A Lightweight Camera SDK for Windows, macOS, and Linux
If you’re looking for a lightweight alternative to OpenCV for camera access on Windows, Linux, and macOS, I’ve created a minimal SDK called lite-camera .
Installation
pip install lite-camera
Quick Usage
import litecam
camera = litecam.PyCamera()
if camera.open(0):
window = litecam.PyWindow(
camera.getWidth(), camera.getHeight(), "Camera Stream")
while window.waitKey('q'):
frame = camera.captureFrame()
if frame is not None:
width = frame0
height = frame1
size = frame2
data = frame3
/r/Python
https://redd.it/1ikizbe
If you’re looking for a lightweight alternative to OpenCV for camera access on Windows, Linux, and macOS, I’ve created a minimal SDK called lite-camera .
Installation
pip install lite-camera
Quick Usage
import litecam
camera = litecam.PyCamera()
if camera.open(0):
window = litecam.PyWindow(
camera.getWidth(), camera.getHeight(), "Camera Stream")
while window.waitKey('q'):
frame = camera.captureFrame()
if frame is not None:
width = frame0
height = frame1
size = frame2
data = frame3
/r/Python
https://redd.it/1ikizbe
PyPI
lite-camera
LiteCam is a lightweight, cross-platform library for capturing RGB frames from cameras and displaying them. Designed with simplicity and ease of integration in mind, LiteCam supports Windows, Linux and macOS platforms.
Terminal Task Manager Using Python
I've built a terminal task manager for programmers, that lets you manage your coding tasks directly from the command line. Key features include:
Adding task
Marking tasks as complete
Listing pending task
Listing completed tasks (filter like today, yesterday, week etc)
I am thinking about adding more features like reminder, time tracking,etc. what would you want to see in this task manager. Comment below
I'd love for you to check it out, contribute and help make it even better
The project is available on GitHub https://github.com/MickyRajkumar/task-manager
/r/Python
https://redd.it/1ikipwh
I've built a terminal task manager for programmers, that lets you manage your coding tasks directly from the command line. Key features include:
Adding task
Marking tasks as complete
Listing pending task
Listing completed tasks (filter like today, yesterday, week etc)
I am thinking about adding more features like reminder, time tracking,etc. what would you want to see in this task manager. Comment below
I'd love for you to check it out, contribute and help make it even better
The project is available on GitHub https://github.com/MickyRajkumar/task-manager
/r/Python
https://redd.it/1ikipwh
GitHub
GitHub - MickyRajkumar/task-manager
Contribute to MickyRajkumar/task-manager development by creating an account on GitHub.
PomdAPI: Declarative API Clients with Tag-Based Caching (HTTP/JSON-RPC) - Seeking Community
Hey everyone,
I’d like to introduce pomdapi, a Python library to simplify creating and caching API calls across multiple protocols (HTTP, JSON-RPC, XML-RPC). It features a clear, FastAPI-like decorator style for defining endpoints, built-in sync/async support, and tag-based caching.
## What My Project Does
- **Declarative Endpoints**: You define your API calls with decorators (@api.query for reads, @api.mutation for writes).
- **Tag-Based Caching**: Tag your responses for easy invalidation. For example, cache getUser(123) under Tag("User", "123") and automatically invalidate it when the user changes.
- **Sync or Async**: Each endpoint can be called synchronously or asynchronously by specifying is_async=True/False.
- **Multi-Protocol**: Beyond HTTP, you can also use JSON-RPC and XML-RPC variants.
- **Swappable Cache Backends** : Choose in-memory, Redis, or Memcached.
Effectively, pomdapi helps you avoid rewriting the usual “fetch => parse => store => invalidate” logic while still keeping your code typed and organized.
## Target Audience
- Developers who need to consume multiple APIs—especially with both sync and async flows—but want a single, typed approach.
- Production Teams wanting a more systematic way to manage caching and invalidation (tag-based) instead of manual or ad-hoc solutions.
- Library Authors or CLI Tool Builders who need to unify caching across various external services—HTTP, JSON-RPC, or even custom protocols.
## Comparison
- Requests + Manual
/r/Python
https://redd.it/1ikjs2s
Hey everyone,
I’d like to introduce pomdapi, a Python library to simplify creating and caching API calls across multiple protocols (HTTP, JSON-RPC, XML-RPC). It features a clear, FastAPI-like decorator style for defining endpoints, built-in sync/async support, and tag-based caching.
## What My Project Does
- **Declarative Endpoints**: You define your API calls with decorators (@api.query for reads, @api.mutation for writes).
- **Tag-Based Caching**: Tag your responses for easy invalidation. For example, cache getUser(123) under Tag("User", "123") and automatically invalidate it when the user changes.
- **Sync or Async**: Each endpoint can be called synchronously or asynchronously by specifying is_async=True/False.
- **Multi-Protocol**: Beyond HTTP, you can also use JSON-RPC and XML-RPC variants.
- **Swappable Cache Backends** : Choose in-memory, Redis, or Memcached.
Effectively, pomdapi helps you avoid rewriting the usual “fetch => parse => store => invalidate” logic while still keeping your code typed and organized.
## Target Audience
- Developers who need to consume multiple APIs—especially with both sync and async flows—but want a single, typed approach.
- Production Teams wanting a more systematic way to manage caching and invalidation (tag-based) instead of manual or ad-hoc solutions.
- Library Authors or CLI Tool Builders who need to unify caching across various external services—HTTP, JSON-RPC, or even custom protocols.
## Comparison
- Requests + Manual
/r/Python
https://redd.it/1ikjs2s
Reddit
From the Python community on Reddit: PomdAPI: Declarative API Clients with Tag-Based Caching (HTTP/JSON-RPC) - Seeking Community
Explore this post and more from the Python community
A new type of interpreter has been added to Python 3.14 with much better performance
Summary: This week I landed a new type of interpreter into Python 3.14. It improves performance by -3-30% (I actually removed outliers, otherwise it's 45%), and a geometric mean of 9-15% faster on pyperformance depending on platform and architecture. The main caveat however is that it only works with the newest compilers (Clang 19 and newer). We made this opt-in, so there's no backward compatibility concerns. Once the compilers start catching up a few years down the road, I expect this feature to become widespread.
Python 3.14 documentation: https://docs.python.org/3.14/whatsnew/3.14.html#whatsnew314-tail-call
(Sorry can't cross-post here) Original post: https://www.reddit.com/r/ProgrammingLanguages/comments/1ikqi0d/a\_new\_type\_of\_interpreter\_has\_been\_added\_to/
/r/Python
https://redd.it/1iks79k
Summary: This week I landed a new type of interpreter into Python 3.14. It improves performance by -3-30% (I actually removed outliers, otherwise it's 45%), and a geometric mean of 9-15% faster on pyperformance depending on platform and architecture. The main caveat however is that it only works with the newest compilers (Clang 19 and newer). We made this opt-in, so there's no backward compatibility concerns. Once the compilers start catching up a few years down the road, I expect this feature to become widespread.
Python 3.14 documentation: https://docs.python.org/3.14/whatsnew/3.14.html#whatsnew314-tail-call
(Sorry can't cross-post here) Original post: https://www.reddit.com/r/ProgrammingLanguages/comments/1ikqi0d/a\_new\_type\_of\_interpreter\_has\_been\_added\_to/
/r/Python
https://redd.it/1iks79k
Python documentation
What’s new in Python 3.14
Editors, Adam Turner and Hugo van Kemenade,. This article explains the new features in Python 3.14, compared to 3.13. Python 3.14 was released on 7 October 2025. For full details, see the changelog...
What is this blank box on the left ? this is on the documentation page of python
Can anyone tell me what is this ??
this is the link : https://docs.python.org/3.13/genindex.html
https://preview.redd.it/r5qpcg242zhe1.png?width=1857&format=png&auto=webp&s=de04daafd00403e68dc3c385d06d3ceb5229eb9c
/r/Python
https://redd.it/1ikwb6i
Can anyone tell me what is this ??
this is the link : https://docs.python.org/3.13/genindex.html
https://preview.redd.it/r5qpcg242zhe1.png?width=1857&format=png&auto=webp&s=de04daafd00403e68dc3c385d06d3ceb5229eb9c
/r/Python
https://redd.it/1ikwb6i
Stuck in a Loop Learning Web Dev—Need Advice!
Hey everyone,
I’ve been learning web development, but I feel stuck in a cycle—watching tutorials, building small projects, fixing bugs, and repeating the same things daily. It feels like I’m not making real progress.
I think I’m too comfortable with what I know and struggle to push myself into bigger challenges. Has anyone else felt this way? How did you break out of the loop and level up?
Would love any advice! Thanks!
/r/django
https://redd.it/1ikr38s
Hey everyone,
I’ve been learning web development, but I feel stuck in a cycle—watching tutorials, building small projects, fixing bugs, and repeating the same things daily. It feels like I’m not making real progress.
I think I’m too comfortable with what I know and struggle to push myself into bigger challenges. Has anyone else felt this way? How did you break out of the loop and level up?
Would love any advice! Thanks!
/r/django
https://redd.it/1ikr38s
Reddit
From the django community on Reddit
Explore this post and more from the django community
Sunday Daily Thread: What's everyone working on this week?
# 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! 🌟
/r/Python
https://redd.it/1il1iwf
# 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! 🌟
/r/Python
https://redd.it/1il1iwf
Reddit
From the Python community on Reddit
Explore this post and more from the Python community
form."field" vs models."field": Defining field parameters in a single location
I plan to pass my models to the forms.py file and I'm wondering if it's possible to define parameters for form.field & models.field in the same line? For example: I want to define
edit: had true and false reversed in first paragraph
/r/djangolearning
https://redd.it/1ikxiji
I plan to pass my models to the forms.py file and I'm wondering if it's possible to define parameters for form.field & models.field in the same line? For example: I want to define
required=True & blank = Flalse.userfirstname = (???).CharField(required=True, blank=False, max_length= 40, verbose_name="First Name")edit: had true and false reversed in first paragraph
/r/djangolearning
https://redd.it/1ikxiji
RedCoffee: Making SonarQube Reports Shareable for Everyone
Hi everyone,
I’m excited to share a new update for [RedCoffee](https://pypi.org/project/redcoffee/), a Python package that generates SonarQube reports in PDF format, making it easier for developers to share analysis results efficiently.
# Motivation:
Last year, while working on a collaborative side project, my team and I integrated SonarQube to track code quality. Since this was purely a learning-focused initiative, we decided to use the SonarQube Community Edition, which met our needs—except for a few major limitations:
* There was no built-in way to share the analysis report.
* Our SonarQube instance was running locally in a Docker container.
* No actively maintained plugins were available to generate reports.
After some research, I found an old plugin that supported PDF reports, but it had not been updated since 2016. Seeing no viable solution, I decided to build RedCoffee, a CLI-based tool that allows users to generate a PDF report for any SonarQube analysis, specifically designed for teams using the Community Edition.
I first introduced RedCoffee on this subreddit around nine months ago, and I received a lot of valuable feedback. Some developers forked the repository, while others raised feature requests and reported bugs. This update includes fixes and enhancements based on that input.
**What's new in the recent update ?**
/r/Python
https://redd.it/1iklryk
Hi everyone,
I’m excited to share a new update for [RedCoffee](https://pypi.org/project/redcoffee/), a Python package that generates SonarQube reports in PDF format, making it easier for developers to share analysis results efficiently.
# Motivation:
Last year, while working on a collaborative side project, my team and I integrated SonarQube to track code quality. Since this was purely a learning-focused initiative, we decided to use the SonarQube Community Edition, which met our needs—except for a few major limitations:
* There was no built-in way to share the analysis report.
* Our SonarQube instance was running locally in a Docker container.
* No actively maintained plugins were available to generate reports.
After some research, I found an old plugin that supported PDF reports, but it had not been updated since 2016. Seeing no viable solution, I decided to build RedCoffee, a CLI-based tool that allows users to generate a PDF report for any SonarQube analysis, specifically designed for teams using the Community Edition.
I first introduced RedCoffee on this subreddit around nine months ago, and I received a lot of valuable feedback. Some developers forked the repository, while others raised feature requests and reported bugs. This update includes fixes and enhancements based on that input.
**What's new in the recent update ?**
/r/Python
https://redd.it/1iklryk
PyPI
redcoffee
A command-line tool to generate PDF for SonarQube Reports
How to Synchronize a Dropdown and Slider in Plotly for Dynamic Map Updates?
Hi all,
I’m working on a dynamic choropleth map using Plotly, where I have:
1. A dropdown menu to select between different questions (e.g., ‘C006’, ‘C039’, ‘C041’).
2. A slider to select the time period (e.g., 1981-2004, 2005-2022, 1981-2022).
The map should update based on both the selected question and period. However, I’m facing an issue:
• When I select a question from the dropdown, the map updates correctly.
• But, when I use the slider to change the period, the map sometimes resets to the first question and doesn’t update correctly based on the selected question.
I need the map to stay synchronized with both the selected question and period.
Here’s the code I’m using:
# Define the full questions for each column
questionlabels = {
'C006': 'Satisfaction with financial situation of household: 1 = Dissatisfied, 10 = Satisfied',
'C039': 'Work is a duty towards society: 1 = Strongly Disagree, 5 = Strongly Agree',
'C041': 'Work should come first even if it means less spare time: 1 = Strongly Disagree, 5 = Strongly Agree'
}
# Combine all periods into a single DataFrame with a new column for the period
meansperiod1merged'Period' = '1981-2004'
meansperiod2merged['Period'] = '2005-2022'
meansperiod3merged'Period' = '1981-2022'
combineddf = pd.concat([meansperiod1merged, meansperiod2merged, meansperiod3merged])
# Create a list of frames for
/r/Python
https://redd.it/1ikm1t1
Hi all,
I’m working on a dynamic choropleth map using Plotly, where I have:
1. A dropdown menu to select between different questions (e.g., ‘C006’, ‘C039’, ‘C041’).
2. A slider to select the time period (e.g., 1981-2004, 2005-2022, 1981-2022).
The map should update based on both the selected question and period. However, I’m facing an issue:
• When I select a question from the dropdown, the map updates correctly.
• But, when I use the slider to change the period, the map sometimes resets to the first question and doesn’t update correctly based on the selected question.
I need the map to stay synchronized with both the selected question and period.
Here’s the code I’m using:
# Define the full questions for each column
questionlabels = {
'C006': 'Satisfaction with financial situation of household: 1 = Dissatisfied, 10 = Satisfied',
'C039': 'Work is a duty towards society: 1 = Strongly Disagree, 5 = Strongly Agree',
'C041': 'Work should come first even if it means less spare time: 1 = Strongly Disagree, 5 = Strongly Agree'
}
# Combine all periods into a single DataFrame with a new column for the period
meansperiod1merged'Period' = '1981-2004'
meansperiod2merged['Period'] = '2005-2022'
meansperiod3merged'Period' = '1981-2022'
combineddf = pd.concat([meansperiod1merged, meansperiod2merged, meansperiod3merged])
# Create a list of frames for
/r/Python
https://redd.it/1ikm1t1
Reddit
From the Python community on Reddit
Explore this post and more from the Python community
ParLlama v0.3.15 released. Supports Ollama, OpenAI, GoogleAI, Anthropic, Groq, Bedrock, OpenRouter
# What My project Does:
PAR LLAMA is a powerful TUI (Text User Interface) written in Python and designed for easy management and use of Ollama and Large Language Models as well as interfacing with online Providers such as Ollama, OpenAI, GoogleAI, Anthropic, Bedrock, Groq, xAI, OpenRouter
# Whats New:
# v0.3.15
* Added copy button to the fence blocks in chat markdown for easy code copy.
# v0.3.14
* Fix crash caused some models having some missing fields in model file
# v0.3.13
* Handle clipboard errors
# v0.3.12
* Fixed bug where changing providers that have custom urls would break other providers
* Fixed bug where changing Ollama base url would cause connection timed out
# Key Features:
* Easy-to-use interface for interacting with Ollama and cloud hosted LLMs
* Dark and Light mode support, plus custom themes
* Flexible installation options (uv, pipx, pip or dev mode)
* Chat session management
* Custom prompt library support
# GitHub and PyPI
* PAR LLAMA is under active development and getting new features all the time.
* Check out the project on GitHub or for full documentation, installation instructions, and to contribute: [https://github.com/paulrobello/parllama](https://github.com/paulrobello/parllama)
* PyPI [https://pypi.org/project/parllama/](https://pypi.org/project/parllama/)
# Comparison:
I have seem many command line and web applications for interacting with LLM's but have not found any TUI related applications
# Target Audience
Anybody that loves
/r/Python
https://redd.it/1il7vim
# What My project Does:
PAR LLAMA is a powerful TUI (Text User Interface) written in Python and designed for easy management and use of Ollama and Large Language Models as well as interfacing with online Providers such as Ollama, OpenAI, GoogleAI, Anthropic, Bedrock, Groq, xAI, OpenRouter
# Whats New:
# v0.3.15
* Added copy button to the fence blocks in chat markdown for easy code copy.
# v0.3.14
* Fix crash caused some models having some missing fields in model file
# v0.3.13
* Handle clipboard errors
# v0.3.12
* Fixed bug where changing providers that have custom urls would break other providers
* Fixed bug where changing Ollama base url would cause connection timed out
# Key Features:
* Easy-to-use interface for interacting with Ollama and cloud hosted LLMs
* Dark and Light mode support, plus custom themes
* Flexible installation options (uv, pipx, pip or dev mode)
* Chat session management
* Custom prompt library support
# GitHub and PyPI
* PAR LLAMA is under active development and getting new features all the time.
* Check out the project on GitHub or for full documentation, installation instructions, and to contribute: [https://github.com/paulrobello/parllama](https://github.com/paulrobello/parllama)
* PyPI [https://pypi.org/project/parllama/](https://pypi.org/project/parllama/)
# Comparison:
I have seem many command line and web applications for interacting with LLM's but have not found any TUI related applications
# Target Audience
Anybody that loves
/r/Python
https://redd.it/1il7vim
GitHub
GitHub - paulrobello/parllama: TUI for Ollama and other LLM providers
TUI for Ollama and other LLM providers. Contribute to paulrobello/parllama development by creating an account on GitHub.
Sync clipboard across guest and host with both running on wayland
# What My Project Does
WayClipSync enables clipboard sharing between guest and host in wayland sessions.
# Target Audience
People who like to tinker with different virtual machines and use wayland compositors that do not automatically support the clipboard sync.
# Comparison
# Note
This program requires
Github
/r/Python
https://redd.it/1il8lcd
# What My Project Does
WayClipSync enables clipboard sharing between guest and host in wayland sessions.
# Target Audience
People who like to tinker with different virtual machines and use wayland compositors that do not automatically support the clipboard sync.
# Comparison
spice-vdagent only works on X-org. On wayland the simplest way to copy from host is xsel -ob and send to host from guest is xsel -ib. It was annoying for me to remember to use this command, so I made this.# Note
This program requires
wl-clipboard to workGithub
/r/Python
https://redd.it/1il8lcd
GitHub
GitHub - azmat-y/WayClipSync: Enable clipboard sharing between guest and host in wayland using wl-clipboard
Enable clipboard sharing between guest and host in wayland using wl-clipboard - azmat-y/WayClipSync
Tailwind v4 problems with Flask
Hey guys I installed Tailwind v4 with CLI to work with Flask but I am having problems in that the UI does not show everything it must show (for example, colors). Anyone with the same issue? By the way v3 works fine!
/r/flask
https://redd.it/1il8x4i
Hey guys I installed Tailwind v4 with CLI to work with Flask but I am having problems in that the UI does not show everything it must show (for example, colors). Anyone with the same issue? By the way v3 works fine!
/r/flask
https://redd.it/1il8x4i
Reddit
From the flask community on Reddit
Explore this post and more from the flask community
XSS in django-impersonate 1.9.3 and django-gravatar2 1.4.4
https://stsewd.dev/posts/xss-in-djang-impersonate-and-django-gravatar2/
/r/django
https://redd.it/1il713q
https://stsewd.dev/posts/xss-in-djang-impersonate-and-django-gravatar2/
/r/django
https://redd.it/1il713q
Santos Gallegos
XSS in django-impersonate 1.9.3 and django-gravatar2 1.4.4
Details about two cross-site scripting vulnerabilities that I reported to django-impersonate and django-gravatar2.
IntentGuard - verify code properties using natural language assertions
I'm sharing IntentGuard, a testing tool that lets you verify code properties using natural language assertions. It's designed for scenarios where traditional test code becomes unwieldy, but comes with important caveats.
What My Project Does:
Lets you write test assertions like "All database queries should be parameterized" or "Public methods must have complete docstrings"
Integrates with pytest/unittest
Uses a local AI model (1B parameter fine-tuned Llama 3.2) via llamafile
Provides detailed failure explanations
MIT licensed
✅ Working Today:
Basic natural language assertions for Python code
pytest/unittest integration
Local model execution (no API calls)
Result caching for unchanged code/assertions
Self-testing capability (entire test suite uses IntentGuard itself)
⚠️ Known Limitations:
Even with consensus voting, misjudgments can happen due to the weakness of the model
Performance and reliability benchmarks are unfortunately not yet available
Why This Might Be Interesting:
Could help catch architectural drift in large codebases
Useful for enforcing team coding standards
Potential for documentation/compliance checks
Complements traditional testing rather than replacing it
Next Steps:
1. Measure the performance and reliability across a set of diverse problems
2. Improve model precision by expanding the training data and using a stronger base model
Installation & Docs:
GitHub Repository
Comparison: I'm not aware of any direct alternatives.
Target Audience: The tool works but needs rigorous evaluation -
/r/Python
https://redd.it/1ilfqtm
I'm sharing IntentGuard, a testing tool that lets you verify code properties using natural language assertions. It's designed for scenarios where traditional test code becomes unwieldy, but comes with important caveats.
What My Project Does:
Lets you write test assertions like "All database queries should be parameterized" or "Public methods must have complete docstrings"
Integrates with pytest/unittest
Uses a local AI model (1B parameter fine-tuned Llama 3.2) via llamafile
Provides detailed failure explanations
MIT licensed
✅ Working Today:
Basic natural language assertions for Python code
pytest/unittest integration
Local model execution (no API calls)
Result caching for unchanged code/assertions
Self-testing capability (entire test suite uses IntentGuard itself)
⚠️ Known Limitations:
Even with consensus voting, misjudgments can happen due to the weakness of the model
Performance and reliability benchmarks are unfortunately not yet available
Why This Might Be Interesting:
Could help catch architectural drift in large codebases
Useful for enforcing team coding standards
Potential for documentation/compliance checks
Complements traditional testing rather than replacing it
Next Steps:
1. Measure the performance and reliability across a set of diverse problems
2. Improve model precision by expanding the training data and using a stronger base model
Installation & Docs:
pip install intentguardGitHub Repository
Comparison: I'm not aware of any direct alternatives.
Target Audience: The tool works but needs rigorous evaluation -
/r/Python
https://redd.it/1ilfqtm
GitHub
GitHub - kdunee/intentguard: A Python library for verifying code properties using natural language assertions.
A Python library for verifying code properties using natural language assertions. - kdunee/intentguard
Need advice with search functionality design
Hi, I've been developing my first project in Django and one of its features is that users can search for movies/series by title (shouldn't match the exact name) and add them to their watchlist. The problem I ran into is that I'm not sure of how to provide the search results.
The first thing that came to my mind was to use API that I'd found on rapidapi, but the free plan offers only 500 requests per month which might get problematic when the app is used by multiple users. Then I thought I would query a database first and if there are no matches, I'd make an API call and store it into the database.
However, now I can't know for sure if the search query contains any possible match. For example, it can be some nonsense, which won't retrieve any data from the db and I would need to make API request to find out that there are no such movies. Or I can't be certain that the query will get enough data from the db. Like, if the first 3 Jurassic Park movies are in the db and a user searches for 'jurassic', they
/r/django
https://redd.it/1ilazjf
Hi, I've been developing my first project in Django and one of its features is that users can search for movies/series by title (shouldn't match the exact name) and add them to their watchlist. The problem I ran into is that I'm not sure of how to provide the search results.
The first thing that came to my mind was to use API that I'd found on rapidapi, but the free plan offers only 500 requests per month which might get problematic when the app is used by multiple users. Then I thought I would query a database first and if there are no matches, I'd make an API call and store it into the database.
However, now I can't know for sure if the search query contains any possible match. For example, it can be some nonsense, which won't retrieve any data from the db and I would need to make API request to find out that there are no such movies. Or I can't be certain that the query will get enough data from the db. Like, if the first 3 Jurassic Park movies are in the db and a user searches for 'jurassic', they
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D Simple Questions Thread
Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead!
Thread will stay alive until next one so keep posting after the date in the title.
Thanks to everyone for answering questions in the previous thread!
/r/MachineLearning
https://redd.it/1ilhw29
Please post your questions here instead of creating a new thread. Encourage others who create new posts for questions to post here instead!
Thread will stay alive until next one so keep posting after the date in the title.
Thanks to everyone for answering questions in the previous thread!
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R LIMO: Less is More for Reasoning
>We present a fundamental discovery that challenges our understanding of how complex reasoning emerges in large language models. While conventional wisdom suggests that sophisticated reasoning tasks demand extensive training data (often >100,000 examples), we demonstrate a striking phenomenon: complex mathematical reasoning abilities can be effectively elicited with surprisingly few examples. This finding challenges not only the assumption of massive data requirements but also the common belief that supervised fine-tuning primarily leads to memorization rather than generalization. Through comprehensive experiments, our proposed model LIMO demonstrates unprecedented performance and efficiency in mathematical reasoning. With merely 817 curated training samples, LIMO achieves 57.1% accuracy on the highly challenging AIME benchmark and 94.8% on MATH, improving the performance of previous strong SFT-based models from 6.5% to 57.1% on AIME and from 59.2% to 94.8% on MATH, while only using 1% of the training data required by previous approaches. Most remarkably, LIMO demonstrates exceptional out-of-distribution generalization, achieving 40.5% absolute improvement across 10 diverse benchmarks, outperforming models trained on 100x more data, directly challenging the prevailing notion that SFT inherently leads to memorization rather than generalization. Synthesizing these pioneering results, we propose the Less-Is-More Reasoning Hypothesis (LIMO Hypothesis): In foundation models where domain knowledge has been comprehensively encoded during pre-training, sophisticated reasoning capabilities can emerge through minimal but precisely orchestrated demonstrations of cognitive processes.
/r/MachineLearning
https://redd.it/1ile9nu
>We present a fundamental discovery that challenges our understanding of how complex reasoning emerges in large language models. While conventional wisdom suggests that sophisticated reasoning tasks demand extensive training data (often >100,000 examples), we demonstrate a striking phenomenon: complex mathematical reasoning abilities can be effectively elicited with surprisingly few examples. This finding challenges not only the assumption of massive data requirements but also the common belief that supervised fine-tuning primarily leads to memorization rather than generalization. Through comprehensive experiments, our proposed model LIMO demonstrates unprecedented performance and efficiency in mathematical reasoning. With merely 817 curated training samples, LIMO achieves 57.1% accuracy on the highly challenging AIME benchmark and 94.8% on MATH, improving the performance of previous strong SFT-based models from 6.5% to 57.1% on AIME and from 59.2% to 94.8% on MATH, while only using 1% of the training data required by previous approaches. Most remarkably, LIMO demonstrates exceptional out-of-distribution generalization, achieving 40.5% absolute improvement across 10 diverse benchmarks, outperforming models trained on 100x more data, directly challenging the prevailing notion that SFT inherently leads to memorization rather than generalization. Synthesizing these pioneering results, we propose the Less-Is-More Reasoning Hypothesis (LIMO Hypothesis): In foundation models where domain knowledge has been comprehensively encoded during pre-training, sophisticated reasoning capabilities can emerge through minimal but precisely orchestrated demonstrations of cognitive processes.
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FastAPI Guard - A FastAPI extension to secure your APIs
Hi everyone,
I've published FastAPI Guard some time ago:
Documentation: rennf93.github.io/fastapi-guard/
GitHub repo: github.com/rennf93/fastapi-guard
What is it?
FastAPI Guard is a security middleware for FastAPI that provides:
- IP whitelisting/blacklisting
- Rate limiting & automatic IP banning
- Penetration attempt detection
- Cloud provider IP blocking
- IP geolocation via IPInfo.io
- Custom security logging
- CORS configuration helpers
It's licensed under MIT and integrates seamlessly with FastAPI applications.
Comparison to alternatives:
-
-
-
-
Key differentiators:
- Combines multiple security layers in single middleware
- Automatic IP banning based on suspicious activity
- Built-in cloud provider detection
- Daily-updated IP geolocation database
- Production-ready configuration defaults
Target Audience:
FastAPI developers needing:
- Defense-in-depth security strategy
- IP-based access control
- Automated threat mitigation
- Compliance with geo-restriction requirements
- Penetration attempt monitoring
Feedback wanted
Thanks!
/r/Python
https://redd.it/1ilhbkk
Hi everyone,
I've published FastAPI Guard some time ago:
Documentation: rennf93.github.io/fastapi-guard/
GitHub repo: github.com/rennf93/fastapi-guard
What is it?
FastAPI Guard is a security middleware for FastAPI that provides:
- IP whitelisting/blacklisting
- Rate limiting & automatic IP banning
- Penetration attempt detection
- Cloud provider IP blocking
- IP geolocation via IPInfo.io
- Custom security logging
- CORS configuration helpers
It's licensed under MIT and integrates seamlessly with FastAPI applications.
Comparison to alternatives:
-
fastapi-security: Focuses more on authentication, while FastAPI Guard provides broader network-layer protection-
slowapi: Handles rate limiting but lacks IP analysis/geolocation features-
fastapi-limiter: Pure rate limiting without security features-
fastapi-auth: Authentication-focused without IP managementKey differentiators:
- Combines multiple security layers in single middleware
- Automatic IP banning based on suspicious activity
- Built-in cloud provider detection
- Daily-updated IP geolocation database
- Production-ready configuration defaults
Target Audience:
FastAPI developers needing:
- Defense-in-depth security strategy
- IP-based access control
- Automated threat mitigation
- Compliance with geo-restriction requirements
- Penetration attempt monitoring
Feedback wanted
Thanks!
/r/Python
https://redd.it/1ilhbkk
GitHub
GitHub - rennf93/fastapi-guard: A security library for FastAPI that provides middleware to control IPs, log requests, and detect…
A security library for FastAPI that provides middleware to control IPs, log requests, and detect penetration attempts. It integrates seamlessly with FastAPI to offer robust protection against vario...
Django REST framework, Django Ninja, FastAPI with Pydantic, and Golang API serialization performance.
A few months ago I wrote a post about comparing the API serialization performance retrieve large amount of records in Django REST Framework, Django Ninja, and Golang. I provided a step-by-step guide in optimization for Django as well as dockerized example for each framework tested. A lot of you liked it, and one common ask was, how does FastAPI compared.
It's taken me a while to sit down and work on this again, but I have now included a dockerized example of FastAPI with SQLAlchemy and Pydantic on the same set of data models with exact same API for comparison. I also added a dockerized example of Locust to do testing now. You can check it out and test it for yourself: https://github.com/oscarychen/building-efficient-api
I gotta say I was expecting FastAPI with Pydantic to perform not far behind Django Ninja, because the serialization of response data mechanism was expected to be similar. I am actually surprised FastAPI is quite a bit faster. Obviously, if we strip parsing response payload through the schema/serializer, Django REST Framework and Django Ninja are a lot faster, but typically on most APIs we would have those mechanism as part of response data parsing, and FastAPI does it significantly
/r/django
https://redd.it/1ilp876
A few months ago I wrote a post about comparing the API serialization performance retrieve large amount of records in Django REST Framework, Django Ninja, and Golang. I provided a step-by-step guide in optimization for Django as well as dockerized example for each framework tested. A lot of you liked it, and one common ask was, how does FastAPI compared.
It's taken me a while to sit down and work on this again, but I have now included a dockerized example of FastAPI with SQLAlchemy and Pydantic on the same set of data models with exact same API for comparison. I also added a dockerized example of Locust to do testing now. You can check it out and test it for yourself: https://github.com/oscarychen/building-efficient-api
I gotta say I was expecting FastAPI with Pydantic to perform not far behind Django Ninja, because the serialization of response data mechanism was expected to be similar. I am actually surprised FastAPI is quite a bit faster. Obviously, if we strip parsing response payload through the schema/serializer, Django REST Framework and Django Ninja are a lot faster, but typically on most APIs we would have those mechanism as part of response data parsing, and FastAPI does it significantly
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https://redd.it/1ilp876
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