What Generative AI Actually Does (And Why Most People Get It Completely Wrong)
生成式人工智慧到底在做什麼?(大多數人都理解錯了)
Ever felt lost when someone talks about generative AI or large language models (LLMs)? You're not alone—most people have it backwards. Here's the thing: generative AI isn't magic, and it's definitely not thinking like a human. It's more like a really sophisticated pattern-matching machine that learned from billions of examples. Think of it like this: if you've read thousands of books, you can probably guess what word comes next in a sentence pretty accurately. That's basically what ChatGPT does, just at an insane scale. For product managers especially, understanding this matters because it changes how you think about what these tools can actually do (and what they can't). The difference between hype and reality? It's all in understanding what's really happening under the hood. Read the full breakdown to stop nodding along in meetings and actually know what you're talking about.
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.