synchronous vs asynchronous

Can you recommend a YouTube video that explains synchronous vs asynchronous programming in depth

/r/djangolearning
https://redd.it/1kkxxn0

sqlalchemy-memory: a pure‑Python in‑RAM dialect for SQLAlchemy 2.0

# What My Project Does

sqlalchemy-memory is a fast in‑RAM SQLAlchemy 2.0 dialect designed for prototyping, backtesting engines, simulations, and educational tools.

It runs entirely in Python; no database, no serialization, no connection pooling. Just raw Python objects and fast logic.

SQLAlchemy Core & ORM support
No I/O or driver overhead (all in-memory)
Supports group\_by, aggregations, and case() expressions
Lazy query evaluation (generators, short-circuiting, etc.)
Indexes are supported. SELECT queries are optimized using available indexes to speed up equality and range-based lookups.
Commit/rollback simulation

# Links

[GitHub Project link](https://github.com/rundef/sqlalchemy-memory)
Documentation link
[Benchmarks vs SQLite in-memory](https://sqlalchemy-memory.readthedocs.io/en/latest/benchmarks.html)
Blogpost: Beyond SQLite: Supercharging SQLAlchemy with a Pure In-Memory Dialect

# Why I Built It

I wanted a backend that:

Behaved like a real SQLAlchemy engine (ORM and Core)
Avoided SQLite/driver overhead
Let me prototype quickly with real queries and relationships

# Target audience

Backtesting engine builders who want a lightweight, in‑RAM store compatible with their ORM models
Simulation and modeling developers who need high-performance in-memory logic without spinning up a database
Anyone tired of duplicating business logic between an ORM and a memory data layer

Note: It's not a full SQL engine: don't use it to unit test DB behavior or verify SQL standard conformance. But for in‑RAM logic with SQLAlchemy-style

/r/Python
https://redd.it/1kmg3db

DBOS - Lightweight Durable Python Workflows

Hi r/Python – I’m Peter and I’ve been working on DBOS, an open-source, lightweight durable workflows library for Python apps. We just released our 1.0 version and I wanted to share it with the community!

GitHub link: https://github.com/dbos-inc/dbos-transact-py

What My Project Does

DBOS provides lightweight durable workflows and queues that you can add to Python apps in just a few lines of code. It’s comparable to popular open-source workflow and queue libraries like Airflow and Celery, but with a greater focus on reliability and automatically recovering from failures.

Our core goal in building DBOS is to make it lightweight and flexible so you can add it to your existing apps with minimal work. Everything you need to run durable workflows and queues is contained in this Python library. You don’t need to manage a separate workflow server: just install the library, connect it to a Postgres database (to store workflow/queue state) and you’re good to go.

When Should You Use My Project?

You should consider using DBOS if your application needs to reliably handle failures. For example, you might be building a payments service that must reliably process transactions even if servers crash mid-operation, or a long-running data pipeline that needs to resume from checkpoints rather

/r/Python
https://redd.it/1kml2h9

Beam Pod - Run Cloud Containers from Python

Hey all!

Creator of [Beam](https://beam.cloud) here. Beam is a Python-focused cloud for developers—we let you deploy Python functions and scripts without managing any infrastructure, simply by adding decorators to your existing code.

**What My Project Does**

We just launched [Beam Pod](https://docs.beam.cloud/v2/pod/web-service), a Python SDK to instantly deploy containers as HTTPS endpoints on the cloud.

**Comparison**

For years, we searched for a simpler alternative to Docker—something lightweight to run a container behind a TCP port, with built-in load balancing and centralized logging, but without YAML or manual config. Existing solutions like Heroku or Railway felt too heavy for smaller services or quick experiments.

With Beam Pod, everything is Python-native—no YAML, no config files, just code:

from beam import Pod, Image

pod = Pod(
name="my-server",
image=Image(python_version="python3.11"),
gpu="A10G",
ports=[8000],
cpu=1,
memory=1024,
entrypoint=["python3", "-m", "http.server", "8000"],
)
instance = pod.create()



/r/Python
https://redd.it/1kmlmvo

Paid Bug Fix Opportunity for LBRY Project (USD) — Python Developers Wanted

Hi r/Python,

I'm posting to help the LBRY Foundation, a non-profit supporting the decentralized digital content protocol LBRY. 

We're currently looking for experienced Python developers to help resolve a specific bug in the LBRY Hub codebase. This is a paid opportunity (USD), and we’re open to discussing future, ongoing development work with contributors who demonstrate quality work and reliability.

Project Overview:

Project Type: Bug fix for LBRY’s open-source Python hub codebase 
What the LBRY Project Does: LBRY is a decentralized and user-controlled media platform
Language: Python 
Repo: https://github.com/LBRYFoundation/hub 
Payment: USD (details negotiated individually) 
Target Audience: Current and future users of the LBRY desktop app
Comparison: Unlike traditional media platforms like YouTube or Vimeo, LBRY is a fully decentralized, open-source protocol that gives users and creators full ownership and control over their content. Contributing to LBRY means working on infrastructure that supports freedom of speech, censorship resistance, and user empowerment—values not typically prioritized in centralized alternatives. This opportunity offers developers a chance to impact a real, live network of users while working transparently in the open-source space.
Communication: You can reply here or reach out via LBRY’s ‘Developers’ Channel on Discord

We welcome bids from contributors who are passionate about open-source and decentralization. Please comment below or connect on Discord if you’re interested or have questions!

/r/Python
https://redd.it/1kmrd8o

Seeking Guidance on Enterprise-Level Auth in Flask: Role-Based Access & Best Practices

Hello, I’m building an enterprise application that requires robust authentication/authorization (user roles, permissions, etc.). I’ve used Flask-Login for basic auth, but I’m struggling to implement scalable role-based access control (RBAC) for admins, managers, and end-users.

For the experts:
1. What approach would you recommend for enterprise-grade auth in Flask?
- How do you structure roles/permissions at scale (e.g., database design)?
2. What are critical security practices for production ?
3. Resources: Are there tutorials, books, or open-source projects that demonstrate professional Flask auth workflows?

Current Setup:
- Flask-Login (basic sessions)
- SQLAlchemy for user models

Any advice or war stories from real-world projects would be invaluable!

TL;DR: Need advice/resources for enterprise auth in Flask: role-based access, security best practices, and scaling beyond Flask-Login.

/r/flask
https://redd.it/1kmmfdf

D Rejected a Solid Offer Waiting for My 'Dream Job'

I recently earned my PhD from the UK and moved to the US on a talent visa (EB1). In February, I began actively applying for jobs. After over 100 applications, I finally landed three online interviews. One of those roles was a well-known company within driving distance of where I currently live—this made it my top choice. I’ve got kid who is already settled in school here, and I genuinely like the area.

Around the same time, I received an offer from a company in another state. However, I decided to hold off on accepting it because I was still in the final stages with the local company. I informed them that I had another offer on the table, but they said I was still under serious consideration and invited me for an on-site interview.

The visit went well. I confidently answered all the AI/ML questions they asked. Afterward, the hiring manager gave me a full office tour. I saw all the "green flags" that Chip Huyen mentions in her ML interview book: told this would be my desk, showed all the office amenities, etc. I was even the first candidate they brought on site. All of this made me feel optimistic—maybe

/r/MachineLearning
https://redd.it/1kmpzpy

Thursday Daily Thread: Python Careers, Courses, and Furthering Education!

# 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! 🌟

/r/Python
https://redd.it/1kmufcq

Microsoft layoffs hit Faster CPython team - including the Technical Lead, Mark Shannon

From Brett Cannon:

> There were layoffs at MS yesterday and 3 Python core devs from the Faster CPython team were caught in them.

> Eric Snow, Irit Katriel, Mark Shannon

IIRC Mark Shannon started the Faster CPython project, and he was its Technical Lead.

/r/Python
https://redd.it/1kmwdbu

DjangoCon Europe and beyond
https://www.djangoproject.com/weblog/2025/may/14/djangocon-europe-and-beyond/

/r/django
https://redd.it/1kmzzbf

Blame as a Service: Open-source for Blaming Others

Blame-as-a-Service (BaaS) : When your mistakes are too mainstream.

Your open-source API for blaming others.
😀
https://github.com/sbmagar13/blame-as-a-service

/r/Python
https://redd.it/1kmxawf

Query and Eval for Python Polars

I am a longtime pandas user. I hate typing when it comes to slicing and dicing the dataframe. Pandas query and eval come to the rescue.

On the other hand, pandas suffers from the performance and memory issue as many people have discussed. Fortunately, Polars comes to the rescue. I really enjoy all the performance improvements and the lazy frame just makes it possible to handle large dataset with a 32G memory PC.

However, with all the good things about Polars, I still miss the query and eval function of pandas, especially when it comes to data exploration. I just don’t like typing so many pl.col in a chained conditions or pl.when otherwise in nested conditions.

Without much luck with existing solutions, I implemented my own version of query, eval among other things. The idea is using lark to define a set of grammars so that it can parse any string expressions to polars expression.

For example,
“1 < a <= 3” is translated to (pl.col(‘a’)> 1) & (pl.col(‘a’)<=3), “a.sum().over(‘b’)” is translated to pl.col(‘a’).sum().over(‘b’), “ a in @A” where A is a list, is translated to pl.col(‘a’).isin(A), “‘2010-01-01’ <= date < ‘2019-10-01’” is translated accordingly for date time columns. For my

/r/Python
https://redd.it/1kmy3xm

Refinedoc - Little text processing lib

Hello everyone!

I'm here to present my latest little project, which I developed as part of a larger project for my work.

What's more, the lib is written in pure Python and has no dependencies other than the standard lib.

What My Project Does

It's called Refinedoc, and it's a little python lib that lets you remove headers and footers from poorly structured texts in a fairly robust and normally not very RAM-intensive way (appreciate the scientific precision of that last point), based on this paper https://www.researchgate.net/publication/221253782\_Header\_and\_Footer\_Extraction\_by\_Page-Association

I developed it initially to manage content extracted from PDFs I process as part of a professional project.



When Should You Use My Project?

The idea behind this library is to enable post-extraction processing of unstructured text content, the best-known example being pdf files. The main idea is to robustly and securely separate the text body from its headers and footers which is very useful when you collect lot of PDF files and want the body oh each.


Comparison

I compare it with pymuPDF4LLM wich is incredible but don't allow to extract specifically headers and footers and the license was a problem in my case.



I'd be delighted to hear your feedback on the code or lib as such!



https://github.com/CyberCRI/refinedoc

/r/Python
https://redd.it/1kn4lfx

PyTorch vs. Keras/Tensorflow D

Hey guys,

I am aware of the intended use cases, but I am interested to learn what you use more often in your projects. PyTorch or Keras and why?

/r/Python
https://redd.it/1kn4132

R AlphaEvolve: A coding agent for scientific and algorithmic discovery

Paper: https://storage.googleapis.com/deepmind-media/DeepMind.com/Blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/AlphaEvolve.pdf

Abstract:

In this white paper, we present AlphaEvolve, an evolutionary coding agent that substantially enhances
capabilities of state-of-the-art LLMs on highly challenging tasks such as tackling open scientific problems
or optimizing critical pieces of computational infrastructure. AlphaEvolve orchestrates an autonomous
pipeline of LLMs, whose task is to improve an algorithm by making direct changes to the code. Using
an evolutionary approach, continuously receiving feedback from one or more evaluators, AlphaEvolve
iteratively improves the algorithm, potentially leading to new scientific and practical discoveries. We
demonstrate the broad applicability of this approach by applying it to a number of important computational problems. When applied to optimizing critical components of large-scale computational
stacks at Google, AlphaEvolve developed a more efficient scheduling algorithm for data centers, found
a functionally equivalent simplification in the circuit design of hardware accelerators, and accelerated the training of the LLM underpinning AlphaEvolve itself. Furthermore, AlphaEvolve discovered
novel, provably correct algorithms that surpass state-of-the-art solutions on a spectrum of problems
in mathematics and computer science, significantly expanding the scope of prior automated discovery
methods (Romera-Paredes et al., 2023). Notably, AlphaEvolve developed a search algorithm that found a
procedure to multiply two 4 × 4 complex-valued matrices using 48 scalar multiplications; offering the
first improvement, after 56 years, over Strassen’s algorithm in this setting.

/r/MachineLearning
https://redd.it/1kmxi4z

I built an Interactive reStructuredText Tutorial that runs entirely in your browser

Hey r/Python!

I wanted to share a project I've been working on: an Interactive reStructuredText Tutorial.

What My Project Does

It's a web-based, hands-on tutorial designed to teach reStructuredText (reST), the markup language used extensively in Python documentation (like Sphinx, docstrings, etc.). The entire tutorial, including the reST rendering, runs directly in your browser using PyScript and Pyodide.

You get a lesson description on one side and an interactive editor on the other. As you type reST in the editor, you see the rendered HTML output update instantly. It covers topics from basic syntax and inline markup to more complex features like directives, roles, tables, and figures.

There's also a separate Playground page for free-form experimentation.

Why I Made It

While the official reStructuredText documentation is comprehensive, I find that learning markup languages is often easier with immediate, interactive feedback. I wanted to create a tool where users could experiment with reST syntax and see the results without needing any local setup. Building it with PyScript was also a fun challenge to see how much could be done directly in the browser with Python.

Target Audience

This is for anyone who needs to learn or brush up on reStructuredText:

Python developers writing documentation or docstrings.
Users of Sphinx or

/r/Python
https://redd.it/1kn6ysa

Very Basic Blog website with Flask

https://redd.it/1kn5s9h
@pythondaily

R AlphaEvolve: A Gemini-powered coding agent for designing advanced algorithms

> Large language models (LLMs) are remarkably versatile. They can summarize documents, generate code or even brainstorm new ideas. And now we’ve expanded these capabilities to target fundamental and highly complex problems in mathematics and modern computing.
Today, we’re announcing AlphaEvolve, an evolutionary coding agent powered by large language models for general-purpose algorithm discovery and optimization. AlphaEvolve pairs the creative problem-solving capabilities of our Gemini models with automated evaluators that verify answers, and uses an evolutionary framework to improve upon the most promising ideas.
AlphaEvolve enhanced the efficiency of Google's data centers, chip design and AI training processes — including training the large language models underlying AlphaEvolve itself. It has also helped design faster matrix multiplication algorithms and find new solutions to open mathematical problems, showing incredible promise for application across many areas.


For all the Evolutionary Algorthim fans out there, here's a really interesting paper that Deepmind published where they show AlphaEvolve designing advanced algorithms like improving matrix multiplication (which is a big deal in ML optimization)

Paper link: https://deepmind.google/discover/blog/alphaevolve-a-gemini-powered-coding-agent-for-designing-advanced-algorithms/

Interview with team:
https://youtu.be/vC9nAosXrJw?si=rzZSorXqgbqChFJa

/r/MachineLearning
https://redd.it/1kmzpg0

flask wiki got a new server to run the website.

/r/flask
https://redd.it/1kn088t

Introducing Pyrefly: A fast type checker and IDE experience for Python, written in Rust

Blog post: https://engineering.fb.com/2025/05/15/developer-tools/introducing-pyrefly-a-new-type-checker-and-ide-experience-for-python/

Podcast: https://engineering.fb.com/2025/05/15/developer-tools/open-sourcing-pyrefly-a-faster-python-type-checker-written-in-rust/

Source code: https://github.com/facebook/pyrefly

/r/Python
https://redd.it/1knh1uu

Python for Good - Save the Date!

Hey Pythonistas!

Do you:

✅ Get excited about writing Python code?
✅ Want to use your skills for some serious good in the world?
✅ Interested in hanging out with the coolest, kindest, most awesome people in the Python community?
✅ Want to make dozens of new close friends?

If you're nodding enthusiastically right now, block off August 28-31st for Python for Good! Registration opens June 1st, but we wanted to give you a heads-up so you can plan accordingly!

Never heard of Python for Good? Python for Good operates year round but the event is basically summer camp for nerds! And it's ALL-INCLUSIVE (yes, you read that right) - lodging, meals, everything - at a gorgeous retreat space overlooking the Pacific Ocean. By day, we code for awesome causes. By night? We unleash our inner geeks with board games, nature hikes, campfire s'mores, epic karaoke battles, and other community building activities!

This is definitely NOT a hackathon. We work on real problems from real nonprofits (who'll be right there with us!), creating or contributing to existing open source solutions that will continue to make a difference long after the event wraps up.

Sounds like fun? Or maybe something your company would love to support? Hit us up!

/r/Python
https://redd.it/1knhkex