Python

Sunday Daily Thread: What's everyone working on this week?

2026-05-10T00:00:12+00:00

Weekly Thread: What's Everyone Working On This Week? 🛠️

Hello r/Python! It's time to share what you've been working on! Whether it's a work-in-progress, a completed masterpiece, or just a rough idea, let us know what you're up to!

How it Works:

Show & Tell: Share your current projects, completed works, or future ideas.
Discuss: Get feedback, find collaborators, or just chat about your project.
Inspire: Your project might inspire someone else, just as you might get inspired here.

Guidelines:

Feel free to include as many details as you'd like. Code snippets, screenshots, and links are all welcome.
Whether it's your job, your hobby, or your passion project, all Python-related work is welcome here.

Example Shares:

Machine Learning Model: Working on a ML model to predict stock prices. Just cracked a 90% accuracy rate!
Web Scraping: Built a script to scrape and analyze news articles. It's helped me understand media bias better.
Automation: Automated my home lighting with Python and Raspberry Pi. My life has never been easier!

Let's build and grow together! Share your journey and learn from others. Happy coding! 🌟

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Tuesday Daily Thread: Advanced questions

2026-06-02T00:00:05+00:00

Weekly Wednesday Thread: Advanced Questions 🐍

Dive deep into Python with our Advanced Questions thread! This space is reserved for questions about more advanced Python topics, frameworks, and best practices.

How it Works:

Ask Away: Post your advanced Python questions here.
Expert Insights: Get answers from experienced developers.
Resource Pool: Share or discover tutorials, articles, and tips.

Guidelines:

This thread is for advanced questions only. Beginner questions are welcome in our Daily Beginner Thread every Thursday.
Questions that are not advanced may be removed and redirected to the appropriate thread.

Recommended Resources:

If you don't receive a response, consider exploring r/LearnPython or join the Python Discord Server for quicker assistance.

Example Questions:

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

Let's deepen our Python knowledge together. Happy coding! 🌟

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New Humble Bundle of Python ebooks benefiting the Python Software Foundation

2026-06-01T18:23:27+00:00

Pay at least $36 for 15 ebooks from No Starch Press benefiting the PSF: https://www.humblebundle.com/books/python-good-stuff-no-starch-books

Hello, I'm Al Sweigart, author of a few books in the bundle. Here's some info about them:

Automate the Boring Stuff with Python - I wrote this to be a programming book for office workers who wanted to escape Excel. It's a book for complete beginners with no coding experience, or for folks who want to skip to Part 2 and learn about several useful packages in the Python ecosystem for web scraping, graph generation, image manipulation, text-to-speech, OCR, regex, sending mobile notifications, and more. Automate is now in it's third edition.
Cracking Codes with Python - This was the third book I wrote (and self-published), and then No Starch published a new edition under a new title. (It was previously called Hacking Secret Ciphers with Python.) I had found several "ciphers and code breaking" books that discussed ciphers (The Code Book: The Science of Secrecy from Ancient Egypt to Quantum Cryptography by Simon Singh is great) but I didn't find any books on writing code to do the code breaking. I wanted Python programs you could literally run on ciphertext that would actually work. Writing this book was a lot of fun. It's also aimed at completely new programmers, using encryption and code breaking programs as the example programming projects.
The Big Book of Small Python Projects - As a kid I loved books like BASIC Computer Games that just listed the source code for actual programs you could run. I learned way more from having these small examples, so I wanted an updated version of this. (Admittedly, a lot of those BASIC games were buggy or just not fun.) There are 81 programs that use text-based user interfaces (TUI), not out of old-school nostalgia but because it's really helpful to learners to have the program source code and program output be the same medium: text. Like, you can look at the text output and find the print() call that caused it. It makes coding less abstract.

(Note that my books are released under a Creative Commons license and can be found online, but these ebooks have much nicer formatting than the HTML pages on my website.)

No Starch Press is my publisher, but I genuinely do love their books. The ones in this bundle that are on my to-read list that I'm especially excited about:

Practical Deep Learning: 2nd Edition - I've been wanting to read this since the first edition, especially now that I'm diving into LLMs more. This book doesn't shy away from technical details but it's not a textbook: there's actual practical information here.
Make Python Talk - I've already read this and used some of it as the basis for a PyCon talk on text-to-speech and speech recognition. This is stuff that was really unreliable twenty years ago, but these days it's so easy to add it to your Python scripts with just a few lines of code.
Computer Science from Scratch - One of my biggest gripes with CS education is that they often talk about concepts in some abstract way on a whiteboard or in Powerpoint slides, and they don't just give you code you can play with. I'm really interested in diving into this one.
Python for Excel Users - My Automate book touches on using Python and spreadsheets, but I'm glad there's an entire book on the topic now.

But of course, Python Crash Course by Eric Matthes is a great book for beginners who want to learn to code. (It consistently beats Automate the Boring Stuff on Amazon.) This is a great collection of ebooks.

Remember to max out the amount of your payment goes to the Python Software Foundation. Scroll down to and click Adjust Donation, then click Custom Amount to edit what percentage of your contribution is split between Developers/Publishers, Humble Bundle, and Charity.

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Python bindings for the C++ DataFrame

2026-06-02T00:16:41+00:00

Grizzlars is Python bindings for the C++ DataFrame with an interface almost identical to Pandas. It is a bit of work in progress. It depends on an older version of C++ DataFrame than the latest release. But it is moving along surely.

submitted by /u/hmoein
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excalidraw-render: render .excalidraw files to PNG/SVG in pure Python, no Node, no headless browser

2026-06-01T20:15:51+00:00

Shipping a small tool I built for a docs pipeline at work. Cleanly converts .excalidraw to PNG or SVG without needing Node, node-canvas, or a headless Chrome.

Quick taste:

pip install excalidraw-render

excalidraw-render diagram.excalidraw # → diagram.png

excalidraw-render diagram.excalidraw -f svg

excalidraw-render ./docs/ # batch mode

Why a new tool? The existing options:

`excalidraw_export` (npm): SVG-only, depends on node-canvas which is famously hard to install
`@excalidraw/excalidraw` (npm): React library, needs a headless browser to render

For automating diagram exports in CI/docs generators, both are heavy. excalidraw-render is just Python + cairosvg.

Honest trade-off: no roughjs hand-drawn style — that's a JS library with no native Python port. The output is clean vector instead, which is usually what doc pipelines want. A pyroughjs port is on the roadmap.

v0.1 covers most element types (rectangle, ellipse, diamond, arrow with all 10 arrowhead variants, line, text, freedraw, image, frame). v0.2 adds PDF/JPEG, terminal output for iTerm2/Kitty/Sixel, a Markdown preprocessor that inlines .excalidraw references, and watch mode.

Also brew-installable on macOS: brew install shivama205/tap/excalidraw-render

Repo: https://github.com/shivama205/excalidraw-render (Listed in [awesome-svg github] under Optimization & Tools.)

Feedback welcome, especially on element edge cases I might've missed.

submitted by /u/shivama205
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How I handle OCR fallback and per-language field parsing when extracting data from PDFs in Python (w

2026-06-01T20:20:02+00:00

I've been working on a document processing tool that extracts structured data from PDFs (invoices, bank statements, contracts) and I ran into two problems that aren't well documented anywhere: OCR fallback strategy and per-language field normalization. Sharing what worked.

**Problem 1: Silent OCR failure**

Most guides tell you to use `pdfplumber` or `PyMuPDF` to extract text. What they don't tell you is that scanned PDFs return an empty string (or worse, garbage spacing characters) without raising any exception. You'll process it, send it to an LLM, and get hallucinated data back – all silently.

My solution: check text length and density *before* calling the LLM. If the extracted text is below a threshold (I use 50 meaningful characters per page), fall back to Tesseract OCR:

```python

import pdfplumber

import pytesseract

from pdf2image import convert_from_bytes

def extract_text_with_fallback(pdf_bytes: bytes) -> str:

with pdfplumber.open(io.BytesIO(pdf_bytes)) as pdf:

text = ''.join(p.extract_text() or '' for p in pdf.pages)

# Scanned PDF check: meaningful chars per page

pages = len(pdf.pages) if pdf.pages else 1

if len(text.strip()) / pages < 50:

images = convert_from_bytes(pdf_bytes, dpi=300)

text = '\n'.join(pytesseract.image_to_string(img) for img in images)

return text

```

The `dpi=300` matters a lot – at 150dpi Tesseract misses characters on dense invoices. 300 is the sweet spot between accuracy and speed.

**Problem 2: Per-language field normalization**

European invoices are a nightmare. The same field can be:

- `Total` / `Totale` / `Gesamtbetrag` / `Montant total`

- Dates as `31/12/2024` (IT), `31.12.2024` (DE), `2024-12-31` (ISO)

- Decimals as `1.234,56` (IT/DE) vs `1,234.56` (EN)

Instead of trying to make one regex rule to catch all formats, I built a simple language detector that runs on a short sample of the text, then loads a locale-specific normalization config:

```python

LOCALE_CONFIGS = {

'it': {'decimal_sep': ',', 'thousand_sep': '.', 'date_formats': ['%d/%m/%Y', '%d-%m-%Y']},

'de': {'decimal_sep': ',', 'thousand_sep': '.', 'date_formats': ['%d.%m.%Y']},

'en': {'decimal_sep': '.', 'thousand_sep': ',', 'date_formats': ['%m/%d/%Y', '%Y-%m-%d']},

'fr': {'decimal_sep': ',', 'thousand_sep': ' ', 'date_formats': ['%d/%m/%Y']},

}

def normalize_amount(raw: str, locale: str) -> float:

cfg = LOCALE_CONFIGS.get(locale, LOCALE_CONFIGS['en'])

cleaned = raw.replace(cfg['thousand_sep'], '').replace(cfg['decimal_sep'], '.')

return float(re.sub(r'[^\d.]', '', cleaned))

```

For language detection I use `langdetect` on the first 500 characters of extracted text – fast, lightweight, accurate enough for this use case.

Hope this helps anyone building document processing pipelines. Happy to answer questions on edge cases I've hit.

submitted by /u/LorenzoNardi
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Monday Daily Thread: Project ideas!

2026-06-01T00:00:05+00:00

Weekly Thread: Project Ideas 💡

Welcome to our weekly Project Ideas thread! Whether you're a newbie looking for a first project or an expert seeking a new challenge, this is the place for you.

How it Works:

Suggest a Project: Comment your project idea—be it beginner-friendly or advanced.
Build & Share: If you complete a project, reply to the original comment, share your experience, and attach your source code.
Explore: Looking for ideas? Check out Al Sweigart's "The Big Book of Small Python Projects" for inspiration.

Guidelines:

Clearly state the difficulty level.
Provide a brief description and, if possible, outline the tech stack.
Feel free to link to tutorials or resources that might help.

Example Submissions:

Project Idea: Chatbot

Difficulty: Intermediate

Tech Stack: Python, NLP, Flask/FastAPI/Litestar

Description: Create a chatbot that can answer FAQs for a website.

Resources: Building a Chatbot with Python

Project Idea: Weather Dashboard

Difficulty: Beginner

Tech Stack: HTML, CSS, JavaScript, API

Description: Build a dashboard that displays real-time weather information using a weather API.

Resources: Weather API Tutorial

Project Idea: File Organizer

Difficulty: Beginner

Tech Stack: Python, File I/O

Description: Create a script that organizes files in a directory into sub-folders based on file type.

Resources: Automate the Boring Stuff: Organizing Files

Let's help each other grow. Happy coding! 🌟

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What is a good project idea for science?

2026-06-01T18:03:21+00:00

Looking for an interesting physics(theoretical or not) or chemistry project to work with in python.

Please include your ideas and reasons below.

Also I might send progress through, so please send suggestions for additions!

:D

submitted by /u/Cold-Detective1356
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Sunday Daily Thread: What's everyone working on this week?

2026-05-31T00:00:06+00:00

Weekly Thread: What's Everyone Working On This Week? 🛠️

Hello r/Python! It's time to share what you've been working on! Whether it's a work-in-progress, a completed masterpiece, or just a rough idea, let us know what you're up to!

How it Works:

Show & Tell: Share your current projects, completed works, or future ideas.
Discuss: Get feedback, find collaborators, or just chat about your project.
Inspire: Your project might inspire someone else, just as you might get inspired here.

Guidelines:

Feel free to include as many details as you'd like. Code snippets, screenshots, and links are all welcome.
Whether it's your job, your hobby, or your passion project, all Python-related work is welcome here.

Example Shares:

Machine Learning Model: Working on a ML model to predict stock prices. Just cracked a 90% accuracy rate!
Web Scraping: Built a script to scrape and analyze news articles. It's helped me understand media bias better.
Automation: Automated my home lighting with Python and Raspberry Pi. My life has never been easier!

Let's build and grow together! Share your journey and learn from others. Happy coding! 🌟

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Perspective on pypi numbers

2026-05-31T21:08:28+00:00

Hey all,

I'm new the world of interpreting pypi numbers and peaks and trends. What would you say about this? https://pepy.tech/projects/gitgalaxy?timeRange=threeMonths&category=version&includeCIDownloads=true&granularity=daily&viewType=line&versions=Total%2C2.*%2C1.* I've got 11k downloads in 2 months but 36 GitHub stars. Is this a normal ratio? Are most of these bots? It seems like GitHub stars are rare but downloads have some basal amount of noise values? Or is this a strong signal that some ppl have found value in my project? why are the peaks so peaky?

submitted by /u/Chunky_cold_mandala
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CVE-2026-48710: A Maintainer's Perspective

2026-05-29T15:40:48+00:00

This is my reply to the vulnerability CVE-2026-48710:

https://marcelotryle.com/blog/2026/05/28/cve-2026-48710-a-maintainers-perspective

submitted by /u/Aggravating-Mobile33
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News : MiniMax M2.7 works well on python with openai python package.

2026-05-30T23:39:44+00:00

MiniMax M2.7 works well on Python with the OpenAI Python package. See my test on my blog: https://python-catalin.blogspot.com/2026/05/python-31011-minimax-m27-tested-with.html

submitted by /u/catafest
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How to deal with slop PR's as a maintainer?

2026-05-29T05:04:17+00:00

says you are the maintainer of a small (50-100 stars) library.

You see someone fork your repo, mention one of your issues in his commits, so your are happy, someone taking true interest in your work!

You take a look at his branch, and there you see pure AI slop, with files at the repo root (not in the src), tests with print statement even tough you use pytest and it's clearly explained in the contributing doc, and purely hallucinated imports like "from my lib import Foo, Bar" even tough there's never any mention of these two in the code or the documentation (and thus completely incomprehensible code with subclasses from these hallucinated types, etc...)

how to best deal with this without appearing hostile to other potential future contributors?

I want contributors, I'm very happy for anyone taking a look at my work, but at the same time that person has other forks of repos where it just seems to be hunting for "good first issues" label, and thus I'm not sure on the value of giving an honest review if it's not clear on wether there's a genuine intention to resolve the issue or just collect cool github points.

EDIT 11h later:

Thanks to everyone who gave his perspective!!

I don't think I have the time immediately to answer to everyone but there's a lot of good advice here.

By the way LMAO I should have linked my lib to maybe get actual contributors, this post is doing views.

Hint: it's the top one ranked in this comparison ->
https://www.reddit.com/r/Python/comments/1rj3ct7/a_comparison_of_rustlike_fluent_iterator_libraries/

submitted by /u/Beginning-Fruit-1397
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Saturday Daily Thread: Resource Request and Sharing! Daily Thread

2026-05-30T00:00:05+00:00

Weekly Thread: Resource Request and Sharing 📚

Stumbled upon a useful Python resource? Or are you looking for a guide on a specific topic? Welcome to the Resource Request and Sharing thread!

How it Works:

Request: Can't find a resource on a particular topic? Ask here!
Share: Found something useful? Share it with the community.
Review: Give or get opinions on Python resources you've used.

Guidelines:

Please include the type of resource (e.g., book, video, article) and the topic.
Always be respectful when reviewing someone else's shared resource.

Example Shares:

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

Example Requests:

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

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

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What Python automation saved you the most hours over the last year?

2026-05-30T03:35:38+00:00

Not looking for side projects.

I mean automations that actually became part of your workflow.

Examples:

Data processing pipelines

Report generation

Monitoring scripts

Internal tools

File organization systems

The most valuable automation I built was probably only ~200 lines of code, but it eliminated a repetitive task I was doing daily.

What Python script ended up having the highest ROI for you?

submitted by /u/Bladerunner_7_
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How we cut LLM token usage 89% in a ReAct agent using intent classification — architecture writeup

2026-05-30T17:01:50+00:00

We're building an AI agent that runs SQL queries against PostgreSQL databases and generates charts, anomaly reports, and analysis from natural language queries.

The agent is a SingleLLM ReAct loop — one model, one growing conversation, up to 15 iterations. No multi-agent orchestration, no separate planner.

The biggest performance problem we hit: the tool registry has 50+ tools. Sending all tool schemas to the LLM every iteration costs ~18,000 tokens per call. With 15 iterations that's 270,000 tokens per query just for tool definitions before any real work.

Our fix: intent classification before the loop starts.

The LLM classifies the query into 1 of 13 intents (explore, analyze, time, segment, quality, report, predict, etc.) and we only pass the relevant tool group. 18K → 2K tokens per iteration. 89% reduction with no loss in output quality.

We also added:

- Dynamic intent recheck every 3 iterations (queries shift mid-loop)

- Intent-based model routing (Nova Micro for explore, Nova Lite for reasoning tasks)

- Tool call deduplication to prevent repeated list_tables fetches

- Parallel tool execution via asyncio.gather

- Separate retry logic for connection errors vs SQL syntax errors

Full architecture writeup with code, flowcharts, and the full ReAct loop mechanics here:

https://vivekmind.com/blog/the-singlellm-agent-how-one-model-one-loop-and-15-iterations-build-a-reasoning-engine

Happy to answer questions about any of it — particularly around the intent classification design or the artifact emission pipeline.

submitted by /u/Vivek-Kumar-yadav
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My 4-step in Python (Logic -> Pseudocode -> Code -> AI). What do you think?

2026-05-30T10:19:38+00:00

Hey guys,

Just wanted to share my current learning workflow as a Python beginner. I see a lot of advice warning against over-relying on AI, so I built a system that forces me to think first:

Step 1: Map out the logic.
Step 2: Write the pseudocode.
Step 3: Code it out and try to polish/refactor it using my own brain power first.
Step 4: Use Gemini in VS Code only when I'm completely stuck, making sure to ask it for a deep, clear explanation of the code it provides.

Building the logic first and using AI as a tutor rather than a code generator has drastically improved my retention.

For those who use AI while learning, how do you make sure you're still actually learning?

submitted by /u/Candy_Sombrelune
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What's Python cloud hosting using these days?

2026-05-29T00:44:59+00:00

Hey, everyone!

I'm trying to find a decent home for my side projects (FastAPI stuff, one Django app, and a Celery worker that scrapes data overnight). Nothing crazy, I just want to SSH in, set up a venv, point Gunicord at my WSGI app, and not have the provider babysit my stack. Pay-as-you-go is a big plus since some of my bots only run a few hours a day.

So far I've looked at DO, Vultr, Serverspace and Linode - each has its own quirks around pricing, regions, and how much hand-holding they do.

What are you all running your Python apps on? Any hidden gem I'm missing, especially for heavier ML workloads or Celery queues?

Thanks!

submitted by /u/Angeeliiccc
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Jupyter notebooks touching production data are application code from a security standpoint

2026-05-29T06:45:55+00:00

Started auditing how our data team works and the security picture was worse than expected. Notebooks querying production databases directly, credentials hardcoded in cells because environment variable setup felt like friction, code that's been copied between notebooks so many times the original author is impossible to trace.

None of it goes through any review process that the engineering team's code goes through. No SAST, no security-minded PR review, no scanning of any kind. The assumption seems to be that notebooks are exploratory and therefore informal, but at some point exploratory code started running against production data with production access and that distinction stopped meaning anything.

These notebooks often have broader data access than the application code because the people writing them needed to move fast and used their own credentials. That access never got revisited.

submitted by /u/UnhappyPay2752
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Free: Invalid Pointer - Running SQLAlquemy and Tensorflow in Google Vertex AI

2026-05-30T00:09:29+00:00

I can't share the code because it was a company-wide error.

The problem was that while running a Docker container within the Google Cloud platform using their tool called Google Vertex Jobs, I encountered the following error: "Free: Invalid Pointer". Inside this container, a Python script runs containing the model training I do using TensorFlow, and I also connect to the database using SQLAlchemy. However, I encountered this error where the script stopped executing the rest of the code.

Okay, up to this point it's confusing because it didn't generate a Python exception. I analyzed the executions, even within the SQLAlchemy functions, and when I removed the code, the script worked normally without this problem.

The alternative I found was to add the SQLAlchemy executions to a parallel process, separate from the model training execution structure. This allowed me to run the script without problems. Has anyone else experienced this issue? Or can you recommend an alternative?

submitted by /u/sc0v0ne
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I'm using ipynb notebook format to store conversations with AI data analyst

2026-05-29T15:28:46+00:00

Hi there!

I've seen many AI data analyst projects - basically you have a chat, which has access to your data and documents and you can ask it any questions. Then it is using code and tools to provide repsponses. I create such AI data analyst and I have used ipynb notebooks format to store the conversation. I think it is perfect format for this. I can keep text, code and outputs in the single file. What is more, it is easy to publish as static web page.

What do you think about such use case for famous ipynb format? What else are you using to store conversations with AI?

submitted by /u/pplonski
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Friday Daily Thread: r/Python Meta and Free-Talk Fridays

2026-05-29T00:00:35+00:00

Weekly Thread: Meta Discussions and Free Talk Friday 🎙️

Welcome to Free Talk Friday on /r/Python! This is the place to discuss the r/Python community (meta discussions), Python news, projects, or anything else Python-related!

How it Works:

Open Mic: Share your thoughts, questions, or anything you'd like related to Python or the community.
Community Pulse: Discuss what you feel is working well or what could be improved in the /r/python community.
News & Updates: Keep up-to-date with the latest in Python and share any news you find interesting.

Guidelines:

All topics should be related to Python or the /r/python community.
Be respectful and follow Reddit's Code of Conduct.

Example Topics:

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

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

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Best Way to Protect Python Windows Software Without Antivirus False Positives?

2026-05-29T06:30:52+00:00

I am developing a Windows-based software in Python and currently using PyArmor to protect it. However, Windows Security and some antivirus programs are detecting it as a virus because of the PyArmor protection/obfuscation.

What is the best way to protect my software from cracking, reverse engineering, or piracy without triggering antivirus false positives?

submitted by /u/fxboshop
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Thursday Daily Thread: Python Careers, Courses, and Furthering Education!

2026-05-28T00:00:05+00:00

Weekly Thread: Professional Use, Jobs, and Education 🏢

Welcome to this week's discussion on Python in the professional world! This is your spot to talk about job hunting, career growth, and educational resources in Python. Please note, this thread is not for recruitment.

How it Works:

Career Talk: Discuss using Python in your job, or the job market for Python roles.
Education Q&A: Ask or answer questions about Python courses, certifications, and educational resources.
Workplace Chat: Share your experiences, challenges, or success stories about using Python professionally.

Guidelines:

This thread is not for recruitment. For job postings, please see r/PythonJobs or the recruitment thread in the sidebar.
Keep discussions relevant to Python in the professional and educational context.

Example Topics:

Career Paths: What kinds of roles are out there for Python developers?
Certifications: Are Python certifications worth it?
Course Recommendations: Any good advanced Python courses to recommend?
Workplace Tools: What Python libraries are indispensable in your professional work?
Interview Tips: What types of Python questions are commonly asked in interviews?

Let's help each other grow in our careers and education. Happy discussing! 🌟

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Apache Fory Serialization 1.0.0 Released Now

2026-05-27T14:53:50+00:00

Hi everyone,

Apache Fory 1.0 has been released recently.

Fory is a fast multi-language serialization framework for native objects, Schema IDL, and cross-language data exchange. It supports Java, Python, C++, Go, Rust, JavaScript/TypeScript, C#, Swift, Dart, Scala, and Kotlin.

The main idea is simple: in many systems, data is not just a flat schema message. Applications often need to serialize idiomatic domain objects, nested containers, polymorphic types, object references, shared references, or even circular object graphs. Fory is designed to handle these cases efficiently while still supporting cross-language data exchange when needed.

With 1.0, Fory has reached a more stable point:

Cross-language serialization is now the default path across supported languages
Schema IDL supports richer object models, including shared and circular references
Decimal and bfloat16 support were added
Nested container and field codec support has improved across runtimes
Kotlin, Scala, Android, Swift, and Dart support have been expanded
Benchmarks and documentation have been refreshed

Fory is not meant to replace Protobuf everywhere. Fory is more focused on cases where you want high-performance serialization while preserving more of the native object model, or where the same data model needs to move across multiple runtimes without too much glue code.

Links:

GitHub: https://github.com/apache/fory
Website: https://fory.apache.org/
Release note: https://fory.apache.org/blog/fory_1_0_0_release/

I would be interested in feedback from people who have worked with Protobuf, FlatBuffers, Kryo, JDK serialization, pickle/cloudpickle, Avro, MessagePack, or Arrow-based systems.

What serialization problems are still painful in your multi-language systems?

submitted by /u/Shawn-Yang25
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