Automated Code Quality Checks with CI Pipeline for Legal Advisor AI Project

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

Day 37 of 100 Days of Coding #100Daysofcoding #coding #dsa 1.Binary Tree Inorder Traversal Pseudocode : 1.Create an empty list res to store result 2.Define a function inorder(node) 3.If node is null, return 4.Call inorder(node.left) → go left first 5.Add node.val to res → process current node 6.Call inorder(node.right) → go right 7.Start recursion by calling inorder(root) 8.Return res In short: Left → Node → Right Code: class Solution:     def inorderTraversal(self, root: Optional[TreeNode]) -> List[int]:         res=[]         def inorder(root):             if not root:                 return             inorder(root.left)             res.append(root.val)             inorder(root.right)         inorder(root)         return res Time Complexity- O(n) Each node is visited exactly once Space Complexity- O(n) recursive stack function call is pushed onto the call stack (managed by Python internally).

So I was digging into this 400-line Python module that's been causing intermittent 502s in prod for months. The retry logic was all over the place, and to be honest, it was a bit of a mess. I decided to set up a multi-agent code review pipeline using CrewAI to tackle it. Here's how I configured the agents: • Agent 1 ("The Critic"): Reviews code for logic errors, race conditions, edge cases • Agent 2 ("The Architect"): Suggests structural improvements and refactors • Agent 3 ("The Tester"): Writes unit tests for uncovered paths Fed the module into the pipeline, and within 12 minutes, Agent 1 flagged an off-by-one error in the backoff calculation 🤦♂️. That one error had been causing those 502s for 2 months. Agent 2 suggested extracting the retry policy into a config-driven strategy pattern, which made a lot of sense. And Agent 3 generated 14 test cases, 3 of which exposed edge cases nobody had thought of 🚀. What really impressed me was how quickly these agents were able to identify issues that had been evading us for so long. The fact that Agent 1 caught that off-by-one error in the backoff calculation was huge - it's a great example of how AI can help with the tedious parts of code review 💻. And now that we've got this pipeline set up, I'm excited to see how it'll help us improve our code quality going forward 📈. #AIAgents #SoftwareEngineering #DeveloperProductivity #CrewAI #CodeReview #Automation #BuildInPublic #DevTools

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

Project 2: I built a small project where I used a basic ML model to detect failure patterns in API logs. The idea was simple — convert log text into numbers, train the model to recognise words that usually indicate failure, and when it detects something like that, trigger an auto-healing script. It’s a very basic setup, but it helped me understand how ML can be plugged into real system behaviour like monitoring and recovery instead of just predictions. Code: https://lnkd.in/ecRr4RnR Still learning, but this was a good step in connecting ML with DevOps concepts. #MLOps #DevOps #Python

Manual test writing is slow. Good agents know how to skip it. I built 6 AI agents that work together as a multi-agent testing system. You give it a URL. It delivers a complete test suite — locators, test cases, feature files and step definitions. Automatically. The self-healing agent fixes broken locators on its own. No human needed. Repo is live and open source. https://lnkd.in/eTHk4KZV #AIAgents #TestAutomation #LangChain #LangGraph #Playwright #Python #SDET #QA #AI

Just published a new Medium article on using 𝑺𝑲𝑰𝑳𝑳.𝒎𝒅 in production LLM systems. At its core, 𝑺𝑲𝑰𝑳𝑳.𝒎𝒅 enables 𝐦𝐨𝐝𝐮𝐥𝐚𝐫 𝐩𝐫𝐨𝐦𝐩𝐭𝐬, 𝐫𝐮𝐧𝐭𝐢𝐦𝐞 𝐬𝐤𝐢𝐥𝐥 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐨𝐧, and 𝐬𝐜𝐚𝐥𝐚𝐛𝐥𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬-𝐫𝐮𝐥𝐞 𝐦𝐚𝐧𝐚𝐠𝐞𝐦𝐞𝐧𝐭. Instead of burying prompt logic inside Python strings, it allows rules to be organized clearly: • extraction rules • negative examples • domain constraints • validation instructions • exception handling At runtime, the system dynamically routes and loads only the relevant skill instead of sending a monolithic prompt with every LLM call. This directly improves 𝐭𝐨𝐤𝐞𝐧 𝐞𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐜𝐲, 𝐦𝐨𝐝𝐮𝐥𝐚𝐫𝐢𝐭𝐲, 𝐦𝐚𝐢𝐧𝐭𝐚𝐢𝐧𝐚𝐛𝐢𝐥𝐢𝐭𝐲, 𝐚𝐧𝐝 𝐩𝐫𝐨𝐦𝐩𝐭 𝐯𝐞𝐫𝐬𝐢𝐨𝐧𝐢𝐧𝐠, while making business-rule-heavy LLM workflows much easier to scale. In the article, I walk through a practical invoice extraction pipeline example and how this architectural boundary improved code reviews, evaluation, and iteration speed. #LLM #GenAI #PromptEngineering #RAG #AIEngineering #MLOps

Spinning up a quick AI agent demo in Python or drafting an orchestration framework is the easy part. The bottleneck isn't the models anymore. It is the infrastructure. Right now, if you want to deploy traditional software, you have a safety net. You have CI/CD pipelines, unit tests, and regression blockers. In AI agent development, this discipline does not exist. Prompt changes and tool updates are being pushed with zero automated quality gating. More to come .... stay tuned #qualLoop #CICD_gating #AI_Evaluation #orchestration

Low-code tools like n8n are great… until they become your biggest production bottleneck. I’ve seen this happen multiple times: You prototype fast 🚀 Everything works ✅ Then suddenly… • Workflows become hard to debug • Scaling becomes unpredictable • Teams start rewriting everything manually • “Temporary” logic becomes permanent tech debt At that point, low-code stops being an accelerator… and starts becoming friction. So, I asked a simple question: 👉 𝑊ℎ𝑎𝑡 𝑖𝑓 𝑤𝑒 𝑑𝑖𝑑𝑛’𝑡 𝑟𝑒𝑤𝑟𝑖𝑡𝑒 𝑤𝑜𝑟𝑘𝑓𝑙𝑜𝑤𝑠… 𝑏𝑢𝑡 𝑐𝑜𝑚𝑝𝑖𝑙𝑒𝑑 𝑡ℎ𝑒𝑚 𝑖𝑛𝑠𝑡𝑒𝑎𝑑? That’s how I built nCode, an open-source transpilation pipeline that converts n8n workflows into production-ready Python projects. ⚙️ Under the hood (high level) Instead of treating workflows as runtime configs, nCode treats them like a source language: • Parse & validate workflow JSON • Build DAG representation • Topological sort execution • Detect runtime (FastAPI vs script) • Generate Intermediate Representation (IR) • Emit clean, executable Python code 🧠 Design choices that mattered • Deterministic generation → same input, same output (debuggable & traceable) • Handler-based node system → plug-and-play extensibility • Shared context → manages imports, dependencies, variable flow • Expression translation layer → supports n8n-style expressions • Graceful fallback → unsupported nodes → TODO scaffolds (no hard failures) 🛠️ But here’s the real learning: Building OSS is not just writing code. It’s about: • CI pipelines • Security scanning • Documentation • Contribution workflows • Making it easy for strangers to trust and contribute 🤔 Now I’m exploring: • Deterministic vs AI-assisted code generation • Multi-language targets (Java, Go) • Handling complex node behaviors at scale If you’ve worked on: compilers / codegen / workflow engines 👉 I’d love your take: Would you prioritize determinism… or introduce AI into the generation pipeline? Also, if this sounds interesting, contributors are welcome 🙌 Repository URL: opsingh861/nCode Docs: Home | nCode #opensource #compilerdesign #systemdesign #backendengineering #python #fastapi #developertools #softwarearchitecture #n8n #buildinpublic #engineering

I built a multi agent Pull Request reviewer using python, github mcp and crew ai and gpt-4o-mini. List of features: - Security Auditor: Scans PR diffs for C++ memory leaks, Go goroutine/channel issues, TypeScript type-safety bypasses, and general injection vectors - Documentation Reviewer: Checks for missing README updates, function docstrings, inline comments, API docs, changelog entries - Performance Optimizer: Analyzes Big-O inefficiencies, unbounded memory allocations, database N+1 loops, and sub-optimal latency patterns - Senior Engineer: Reads all reports, weighs severity vs. PR value, renders APPROVE / REQUEST_CHANGES / COMMENT verdict These 4 steps execute sequentially and after produces the final merge verdict directly into output/review_report.md. I have documented the whole approach in GitHub, including the proper approach, including commands and testing steps. Do check it out. GitHub link: https://lnkd.in/gF2kjUBr Let me know your thoughts. Thank you for reading. #tech #agent #ai #github #linkedin #connections #gpt