Why AI Chatbots Suddenly Start Talking Nonsense When You Ask Them Hard Questions
為什麼 AI 在回答難問題時會開始胡說八道?
Ever notice how ChatGPT gives you brilliant answers for everyday stuff, but completely makes things up when you ask about cutting-edge research? Here's what's actually happening: LLMs are basically remix machines—they're phenomenal at mixing and matching existing knowledge, so they crush normal questions. But the moment you push them toward the frontier of what's actually unknown (where current knowledge ends and new discovery begins), they hit a wall. Their training data gets sparse and messy, so the model literally doesn't know what to do and just... guesses. It's like asking someone to describe a country they've never been to—they'll confidently make stuff up instead of admitting they don't know. This might explain why AI hallucinations seem worse lately: we're asking these models questions that are genuinely beyond what their training data covers. The real issue isn't that the AI is broken; it's that we're asking it to do something it was never designed for.
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.