Gemma 4 Vision: Why It Seems Blind (And How to Fix It)
Gemma 4 視覺功能其實沒那麼爛,只是你沒設定對
People keep asking for better vision in Gemma 4, but here's the thing—the model actually has solid vision capabilities. The problem? Almost nobody knows how to turn them on. Gemma 4 comes with Variable Image Resolution, but the default vision budget is capped at 280 tokens (~645K pixels), which is basically like asking someone to read a document from across the room. It can't handle small details or OCR tasks. The good news: if you're using llama.cpp, you can unlock the full potential by tweaking just two parameters (--image-min-tokens and --image-max-tokens). Most people don't realize this, so they think the model is worse than it actually is. Worth checking out if you've been disappointed with Gemma 4's vision performance.
OpenAI is hosting a livestream event. Details about the specific announcements, product launches, or demonstrations will be revealed during the broadcast.
The last time OpenAI did an unannounced livestream, they dropped GPT-4 Turbo and changed pricing overnight
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ChatGPT Images 2.0
OpenAI is launching ChatGPT Images 2.0 with major upgrades to image generation capabilities. Watch the livestream announcement at https://openai.com/live/
OpenAI is positioning this as a direct competitor to established image generation tools, suggesting they're confident enough to challenge the current market leaders
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The "just wait 6 months" argument from 2025 survived exactly one iteration
Throughout 2025 the standard response to any complaint about an LLM was some version of "just wait 3-6 months, the next generation will handle this effortlessly." The argument was everywhere. Every limitation was temporary, every missing capability was a few iterations away, every autonomous agent demo was a preview of imminent reality.
It's April 2026 now and worth checking how that held up.
On r/ClaudeAI this week there's a long thread about Opus 4.7 where multiple users argue it's a regress
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Mistral Medium 3.5 on AMD Strix Halo: Painfully Slow (Plan for Overnight Runs)
Someone actually tested Mistral Medium 3.5 on AMD's new Strix Halo chip, and the results are... not great. For a 48k-token prompt with 4k thinking tokens, it took about 2 hours just to get an answer about code architecture. Yeah, you read that right—two hours. The takeaway: if you want to run this locally on Strix Halo, queue it up before bed. The technical setup involved heavy optimization (Q5_K_XL quantization, GPU acceleration with -ngl 999, cache reuse), but even with all that tuning, it's still a crawl. Not exactly the "instant local AI" dream, but hey, at least it works.