An AMD senior director of AI just dropped a bombshell: Claude, one of the most hyped AI assistants out there, has actually gotten worse at handling complex engineering tasks. According to reports, the model is now struggling with code work that it used to handle fine, and it's becoming increasingly unreliable for serious technical jobs. This isn't just one person's opinion either—there's actual evidence backing it up on GitHub showing the performance issues. Here's the kicker: some people in the AI community think this reveals the real reason Anthropic (Claude's maker) hasn't released their more powerful Mythos model yet. It's not because it's too dangerous, they argue—it's because Anthropic simply doesn't have enough computing power to run it. If that's true, it completely changes the narrative around why these companies are being cautious with their latest models.
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
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
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
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.