I am almost positive this will get caught by some AI-driven filter here too, but I would like to ask developers if the "AI bubble-everything" has changed your willingness to open source your own work products going forward?

EDIT (T+1h) Just to clarify, this is not about whether AI is detrimental to (yours or others') development quality, what the best policy on contributions is or how to enforce it. It's just about the impact of how others use AI on your own approach to publicly sharing own code.

You know you will be getting your source forked by - i.a. - low quality AI-enabled whizz kids which will "add value" to the product with the latest designs and whatnot, then have your userbase inquire why you are not "catching up" or "collaborating" with the zealous contributors. Receiving barrage of agentic PRs to review. While at that, let's not forget bogus security reports. And to top it off, have Microsoft constantly scan your (presumably GitHub) repo to train more of the ecosystem to enable the same.

OS is not about the thankless job alone anymore, it is becoming a liability to share one's own stuff, not to mention we have reached cast-pearls-before-swine (sorry) scenario where new projects have it hard to distinguish themselves and provide anything valuable back for the original author.

Hey everyone! After spending the last few weeks diving into API development, I decided to build something useful and open-source it. I present: Slugger — a dead-simple SEO Slug Generator API.

What it does: It converts messy text into clean, SEO-friendly URL slugs. Think "Hello World!!! 🚀" → "hello-world". Super niche, but genuinely useful if you're building content platforms or CMSs.

Why?? Honestly? I wanted to learn. I was frustrated with how scattered my learning felt, so I decided to solve a real problem (poorly at first ) and ship it. Along the way, I picked up:

• Building and documenting REST APIs
• Containerization with Docker
• Open-source best practices (README, licensing, etc.)
• How to handle edge cases (emojis, special characters, multiple languages)

learnings:The hardest part wasn't the code—it was writing clear documentation. I can't tell you how many times I rewrote the README because something that made sense to me was gibberish to others.

The repo: github.com/Merth1470/slugger

It's 97% Python, runs in Docker, and has near zero dependencies (well, minimal ones). If you've ever wanted to see a beginner's first open-source project, here it is—warts and all. Feel free to roast the code or suggest improvements; that's literally why I'm sharing it.

Would love feedback, PRs, or even just to hear if anyone actually finds it useful. And if you're learning too, maybe it'll give you courage to ship something!

I've day-traded options on the side for years. I wanted a journal that could take a position with multiple fills, scale-ins and scale-outs, and get the averages and fees right. TradeZella, TraderSync and Edgewonk do this for minimum $40 a month, closed source, your trading history on their servers. I'd been hacking away at a spreadsheet monstrosity and eventually started to build a proper web application. When I lost my job this past January, I rebuilt it from the ground up with two major requirements: 1) a secure, multi-user SaaS architecture, and 2) easy self-hosting defaults.

So, Tradr: the open-source stock and options trading journal.

• Positions with many fills, fee-accurate realised P&L, and a double-entry ledger underneath so the cash actually reconciles
• A sizing calculator to spit out position sizes based on you account risk preference.
• Greeks and OCC symbol parsing for the options crowd.
• CSV import from whatever broker you use... hypothetically. I only use one broker so I've only tested with that one!
• It never places an order. Nothing in it touches your broker. I'd like to connect read-only to brokerages someday.

Self-hosting is the primary way to run it. One Docker Compose quickstart, about 2 minutes. Feature gating is a single deployment setting that ships off, so a self-hosted install has everything with no caps.

On the license: it's Apache 2.0. There are licenses built to stop somebody else from selling hosting of your code, but none of them count as open source. Anyone can fork Tradr, run it, even sell hosting for it, they just can't call it Tradr. (There is a hosted offering for people who don't want to run a server, running on the opensource builds. That's how I'm trying to pay the hosting & development costs and maybe make a bit on the side. Links to hosted stuff are in the GitHub readme.)

AI disclosure: I do use coding agents heavily, inside a spec-driven-development workflow I also maintain, and I've been living in the app with my own real trades for weeks.

Repo: https://github.com/madmatt112/tradr

This is the first product I've ever launched. Tell me what breaks & what sucks if you want!

Hi there! I'm the developer of the HRConvert2 File Conversion Server (available on Github & Docker) and I figured you guys would appreciate all the time I've been putting into the project lately.

Truth is, I'm going through a lot and this project has been my escape lately. But the progress has been pretty astounding.

Check out the Github and let me know what you guys think!

I've been working on a server emulator for a game for quite a while and was fairly close to releasing but yesterday someone else released one. I'm not sure what the best path forward is now, is it bad form to open source mine anyway? I feel like I'd be releasing with less features implemented and in a different language.

Would I be a dick splitting a community like this (why have two when we should all contribute to the one)? Just kind of sucks for all my work to go to waste

mloda is an open source (Apache-2.0) feature engineering layer with a plugin-based resolver: you request a feature by name, and the resolver picks which plugin computes it.

Until 0.11, a bad request just failed with one line:

No feature groups found for feature name: 'sales__mean_aggr'. Use resolve_feature(name, options=...) to debug feature resolution. For troubleshooting guide, see: https://mloda-ai.github.io/mloda/in_depth/troubleshooting/feature-group-resolution-errors/

Nothing said whether the name was misspelled, the domain was wrong, or a required option was missing, so debugging meant reading the resolver source. Since 0.11, the same failure returns the full elimination trail:

No feature groups found for feature name: 'sales__mean_aggr'. Requested domain: 'marketing'. Feature group(s) eliminated while matching 'sales__mean_aggr': - AggregatedFeatureGroup (domain): declares domain 'default_domain', but the run requested 'marketing' - PandasAggregatedFeatureGroup (domain): declares domain 'default_domain', but the run requested 'marketing' - PolarsLazyAggregatedFeatureGroup (domain): declares domain 'default_domain', but the run requested 'marketing'

Every candidate the resolver considered, and the first gate it failed.

Implementation notes for anyone maintaining a plugin registry or resolver of your own:

• Rejections are recorded as data first: a stage label (one of nine values) plus a reason per candidate. Text is rendered from that.
• A plugin whose match hook raises is contained per candidate, so one broken plugin doesn't blank out the report for the others.
• The preflight (mlodaAPI.diagnose) never raises, and a real run raises the same facts as a typed FeatureResolutionError, so the exception and the preflight report never drift apart.

Curious how other maintainers of plugin/registry-style systems handle this: full rejection trail, or just the most likely cause?

Hey all,

I've been running Pi-hole for a while and got tired of opening the web UI every time I just wanted to check status or unblock something for five minutes. So I built Holeberry, a native macOS menu bar app for it. Free and open source (MIT).

What it does:

• Lives in your menu bar, shows status and total queries/blocked at a glance
• Manage up to two Pi-hole instances at once. Every action (block/unblock/allowlist) applies to both, so they never drift out of sync
• Disable blocking globally with a timer (or indefinitely). An auxiliary countdown will be right in the menu bar; re-enables automatically
• One-click unblock of whatever site broke in your current browser tab (Safari, Chrome/Chromium, Firefox/Zen), without a global disable
  • The standout feature for me and the one I've been using the most
• Browse recently blocked domains and allowlist/unblock them straight from the menu
• Trigger a Gravity update right from the menu bar, on all your servers simultaneously
• Now fully notarized with a paid developer account
• Supports passwordless Pi-hole instances

Why Holeberry and not another app?
I looked around before building this. Some of the existing companion apps for Pi-hole weren't in active development anymore; felt outdated or didn't do the things I actually needed day to day: keep two instances in sync, unblock just the tab I'm on instead of disabling blocking for the whole network, and being able to glance at it quickly. Those ended up being the features I use most, so they became the core of the app.

Requires macOS 14+, supports Pi-hole v6 (and v5, though less tested). Credentials live in Keychain, nothing written to disk.

GitHub: https://github.com/pedrovieira/Holeberry

Happy to answer questions, and open to feature requests / bug reports.

Hey r/opensource,

I've been working on Oasis Editor, an open-source document editor written in TypeScript.

It has its own Canvas-based rendering engine for paged layout, text, selections, images, tables, and document geometry instead of relying entirely on contenteditable.

It also includes a typed command/plugin API, DOCX/PDF workflows, React and Vue adapters, and a headless runtime.

Live playground:
https://celsowm.github.io/oasis-editor/#/editor

GitHub:
https://github.com/celsowm/oasis-editor

I'd love feedback, issues, and contributions — especially around rendering, document layout, plugins, and import/export fidelity.

ps: MIT license

and 3D-acceleration on real hardware GPUs

https://github.com/stubbb/c-shop

Status

Active development, beta. I looking into patching up a few bugs but there is a lot of them that I haven't found yet. Feature requests are welcome.

What I wanted to build

One: quick and capable image editor. Every image editor I could actually use was either an enormous proprietary suite or a web app wearing a desktop costume. I wanted something that starts instantly, needs no system packages beyond a GPU driver, and behaves the way my hands already expect — layers, masks, blend modes, non-destructive adjustments, the same keyboard shortcuts. X11 is opened through dlopen, Vulkan through ash, everything else is pure Rust, so cargo run --release is the whole install. It uses less than 100 MB of RAM idle compared to 2.5 GB from Photopea.

Two: agentic integration. I kept wanting to hand image work to an agent but the LLM cant natively see images. The approach I selected is that the editor drives from a script, headless, and it's the same application the buttons drive — anything the UI gains is reachable from a script.

Example script:

cshop --run 'new 400 240 background=#20304a
text 40 154 "Hello" size=54 color=#ffffff bold
effect drop-shadow distance=6 size=8
export out.png'

The part that makes this usable by something blind is that every run answers with a report — where each layer landed, what each step did, what failed and why — and that measure will size a piece of text before anything is drawn. So a caller places things by number instead of rendering and guessing. Nothing fails silently: a bad command is a failed step with a reason, and the run carries on so one typo doesn't discard the rest.

It can be run locally by an agent on your computer or deployed to a server and interacted with over network as an MCP server.

What's in it

Raster/group/fill/type/shape/adjustment layers; layer and clipping masks; 27 blend modes. Marquee, lasso and magic wand selections with feather, expand, contract, border, grow. Brush, pencil, eraser and clone stamp on one stroke engine. Re-editable type from your installed fonts. Eleven layer effects, all driven off one distance field. Vector shapes and Bézier paths from SDFs. Fourteen adjustments, each available destructively and as an adjustment layer. Thirty filters. Lens correction as one resampling pass rather than four. ICC profiles, real CMYK, 16-bit export. A native .cshop project format, plus PSD in and out.

Deep learning features are an optional add-on (heavy stuff)

There's a pack you opt into that adds seven models — detect, segment, depth, inpaint, semantic separation, denoise, upscale. It exists because "cut the dog out of this picture" and "clean up this sky" should be things you ask for, not things you do by hand. The whole cut-out is:

detect                        # → dog 90% at 4,303 632x501
segment class=dog feather=1   # the dog becomes the selection
layer via-copy

They run in a separate process behind a line of JSON, deliberately. A neural runtime is tens of megabytes of platform-specific machine code that changes every few months; putting it inside the binary would cost the binary its whole character for a feature most sessions never touch. Not installing it costs nothing and breaks nothing. At least I hope :)

Today the detection model holds only 80 classes and it works well for basic concepts: person, dog, vehicle but it wont be useful for anything nuanced. Segmentation and depth are nice, generative fill is surprisingly good. Noise removal is world-class but takes long time. I used the SOTA model.

Hello, I had created Testing Files Generator for QA/Developers

Want to create 5k log files? Do size boundary testing? Generate 2GB PNG file? Generate archive with 200 files? This tool can do it all. If you like the project, please ⭐it.

Why it's different from other generators?

- Exact size of files, if we ask for 314832 bytes, we get it, want 500kb? 5mb? 1GB? No problem. One file or 9000 files? No problem

- Generates files come with "manifest" which says how the files should behave + we can do easy cleanup

- "Recipe" system that we can store in a testing repo, create recipe that generates a 500-page PDF, a zip with 200 files and 50 csv files, no problem, same seed.

- 100% offline, no network connections, free for commercial and personal use. Windows/Linux/macOS. Min files sizes are based on the real formats, you can open generated files with software that open the format.

- Format got own values, so you can create xlsx with 4 rows and 4 collumns.

-Preset system, we can do "boundary testing" let's say we put 10mb, and we get file under 10mb, over 10mb, etc.

- More on GitHub :-)

This is V0.2 version, so there is a long way ahead, not all things are working, More format and presets will come soon. If you got any ideas feel free to drop them.

Supported formats:

📄 Documents: pdf, docx (Word), xlsx (Excel), pptx (PowerPoint)

🖼️ Images: png, jpg, bmp, gif, ico, svg

📝 Text: txt, md, csv, json, xml, html, log

🗜️ Archives: zip, targz (.tar.gz)

🔊 Audio: wav

What format's or features would you like to see in that project?

tiff, webp, avif, jpg xl are next formats. To keep the determinism of the generated files, I try to avoid external libraries, but for some formats it's just a bit more work (AVIF or JPG XL), so in those ones I'm heading towards external libraries. It may also change in the future, because I want to add more settigns to each file type.

PS: Why this name? it's simple, and I got no idea for other,

PS2: about ARM on Linux and Windows (GUI), there is CLI, GUI would be a extra work, but I will look into it in the future.

In the future I will need to think about supporting bigger files sizes, but in current implementation the memory is a problem, but some things could work better in the engine, so I will probably work on it first.

There is Linux build, Windows build is signed, macOS build is notarized. Separate file for CLI and separate for GUI. The language is Go (1.26.7, I will need to upgradr to 1.2.7), GUI is Fyne. (2.8.1), Testing Files Generator

I Saw the cute little faces that x.ai/bot have and wanted to give a spin on the idea... so I created this light weight super importable free repo!

you can either download it yourself and go crazy or just import it from the site. would love to see what people do with it!

https://github.com/bwndapp/bfce

or just import from https://bfce.bwnd.app/

Hello folks!

I would like to share an update about BentoPDF. It's an open source privacy first PDF toolkit that runs in your browser.

1. You can now actually edit text inside PDFs

BentoPDF now allows you to click existing text, edit it, and have the text reflow while preserving the original fonts and styling.

It also includes:

• Bold, italic, underline, strikethrough, superscript, subscript, font family/size, colours, outlines, character spacing and line spacing
• Left, centre, right and justified alignment
• Bulleted and numbered lists with indentation
• RTL and LTR text alignment
• Find and replace across the document
• Edit images by rotating, flipping, duplicating, resizing or deleting them
• Object alignment, distribution, rotation, flipping, duplication

Please note this is a work in progress. You may encounter bugs, which you can report and I will look into.

2. Hyper Compress

Hyper is an open source PDF compression engine built to solve specific problems.

• Compression sometimes returns a file larger than the original. Hyper's result is always binary: it either produces a smaller PDF or returns the original. This helps create predictable workflows.
• Hyper includes a true lossless mode. It preserves searchable text, document structure and PDF conformance, rather than rebuilding the document from scratch like Ghostscript.
• It runs everywhere: CLI, Node SDK, C API, self hosted service, and WebAssembly build.

Repository and benchmarks: https://github.com/alam00000/bentopdf-hyper-compress

I also compared it to Adobe's compression API, but only managed around 100 PDFs where results were within 5%. Testing on a bigger corpus got expensive.

3. Kura

Kura is a PDF standards, conversion and preflight engine.

It supports:

• All 11 PDF/A conformance levels: PDF/A-1a, PDF/A-1b, PDF/A-2a, PDF/A-2b, PDF/A-2u, PDF/A-3a, PDF/A-3b, PDF/A-3u, PDF/A-4, PDF/A-4e and PDF/A-4f
• Accessibility: PDF/UA-1 and PDF/UA-2
• Print production: PDF/X-1a, PDF/X-3, PDF/X-4, PDF/X-4p, PDF/X-5g, PDF/X-5n and PDF/X-5pg
• Engineering and variable data printing: PDF/E-1 and PDF/VT
• E-invoices: Factur-X, ZUGFeRD, XRechnung and Order-X
• 396 bundled print-preflight profiles

It has been tested against several standards suites, including the veraPDF corpus, Isartor, BFO, Ghent Output Suite 5.0, the PDF/UA Reference Suite and Cal Poly's PDF/VT suite.

Across 30,677 PDF conversions it had zero crashes and zero timeouts, with a 0.05 second median conversion time.

Like Hyper, it ships as a CLI, C library, npm package, Docker image and WebAssembly build.

Repository and benchmarks: https://github.com/alam00000/bentopdf-kura

The release also contains other improvements and bug fixes: https://github.com/alam00000/bentopdf/releases/tag/v2.8.8

Thank you and have a great weekend!

https://www.reddit.com/r/Luanti/s/FS7ZvaAlTE

Hello folks!

I am mildly active here and I *think* I've gotten some approval to post about the work we are doing. I am the founder / CEO of Imagination Foundry -- a fully community funded and run, co-op organized company that has built and run [whatiff.chat](http://whatiff.chat) . Our goal is to make AI transparent, break up lock-in, and provide the tools that regular people need to remember better over time and generally make their lives better.

I've written before about how I think [transparency in AI is incredibly important](https://substack.com/home/post/p-202193358) (this honestly buries the lede a bit)

To that end, we have just recently open sourced our entire project -- you can run it locally, remix it, add things, etc. I'm sharing here in case anyone is interested in using it, or might want to take a look at a reasonably mature app and how we built things. Totally free.

If you have any questions, or just want to talk AI memory, etc, Hit me up!

[https://github.com/theimaginaryfoundation/what-iff\](https://github.com/theimaginaryfoundation/what-iff)

I just saw this excellent Youtube video made by Jono Bacon. Hope it's OK to share it here. It gives a nice overview on this amazing technological and human adventure, with a lot of significant facts we tend to forget, which contributed to where we are now.

I'm looking into it but have no clue which intelligence model to pick for more accurate calorie count.

I've been building this for too long as a hobby, idea is to make Ai workflow outputs more predictably good though context management and defined domain rules, any feedback appreciated:

https://github.com/MattCreigh/beagle

https://github.com/word-sys/word-sys-pdf-editor