TIMRUN inference runtime and Python SDK v2 released

𝗜 𝗕𝘂𝗶𝗹𝘁 𝗠𝘆 𝗢𝘄𝗻 𝗠𝗖𝗣 𝗦𝗲𝗿𝘃𝗲𝗿 — 𝗛𝗲𝗿𝗲’𝘀 𝗪𝗵𝘆 𝗬𝗼𝘂 𝗠𝗶𝗴𝗵𝘁 𝗪𝗮𝗻𝘁 𝗧𝗼, 𝗧𝗼𝗼 Over the past week, I’ve been exploring how Model Context Protocol (MCP) is quietly changing the way AI tools like GitHub Copilot Chat interact with real-world systems. We already saw what’s possible with Playwright’s MCP (if you haven’t read my breakdown yet, check this : https://lnkd.in/g_Wa9nCC) — it’s a brilliant example of how a product MCP exposes tool-specific capabilities. Just like Playwright, even Postgres now has its own MCP extension (https://lnkd.in/gYwHH7rF). And MCPs are popping up everywhere — from monitoring tools to testing frameworks. So I started thinking…  • What happens when your enterprise wants to bring its own data model, its own rules, and its own workflows into this ecosystem?  • What if your AI assistant could reason directly over your database, APIs, or CI/CD pipelines — securely and contextually? That’s where Custom MCP Servers come in. I built one in Python using FastMCP and Postgres — complete with schema discovery, sample queries, and contextual data exploration — all accessible right inside VS Code Copilot Chat. 𝗚𝗶𝘁𝗛𝘂𝗯: https://lnkd.in/gYjzhFgy 𝗙𝘂𝗹𝗹 𝗕𝗹𝗼𝗴: https://lnkd.in/gvzhpeKE 𝗬𝗼𝘂𝘁𝘂𝗯𝗲 𝗩𝗶𝗱𝗲𝗼: https://lnkd.in/gNCJYkvS 𝗪𝗵𝘆 𝘁𝗵𝗶𝘀 𝗺𝗮𝘁𝘁𝗲𝗿𝘀 Product MCPs (like Playwright, Postgres) expose tool capabilities. Custom MCPs expose business capabilities. Together, they create a bridge between AI assistants and enterprise data ecosystems — safely, contextually, and locally. If you’re exploring AI-native DevOps, data, or testing workflows — this space is worth watching closely. The MCP era is just beginning. #MCP #AI #GitHubCopilot #FastMCP #Python #Postgres #DeveloperTools #VSCode #AIEngineering #OpenAI #SlayItCoder

Ever built a Flask or FastAPI endpoint and found yourself writing the same tedious data validation logic over and over? 🙋♂️ app.route('create/user') def create_user(): data = request.get_json() if not data.get('name'): return {'error': 'Name is required'}, 400 This approach is repetitive, error-prone, and clutters your business logic. I finally got fed up and went looking for a better way. That's when I found Pydantic. It's not the flashiest tool, but it has quietly become my secret weapon for bulletproof data validation in Python. Here's why it's a game-changer: 1. Type Hints as Validation: You define your data structure using Python's type hints, and Pydantic handles the validation automatically. 2. Clean & Readable: Your validation logic moves from messy if statements to clean, declarative class definitions. 3. Powerful & Flexible: It handles complex nested models, custom validators, and integrates seamlessly with FastAPI. It's one of those libraries that makes your Python code more robust, your development faster, and eliminates entire categories of bugs before they happen. What's your "non-glamorous" Python tool that you can't live without? I'm always looking for new ones to add to the toolkit. #Python #WebDevelopment #FastAPI #Flask #Pydantic #SoftwareEngineering #DeveloperTools #Coding #BackendDevelopment

🚀 𝗦𝗽𝗲𝗲𝗱 𝗨𝗽 𝗬𝗼𝘂𝗿 𝗣𝘆𝘁𝗵𝗼𝗻 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀 𝘄𝗶𝘁𝗵 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆! 🚀 Ever felt like your Python apps could be faster?   The secret often isn’t 𝗺𝗼𝗿𝗲 𝗰𝗼𝗱𝗲, it’s 𝗯𝗲𝘁𝘁𝗲𝗿 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆. Concurrency lets your program handle 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗲 𝘁𝗮𝘀𝗸𝘀 𝗮𝘁 𝘁𝗵𝗲 𝘀𝗮𝗺𝗲 𝘁𝗶𝗺𝗲, unlocking massive performance gains.   It’s especially powerful for I/O-bound tasks (like API calls or file operations) and CPU-bound tasks (like data crunching or heavy computations). ⚙️ 𝗧𝗵𝗲 3 𝗠𝗮𝗶𝗻 𝗪𝗮𝘆𝘀 𝘁𝗼 𝗗𝗼 𝗖𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝗰𝘆 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 🧵 1. 𝗧𝗵𝗿𝗲𝗮𝗱𝗶𝗻𝗴   Perfect for I/O-bound tasks.   Even though Python’s GIL limits true parallelism, 𝘁𝗵𝗿𝗲𝗮𝗱𝘀 are great for handling multiple network requests or file reads simultaneously, making your app feel faster and more responsive. ⚡ 2. 𝗔𝘀𝘆𝗻𝗰𝗜𝗢   Modern, elegant, and event-driven.   With 𝗮𝘀𝘆𝗻𝗰 and 𝗮𝘄𝗮𝗶𝘁, you can run thousands of concurrent I/O operations efficiently.   Great for APIs, web scraping, or anything that waits on external data. 🧠 3. 𝗠𝘂𝗹𝘁𝗶𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴   The key to 𝘁𝗿𝘂𝗲 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹𝗶𝘀𝗺 in Python.   By running multiple processes, you can fully use all CPU cores. Perfect for data processing, simulations, or ML workloads. 🧩 𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗥𝗶𝗴𝗵𝘁 𝗧𝗼𝗼𝗹 • For web scraping or API calls → use 𝘁𝗵𝗿𝗲𝗮𝗱𝗶𝗻𝗴 or 𝗮𝘀𝘆𝗻𝗰𝗶𝗼 • For CPU-heavy tasks → go with 𝗺𝘂𝗹𝘁𝗶𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 The right choice can transform a slow, blocking program into a fast and scalable one. Understanding concurrency is a game-changer for performance-minded developers.   Once you get it right, you’ll never look at sequential code the same way again. What’s your go-to concurrency model in Python?   💬 Drop your thoughts in the comments. Let’s share some performance wisdom! #Python #Concurrency #AsyncIO #Threading #Multiprocessing #Performance #SoftwareEngineering #Backend #PythonDeveloper #TechTips #DevCommunity

Most data pipelines break not because of bad data. But because they weren’t designed with tests in mind. In my latest article, I explain how I apply Test-Driven Development (TDD) to data pipelines in Python. Step by step: • Write the first test before the function exists • Make it pass with the simplest implementation • Build transformations one test at a time • Validate the end-to-end pipeline • Scale to Airflow, Spark, or Glue with confidence By designing for tests, your pipelines become predictable, maintainable, and production-ready. From the very first line of code. Read here: https://lnkd.in/d6B_mtj9

Python 3.14 is out! 🐍🚀 I guess that's stale news! It dropped back on Oct 7th, but if you work with data or ML, it’s worth a closer look. This release packs features that make Python smarter, faster, and more scalable. Here are the biggest highlights and what they mean (in plain terms): 🔹 Template String Literals (`t""`): Think of f-strings, but super-powered. You can now embed expressions and customize how they’re processed. It's great for templating SQL, logs, or reports safely. 🔹 Deferred Evaluation of Annotations: Type hints are now “lazy.” No more errors from circular imports or forward references. Your ML modules and pipelines can be cleaner and load faster. 🔹 Subinterpreters in the Standard Library: True concurrency is closer than ever. Python can now run multiple isolated interpreters in a single process; a big deal for parallel data loading or model serving. 💡 Impact for ML pros: * Easier-to-maintain, modular codebases * Smoother parallelism in data pipelines * Better tooling for building scalable, production-ready AI apps The future of Python in data science keeps getting brighter. 🌟 Are there downsides to any of this improvements? Please share in the comment section. #Python314 #DataScience #MachineLearning #MLops #Python

🚀 LeetCode #1611 – Minimum One Bit Operations to Make Integers Zero Today, I tackled one of the more fascinating bit-manipulation problems on LeetCode — "Minimum One Bit Operations to Make Integers Zero". 🧩 Problem Overview Given an integer n, you must transform it into 0 using two operations: Flip the rightmost (0th) bit. Flip the ith bit if the (i−1)th bit is 1 and all lower bits are 0. The goal is to find the minimum number of operations required. The challenge is to transform an integer n into 0 using specific bit operations that flip bits based on certain constraints. At first glance, it seems like a complex recursive search problem, but the key insight lies in recognizing the pattern of Gray codes. 🔍 Key Insight: The problem follows the structure of reflected Gray code transformations. By analyzing how bits flip in the Gray code sequence, we can derive a recursive relationship that efficiently computes the minimum operations. 💡 Recursive Relation: If f(n) is the minimum number of operations for integer n: f(0) = 0 f(n) = (1 << (k + 1)) - 1 - f(n ^ (1 << k)) where k is the position of the most significant bit (MSB) in n. 🧠 Example Walkthrough n = 3 → binary 11 → result = 2 n = 6 → binary 110 → result = 4 ⚙️ Complexity Time: O(log n) Space: O(log n) (due to recursion depth) 🧩 Takeaway This problem was a great reminder that: Many bit-manipulation problems have elegant recursive or mathematical patterns hidden beneath them. Recognizing symmetry and recursion in binary transformations often leads to O(log n) solutions. #LeetCode #Python #BitManipulation #GrayCode #Algorithms #DataScience

**I wish I had this tool in my previous role.** I built an AI-powered Code Review Agent. **What it does:** 🔒 Security scanning (SQL injection, XSS, hardcoded secrets, command injection) 📊 Code quality analysis (complexity, maintainability, best practices) 🤖 AI-powered reviews using Claude for context-aware feedback ⚡ GitHub Actions integration for automated PR reviews **The impact:** It detects 10+ types of security vulnerabilities across Python, JavaScript, TypeScript, and more. **Why this matters:** Security reviews are crucial but time-consuming. Junior developers need guidance. Senior developers need to catch what they miss when moving fast. This tool provides that safety net—instantly. The best part? It runs automatically on every PR. No manual trigger needed. No waiting for security team review cycles. **Tech Stack:** Python, Claude AI API, pattern-based detection with AST analysis, GitHub Actions This is my 3rd AI agent project, and each one reinforces my belief: AI isn't replacing engineers—it's augmenting the workflows that keep codebases secure and maintainable. Open source and ready to use on your repos today. 🔗 https://lnkd.in/eFMPy9KR #AI #Security #DevSecOps #CodeReview #EngineeringLeadership #Python #ClaudeAI #Automation

𝘚𝘵𝘰𝘱 𝘞𝘳𝘪𝘵𝘪𝘯𝘨 𝘖𝘯𝘦 𝘋𝘈𝘎 𝘱𝘦𝘳 𝘗𝘪𝘱𝘦𝘭𝘪𝘯𝘦 A common anti-pattern: 50 pipelines → 50 hand-written DAGs. It scales poorly, creates drift, and forces teams to maintain orchestration instead of improving their actual data platform. A better approach: a 𝗗𝗔𝗚 𝗙𝗮𝗰𝘁𝗼𝗿𝘆. You build one Python module that: Reads a folder of YAML configs Generates DAGs dynamically Enforces retries, naming, SLOs, owners, security, and alert patterns automatically A new pipeline becomes a new YAML file. No extra Python. No drift. No bespoke orchestration logic hiding in someone’s repo. This is usually where engineers realize they’re not building pipelines anymore — they’re building a platform others can reliably use.

Stop using lists when you don’t need them. Here’s why Python generators might quietly be one of the most powerful features in the language. ⚡ A generator doesn’t store data. It creates data — one item at a time, only when you need it. That means: ✅ Near-zero memory usage ✅ Faster for large datasets ✅ Cleaner, more readable code 3 ways to create a generator: # 1. Function with 'yield' def numbers():   for i in range(5):     yield i # 2. Generator expression g = (n for n in range(3, 5)) next(g) # 3 # 3. Class-based iterator class Numbers:   def __iter__(self): ...   def __next__(self): ... In practice, the function way win 99% of the time, less code, more clarity. Where it shines: - Reading massive log files - Streaming API data - Processing large DB results - Building data pipelines Tip: Generators are lazy, they produce values only when needed. That’s why they’re fast and memory-efficient. Because sometimes… the best optimization isn’t to store everything, but to create just what you need. #Python #CodingTips #BackendDevelopment #Performance #CleanCode

How Pydantic AI Turned My Chaotic Data Into a Super‑Smart Python Model 🤖 I was juggling a legacy API that returned nested JSON like a tangled ball of yarn—lists inside dicts, optional fields, and a few hidden “type‑mismatch” bugs that broke the whole pipeline. Every time I wrote a new class, I added manual checks, and the codebase grew into a nightmare of try/except blocks. Enter Pydantic AI. I fed it a single example payload, and it instantly generated a hierarchy of BaseModel classes with proper type hints, default values, and validators for the edge cases. The next day, the same API response passed through the model without a single runtime error, and the auto‑generated docs showed exactly what each field meant. Adding a new optional field? Just update the example and let Pydantic AI regenerate—no more hand‑rolled parsing logic. Now my services serialize, deserialize, and validate data in one line, and the code reads like a story instead of a maze. #Python #PydanticAI #DataValidation