I tested 9 local models on the same flight sim prompt, all Q8, different Q providers, MLX
我在相同的飛行模擬提示詞上測試了9個本地模型,全部Q8量化,不同的量化提供者,使用MLX
I gave 9 local models the same flight combat sim prompt. The results broke a few of my assumptions about quant providers and parameter count.
All 8-bit MLX, M3 Max 128GB, served via omlx, prompted through Claude Code. Same prompt every time — single-file HTML, three selectable planes (jet, prop, wildcard of the model's choice), dynamic enemies, tracers, damage, crash spiral on loss. Counted prompts-to-final and graded on "does it actually play."
https://alextzk.github.io/flight-combat-llm-comp
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.