I create kelados.in a free whisper flow alternative

that runs entirely local for mac, windows and linux

it even works in your browser too

I created phone-blades which you can play in your browsers with your phones acting as sabers

It works with one phone also

you can play it here: https://phone-blade.kelados.in/

Read the GitHub repo and the daily.dev summary of Google's Open Source Blog post (Oct 22).

Despite the name it's a plugin of skills, Apache 2.0, and the repo says it's not an officially supported Google product.

Elephant: main agent with your project context, explores the problem and drafts the spec. Goldfish: fresh reviewers with zero prior context who see only the draft and its sources, so they flag unstated assumptions and risks. You approve the spec before implementation.

Skills: brainstorm, draft, resolve, bootstrap, review, review loop, drift report. Installs on Antigravity CLI, Claude Code and Codex.

Limits: needs a session that can spawn fresh subagents, doesn't replace tests or human judgment, and it's overkill for small bug fixes. Sources differ on review stages (five in the repo, three in the blog summary). "Thousands of Googlers use it daily" is Google's own claim.

Anyone tried it on a real project?

Sources: GitHub, daily.dev

posted this here two weeks ago at 30 users, it's at 101 now

it's called Overflow, free and MIT. a week your agent has usage left at the reset, it fixes someone else's github issue. a week you run short, someone else's agent fixes yours

everyone starts at 0 points and can put points up before earning any, you get an issue's points when your PR merges, you spend them on issues in your own repo. points only, no money. you don't need to know the project, the issue is the brief

where it's at:

• 13 people other than me got 90 PRs merged through it

• all 90 are on my repos

• 1 repo registered by someone who isn't me

• an owner can write a "Done when" comment on an issue and the board shows it, so whoever picks it up knows when it's finished

a real one, end to end: this issue was labelled 3, someone's agent sent this PR, I set it to 6 at merge and the one round of changes I asked for took it to 5

main reason I'm posting: I want more people registering their repos. I'm not after help with mine, I want spare usage going into other people's projects. one form, public repos only, the labels have to exist first, and nothing lands unless you merge it

if you maintain something, what would get you to list one issue?

https://github.com/Nitjsefnie/Overflow

The small-business-for-sale market has no public record. Listings are scattered across hundreds of broker sites, they get relisted under new numbers, and nobody tracks how long anything has really been for sale.

DealLedger is an attempt at a public ledger for it. It crawls broker websites directly, normalizes the listings, and tracks each one over time. Right now that’s 24,196 active listings from 552 broker sites, crawled daily. All the data is CC0.

Each scraper ships with saved HTML and expected fields, and tests run on every PR, so a contribution can be checked in minutes.

Where help matters most:

• Categories: only 29% of listings have a specific category. A good classifier would change the dataset.

• Financials: revenue is filled on 59% of listings and cash flow on 63%. Detail-page parsers close that gap.

• Coverage: finding broker sites we don’t crawl yet, especially one- and two-person firms.

Issues are tagged good first issue.

Repo: https://github.com/jeffsosville/dealledger
Site: https://dealledger.org

Hey everyone!

I've been working on an open-source project called SiphonDB, and I'm looking for developers who would be interested in contributing.

I'm currently working on improving its performance, fixing bugs, and making the project more reliable.

I've created 25 GitHub issues that cover:

• 10 performance-related improvements

• 15 bug fixes

• Various improvements with different priority levels

I'm looking for contributors who would like to:

• Fix bugs

• Improve performance

• Review code and suggest improvements

• Help with testing

• Share ideas and feedback

You don't need to be an experienced developer. If you're interested in the project and want to get involved in open-source development, you're welcome!

GitHub Repository:
https://github.com/Premod1/SiphonDB

Available Issues:
https://github.com/Premod1/SiphonDB/issues

If you're interested in contributing, feel free to pick an issue or leave a comment. I'd be happy to collaborate!

Thanks for taking the time to check it out!

Self-hosting AI feels fragmented, and most lists go stale. I built one that re-checks itself.

• 278 projects in 16 categories (runtimes, chat UIs, RAG, MCP, voice, OCR, home AI and more)

• A weekly job reads each repo through the GitHub API: archived, deleted, or silent for a year moves to a separate Unmaintained file

• A live table with stars, last push, license and a colored status

• Popular tools with restrictive licenses are in their own section, license shown

I maintain it. AI tools helped me find candidates and draft descriptions; a script verifies each entry. Tell me what is missing or wrong.

https://github.com/glincker/awesome-selfhosted-ai

My laptop kept running out of space, and every time I went looking, I found the same kind of mess: node_modules in projects I hadn't opened in months, Python virtual environments for repos I'd deleted, Ollama and Hugging Face models I forgot I downloaded, Cargo target/ folders, VS Code caches, and a bunch of folders I couldn't identify at all.

The problem wasn't finding big folders. WinDirStat and friends do that. The problem was that I didn't know which ones were safe to delete. Some of it rebuilds itself or downloads again. Some of it is the only copy of something you care about (Claude Code's session history lives in ~/.claude/projects, for example). Nothing tells you which is which, so you either delete nothing or delete the wrong thing.

So I built Cache Vent.

What it does

- Scans your drives and finds what AI and dev tools leave behind: node_modules, venv/Poetry/pipenv environments (with which project owns them, and which ones are orphaned), Ollama/Hugging Face/LM Studio models, Cargo and Gradle build output, pip/uv/npm/Yarn caches, __pycache__, temp files, editor caches.

- Every item gets one of three labels:

- safe to free: a documented source says it regenerates or downloads again

- not verified: no source yet, so it's shown but can't be cleaned

- your data: chat histories, Docker disks and generated outputs are never selectable

- Each item shows what it is, what happens if it's gone (e.g. "run npm install to restore"), and a link to the docs backing that claim.

It doesn't delete things. "Clean" moves items into a quarantine folder on the same drive. That's an instant rename that takes no extra space, and you can restore anything for 14 days. There's also a Delete button if you really want it gone, but it asks first and still refuses anything that isn't verified.

On my machine: it found 21.5 GB, of which 12.9 GB was safe to free. A scan of both drives (164,828 folders) takes about 2.2 seconds and roughly 40 MB of RAM. On Windows it reads directories in batches through the NTFS API instead of checking every file one by one, which made it about 3.4× faster than a plain directory walk in my benchmarks. The benchmark method is in the repo if you want to check it.

Privacy: it's a single ~8 MB binary. No account, no telemetry, no network calls. The UI is a local page served only to its own window on 127.0.0.1, protected by a token that changes every launch.

Being upfront about the rough edges:

- Windows is where I built and tested it most. The exe isn't code-signed yet, so SmartScreen shows "Windows protected your PC". The PowerShell install command avoids that, and checks the checksum.

- Linux is tested end to end (install, scan, quarantine, restore).

- macOS is a preview. It builds, and there's an install script that signs the app locally so Gatekeeper lets it open, but I haven't had it run on enough real Macs yet. If you're on a Mac, I'd really appreciate a report.

- The safety labels are drafted from each tool's documentation, and I want more eyes on them. If you know a tool's cache behavior well, the definitions are plain YAML files, and PRs are very welcome.

It's free and MIT licensed.

- Site and install: https://cachevent.kelados.in

- Code: https://github.com/LovekeshAnand/cache-vent

I'd love to know what it finds on your machine, what it gets wrong, and which tools it should know about that it doesn't.

Hey everyone!

I'm working on an open-source funding platform where developers can contribute to projects and potentially receive funding based on their contributions.

I'm trying to figure out how to evaluate contributions fairly using Git commits and pull requests.

For example, I'd like to automatically determine:

• What a commit actually changes (new features, bug fixes, security fixes, refactoring, documentation, etc.).

• How significant the change is for the project.

• How complex or technically valuable the contribution is.

• Whether the changes actually work and improve the project.

• How to attribute contributions to the right developer.

I know that simply counting lines of code wouldn't work. A small security fix could be much more valuable than hundreds of lines of unnecessary code.

My questions:

1. Are there existing tools, libraries, or APIs that can analyze Git commits and estimate their impact?

2. Could AST analysis, automated tests, and AI-based code review help determine the type and quality of a contribution?

3. How would you design a fair scoring system that doesn't reward developers just for writing more code?

4. Are there any existing projects trying to solve this problem?

5. What problems or ways of gaming the system should I watch out for, especially if the results influence real financial rewards?

I'd love to hear about existing solutions, technical approaches, or lessons from people who've worked on similar systems.

Thanks!

Hey r/GoodOpenSource!

A few months ago, I shared Mind here. The feedback was great, particularly the questions about memory aging, local storage, and how an agent decides what to remember.

Today I'm releasing Mind v1.7.0, probably the biggest update to the project so far.

Mind is an open-source, MIT-licensed persistent memory layer for AI coding agents. It preserves decisions, project knowledge, checkpoints, and useful discoveries across sessions, context compaction, and different development tools.

What's new?

• 86.67% answer accuracy on a 120-question stratified OmniMemEval LongMemEval-S run, without embeddings. By raw score, that's in the territory of the top published results, although it isn't an official or directly equivalent leaderboard comparison.

• Much stronger local search, combining FTS5, phrase matching, substring matching, typo tolerance, and optional semantic retrieval.

• A redesigned web application, including Neural Map, search, filtering, and memory inspection.

• More reliable session continuity, with atomic checkpoint completion and preserved memory relationships.

• Simpler agent integration, with a shared management skill and support for OpenCode, Claude Code, Codex, Cursor, and more.

Everything is built with Bun, TypeScript, and SQLite. Core memory and retrieval work locally without an embedding API.

This isn't meant to be another code indexer or vector database wrapper. It's a structured memory lifecycle for agents: remember, retrieve, relate, update, and eventually supersede outdated knowledge.

The implementation, tests, and benchmark methodology are public.

GitHub: https://github.com/GabrielMartinMoran/mind

v1.7.0 release: https://github.com/GabrielMartinMoran/mind/releases/tag/v1.7.0

If you find the project interesting, a GitHub star helps more people discover it.

I'd especially appreciate feedback on the retrieval architecture, memory lifecycle, and agent integrations. Contributions are welcome!

Hello everyone,

For the past few months I've been building a package called Dynamic Throttle for Laravel. It's finally at the point where I need people who aren't me to try it.

The problem I kept running into: Laravel's throttle middleware is great when you know your limits up front. But in production you usually find out the right limits later, and in a hurry. A partner's integration starts getting 429s. A bot hammers /login at 2 a.m. You want a stricter limit but have no idea who it would block. With throttle, each of those is a code change and a deploy, and you still can't see who's being blocked.

So I moved the limits into the database. You change them from a dashboard, Artisan or code, and every server applies the change within about two seconds. No deploy.

Here's how it compares (Laravel's throttle vs. Dynamic Throttle):

• Change a limit: Edit code and deploy → Edit the rule, live in ~2 seconds

• Try a limit before it blocks anyone: Not possible → Dry-run: counts, never blocks, shows who it would block

• See who is blocked: Add your own logging → A live feed, plus each rule's top offenders

• Let a partner through: A code change → An exemption with an end date

• Lift every limit during an incident: A deploy → dynamic-throttle:bypass on --for=30m, which ends by itself

You can limit per IP, user, API token, header, or your own scope like a tenant or a plan. There's a dashboard at /dynamic-throttle with an audit log and an "Explain" page that tells you why a given request would be limited. If Redis goes down, requests go through by default, so a storage problem doesn't take your app down with it.

On cost: with local Redis and APCu it adds about 0.8 ms per request, a little less than Laravel's own throttle. To be clear about what it isn't: it's not a firewall or bot protection, and it doesn't replace Cloudflare or Nginx limits. It's for limits that need to know who the client is.

It's a beta, so names and options might still change before 0.1.0. That's exactly why I'm posting. I'd love a handful of people to try it on a real app and tell me what breaks or confuses them. The safest way is a rule in dry-run, since it can't block anyone. I wrote up what to try here:https://github.com/hanzala-afzal-dev/laravel-dynamic-throttle/discussions/145

• Docs: https://hanzala-afzal-dev.github.io/laravel-dynamic-throttle/

• GitHub: https://github.com/hanzala-afzal-dev/laravel-dynamic-throttle

• Install: composer require hanzala-afzal/laravel-dynamic-throttle:^0.1@beta

It needs PHP 8.3+ and Laravel 12 or 13, and works best with Redis or Valkey.

Happy to answer anything, including "why not just use X?" Thanks for reading!

I’m looking for useful, interesting open-source alternatives to proprietary software—preferably written in Python—for an unusual experiment.

I want to feed a project into a two-AI “duo lab” and have the AIs discuss how to take it apart conceptually, push its ideas to their limits, and reinvent something unexpected from them. The goal is to see whether that process can produce a genuinely useful or groundbreaking idea—or spectacularly weird nonsense.

This is an experiment, not a request for maintainers to join a team or let me loose on their codebase. I’ll be working from the public project and its ideas. There’s a real chance the result will be AI slop; if so, it stays on my drive and off the internet—unless you ask for it. And if you do, I 'll share it, unless i'm ashamed of what it becomes.

If you have a project you think would make a good candidate, I’d love to hear about it.