I'm running Qwen 3.6-35B-A3B with 8-bit quantization and 64K context through OpenCode on my MacBook Pro M5 Max 128GB, and it's as good as Claude
我在 MacBook Pro M5 Max 128GB 上透過 OpenCode 執行 Qwen 3.6-35B-A3B(8 位元量化、64K 上下文),效能與 Claude 相當
of course this is just a trust me bro post but I've been testing various local models (a couple gemma4s, qwen3 coder next, nemotron) and I noticed the new qwen3.6 show up on LM Studio so I hooked it up.
VERY impressed. It's super fast to respond, handles long research tasks with many tool calls (I had it investigate why R8 was breaking some serialization across an Android app), responses are on point. I think it will be my daily driver (prior was Kimi k2.5 via OpenCode zen).
FeelsGoodman, no
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.