𝐭 𝐨𝐧 𝐆𝐢𝐭𝐇𝐮𝐛,  and if you want to shape where it goes next, Want native support for your stack? The repo is open and contributions very welcome! Built this with Claude as my co-pilot, the irony of using AI to build a tool that keeps AI in check isn't lost on me. 😄 𝐋𝐢𝐧𝐤 𝐢𝐧 𝐜𝐨𝐦𝐦𝐞𝐧𝐭𝐬 👇 #SoftwareArchitecture #Devtools #AIEngineering #CleanArchitecture #CleanCode #TechDebt #SoftwareEngineering #PyPI #npm #NuGet

No one asked for a shared package. I built one anyway. Multiple teams at a global pharmaceutical company were running the same logic. Fetch data from source. Transform it. Write to ADLS Gen2. Each team had their own version. Assumption: custom code per team is safer. Easier to change without breaking someone else’s pipeline. Reality: five codebases with five variations of the same bug. Every upstream schema change meant five separate fixes. I built an OOP-based Python package. Parameterized. Modular. One abstraction for retrieval, one for transformation, one for storage. Other teams started using it. Then more teams. It became the default pattern not because someone mandated it, but because it was simply better. Reusability isn’t about efficiency. It’s about reducing drift between what you intended and what ten teams independently decided to implement. The hardest part wasn’t the code. It was designing the interface so teams could configure it without needing to understand what was underneath. That’s the real engineering skill. Not writing a good function. Writing one that other engineers trust enough not to rewrite. What’s a pattern you built that spread further than you expected? #DataEngineering #Python #AzureDatabricks