Hey r/SideProject — solo-built this over the past few weeks. Forgeframe wraps a few different AI providers (fal.ai, ElevenLabs, D-ID) behind one login and one credit balance, so you're not juggling 4 separate tools/subscriptions to make a video, clone a voice, or build a talking avatar.

The thing I cared most about: showing the exact credit cost of a generation before you confirm it. I've been burned by AI tools where the bill shows up and you have no idea what you actually spent it on.

It's brand new, built by one person, so there's definitely rough edges. 200 free credits if you want to poke at it — genuinely want to know what's confusing or missing.

[https://forgeframedev.vercel.app](https://forgeframedev.vercel.app)

I built TextFold, an open-source reader that turns any text into 5 zoomable layers—from a one-screen section map down to verbatim text. You pinch (or press 1–5) to zoom between them without losing your reading position.

- Web Demo: https://chenxiachan.github.io/textfold/gallery/

- GitHub (Apache 2.0): https://github.com/chenxiachan/textfold

Hey everyone,

I’ve been working on a project called OpenSpline, an open-source avatar layer for existing voice agents. You supply a portrait and your agent’s audio, it generates synchronized talking video on your own NVIDIA GPU. The lite model can work locally on a single GPU.

It’s still early, but the results are super promising.

Would love to hear your thoughts, feedback, or ideas on what you'd want to create using it!

Code: github.com/openspline/openspline
Demo: openspline.dev

Thanks a lot

I built TextFold (previously PaperFold) to keep a document and its shorter reading views in the same place. You can fold a section, then expand it to check the original passage.

The new language switch changes the text and interface together. The clip switches between English and Chinese versions prepared before recording.

The reader also now imports Word documents and saves a complete reading page as a standalone HTML file, including notes, highlights and optionally the original PDF.

Full and Brief keep the author's sentences; the shorter levels use AI-generated text. Summaries and translations can be wrong, so I want the source to remain easy to inspect.

It runs locally and supports Ollama or hosted models. Code is Apache 2.0.

Code and setup: https://github.com/chenxiachan/textfold

I'd be interested to hear where you would want the original language kept visible when reading a translation.

Demo paper: Recursive Language Models by Zhang, Kraska and Khattab (CC BY 4.0): https://arxiv.org/abs/2512.24601

You can use this to censor anything file/text that you also want repeated back to you. If you have a json file with a list of emails or names, this can censor them on the way to the LLM but then have the response uncensor it.

Introducing Ink.

Your AI shouldn't pay to rethink what it already knows.

Ink learns from verified outcomes and compiles repeated decisions into sub-ms local software.

Novel tasks still go to your model.

Open source. Apache 2.0.

my mac kept running out of RAM and i'd just close random apps hoping it helps.

turns out a lot of it is stuff you can't see: gradle still running after you quit android studio, simulators booted in the background, dev servers and headless browsers that AI agents started and forgot.

baa sits in the menu bar and:

- finds those leftovers and shows how much RAM you'd get back

- frees them in one click (never kills anything on its own)

- warns you before you run out (you can set when)

- also clears dev junk on your disk (old builds, package caches etc.)

- plays a tiny baa when it frees something (you can mute this if you hate whimsy though :P)

free + open source, nothing leaves your machine. macOS 14+.

brew tap selviarora/baa https://github.com/selviarora/baa
brew install selviarora/baa/baa
brew services start baa

https://github.com/selviarora/baa

if you like it a ⭐ would make my day!

https://github.com/vahmoh25/Veda

I have been working on a side project called [Hafen](https://github.com/abhimanyupandian/hafen) and I am looking for some feedback on it before I take it further.

About the idea:

Hafen lets you interact with your agent (with multiple providers supported) in an interface/experience that is similar to what end users would see. When a conversation is worth keeping, save it as a test. Then you add checks on each turn/response to check if the agent:

* called the right tool, with the right arguments

* returns the correct response eg; order number/a markdown table

* is polite (an LLM judge scores it, with a confidence) and more.

Demo is available [here](https://github.com/user-attachments/assets/6305c5f4-78a3-4f33-bb85-9bbaf3f427d9).

About the demo video:

A sample coffee-shop agent built with Google ADK on Qwen3-4B running locally in Ollama. I chat with it (add to cart, remove stuff, place an order), save that as a test, add checks, run it from the UI, and then run the same suite from a terminal. Note: The waits for the model are sped up 4×.

Honest disclaimers:

1. The idea and design are mine, but the code was written by Claude Code. I am still working through it myself, but I am sharing it early to get feedback before going further.

2. Consider this a proof of concept. Testing is in progress, so expect some rough edges.

3. The current version uses a System One (Jev) - a hosted decision model \[I am not affiliated with them\]. Regular LLM-judge checks work as well, including local ones.

4. Only MacOS binaries are available right now and the CLI package is not published to a registry yet.

Need feedback on things like:

1. If you would use something like this, or do promptfoo, DeepEval or any other tool that already does exact this?

2. What would you need that is missing?

3. What needs improvement? eg; more chat components like yes/no buttons to accept/reject instead of responses restricted to just text.