Gemma 4 Just Beat ChatGPT and Gemini—And It's Because You Can Actually Run It Yourself
Gemma 4 打敗 ChatGPT 和 Gemini——因為你可以自己執行它
Here's a wild story about why owning your own AI actually matters. The author uses AI to translate a Chinese novel chapter-by-chapter, and the plot has characters with secret identities that require the AI to track context clues and maintain consistency across translations. When they started, GPT-4 and other big models handled this fine—but over time, those same models got worse at it. Why? The author suspects it's because of model degradation and increased censorship filters that make the models more cautious and less able to follow complex narrative threads. Then they tried Gemma 4, an open-source model they could run locally, and it crushed both ChatGPT and Gemini at the same task. The kicker: because it's open-source and runs on their own hardware, there's no corporate filter watering down its capabilities. It's a perfect example of why 'if you don't run it, you don't own it' actually matters in practice—not just as a slogan.
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.