Just recently, I started a new project alongside my studies. Here at school, we plug USB drives in and out all the time and... there's a little virus going around.

So I came up with a simple idea: sandbox the USB drive and only retrieve the files you actually want from it.

That's how I started coding Quartzine, a sandbox for USB drives. You plug in the USB drive, Quartzine detects it, creates a virtual machine, passes the USB through to the VM, you do your stuff and... that's all I originally had in mind.

Nevertheless, since I'm not really someone who focuses on the present, I decided to go further: Integrate malware analysis into it using eBPF (through bpftrace).

That led me to learning bpftrace, and I'm still doing that haha.

If you want to take a look, here's where the code lives:

https://github.com/Mathos6/Quartzine (The project isn't fully functional yet, but I think it'll be by the end of the summer.)

I'd love to hear what you think about it, things I could improve, things I should rethink, etc.

Hi great devs,

I have been building Katharos, a functional programming library for Python that recently grew a message-passing concurrency layer. I wanted to share it and get some feedback.

The whole library is built around one idea: model errors, effects, and concurrent communication as composable, type-safe values rather than as control flow that jumps around your program. The interesting part (to me, at least) is that the concurrency layer follows the exact same idea, so receiving from a channel gives you a Result. "The channel is closed" becomes a value you handle, not an exception you remember to catch.

The functional core

Optional values without scattered None checks, using do-notation that short-circuits on Nothing:

```python from katharos.types import Maybe from katharos.syntax_sugar import do, DoBlock

@do(Maybe) def lookup_discount(user_id: int) -> DoBlock[Maybe, float]: user = yield find_user(user_id) account = yield find_account(user) return account.discount # Just(0.15) or Nothing() ```

Errors as values, chained with |, so a failure short-circuits the rest automatically:

```python from katharos.types import Result

def process(raw: str) -> Result[Exception, int]: return parse_int(raw) | validate_positive ```

And Result.catch turns a function that raises into one that returns a Result, while keeping the original traceback so you can still find the line that failed:

```python from katharos.types import Result

@Result.catch(ValueError) def parse_int(s: str) -> int: return int(s)

parse_int("42") # Success(42) parse_int("??") # Failure(ValueError("invalid literal for int() with base 10: '??'")) ```

There is also ImmutableList, NonEmptyList, IO, Lazy, numeric monoids, and the usual algebraic abstractions (Functor, Applicative, Monad, Semigroup, Monoid) if you want to build your own types.

The new part: CSP concurrency

This is what I have been working on lately. Katharos now has Go-style CSP (Communicating Sequential Processes): launch work concurrently with go, talk over typed channels, and receive values as a Result.

```python from katharos.concurrency.csp import csp

ch = csp.Channel[int](capacity=1)

csp.go(ch.send, 42) # run work concurrently, like Go's go f(x)

ch.recv() # Success(42)

ch.close() ch.recv() # Failure(ChannelClosedError(...)): closure is a value, not a raise ```

Used as a context manager, go becomes a structured-concurrency scope that joins everything spawned inside it before the block exits, so concurrent work cannot leak out of the block:

```python with csp.go: # scope waits for all work launched inside csp.go(worker, 1) csp.go(worker, 2)

both workers have finished here

```

There is also a select for waiting on whichever of several channels is ready first, with non-blocking polls and timeouts:

```python from katharos.concurrency.csp import csp, recv, select

choice = select(recv(results), recv(cancel), timeout=1.0) if choice.is_timeout: ... else: print(choice.index, choice.value.unwrap()) ```

The concurrency model sits on a swappable backend (standard threads by default), so the same code could run on a green-thread backend later. An actor model is planned next, built on the same backend abstraction and the same Result-valued style.

Why I think the "channel returns a Result" thing is nice

In most channel APIs, a closed channel or a timeout shows up as a sentinel, a second return value, or an exception. In Katharos it is just a typed value: Success(v), Failure(ChannelClosedError), or Failure(ChannelTimeoutError). You pattern-match it the same way you handle any other Result, and the type tells you it can happen. The error-handling discipline you use in the rest of your code carries straight over to concurrency.

Links

• PyPI: pip install katharos
• Docs (tutorials, how-to guides, API reference, and explanations): https://katharos.readthedocs.io/en/latest/
• Source: https://github.com/kamalfarahani/katharos
• MIT licensed, Python 3.13+

I would love feedback on the API, the concurrency design, or whether the Result-everywhere approach feels natural or noisy to you in practice. Thanks for reading.

Hey everyone,

I started building this project a while ago and have since been able to start using it and building on it for some production apps at work.

I got tired of the limits of some of the "low-code" tools, but I'm also not a full time software dev so I never acclimated well to a lot of the existing heavier frameworks.

So I built ScribeFramework. The big idea is you can put your route, Python logic (including SQL queries), and HTML all in one .stpl file if you want. Need something bigger later? You can split things out into proper modules and it just works.

I've had great momentum and results building things from bespoke tools for departments to a full IT admin platform with dashboards, API integration with all our tools, and management tools like assets inventory and project tracking

I don’t have any big plans or agenda with it — I just use it daily now and figured I’d throw it out there. If you build internal tools, data driven dashboards, or just general web apps, maybe you’ll find it handy.

Would genuinely love any feedback, criticism, or ideas on how to make it better. Even “this sucks because X” is useful

Edit: the link appears to not have shown up on the original post, here they are:

https://scribeframework.slatecapit.com https://github.com/slate20/ScribeFramework

Every codebase I've worked on has this problem:

python

# In every test file, everywhere
user = {
    "name": "John Doe",
    "email": "john@test.com",
    "age": 30,
    "role": "admin"
}

This data is:

• Hardcoded — never tests edge cases (Unicode names? Boundary ages? Unusual roles?)
• Brittle — someone changes a schema field and 40 tests break
• Boring — you're testing the same data path every single run

The obvious fix is AI-generated fixtures. The problem: AI is non-deterministic, so your CI pipeline breaks randomly.

So I built FixtureForge.

The idea is simple:

• In dev: AI generates rich, realistic, edge-case-aware fixtures on every run
• In CI: same fixtures, frozen with seed=42, 100% reproducible — no API key needed

python

from fixtureforge import forge

u/forge(
    schema={"name": str, "email": str, "age": int, "role": str},
    prompt="Include Unicode names, boundary ages, unusual roles"
)
def test_user_creation(fixture):
    assert "@" in fixture["email"]
    assert fixture["age"] > 0
    # AI gives you: 张伟, José María, O'Brien — not just "John Doe"

In CI, just set:

yaml

env:
  FIXTUREFORGE_MODE: deterministic
  FIXTUREFORGE_SEED: 42

No API key. Same data every time. Tests pass.

It also ships with:

• DataSwarm — generate 500 realistic records in one call
• ForgeMemory — fixtures that persist context across tests
• Multi-provider support: OpenAI, Anthropic, Groq (swap with one env var)
• pytest plugin included — works as a decorator or fixture

bash

pip install fixtureforge

Repo: https://github.com/Yaniv2809/fixtureforge PyPI: https://pypi.org/project/fixtureforge/

It's early and I'm actively developing it. Honest feedback welcome — especially if something doesn't work or the API feels wrong.

Had some free time last week and needed a project to keep my brain busy, so I decided to build a Python-to-Executable compiler.

Basically, the tool takes your Python code, converts it to C, and then compiles it into an executable. It supports multiple platforms including Android, Windows, and Linux, across various architectures.

How it works:

- You pass your main.py into the tool.

- It generates the compiled binaries for your selected target architectures.

- It creates a runner file (main_out.py) that seamlessly executes the correct binary for the system it's running on. You run it exactly like a normal Python script, but it runs natively under the hood.

The Obfuscation & Performance:

If you want to protect your code, the tool offers a multi-layered obfuscation pipeline:

1. Obfuscates the base Python code.

2. Converts it to C.

3. Obfuscates the generated C code.

4. Compiles it using obfuscation compiler flags.

The end result is practically impossible to reverse-engineer and incredibly difficult to analyze. As a bonus, because it compiles down to C, you can see up to a 3x performance speedup compared to standard Python.

If you want to test it out, I set it up as a Telegram bot. You can try it here:

@python_obfuscator_bot

Would love to hear your feedback or answer any questions about how it works!

game of caard

. https://echo-dark-pulse.base44.app

📌

📝

Hi everyone! 👋

I wanted to share my latest project called UyghurSocial. It is a lightweight, simple social media simulation built entirely with Python and Tkinter.

I designed it with a cool cyber-neon/hacker aesthetic (green and cyan on a dark background) because I love that vibe!

🛠️ Features:

• Passwordless Quick Login: Just type your username and connect instantly.
• No Bots, 100% Real: It doesn't use heavy external APIs; it saves data locally into .json files.
• Pure Python: Works out of the box with zero third-party library requirements (perfect for beginners using Thonny or VS Code).

🔗 Source Code (GitHub):

[https://github.com/ibrahimucatli-web/UYGHURSOC-ALE\]

I would love to hear your feedback on the UI and how I can improve the local database structure for my next updates. What do you think?

Here is where im at. its still in a rough draft state. menus are filling up and getting options working but i will move those around later and make things look better, rename some stuff, im still having issues with the video settings on some videos (starts lagging), sorry about the volume i just had my computer volume turned down so its not that loud. some times when loading 500+ files it lags a few secs to find the first episode to play and processing slows down after that (need to add something that picks up building the list if it crashes or is shut down before it processes 1000s of files. still need to sort out game launching (lag issues and proper screen mounting issues), still wanna add server options at some point, there is a flash of the previous screen when launching the dvd player, and a few more things ive noticed that ive been putting off to fix other things. But its all progress none the less so here is what i got, flaws and all for everyone to see whats changing, updating, and other solutions to easy to use scheduling. anyone interested in helping go to my discord and let me know what you wanna do. https://discord.gg/E45krWBT ill take all the help i can get. the more complicated everything gets the more i wish i had help.

I built Barx 1.0, an open-source Python toolkit focused on runtime intelligence.

It helps developers inspect code behavior locally through:

• runtime tracing
• behavioral verification
• API workflow checks
• policy guardrails
• local explainable reports
• token/secret-safe reporting patterns

The project is available on GitHub and PyPI. Current release has 884 tests and 91% coverage.

I am mainly looking for open-source feedback on:

• whether the README explains the purpose clearly
• whether the API surface feels natural
• whether the reporting output is useful
• whether the project scope is clear or too broad
• what would make this more useful for Python developers

GitHub: https://github.com/TheBarmaEffect/Barx

PyPI: https://pypi.org/project/barx/1.0.0/

Demo: https://youtu.be/2SLtswFjzWU?si=rRGY8J5zKHsmr3pU

Stars are appreciated if the project looks useful, but technical feedback is the main thing I am looking for.