Optimizing Select AI for Python 1.3 Pooling

Generating tokens is the solved problem. What breaks in production is everything around it: streams that don't survive a page refresh, sessions tied to a single connection, agents with no signal when the user has gone. V3 of the Ably Python SDK is built for that layer. AI Transport support is now fully available. Check it out: Link in comments

Python is undergoing an important change... Recently, I came across a post about FastAPI mentioning support for free-threading, which led me to reflect on its practical impact. With the removal of the GIL, Python’s concurrency model evolves: - Threads can now run in true parallelism - Improved performance for CPU-bound workloads - New architectural possibilities This raises an important question: When should you use async vs threads? 🧠 A simple way to think about it: - I/O-bound → async - CPU-bound → threads In practice, the most common approach will be combining both: async for I/O and threads for CPU-intensive tasks. This shift reduces the need for multiprocessing and makes Python more competitive in concurrent workloads. I plan to start exploring this model in real-world projects and share the learnings soon. How do you see this impacting the architecture of your systems? #python #freethreaded #fastapi #multiprocessing #concurrency #threads #performance

Free Threading — Python's way to "goroutines", sort of. I’ve been experimenting with the new python3.14t builds. By combining Free-threading with AsyncIO, you can now: 1. Spawn worker threads (Parallelism). 2. Run an AsyncIO loop inside each (Concurrency). 3. Use queue.Queue as your "Channels" for thread-safe communication. The Result? True parallelism without the memory overhead of multiple processes. I’ve broken down the benchmarks and the "serialization tax" of subinterpreters in my latest write-up. If you're building high-scale backends, this is required reading. Read more: https://lnkd.in/gp3KBR_G #Python #DistributedSystems #Concurrency #Scalability #Performance

Async Python Event Loop Behavior Enables Deterministic Workflow Recovery 📌 Async Python’s event loop hides a powerful secret: deterministic initialization before concurrency kicks in. This lets developers build resilient, replayable workflows - perfect for fault-tolerant systems like finance or AI agents. No more race conditions; just predictable step ordering from the start. 🔗 Read more: https://lnkd.in/dWUSV-ZK #Asyncio #Python #Eventloop #Deterministic #Workflowrecovery

Interesting idea - monty from pydantic - LLM writes python, that is later executed in very tight sandbox, and that python calls your actual tools. What do you think? Will it fly?

Python is where AI gets built. The agents, the orchestration layers, the systems that turn LLM calls into products people actually use... What's changed over the past year is the bar. Teams that used to ship prototypes are now running agents in production, with real users who notice when sessions break or streams drop. That shift is what the newest version of the Ably Python SDK is built for. https://lnkd.in/ebHCrafj

🚀 Day 13 of My Python + DSA Journey Today’s problem focused on frequency counting 👇 ✅ Majority Element (#169) 💡 Majority Element Find the element that appears more than n/2 times 🔍 Approach: Used hashmap → counted frequency and returned element exceeding n/2 ⏱ O(n) time | O(n) space 🔥 What I learned today: • Hashmaps make counting problems simple • Frequency-based logic is very common in arrays • Early exit improves efficiency Getting faster at recognizing patterns ⚡ #Day13 #LeetCode #Python #DSA #CodingJourney #100DaysOfCode

Learn how to create a chatbot with Python and Dialogflow, including step-by-step guide, advanced features, and real-world applications, with a focus on creating a chatbot with Python https://lnkd.in/g8GFzcvs #CreateChatbotWithPython Read the full article https://lnkd.in/g8GFzcvs

Learn how to create a chatbot with Python and Dialogflow, including step-by-step guide, advanced features, and real-world applications, with a focus on creating a chatbot with Python https://lnkd.in/g8GFzcvs #CreateChatbotWithPython Read the full article https://lnkd.in/g8GFzcvs

Understanding closures and variable scoping is what separates a good Python developer from a great one. 💻 In this video, I break down the "Late Binding" behavior in Python lambdas. It’s a common pitfall when creating functions inside loops, leading to bugs that are incredibly hard to track down in large codebases. Key Takeaway: Default arguments are evaluated at definition time, but closures capture variables by reference. To fix this, we use the i=i trick to force early binding. Have you ever encountered this "Late Binding" bug in your production code? #PythonProgramming #SoftwareDevelopment #CleanCode #ComputerScience #TechEducation #BackendDeveloper