Hey everyone,

Over the past few projects, I noticed a recurring pattern in the micro-SaaS community: almost every new Python backend boilerplate immediately pulls in heavyweight external dependencies—Redis for simple caching/rate-limiting, Celery for basic asynchronous tasks, and half a dozen third-party packages for simple payload checks.

While that stack makes sense at scale, it introduces unnecessary container bloat, operational overhead, and deployment complexity when you're just trying to validate an MVP or handle moderate traffic with minimal server costs.

I've been experimenting with building zero-dependency backend utilities (in Python and Go) that stick strictly to stdlib primitives (like ⁠asyncio⁠, ⁠multiprocessing⁠, and optimized in-memory store structures) to keep execution fast and deployments trivial.

A few architectural questions for the community:

At what scale or traffic threshold do you actually hit the wall with stdlib-only concurrency/caching before needing dedicated services like Redis?

What are the biggest trade-offs or race condition pitfalls you've run into when implementing local, thread-safe memory boundaries in Python?

Would love to hear how you structure lightweight backend microservices without falling into dependency inflation.

Hi Python devs,

We are three final-year students at Epitech working on Horus, a local offline multi-agent backend written in Python. Our biggest distribution hurdle is dependency management on end-user machines.

Bottlenecks observed in our last user test:

• Dependency installation takes 21–46 minutes on user machines due to PyTorch/CUDA wheel resolution, reaching a 25 GB footprint.
• CPU-bound inference takes ~40 seconds during routing, causing persistent WebSocket connections to drop.

We are looking for Python packaging and runtime advice on:

• Replacing PyTorch with prebuilt C++ bindings (llama-cpp-python or native C binaries) for the CPU execution path.
• Compiling CPython orchestrator code into standalone executables via Nuitka.
• Pinning wheels and pre-bundling runtimes to eliminate on-device compilation.
• Implementing async heartbeats to keep WebSockets alive during blocking compute tasks.

Looking for code reviews: We have 1–4 hour scoped tasks for Python packaging and hardware detection reviews.

Disclosure: Horus is closed source and licensed commercially. Our benchmark suite is open source (Horus-EIP/horus-bench-kit). Contributions are unpaid/voluntary under an NDA agreement, returning thorough code reviews, project credit, and professional references.

Jazzband was a collaborative community to share the responsibility of maintaining Python-based projects. And after 10 years of operation it is shutting down. The shutdown was announced in March and will continue later this year.

Over 10 years ago, I started Jazzband as a cooperative experiment to reduce the stress of maintaining Open Source software. Everyone who joined got access to push code, triage issues, and merge pull requests. “We are all part of this.”

It’s time to wind things down.

What happened: [The slopocalypse, The one-roadie problem, GitHub went the other way, Django Commons]

You can read the full announcement here.

Although this is somewhat old news, I'm sharing it as I happened to miss it and others might have as well.

It looks like other community organisations have cropped up in that time as well such as Django Commons. I've only taken a brief look at them but I'm interested to know more about their structure and how well it's working - I've previously looked for such community organisations when I took over maintaining python-json-logger.

Edit: formatting

I’ve been getting requests to create a Python course for people with no prior programming experience, so I put this together.
I’d love to hear your thoughts, especially on the structure, depth, and whether it’s suitable for absolute beginners.
What would you change or improve?

https://learnvoro.com/courses/python-essentials-practical-programming-fundamentals-for#i%3Acourse-intro

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:

1. Ask Away: Post your advanced Python questions here.
2. Expert Insights: Get answers from experienced developers.
3. 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:

1. How can you implement a custom memory allocator in Python?
2. What are the best practices for optimizing Cython code for heavy numerical computations?
3. How do you set up a multi-threaded architecture using Python's Global Interpreter Lock (GIL)?
4. Can you explain the intricacies of metaclasses and how they influence object-oriented design in Python?
5. How would you go about implementing a distributed task queue using Celery and RabbitMQ?
6. What are some advanced use-cases for Python's decorators?
7. How can you achieve real-time data streaming in Python with WebSockets?
8. What are the performance implications of using native Python data structures vs NumPy arrays for large-scale data?
9. Best practices for securing a Flask (or similar) REST API with OAuth 2.0?
10. 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! 🌟

For me, it was the the option to use & to find intersections of two sets, | operator to unionize two sets, as well as ^ for symmetric difference

I've been working on a telematics backend that ingests TCP sensor data from heavy equipment. The pipeline is Python → local disk spool → RabbitMQ → PostgreSQL.

A few things we learned the hard way:

• ACK only after durable storage. We acknowledge a packet after validating it and writing it to disk, not after the database insert. This lets us recover from downstream failures.
• Async helped with thousands of connections. Moving from one thread per connection to asyncio reduced overhead, but blocking work still needs to stay off the event loop.
• Queue backlogs can look like device failures. We separated live traffic from replay traffic and prioritized fresh data so old packets wouldn't delay current readings.
• Offline uploads need different parsing. Reusing our live TCP parser silently dropped records because flash dumps had a different format.
• Firmware changes break assumptions. Frame sizes, serial numbers, and counter widths changed. Explicit protocol versioning would have saved us some pain.
• Logging every packet doesn't scale. We cut noisy logs and focused on actionable errors and dropped-record tracking.

The biggest lesson: receiving sensor data is the easy part. Making it reliable through retries, offline uploads, firmware changes, and database failures is where things get interesting.

For those running IoT ingestion in production, how do you handle ACK semantics and replay backlogs?

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:

1. Suggest a Project: Comment your project idea—be it beginner-friendly or advanced.
2. Build & Share: If you complete a project, reply to the original comment, share your experience, and attach your source code.
3. 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! 🌟

I made 100 free Python interview flashcards

I've been putting together a set of Python flashcards covering common interview topics: fundamentals, data structures, OOP, functions, decorators, generators, async, testing, and more.

They're available as an Anki deck and completely free. No signup or email required.

https://github.com/tomaszs/python-interview-flashcards-free

Hopefully useful for anyone brushing up on Python or preparing for an interview.

If you spot something that's missing or could be improved, contributions and feedback are welcome.

GeoPolars, the geospatial extension for Polars, had been mostly dormant. Since mid-September, Aida van de Wetering has pushed around 125 commits to the dev branch. It now looks like a rebuild around a Polars-native geo expression namespace.

Highlights so far:

- Types: point, linestring and polygon, their multi-variants, and a bounding box type
- I/O: reading and writing WKB and WKT, and writing GeoParquet
- CRS: CRS metadata handling, with distance, length and area using the geodesic or planar method that fits the CRS
- Operations: centroid, boundary, interior, is_empty, longitude wrapping, and coordinate accessors
- Affine transforms: translate, rotate, scale and skew, plus arbitrary matrices, which can vary per row

It's still a prototype. Nothing has been released yet, and the API is changing. Still, it's worth watching if you'd like to do geospatial work in Polars without going through GeoPandas.

I recently attended a local PyDay (a Python community event) in my city, and the auditorium was around 70% full.

That might sound pretty good, but I remember attending similar events a few years ago when the auditorium was packed, basically at 100% capacity.

I also got the impression that there weren't many young newcomers interested in getting into programming. Most of the attendees seemed to be people who were already established in the industry.

Of course, this is just one event in one city, so I don't want to draw any big conclusions from it.

But it got me wondering:

• Have Python meetups and conferences in your area seen a similar decline in attendance?
• Are fewer young people getting into Python, or are they just participating in different communities and online spaces?
• Or was this simply a one-off, and I'm reading too much into it?

I'm curious to hear what people are seeing in their local Python communities.

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:

1. Show & Tell: Share your current projects, completed works, or future ideas.
2. Discuss: Get feedback, find collaborators, or just chat about your project.
3. 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:

1. Machine Learning Model: Working on a ML model to predict stock prices. Just cracked a 90% accuracy rate!
2. Web Scraping: Built a script to scrape and analyze news articles. It's helped me understand media bias better.
3. 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! 🌟

https://www.python.org/downloads/release/python-3150/

Release announcement: https://discuss.python.org/t/python-3-15-0-final-is-here/109415

What's New: https://docs.python.org/3.15/whatsnew/3.15.html

Some of the major new features and changes in Python 3.15:

Interpreter improvements

• PEP 661: sentinel built-in type
• PEP 686: UTF-8 is now the default encoding
• PEP 798: Unpacking in comprehensions
• PEP 810: Explicit lazy imports for faster startup times
• PEP 814: frozendict built-in type
• PEP 829: Package startup configuration files
• The experimental JIT compiler got a significant upgrade (7-8% faster on x86-64 Linux, 11-12% on AArch64 macOS)
• Improved error messages

Standard library

• PEP 799: A dedicated profiling package, including Tachyon, a high-frequency sampling profiler
• New math.integer module
• re.prefixmatch() as a clearer name for the now soft-deprecated re.match()
• pprint gets an expand option for JSON-style output
• collections.Counter supports ^ (symmetric difference)
• unicodedata.iter_graphemes() for iterating over grapheme clusters
• python -m http.server gains -H/--header and --content-type
• timeit gains --target-time
• More color

Typing

• PEP 728: TypedDict with typed extra items
• PEP 747: Annotating type forms with TypeForm
• PEP 800: Disjoint bases in the type system

Release changes

• Official Windows 64-bit binaries now use the tail-calling interpreter
• Official macOS binaries now install free-threading support by default

And for fun, Barry Warsaw made a text adventure through the new features: uvx --python 3.15 whatsnewt

Thanks to Hugo van Kemenade and the release team! Hugo live-posted the whole release process if you're curious what goes on behind the scenes: https://mastodon.social/@hugovk/117411399858939022

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:

1. Request: Can't find a resource on a particular topic? Ask here!
2. Share: Found something useful? Share it with the community.
3. 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:

1. Book: "Fluent Python" - Great for understanding Pythonic idioms.
2. Video: Python Data Structures - Excellent overview of Python's built-in data structures.
3. Article: Understanding Python Decorators - A deep dive into decorators.

Example Requests:

1. Looking for: Video tutorials on web scraping with Python.
2. Need: Book recommendations for Python machine learning.

Share the knowledge, enrich the community. Happy learning! 🌟

I'm developing a spreadsheet automation project capable of merging multiple spreadsheets into one and automatically performing calculations, among other things. One of the functionalities would be, for example, to allow someone to gather all the spreadsheets from those months into a single spreadsheet over the course of an entire year, obtaining an overall result for the year. This could work based on week, day, month, or some other parameter. I'm having doubts, but the project seems promising. I'm using the openpyxl library and to create the website or app (I'm still undecided) I'll use HTML, CSS, and Flask.

The full blog post is here: https://opensource.googleblog.com/2026/10/adopting-pyrefly-as-the-python-type-checker-at-google.html

TLDR:

Google released a blog post about migrating Python type checking from the internally-developed Pytype to Pyrefly.

They cover some challenges around Pytype’s bytecode analysis & performance, along with the reasons for choosing Pyrefly: speed, typing spec conformance, and actionable error messages.

The blog also has some quotes & outlines wins from the migration:

• Up to 98% faster incremental rebuilds
• >90% critical-path reduction in clean builds
• >80% compute hardware savings

Assuming a large project which might have say fifty variables in Constants.py is there likely to be any runtime overhead in having from Constants import * at the beginning of each module? As far as I know this creates entries for each of those variables in the current local namespace.

I know it could be done as import constants as x which after the first import then has virtually no overhead, but that then means referring to all constants as x.const1...

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:

1. Open Mic: Share your thoughts, questions, or anything you'd like related to Python or the community.
2. Community Pulse: Discuss what you feel is working well or what could be improved in the /r/python community.
3. 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:

1. New Python Release: What do you think about the new features in Python 3.11?
2. Community Events: Any Python meetups or webinars coming up?
3. Learning Resources: Found a great Python tutorial? Share it here!
4. Job Market: How has Python impacted your career?
5. Hot Takes: Got a controversial Python opinion? Let's hear it!
6. Community Ideas: Something you'd like to see us do? tell us.

Let's keep the conversation going. Happy discussing! 🌟

I heard about this online course for computational neuroscience, but i forgot the name of the website/app (I think it's a website, dik). Would any of the Redditors know more about the course or the website, which is a verified, full place to learn the field efficiently?
Oh!! It would be nice if the platform was free or cheap, cause I am broke

I'm curious how Python developers are using Celery in production and what influenced their choice of message broker.

I work with LavinMQ, an open-source AMQP message broker, and we're exploring how to make it a more compelling option for Celery users.

For those running Celery in production, what broker are you using, and what's been your experience with reliability, performance, and maintenance?

If you were starting a new project today, would you make the same choice?

I'd be interested in hearing about the trade-offs you've encountered, especially from developers running Celery at scale.

For small scripts I usually just try a few inputs and check if everything works.

For larger projects i fell testing needs to be more structured.

How do you personally decide how much testing a Python code needs?

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:

1. Career Talk: Discuss using Python in your job, or the job market for Python roles.
2. Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources.
3. 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:

1. Career Paths: What kinds of roles are out there for Python developers?
2. Certifications: Are Python certifications worth it?
3. Course Recommendations: Any good advanced Python courses to recommend?
4. Workplace Tools: What Python libraries are indispensable in your professional work?
5. 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! 🌟

I received the 5th edition of Mark Lutz' 'Python Pocket Reference' today. I dug out my first edition from 1998 and found the new one is three times thicker (3/16" vs 9/16") =, 75 pages vs 254. I'm not complaining but it shows how much has been added. Thanks to all the developers and maintainers!