snapcompact (from oh-my-pi) swaps the normal LLM summarization for a local render of the conversation into dense pixel font PNG frames. The vision model reads them back. No LLM call, no API key, fully deterministic. For cloud models the pitch is clear, it saves billed vision tokens. But I self host DeepSeek-V4-Flash-Vision-Exp on 2x RTX Pro 6000, so there's no per token billing to save, and compute is cheap enough that I'm not worried about the render cost. The real question is whether a local VLM can read those frames well at all. All the shape tuning and recall evals were done on Claude, Gemini, GPT, Kimi and GLM. I couldn't find a single local model benchmark. The maintainer even flags fidelity risk for code, tables, Unicode and dense terminal output, which is basically a whole coding transcript. There's no clean way to test this myself, it's more of a vibe-based thing. But does anyone actually have an answer? I'd love to hear what other people think, especially anyone who's run a local VLM against snapcompact.   submitted by   /u/anitamaxwynnn69 [link]   [comments]