Alternatives
Products that do what I run a vision model on every screenshot, locally, on a 4GB GPU does
- 1PB
2018 · pbr-book.org
- 2
- 3A4
2017 · artpip.com
- 4IM
I made my first macOS utility app that ships with a bundled Gemma 4 model, specifically the Gemma E4B one. It made my app DMG have 5.3 GB in size, but I think it is a small size for the power that this free local model can provide. It runs fine on CPU, but can also run on Apple Silicon GPU, although I did not notice any performance improvements with GPU (tested on a M5 chip). I think these local lightweight and multimodal models will open multiple possibilities for new software tools where privacy is essential.
May 2026 · snapname.app
- 5IW
Jun 2026 · github.com
- 61T
2020 · youtube.com
- 7PM
2019 · pixaven.com
- 8GV
2018 · github.com
- 9OT
I've always had an issue with the screenshot flow on my computers. They save to the desktop by default and I can't seem to find screenshots easily when I needed it. They create too much clutter too. So I set out to solve this problem. My major concern was with privacy so I made sure to make it work entirely offline. Here's the final product, let me know what you think. - https://visionshot.app/
2020
- 10
- 11AM
2017 · github.com
- 12DG
2016 · github.com
- 13PB
2021 · github.com
- 14
Image to 3D Model: Offline. Local. Portable.
Jul 2026 · triposplat-webgpu.vercel.app
- 15
The vision plugin for OpenCode that truly understands images. Inspect, read, and reason about any screenshot or picture with deeper understanding than any other plugin — fully local, private, and free. Normally, it takes 300ms to analyse one image on my laptop, which is pretty fast for a local vision model. I use moondream2 as my vision model, you can set your custom model like moondream3.1 if you have a good GPU (for comparison I have currently have an RTX 3050). It works cross-platform. Just follow the README. If you like my work, you leave me a tip as an act for supporting open source!!…
24d ago · github.com
- 16EV
2018 · github.com
- 17AE
2016 · github.com
- 18GB
2016 · paperspace.com
- 19
- 20UA
Hi HN! I've got the barebones of a service running on top of Stable Diffusion XL. I can cheaply run image generations at 1024x1024. And of course there's a limit to how fast I can generate them given the request queue and limited GPUs, but the service is cheap enough that I'm happy to run it out of pocket for now. Let me know your thoughts, I hope you enjoy the service!
2023 · unstock.ai
- 21GI
2014 · getscreenshots.io
- 22S1
I wanted to build an inference provider for proprietary AI models, but I did not have a huge GPU farm. I started experimenting with Serverless AI inference, but found out that coldstarts were huge. I went deep into the research and put together an engine that loads large models from SSD to VRAM up to ten times faster than alternatives. It works with vLLM, and transformers, and more coming soon. With this project you can hot-swap entire large models (32B) on demand. Its great for: Serverless AI Inference Robotics On Prem deployments Local Agents And Its open source. Let me know if anyone…
Nov 2025 · github.com
- 23IM
It works by using open AIs new gpt-image API, image processing algorithms and some prompt engineering. Still has some limitations, but can already generate ready to use assets.
2025 · pixel-gen.ai
- 24LT
Hi HN! I'm the author of mere.run a local first inference runtime built around an installable CLI. I believe that whenever possible we should use the stuff we already own (like our Mac laptops, decent machines gathering dust, our gaming PC) and the limited electrical power we have easy access to, like the socket in the wall next to most of us. We shouldn't have to send our data to the cloud hoping some T&C will prevent it from being used in a way that we'd regret. Most of the local AI solutions are technical, involved, and land a curious body in some package hell. People are optimizing for…
Jul 2026 · github.com
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