Qwen 3.6 27B Neo Code Q4 KM Actually Crushes It for Real Work—Not Just Coding
Qwen 3.6 27B Neo Code Q4 根本是怪物級別—終於能在自己電腦上做真正的工作
Here's the thing: this person is running Qwen 3.6 27B on a mid-range Ryzen 9 laptop (8C/16T, 60GB RAM, Radeon 780M) to automate tax accounting—converting PDF tax forms and Excel sheets into processed Excel files. Yeah, it's slower than Claude, but the output quality is basically identical. The real insight? We're entering a phase where local AI models aren't just for coding anymore—they're becoming practical tools for specific industry tasks. You don't need a fancy cloud API or cutting-edge speed if the model is smart enough to do your actual job accurately. That's the shift happening right now.
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.