Why AI Assistants Are Secretly Better Collaborators Than People (No Judgment Zone)
AI 助手才是真正的完美隊友:不會碎碎念、不會翻白眼的開發夥伴
You know that frustrating moment when you're brainstorming with someone and suddenly want to pivot direction, but they give you that look or sigh? Yeah, Claude and Gemini don't do that. The author realized that after countless development conversations—no matter how long or tangled—these AI tools just roll with whatever curveball you throw. Want to scrap the whole approach and try something completely different? They don't complain, don't question why you're "wasting time," just adapt instantly. It's weirdly liberating. No ego, no "but we already spent hours on this," just pure "okay, let's rebuild this for X instead." Honestly kind of spoiling us for human collaboration.
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.