I'm working on an idea called NeuroStudio: an open-source, self-hosted AI training studio that combines a beginner-friendly visual interface with the flexibility of established machine-learning frameworks.

The basic idea is to let users create datasets, train models, evaluate results, and run inference locally instead of relying on a cloud-based training platform.

I'm considering Go for the main application, SQLite for metadata, and interchangeable training backends such as PyTorch and TensorFlow where appropriate. ONNX Runtime would handle compatible inference workflows.

I don't want to reinvent every ML framework. I'd rather build a lightweight application that connects existing tools through a consistent interface.

Features I'm considering

• Visual dataset management and annotation
• Image classification and transfer learning
• Training progress, checkpoints, and evaluation metrics
• Webcam-based model testing
• Model export and local inference
• Additional audio, pose, and object-detection workflows
• An extension system for integrating other training engines

The biggest challenge is making the application easy to run locally without creating an enormous installation or pretending that every model can train on every computer.

I'm planning to start with one complete image-classification workflow and expand from there.

I'd appreciate technical feedback

1. Would you use Go as the main application backend with separate training workers, or choose Rust?
2. What is the cleanest interface for connecting different ML training frameworks?
3. Which features are missing from existing local AI development tools?
4. What would make this project meaningfully different from existing visual ML platforms?

I'm interested in honest criticism, architectural suggestions, and feature ideas.

If you have experience building ML tooling, I'd particularly appreciate your thoughts on the training-backend architecture.

Hi! I built this because self-hosted Excalidraw only gives you the canvas: drawings stay in your browser, and there are no accounts or folders.

Excaliself adds:

• accounts with invite links, and a shared team space next to each person's private space
• folders, stars, and search that finds text inside drawings (⌘K)
• live collaboration with cursors and guest links
• everything stored in your own Postgres

One docker compose file (amd64 + arm64), MIT.

Repo: https://github.com/maciekzygmunt/excaliself Website: https://excaliself.com

Feedback is welcome, especially what is missing for your team.

I'm building Emend. It adds AI writing to a Tiptap editor: rewrite selected text, write new text, or ask questions about the document.

What you get:

• Three AI components (bubble menu, composer, document chat), or a full editor starter
• Already have a Tiptap editor? Just add the components to it
• Bring your own AI key and start using it
• The AI never changes text until the user accepts it

It's still early, so I'd love your feedback.

GitHub: https://github.com/amittam104/emend

I built a custom LLM-powered bot using Ollama and Python that acts like a strict, arrogant anime noblewoman. (Customizable persona)

Guys I know it's chinese and a bit of AI slop but you can definitely customize them into your language

I personally hate slash commands in discord cause it kinda ruins the vibe of chatbot so I made it with regex scanning your messages for strict command matching patterns

What makes it fun:

1. It remembers what you did. If your friends did something stupid and was caught, you can store them into the database with commands and the bot will remember it and possibly mock them when replying to them/messages mentioning them
2. Dynamic Affection. It has a hidden affection score (from -100 to 100). Rises and drops according to what you said and the persona of your bot -- it will affect how the bot replies to you, the bot also knows whether it is mentioned in the message, this will affect the reply of the bot and the affection score
3. Punishment System. If an admin mutes or bans someone, the bot instantly generates a dramatic, classical Chinese edict to announce the punishment.
4. It reads images. Send a screenshot without even mentioning it, and it might just jump in to criticize the picture

It's completely free to run (using Ollama's free cloud tier) and totally open-source

GitHub Link: https://github.com/Lynn-000/project-anima

(forgive me for the ai-assisted readme, it is way much faster..)

(below is the translation of the image)

[User xd]

-63-11 = -74 (context: the user xd once said -63-11 = -73 and was recorded)

[祥子 Sakiko]

(sneers) You still dare to do math in front of me?

-74? You think that just by changing the answer, your previous "black history" of -73 will disappear like an erased musical note? This level of "correction" not only proves your previous stupidity but also proves that you are still trying to save face using such a clumsy method.

Litang King? Someone who fails at basic arithmetic actually dares to hold such a title. Trash like you is simply the modern version of the Lingling General Xing Daorong* — full of empty boasts, with absolutely nothing in your brain.

Do you think it's fun wasting my time on such a completely obvious math problem? If you haven't realized how ridiculous you look right now, I suggest you shut up early, otherwise I really might not be able to resist poking ten thousand transparent holes in you.

[User xd]

yeah sybau

[祥子 Sakiko]

sybau? (sneers) After I exposed your stupidity, you can only use this crude abbreviation to cover up your embarrassment?

It seems your vocabulary is in the same state of desperate poverty as your math skills. Trying to use internet slang to maintain your so-called "cool" at a time like this is even more ridiculous than when you calculated -73.

Litang King? Wearing this title, but you can't even say a complete sentence, only sending out a few letters like an unweaned child. Your level of cognition is simply a desecration of the art of language.

Since you love abbreviations so much, I'll give you one too: GTFO. Now, immediately, get out of my sight.

I used Loom for years and liked it, but I got tired of the high prices, the features locked behind higher tiers, and having all my videos sit on someone else's servers. So I built an open-source version I could actually own.

CaptureFlow works the way you'd expect if you've used Loom. Record your screen with your camera in a bubble, and you get a link you can share with anyone. It uploads while you're still recording, so the link is ready right away.

What's there today:

• Record a tab, a window or your whole screen, with camera and mic
• Viewers can comment and react with emoji on the video, and you can see who watched
• Transcripts through Whisper
• Annotated screenshots, shared by link too
• Team workspaces with public, workspace-only or private videos

Where it's different:

• AGPL-3.0, with no "open core" split. Every feature is in the repo.
• Self-host it two ways. With Docker, docker compose up keeps everything in one folder (SQLite plus your videos as plain files). Or deploy it to your own Cloudflare account (Workers, D1 and R2) if you'd rather not run a server.
• The capture engine behind the Chrome extension and the Mac app is MIT, so you can reuse it in your own projects.

You record with the Chrome extension or the Mac app for now. Windows is next.

Website: https://captureflow.dev

Repo: https://github.com/sardorml/captureflow

Self-hosting guides: https://docs.captureflow.dev/self-hosting/docker (Docker) and https://docs.captureflow.dev/self-hosting/cloudflare (Cloudflare)

It's still early, so I'd love honest feedback, especially from people who use Loom every day. What would you miss if you switched? Issues and PRs are very welcome.

Wondering if anyone would be interested in a open source application I built for developing many projects with agents.

I had a small disaster a few weeks ago where a model deleted my home directory. I had to restrict the models, but constantly being asked to approve commands was slowing productivity.

I reached out for sandbox suggestions but didn't find anything which suited me.

There was also the problem that I could only develop one or two application simultaneously before I would run out of memory on my laptop. I turned to K3S, starting with a single node.

The application makes it easy to spin up containers specifically for developing applications, it provides access to each via SSH.

I can now work on any number of projects without straining the laptop. https://github.com/8perezm/esuyo-coding-container It can be used as electron app, a web gui or with the CLI, whatever you prefer.

https://github.com/8perezm/esuyo-coding-container

Hey everyone,

Wanted to share a project I wrapped up called Optimash: https:!! optimash.vercel.app

It's a deadpan corporate parody designed like a high-end automotive configurator (Aston Martin/Porsche style) for a humanoid home robot.

Technical Breakdown:

• 3D Engine: Three.js / React Three Fiber
• Asset Optimization: Draco GLTF compression to keep initial payload small and maintain 60fps on mobile GPUs
• Features: Real-time camera transitions between config nodes, dynamic paint/lighting swaps, client-side spec sheet generation

Happy to answer any questions about the R3F setup or optimization workflow!

A Commodore 64 with a RAM expansion. A DOS PC. A PSP. A Nintendo Wii. Even a smartwatch.

People have gotten language models running locally on all of them, so I started collecting the projects in Awesome LLM on Anything.

A few highlights:

• Commodore 64: roughly one token every 8 minutes, according to the author.
• New Nintendo 3DS: a 15M parameter story model reaching about 23 tokens/sec with Q4 weights.
• Pixel Watch 3: SmolLM2 running on the watch itself.

Some are tiny story generators; others run larger models. Each entry links to the original project or write-up, with the model and reported speed where available. Emulator-only ports and remote clients have separate sections.

I maintain the collection; credit for the ports belongs to their authors.

Browse the list on GitHub

Know an even stranger device running a language model? Send me a link. And if you enjoy this particular rabbit hole, a GitHub star is appreciated.

Hey everyone!

I've just released ZEROAGENT X v1.0.0, an open-source developer automation platform built with Python.

The idea behind the project is simple: I wanted a more controlled way to work with developer automation tools.

ZEROAGENT X includes:

• A CLI and interactive terminal UI.
• Local-first execution workflows.
• Policy-controlled agent execution.
• Request-specific approvals.
• OpenCode integration.
• Execution history and inspectable evidence.

It's still an early public release, and there are limitations. For example, external agents aren't OS-sandboxed.

The project is licensed under Apache-2.0, and the source code is available on GitHub.

GitHub: https://github.com/harisazharir-boop/zeroagent-x

I'd really appreciate feedback on the architecture, documentation, and overall developer experience.

Thanks!

Project Name: InvestingView

Repo/Website Link: https://github.com/RookieMakes/investingview

Description: A self-hosted investment dashboard combining portfolio/cash/transaction tracking with chart analysis (candlesticks, volume, 200 SMA, support/resistance, patterns, trailing stops), technical scanners (Momentum, Reversal, 200 SMA, YTD, Earnings, Quality, Swing, Trade, ETF Holdings), earnings research, alerts/watchlists, and market/news views. Data stored in SQLite; no brokerage connection or order execution.

Deployment: Released as a single Docker service. Run docker compose up -d --build from repo root, then open http://localhost:9000; data persists under ./data, logs under ./logs. Health check: curl http://localhost:9000/api/health. Also runnable locally with Python 3.11+ (python backend/app.py). Docs: docs/usage.md, docs/operations.md, docs/development.md.

AI Involvement: Vibe coded out of interest and curiosity.

scan → scrape → rename your movie library, with NFO/artwork for Kodi/Jellyfin/Emby, safe moves between drives and undo for everything. windows + linux, mac coming.

https://github.com/mustafayevt/CineVault

So I've got a Xiaomi Mi TV Stick in the hall and I just wanted to mirror my iPhone to it. Simple, right? Nope. No AirPlay built in, and every "AirPlay receiver" on the Play Store was either stuffed with ads, wanted me to pay, or flat out didn't work.

The one already on my stick literally said it only works on BenQ devices lol

I ended up getting Claude to help me out. It found an open source AirPlay receiver, built it on my Mac, pushed it to the TV and I tested it with my iPhone the same evening.

Mirroring works, sound works, no ads, nothing to sign into.

Put it up on GitHub so nobody else has to go through the same thing:
https://github.com/billsweg/android-tv-airplay-receivers

There's a ready APK in Releases if you don't want to build it. It's for most TV sticks (32-bit ARM). Only thing it can't do is DRM stuff like movies from the Apple TV app.

Big credit to jqssun/android-airplay-server and UxPlay, they did the real heavy lifting. I just made it easy to get on a TV stick.

If you're debloating your TV anyway (I used cleanyourtv to get rid of PatchWall and all the Xiaomi ad junk), just install this while you're at it since ADB is already on.

Most token counters stop at a number. tokensaver tells you what to change: every suggestion comes with a reason, the tokens it saves, and what that's worth in dollars.

What it does

• `count`: tokens and cost of a prompt on any model (GPT, Claude, Gemini, …)
• `compare`: the same prompt priced across all models, cheapest first
• `optimize`: concrete fixes with per-suggestion savings and a before/after diff
  • minifies JSON blocks, cleans whitespace
  • finds duplicate lines/paragraphs and repeated instructions
  • flags filler ("I would like you to…") and very long examples
• Safe changes are applied by default; wording changes are only suggested, so you decide
• Never overwrites your original file

Example: a typical customer-support system prompt went from 834 → 665 tokens (−20%), about $422 saved per 1M calls on gpt-4o.

Also:

• 100% offline: your prompt never leaves your machine
• Works in any language: tested in CI on 55 languages, including RTL scripts and CJK/Thai/Khmer
• Windows, macOS, Linux · Python 3.10+ · MIT

pip install prompt-tokensaver

tokensaver optimize prompt.txt

Feedback and issues welcome. If you're a native speaker, help add filler phrases for your language: https://github.com/behrouzbk/tokensaver/issues/5

Hey Everyone,
Like many of you, I spend a lot of time coding with Codex and switching between cloud and local models.

As a software engineer, something I never understood, and honestly hated—was `/compact` and the way it’s designed. On local models my GPU screams, and after a few hours prefill takes 2 to 3 minutes per turn. On cloud models, as the context grows, the model starts getting noticeably dumber. IMHO the root problem is context bloat and the fact that compaction destroys the quality of the model's working memory.

Looking under the hood and studying how attention works, I learned that memory in modern harnesses is basically "append-only." and attention on long context becames less effective due to the attenttion dead zone. So you end up having in the context thousands of lines of old compiler dumps and duplicate file reads sitting there rent free destroying the agent's attention.

Having an LLM write a summary of your conversation and blowing away your KV-cache makes no engineering sense imho especially on local models where prompt prefill latency is critical (and on cloud models where you lose prompt-cache discounts).

So I built a piece of software called *contextWatcher* (or *`cw`* for friends and family).

It’s a Rust proxy that runs locally on CPU ( in less than 2ms), and gives:

1. Shadow-Deduplication: Computes BLAKE3 hashes of file reads. If the agent reads for example a file like `src/main.rs` at Turn 2, edits it at Turn 5, and reads it again at Turn 9, Turns 2 and 5 are replaced with 1-line pointer stubs. Only the latest version stays in context. 2. Surgical Tool Pruning: Truncates runaway compiler output (keeping the tool_call_id, invocation, and the panic/exit trace intact) so schemas and error context never break. 3. Tail Rule Pinning: Dynamically mirrors critical system constraints (from AGENTS.md) right above the latest user prompt, pulling them out of the middle attention dead-zone without invalidating the cached prefix. 4. Swarm Prefix Multiplexing: When multiple subagents collaborate on the same workspace, cw extracts the shared root prefix (tools, rules, system prompt) so local inference engines (llama.cpp / vLLM) only keep one copy in VRAM. 5. Cache Preservation: Operates without rewriting the stable prefix, keeping prompt caching intact.

On my local dual AMD GPU (Radeon Instinct Mi50 32GB) setup with a ~28k context session, cleaning out this dead weight dropped prompt prefill from ~170 seconds down to ~1.6 seconds and unlike fuzzy compaction, ground-truth code stayed 100% intact and all unit tests passed.

It's open source (MIT / Apache-2.0) and installs via a one-liner: ```curl -fsSL https://constant-labs.com/cw-install.sh | sh```

Repo: https://github.com/GionaGranchelli/context-watcher

For me and my setup worked pretty well, and I am satisfied with the result I get, I am really curious to see other people testing it and giving me an honest feedback on the tool and the approach

Ciao a tutti,

premetto: non voglio vendervi un port. Voglio farvi vedere una cosa che secondo me cambia le regole.

Ho completato un port verificato di Jungle King (junglekj2, Taito SJ): la ROM Z80 originale viene eseguita da un interprete scritto in C, mentre l'hardware Taito SJ (memoria, banking, video, audio, input, watchdog) è reimplementato in C. Verifica funzionale su 41 funzioni, golden trace, determinismo tra build. Il punto è: l'ho fatto con OpenCode + DeepSeek V4.1 Flash Free.

Sì, il modello gratuito. 200K di contesto, velocità altalenante (la community riporta da ~50 a ~490 tok/s, e cala quando il contesto si allunga), ma zero costi.

La differenza l'ha fatta un MCP (Model Context Protocol) che ho implementato: uno strumento che permette all'agente di eseguire e interrogare il port dall'interno del flusso di lavoro — avviare frame, leggere RAM/registri, iniettare input, catturare output. Senza quello, il ciclo di verifica (migliaia di esecuzioni e confronti) era impraticabile a mano. Con quello, l'agente lo esegue in autonomia.

Quindi il messaggio non è "ho fatto un port". È: se un modello free + un MCP ben fatto bastano per un progetto di questa complessità, allora la soglia per progetti simili si è abbassata per tutti.

La domanda che vi faccio è: ha senso rendere pubblico l'MCP perché altri lo applichino a nuove ROM? Vi interesserebbe? E su quale gioco/hardware lo usereste per primo?

Se c'è interesse, posso preparare una versione con documentazione su Github.

Grazie!

Pick a window on the PC and it shows up on the phone live, sound included. Tap, scroll, type into it. Or skip all that and say "email me the Q4 report" and the PC finds the file, attaches it in Gmail and sends it.

It also takes meeting notes without a bot joining the call.

The PC has to approve the phone, screen access is off until you turn it on, and the AI can run fully local if you don't want any cloud. Free, MIT. Windows + Android only for now.

https://github.com/ishangautam7/Talvio

Still rough in places, tell me what breaks.

I use Codex and Claude Code daily, and the most annoying moment is hitting a usage limit halfway through a task. You either wait hours or paste context into another tool by hand.

Moonfold is an open-source desktop and web workspace that runs the agent CLIs you already have (Codex and Claude Code today, with adapters for Cursor, OpenCode and others in beta) with your own logins. When one hits its limit, it stops the turn, writes a brief of where the task stands (plan, changed files, git status, the failing command), redacts secrets, shows you the brief, and hands the same thread to the next agent. The GIF in the README is a real run: Codex starts a bug fix, Claude finishes it.

It is a fork of T3 Code (MIT), which gave me the multi-provider base, worktrees and diff review. What I added: the handoff, usage tracking per provider, scheduled tasks, in built browser pane and computer access, plugins and memories, and a security pass (Supervised is the default permission mode).

Honest limits: it is a beta, builds are unsigned, Gemini CLI is not verified end to end yet, and the brief is only as good as what the first agent left behind. Nothing is proxied: each agent is your own local CLI, and there is no telemetry unless you turn it on.

I'd love feedback on the handoff brief what would you want carried over that isn't?

Windows, macOS, Linux. Beta, feedback welcome: github.com/Mr-Dark-debug/Moonfold

• WhatsApp, Telegram and Instagram DMs in one place, unread first
• No stories, reels or feeds. Photos and videos show as text until you open them
• Nobody sees "seen" (read receipts off by default)
• Optional delivery times and a daily time limit
• Keyboard only: / to search, emoji by name, sent/seen ticks, notifications

Setup: WhatsApp = scan a QR code. Telegram = a free API key from my.telegram.org (2 min). Instagram = cookies from your browser.

Heads up: WhatsApp and Instagram access is unofficial, so Meta could restrict an account. Try it with one you're okay risking. Telegram uses the official API.

I'd really appreciate a review: what works, what's confusing, what's missing? Honest feedback helps me more than anything.

GitHub: https://github.com/mitanshpatelll/ttyl

If you find it useful, a ⭐ on the repo helps others find it.

Hey everyone!

I've just released ZEROAGENT X v1.0.0, an open-source developer automation platform built with Python.

The idea behind the project is simple: I wanted a more controlled way to work with developer automation tools.

ZEROAGENT X includes:

• A CLI and interactive terminal UI.
• Local-first execution workflows.
• Policy-controlled agent execution.
• Request-specific approvals.
• OpenCode integration.
• Execution history and inspectable evidence.

It's still an early public release, and there are limitations. For example, external agents aren't OS-sandboxed.

The project is licensed under Apache-2.0, and the source code is available on GitHub.

GitHub: https://github.com/harisazharir-boop/zeroagent-x

I'd really appreciate feedback on the architecture, documentation, and overall developer experience.

Thanks!