I asked ChatGPT to imagine NYC and LA in 100 years—it's obsessed with turning them into jungles
我叫 ChatGPT 畫紐約和洛杉磯 100 年後的樣子——它根本把城市變成叢林
So this person fed ChatGPT a prompt asking it to generate images of what New York and Los Angeles might look like a century from now. The AI went absolutely wild with vegetation—we're talking overgrown parks, buildings wrapped in vines, nature reclaiming the concrete jungle. It's kind of fascinating (and a little dystopian?) how the AI imagined these megacities basically becoming forests. Makes you wonder if that's what the AI thinks will happen if we don't fix climate change, or if it just has a weird obsession with greenery. Either way, the images are pretty wild and definitely worth checking out to see what your favorite cities might look like when Mother Nature takes back over.
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.