Context: We have an internal tool that pushes ~100 videos a week through a lipsync api (sync.so) for client work. Their SDK is fine, you submit a generation and poll status until its done and the jobs take anywhere from 2 to 15 min. Right now i have a dumb while loop with sleep(30) per job and it obviously doesn't scale, Im either polling too often and eating 429s or too slow and jobs sit finished for minutes. They do have webhooks but our tool runs on a box behind the company network so exposing an endpoint is a whole conversation with IT. Whats the sane middle ground here. asyncio with jittered backoff per job? submitted by /u/vedantk21 [link] [comments]

Weekly Thread: Professional Use, Jobs, and Education 🏢 Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment. How it Works: Career Talk: Discuss using Python in your job, or the job market for Python roles. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally. Guidelines: This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar. Keep discussions relevant to Python in the professional and educational context. Example Topics: Career Paths: What kinds of roles are out there for Python developers? Certifications: Are Python certifications worth it? Course Recommendations: Any good advanced Python courses to recommend? Workplace Tools: What Python libraries are indispensable in your professional work? Interview Tips: What types of Python questions are commonly asked in interviews? Let's help each other grow in our careers and education. Happy discussing! 🌟 submitted by /u/AutoModerator [link] [comments]

For example, FastAPI introduced a breaking change in a minor version upgrade. By default, it started rejecting requests without a Content-Type header. With only the major version pinned, uv lock --upgrade upgrades to the latest version. A similar thing has happened with google-auth-oauthlib. And that's what bit us. In our case, everything was fine after the upgrade according to the end-to-end test suite, since most modern HTTP clients add the Content-Type header by default. The issue arose when calls were made using some older Java versions. The customer didn't explicitly add the header, so calls were rejected once their cron had started. Since reading every release note for every dependency is a very dull and time-consuming task, we wrote a Python script that downloads all release notes and added a Claude command to read them, update dependency versions, and update code as required by breaking changes, while keeping the existing state. So far, it's working great. Anyhow, curious to hear how others are dealing with these things? I assume you're not reading every release note for every dependency? submitted by /u/JanGiacomelli [link] [comments]

I've been spending some time with PydanticAI lately, and one thing I really like is how it keeps agent code structured without turning everything into prompt spaghetti. You get a lot of useful building blocks out of the box: • typed outputs • tool calling • retries • dependency injection • graph-based workflows • flexibility across models and providers From an engineering perspective, it's a really nice way to build agents that don't immediately become a maintenance nightmare. What I've noticed, though, is that once you start using those features in real-world workflows, costs can climb faster than you expect. Not because PydanticAI is inefficient—just because richer agent workflows naturally generate more model activity. A few examples: • the same instructions and schemas get sent repeatedly • validation failures trigger retries • tool calls often add extra model turns • context grows as workflows get longer • expensive models end up handling tasks that don't really need them That's actually the problem I built a LLM gateway to help solve. Rather than replacing frameworks like PydanticAI, it sits underneath them as a gateway layer. So you keep PydanticAI as your application framework, but use LLM gateway to handle things like: • routing simple tasks to cheaper models • caching repeated prompt material • switching providers without changing agent code • centralizing cost and model controls What I like about this setup is that it doesn't require rethinking your agent architecture. Take a pretty normal workflow: • a user submits messy text • the agent extracts structured data • validation fails and retries • a tool gets called for enrichment • a final typed response is returned That's exactly the kind of workflow PydanticAI handles well. It's also the kind of workflow where costs quietly stack up in the background: • schemas get repeated • instructions get repeated • retries add more calls • tools add more interactions • a premium model may be used for every step In practice, the biggest savings usually come from a few simple optimizations: • sending extraction and classification tasks to cheaper models • caching repeated context and instructions • reserving stronger models for the steps that actually need them Of course, a gateway isn't a magic fix. If a workflow is looping too much, retrying aggressively, or making unnecessary tool calls, that's still an application-level problem. A gateway can reduce the cost of those mistakes, but it can't eliminate them. That said, if you're already using PydanticAI and starting to feel the impact of retries, tool calls, and growing context windows, putting a gateway underneath it feels like a pretty practical pattern. submitted by /u/Public-Minimum5892 [link] [comments]

Weekly Wednesday Thread: Advanced Questions 🐍 Dive deep into Python with our Advanced Questions thread! This space is reserved for questions about more advanced Python topics, frameworks, and best practices. How it Works: Ask Away: Post your advanced Python questions here. Expert Insights: Get answers from experienced developers. Resource Pool: Share or discover tutorials, articles, and tips. Guidelines: This thread is for advanced questions only. Beginner questions are welcome in our Daily Beginner Thread every Thursday. Questions that are not advanced may be removed and redirected to the appropriate thread. Recommended Resources: If you don't receive a response, consider exploring r/LearnPython or join the Python Discord Server for quicker assistance. Example Questions: How can you implement a custom memory allocator in Python? What are the best practices for optimizing Cython code for heavy numerical computations? How do you set up a multi-threaded architecture using Python's Global Interpreter Lock (GIL)? Can you explain the intricacies of metaclasses and how they influence object-oriented design in Python? How would you go about implementing a distributed task queue using Celery and RabbitMQ? What are some advanced use-cases for Python's decorators? How can you achieve real-time data streaming in Python with WebSockets? What are the performance implications of using native Python data structures vs NumPy arrays for large-scale data? Best practices for securing a Flask (or similar) REST API with OAuth 2.0? What are the best practices for using Python in a microservices architecture? (..and more generally, should I even use microservices?) Let's deepen our Python knowledge together. Happy coding! 🌟 submitted by /u/AutoModerator [link] [comments]

Mypy, Pyrefly, Pyright, ty, Zuban, and possibly more that will come in the future... how are library maintainers expected to cope? TL;DR: If you're a library maintainer, prioritise running as many type-checkers as possible on your test suite. Run at least one on your source code. In the, we share our reasoning about why we think this approach is best, along with a case study for the Polars package. Full blog post: https://pyrefly.org/blog/too-many-type-checkers/ I'd love to hear from the community: 1. What's the biggest friction around running multiple type checkers in CI? 2. Have you ever used a package that doesn't play nicely with your type checker because it depends on the implementation details of a different type checker? submitted by /u/BeamMeUpBiscotti [link] [comments]

Weekly Thread: Project Ideas 💡 Welcome to our weekly Project Ideas thread! Whether you're a newbie looking for a first project or an expert seeking a new challenge, this is the place for you. How it Works: Suggest a Project: Comment your project idea—be it beginner-friendly or advanced. Build & Share: If you complete a project, reply to the original comment, share your experience, and attach your source code. Explore: Looking for ideas? Check out Al Sweigart's "The Big Book of Small Python Projects" for inspiration. Guidelines: Clearly state the difficulty level. Provide a brief description and, if possible, outline the tech stack. Feel free to link to tutorials or resources that might help. Example Submissions: Project Idea: Chatbot Difficulty: Intermediate Tech Stack: Python, NLP, Flask/FastAPI/Litestar Description: Create a chatbot that can answer FAQs for a website. Resources: Building a Chatbot with Python Project Idea: Weather Dashboard Difficulty: Beginner Tech Stack: HTML, CSS, JavaScript, API Description: Build a dashboard that displays real-time weather information using a weather API. Resources: Weather API Tutorial Project Idea: File Organizer Difficulty: Beginner Tech Stack: Python, File I/O Description: Create a script that organizes files in a directory into sub-folders based on file type. Resources: Automate the Boring Stuff: Organizing Files Let's help each other grow. Happy coding! 🌟 submitted by /u/AutoModerator [link] [comments]

Weekly Thread: What's Everyone Working On This Week? 🛠️ Hello r/Python! It's time to share what you've been working on! Whether it's a work-in-progress, a completed masterpiece, or just a rough idea, let us know what you're up to! How it Works: Show & Tell: Share your current projects, completed works, or future ideas. Discuss: Get feedback, find collaborators, or just chat about your project. Inspire: Your project might inspire someone else, just as you might get inspired here. Guidelines: Feel free to include as many details as you'd like. Code snippets, screenshots, and links are all welcome. Whether it's your job, your hobby, or your passion project, all Python-related work is welcome here. Example Shares: Machine Learning Model: Working on a ML model to predict stock prices. Just cracked a 90% accuracy rate! Web Scraping: Built a script to scrape and analyze news articles. It's helped me understand media bias better. Automation: Automated my home lighting with Python and Raspberry Pi. My life has never been easier! Let's build and grow together! Share your journey and learn from others. Happy coding! 🌟 submitted by /u/AutoModerator [link] [comments]

the Steering Council is formally requesting a Standards Track PEP be authored that the community can discuss and the Steering Council can formally accept (or reject), making the case for the JIT as a supported, non-experimental part of CPython https://discuss.python.org/t/an-announcement-from-the-steering-council-regarding-the-jit-project/107638 submitted by /u/JimDabell [link] [comments]

Weekly Thread: Resource Request and Sharing 📚 Stumbled upon a useful Python resource? Or are you looking for a guide on a specific topic? Welcome to the Resource Request and Sharing thread! How it Works: Request: Can't find a resource on a particular topic? Ask here! Share: Found something useful? Share it with the community. Review: Give or get opinions on Python resources you've used. Guidelines: Please include the type of resource (e.g., book, video, article) and the topic. Always be respectful when reviewing someone else's shared resource. Example Shares: Book: "Fluent Python" - Great for understanding Pythonic idioms. Video: Python Data Structures - Excellent overview of Python's built-in data structures. Article: Understanding Python Decorators - A deep dive into decorators. Example Requests: Looking for: Video tutorials on web scraping with Python. Need: Book recommendations for Python machine learning. Share the knowledge, enrich the community. Happy learning! 🌟 submitted by /u/AutoModerator [link] [comments]

In bankers' rounding, x.5 rounds to the nearest even number. So, if x is even, it rounds down... round(2.5) returns 2. If x is odd, it rounds up... round(3.5) returns 4. It was explained that it removes an upward rounding bias when round(x.5) always returns x+1... x.1, x.2, x.3, & x.4 always round down. x.6, x.7, x.8, & x.9 always round up. Four down, four up. x.5 is the right in the middle. If it always rounded up, there would be a slight creep upwards in large datasets. But, whither x.0? x.0 always rounds to x. So, there are five cases where x.y always rounds down, not four. And... round(2.500000000000001) return 3 round(2.5000000000000001) returns 2 ... though that might be more to do with binary representation of floats than rounding rules since 2.5000000000000001 == 2.5 is True. submitted by /u/nemom [link] [comments]

Sometimes I think back to the times when I started using Python in 2018 and how much the language was changing in my first years. From Flask to FastAPI, Pydantic, Streamlit, Polars and Httpx. It was honestly fun to start new projects and explore all these developments and what they allowed you to do. Use it in your new project and surprise yourself with how much faster you can get things done, all while writing much cleaner code. Currently I'm feeling most of the package I see are about AI; frameworks, LLM tooling, RAG, vector databases. Great developments, but they don't change the way I am working with the Language. It sure has something to do with the fact that in the beginning when you start using a language you explore more and develop faster, and a lot of fundamental things were changing around that time (typing, async). But I keep wondering; am I missing out on packages that have changed the way you've used Python? Cause maybe I'm simply not looking in the right place. I'm thinking for example on how frontend frameworks handle state with signals. So, two honest questions: Which package from the last ~3 years really changed how you use/write Python? (Uv and Ruff count) Did the pace of these foundational packages actually slow down, or am I just not in the right information streams? submitted by /u/Proof_Difficulty_434 [link] [comments]

Weekly Thread: Meta Discussions and Free Talk Friday 🎙️ Welcome to Free Talk Friday on /r/Python! This is the place to discuss the r/Python community (meta discussions), Python news, projects, or anything else Python-related! How it Works: Open Mic: Share your thoughts, questions, or anything you'd like related to Python or the community. Community Pulse: Discuss what you feel is working well or what could be improved in the /r/python community. News & Updates: Keep up-to-date with the latest in Python and share any news you find interesting. Guidelines: All topics should be related to Python or the /r/python community. Be respectful and follow Reddit's Code of Conduct. Example Topics: New Python Release: What do you think about the new features in Python 3.11? Community Events: Any Python meetups or webinars coming up? Learning Resources: Found a great Python tutorial? Share it here! Job Market: How has Python impacted your career? Hot Takes: Got a controversial Python opinion? Let's hear it! Community Ideas: Something you'd like to see us do? tell us. Let's keep the conversation going. Happy discussing! 🌟 submitted by /u/AutoModerator [link] [comments]

Hey everyone, I'm researching ideas for a new Python-focused side project and would love input from other Python developers. Rather than building something based on assumptions, I'd like to understand the real pain points people encounter while coding in Python. One idea I'm currently exploring is a tool that analyzes Python errors and tracebacks in real time, then translates them into clear, beginner-friendly explanations. The goal would be to help developers understand not only what went wrong, but also why it happened and how to fix it. That said, I'm still validating the idea and I'm completely open to other suggestions. What are the most frustrating, repetitive, or time-consuming tasks you deal with when working with Python? Are there any small tools, automations, debugging helpers, workflow improvements, or developer utilities that you wish existed? I'd appreciate any feedback, ideas, or examples from your own experience. Thanks! submitted by /u/Evellen_T [link] [comments]

Post all of your code/projects/showcases/AI slop here. Recycles once a month. submitted by /u/AutoModerator [link] [comments]

Weekly Thread: Professional Use, Jobs, and Education 🏢 Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment. How it Works: Career Talk: Discuss using Python in your job, or the job market for Python roles. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources. Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally. Guidelines: This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar. Keep discussions relevant to Python in the professional and educational context. Example Topics: Career Paths: What kinds of roles are out there for Python developers? Certifications: Are Python certifications worth it? Course Recommendations: Any good advanced Python courses to recommend? Workplace Tools: What Python libraries are indispensable in your professional work? Interview Tips: What types of Python questions are commonly asked in interviews? Let's help each other grow in our careers and education. Happy discussing! 🌟 submitted by /u/AutoModerator [link] [comments]

Disclosure: I am affiliated. I wanted to share that as of today, Polars also is available as a Distributed Engine on kubernetes. Polars' goal has always been to make single node processing as performant and easy as possible, and that is something we want to extend to distributed compute as well. Read more in our announcement: https://pola.rs/posts/polars-distributed-available-on-kubernetes/ Happy to answer any questions you might have. submitted by /u/ritchie46 [link] [comments]

I converted my personal notes into a tutorial. Maybe useful for others. Please also feel free to provide feedback. Would love to discover my blind spots. https://www.pulkitagrawal.in/blogs/2026-05/ayncio submitted by /u/Practical_Plan007 [link] [comments]

Usually when you run high-concurrency rate limiting inside FastAPI, you are usually forcing python's single threaded event loop to spend precious time on network driver I/O just to verify a token before the request even hits the application logic. I wanted to see how cleanly I could isolate the Redis network layer outside of python, so I built rustgate using PyO3 and a multi-threaded tokio driver. Disclaimer: This is basically a proof of concept. It's basically tied to another experimental crate I am working on (axum-rate-limiter), and so it's not super configurable or abstracted as of now. Could you use in production? Probably, but why? That being said, the raw performance under a 100-concurrency flood on a heavy, dynamically rerouted endpoint turned out pretty efficient: Pushed 1,128 req/sec without dropping a connection. Fastest response hit 15.3 ms. Fails closed instantly with immediate 429 rejections to protect downstream application logic. The cool part: I benched a naked, no-op /health endpoint (literally just returning {"status": "ok"}) on the same machine, and it maxed out at 1,496 req/sec. The fact that crossing FFI boundaries, handling memory pinning, and doing a multi-threaded Tokio to Redis round-trip only costs ~370 req/s, proves that the Rust integration added almost non existent overhead. I’ve dropped the GitHub link in the comments section below to keep this thread focused on the performance discussion. EDIT: Regarding the benchmarks criticism, I hear you loud and clear, and I will try to update this tomorrow, run it on linux, using `uvloop`, using 8k connections, and will add a proper baseline. submitted by /u/mordechaihadad [link] [comments]

In Pandas, an IntervalArray is created by: > pd.arrays.IntervalArray([pd.Interval(0, 1), pd.Interval(1, 5)]) <IntervalArray> [(0, 1], (1, 5]] Length: 2, dtype: interval[int64, right] Note the `[(0, 1], (1, 5]]`: what's the rationale for the opening bracket being a parenthesis but the closing bracket being square? submitted by /u/ccw34uk [link] [comments]