R Can't attend to present at ICML

Due to visa issues, no one on our team can attend to present our poster at ICML.


Does anyone have experience with not physically attending in the past? Is ICML typically flexible with this if we register and don't come to stand by the poster? Or do they check conference check-ins?



/r/MachineLearning
https://redd.it/1kxs67w

We built a Python SDK for our open source auth platform - would love feedback from Flask devs!!

Hey everyone, I’m Megan writing from Tesseral, the YC-backed open source authentication platform built specifically for B2B software (think: SAML, SCIM, RBAC, session management, etc.). We released our Python SDK and I’d love feedback from Flask devs…. 

If you’re interested in auth or if you have experience building it in Flask, would love to know what’s missing / confusing / would make this easier to use in your stack? Also, if you have general gripes about auth (it is very gripeable) would love to hear them. 

Here’s our GitHub: https://github.com/tesseral-labs/tesseral 

And our docs: https://tesseral.com/docs/what-is-tesseral   

Appreciate the feedback!

/r/flask
https://redd.it/1kxs6ff

Architecture and code for a Python RAG API using LangChain, FastAPI, and pgvector

I’ve been experimenting with building a Retrieval-Augmented Generation (RAG) system entirely in Python, and I just completed a write-up that breaks down the architecture and implementation details.

The stack:

Python + FastAPI
LangChain (for orchestration)
PostgreSQL + pgvector
OpenAI embeddings

I cover the high-level design, vector store integration, async handling, and API deployment — all with code and diagrams.

I'd love to hear your feedback on the architecture or tradeoffs, especially if you're also working with vector DBs or LangChain.

📄 Architecture + code walkthrough



/r/Python
https://redd.it/1ky5bgs

Looking for advice: Applying for a full-stack role with 5-year experience requirement (React/Django) — Internal referral opportunity

Hi everyone,

I’d really appreciate some advice or insight from folks who’ve been in a similar situation.

I was recently referred internally for a full-stack software engineer role that I’m very excited about. It’s a precious opportunity for me, but I’m feeling unsure because the job requires 5 years of experience in designing, developing, and testing web applications using Python, Django, React, and JavaScript.

Here’s my background:

I graduated in 2020 with a degree in Computer Engineering.
I worked for 2.5 years doing manual QA testing on the Google TV platform.
For the past 5 years, I’ve been teaching Python fundamentals and data structures at a coding bootcamp.
I only started learning React and Django a few months ago, but I’ve gone through the official tutorials on both the React and Django websites and have built a few simple full-stack apps. I feel fairly comfortable with the basics and am continuing to learn every day.

While I don't meet the "5 years of professional experience with this exact stack" requirement, I do have relevant technical exposure, strong Python fundamentals, and hands-on experience through teaching and recent personal projects.

If you've been in similar shoes — applying for a role where you didn’t meet all the

/r/django
https://redd.it/1ky3fqc

Recent Noteworthy Package Releases

In the last 7 days, there were these big upgrades.

**Deltalake 1.0.0**

**DeepEval v3.0**

**pytest-asyncio 1.0.0**

**Curlify v3.0.0**

**cachetools v6.0.0**

**Apache Spark 4.0.0**

/r/Python
https://redd.it/1ky8o84

DTC - CLI tool to dump telegram channels.

🚀 What my project does

extract data from particular telegram channel.

Target Audience

Anyone who wants to dump channel.


Comparison

Never thought about alternatives, because I made up this poject idea this morning.

Key features:

📋 Lists all channels you're subscribed to in a nice tabular format
💾 Dumps complete message history from any channel
📸 Downloads attached photos automatically
💾 Exports everything to structured JSONL format
🖥️ Interactive CLI with clean, readable output

# 🛠️ Tech Stack

Built with some solid Python libraries

:

Telethon \- for Telegram API integration
Pandas \- for data handling and table formatting
Tabulate \- for those beautiful CLI tables

Requires Python 3.8+ and works across platforms.

# 🎯 How it works

The workflow is super simple

:



bash
# List your channels
>> list
+----+----------------------------+-------------+
| | name | telegram id |
+====+============================+=============+
| 0 | My Favorite Channel | 123456789 |
+----+----------------------------+-------------+


/r/Python
https://redd.it/1kya1xc

I don't understand the FlaskSQLalchemy conventions

When using the FlaskSQLalchemy package, I don't understand the convention of

class Base(DeclarativeBase):
pass

db=SQLAlchemy(model_class=Base)


Why not just pass in `db=SQLAlchemy(model_class=DeclarativeBase)` ?

/r/flask
https://redd.it/1kyd5l7

Open-source AI-powered test automation library for mobile and web

Hey [r/Python](/r/Python/),

My name is Alex Rodionov and I'm a tech lead of the Selenium project. For the last 10 months, I’ve been working on **Alumnium**. I've already shared it [2 months ago](https://www.reddit.com/r/Python/comments/1jpo96u/i_built_an_opensource_aipowered_library_for_web/), but since then the project gained a lot of new features, notably:

* mobile applications support via Appium;
* built-in caching for faster test execution;
* fully local model support with Ollama and Mistral Small 3.1.

**What My Project Does**
It's an open-source Python library that automates testing for mobile and web applications by leveraging AI, natural language commands and Appium, Playwright, or Selenium.

**Target Audience**
Test automation engineers or anyone writing tests for web applications. It’s an early-stage project, not ready for production use in complex web applications.

**Comparison**
Unlike other similar projects (Shortest, LaVague, Hercules), Alumnium can be used in existing tests without changes to test runners, reporting tools, or any other test infrastructure. This allows me to gradually migrate my test suites (mostly Selenium) and revert whenever something goes wrong (this happens a lot, to be honest). Other major differences:

* dead cheap (works on low-tier models like gpt-4o-mini, costs $20 per month for 1k+ tests)
* not an AI agent (dumb enough to fail the test rather than working around to

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

Problems with Django Autocomplete Light

So, I'm stuck, I'm trying to make two selection boxes, one to select the state, the other to select the city. Both the code and the html are not crashing, but nothing is being loaded into the selection boxes.

Any help would be greatly appreciated!

#models.py
class City(models.Model):
    country = models.CharField(maxlength=50)
    state = models.CharField(maxlength=50)
    city = models.CharField(maxlength=50)

    def str(self):
        return f"{self.name}, {self.state}"
class City(models.Model):
    country = models.CharField(maxlength=50)
    state = models.CharField(maxlength=50)
    city = models.CharField(maxlength=50)


    def str(self):
        return f"{self.name}, {self.state}"

#forms.py
class CreateUserForm(forms.ModelForm):

    def init(self, args, kwargs):
        super().__init__(args, kwargs)
        # Ensure city field has proper empty queryset initially


/r/djangolearning
https://redd.it/1kxt7bn

R How to add confidence intervals to your LLM-as-a-judge

Hi all – I recently built a system that automatically determines how many LLM-as-a-judge runs you need for statistically reliable scores. Key insight: treat each LLM evaluation as a noisy sample, then use confidence intervals to decide when to stop sampling.

The math shows reliability is surprisingly cheap (95% → 99% confidence only costs 1.7x more), but precision is expensive (doubling scale granularity costs 4x more).Also implemented "mixed-expert sampling" - rotating through multiple models (GPT-4, Claude, etc.) in the same batch for better robustness.

I also analyzed how latency, cost and reliability scale in this approach.Typical result: need 5-20 samples instead of guessing. Especially useful for AI safety evals and model comparisons where reliability matters.

Blog: https://www.sunnybak.net/blog/precision-based-sampling

GitHub: https://github.com/sunnybak/precision-based-sampling/blob/main/mixed\_expert.py

I’d love feedback or pointers to related work.

Thanks!

/r/MachineLearning
https://redd.it/1kyl04x

Friday Daily Thread: r/Python Meta and Free-Talk Fridays

# 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! 🌟

/r/Python
https://redd.it/1kyq5i2

bulletchess, A high performance chess library

# What My Project Does

`bulletchess` is a high performance chess library, that implements the following and more:

* A complete game model with intuitive representations for pieces, moves, and positions.
* Extensively tested legal move generation, application, and undoing.
* Parsing and writing of positions specified in [Forsyth-Edwards Notation](https://www.chessprogramming.org/Forsyth-Edwards_Notation) (FEN), and moves specified in both [Long Algebraic Notation](https://www.chessprogramming.org/Algebraic_Chess_Notation#Long_Algebraic_Notation_.28LAN.29) and [Standard Algebraic Notation](https://www.chessprogramming.org/Algebraic_Chess_Notation#Standard_Algebraic_Notation_.28SAN.29).
* Methods to determine if a position is check, checkmate, stalemate, and each specific type of draw.
* Efficient hashing of positions using [Zobrist Keys](https://en.wikipedia.org/wiki/Zobrist_hashing).
* A [Portable Game Notation](https://thechessworld.com/articles/general-information/portable-chess-game-notation-pgn-complete-guide/) (PGN) file reader
* Utility functions for writing engines.

`bulletchess` is implemented as a C extension, similar to NumPy.

# Target Audience

I made this library after being frustrated with how slow `python-chess` was at large dataset analysis for machine learning and engine building. I hope it can be useful to anyone else looking for a fast interface to do any kind of chess ML in python.

# Comparison:

`bulletchess` has many of the same features as `python-chess`, but [is much faster](https://zedeckj.github.io/bulletchess/auto-examples/performance.html). I think the syntax of `bulletchess` is also a lot nicer to use. For example, instead of `python-chess`'s

board.piece_at(E1)

`bulletchess` uses:

board[E1]

You can install wheels with,

pip

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

Mastering Modern Time Series Forecasting : The Complete Guide to Statistical, Machine Learning & Dee

I’ve been working on a Python-focused guide called Mastering Modern Time Series Forecasting — aimed at bridging the gap between theory and practice for time series modeling.

It covers a wide range of methods, from traditional models like ARIMA and SARIMA to deep learning approaches like Transformers, N-BEATS, and TFT. The focus is on practical implementation, using libraries like statsmodels, scikit-learn, PyTorch, and Darts. I also dive into real-world topics like handling messy time series data, feature engineering, and model evaluation.

I’m publishing the guide on Gumroad and LeanPub. I’ll drop a link in the comments in case anyone’s interested.

Always open to feedback from the community — thanks!

/r/Python
https://redd.it/1kz1tkt

🎉 Introducing TurboDRF - Auto Generate CRUD APIs from your django models

# What My Project Does:
🚀 [TurboDRF](https://github.com/alexandercollins/turbodrf) is a new drf module that auto generates endpoints by adding 1 class mixin to your django models:
- Autogenerate CRUD API endpoints with docs 🎉
- No more writng basic urls, views, view sets or serailizers
- Supports filtering, text search and granular perissions

After many years with DRF and spinning up new projects I've really gotten tired of writing basic views, urls and serializers so I've build turbodrf which will do all that for you.


🔗 You can access it here on my github: [https://github.com/alexandercollins/turbodrf](https://github.com/alexandercollins/turbodrf)


✅ Basically just **add 1 mixin to the model you want to expose as an endpoint** and then 1 method in that model which specifies the fields (could probably move this to Meta tbh) and boom 💥 your API is ready.

📜 It also generates swagger docs, integrates with django's default user permissions (and has its own static role based permission system with field level permissions too), plus you get advanced filtering, full-text search, automatic pagination, nested relationships with double underscore notation, and automatic query optimization with select_related/prefetch_related.

💻 Here's a quick example:

```
class Book(models.Model, TurboDRFMixin):
title = models.CharField(max_length=200)
author = models.ForeignKey(Author, on_delete=models.CASCADE)
price = models.DecimalField(max_digits=10,

/r/Python
https://redd.it/1kyywn0

MigrateIt, A database migration tool

# What My Project Does

[MigrateIt](https://github.com/iagocanalejas/MigrateIt) allows to manage your database changes with simple migration files in plain SQL. Allowing to run/rollback them as you wish.

Avoids the need to learn a different sintax to configure database changes allowing to write them in the same SQL dialect your database use.

# Target Audience

Developers tired of having to synchronize databases between different environments or using tools that need to be configured in JSON or native ASTs instead of plain SQL.

# Comparison

Instead of:

```json
{ "databaseChangeLog": [
{
"changeSet": {
"changes": [
{
"createTable": {
"columns": [
{
"column": {
"name": "CREATED_BY",
"type": "VARCHAR2(255 CHAR)"


/r/Python
https://redd.it/1kz30mk

Functional programming concepts that actually work in Python

Been incorporating more functional programming ideas into my Python/R workflow lately - immutability, composition, higher-order functions. Makes debugging way easier when data doesn't change unexpectedly.

Wrote about some practical FP concepts that work well even in non-functional languages: https://borkar.substack.com/p/why-care-about-functional-programming?r=2qg9ny&utm\_medium=reddit

Anyone else finding FP useful for data work?

/r/Python
https://redd.it/1kz6kx3

HELP-Struggling to Scale Django App for High Concurrency

Hi everyone,

I'm working on scaling my Django app and facing performance issues under load. I've 5-6 API which hit concurrently by 300 users. Making almost 1800 request at once. I’ve gone through a bunch of optimizations but still seeing odd behavior.

# Tech Stack

\- Django backend
 \- PostgreSQL (AWS RDS)
 \- Gunicorn with `gthread` worker class
 \- Nginx as reverse proxy
 \- Load testing with `k6` (to simulate 500 to 5,000 concurrent requests)
 \- Also tested with JMeter — it handles 2,000 requests without crashing

# Server Setup

Setup 1 (Current):

\- 10 EC2 servers
 \- 9 Gunicorn `gthread` workers per server
 \- 30 threads per worker
 \- 4-core CPU per server

Setup 2 (Tested):

\- 2 EC2 servers
 \- 21 Gunicorn `gthread` workers per server
 \- 30 threads per worker
 \- 10-core CPU per server

Note: No PgBouncer or DB connection pooling in use yet.
 RDS `max_connections` = 3476.

# Load Test Scenario

\- 5–6 APIs are hit concurrently by around 300 users, totaling approximately 1,800 simultaneous requests.
 \- Each API is I/O-bound, with 8–9 DB queries using annotate, aggregate, filter, and other Django ORM queries and some CPU bound logic.

/r/django
https://redd.it/1kzb8h0

[R] The Resurrection of the ReLU

Hello everyone, I’d like to share our new preprint on bringing ReLU back into the spotlight.

Over the years, activation functions such as GELU and SiLU have become the default choices in many modern architectures. Yet ReLU has remained popular for its simplicity and sparse activations despite the long-standing “dying ReLU” problem, where inactive neurons stop learning altogether.

Our paper introduces **SUGAR** (Surrogate Gradient Learning for ReLU), a straightforward fix:

* Forward pass: keep the standard ReLU.
* Backward pass: replace its derivative with a smooth surrogate gradient.

This simple swap can be dropped into almost any network—including convolutional nets, transformers, and other modern architectures—without code-level surgery. With it, previously “dead” neurons receive meaningful gradients, improving convergence and generalization while preserving the familiar forward behaviour of ReLU networks.

**Key results**

* Consistent accuracy gains in convolutional networks by stabilising gradient flow—even for inactive neurons.
* Competitive (and sometimes superior) performance compared with GELU-based models, while retaining the efficiency and sparsity of ReLU.
* Smoother loss landscapes and faster, more stable training—all without architectural changes.

We believe this reframes ReLU not as a legacy choice but as a revitalised classic made relevant through careful gradient handling. I’d be happy to hear any feedback or questions you have.

**Paper:** [https://arxiv.org/pdf/2505.22074](https://arxiv.org/pdf/2505.22074)

\[Throwaway because I do

/r/MachineLearning
https://redd.it/1kz5t16

Windows Task Scheduler & Simple Python Scripts

Putting this out there, for others to find, as other posts on this topic are "closed and archived", so I can't add to them.

Recurring issues with strange errors, and 0x1 results when trying to automate simple python scripts. (to accomplish simple tasks!)
Scripts work flawlessly in a command window, but the moment you try and automate... well... fail.
Lost a number of hours.

Anyhow - simple solution in the end - the extra "pip install" commands I had used in the command prompt, are "temporary", and disappear with the command prompt.

So - when scheduling these scripts (my first time doing this), the solution in the end was a batch file, that FIRST runs the py -m pip install "requests" first, that pulls in what my script needs... and then runs the actual script.

my batch:
py.exe -m pip install "requests"
py.exe fixip3.py

Working perfectly every time, I'm not even logged in... running in the background, just the way I need it to.

Hope that helps someone else!

Andrew

/r/Python
https://redd.it/1kzcj8w

What is the best way to parse log files?

Hi,

I usually have to extract specific data from logs and display it in a certain way, or do other things.

The thing is those logs are tens of thousands of lines sometimes so I have to use a very specific Regex for each entry.

It is not just straight up "if a line starts with X take it" no, sometimes I have to get lists that are nested really deep.

Another problem is sometimes the logs change and I have to adjust the Regex to the new change which takes time

What would you use best to analyse these logs? I can't use any external software since the data I work with is extremely confidential.

Thanks!

/r/Python
https://redd.it/1kzhq0i