A developer just released a heavily optimized version of NVIDIA's Nemotron-3-Super-120B that cuts the model size in half (from 120B to 64B parameters) while actually getting *better* at math. Here's what they did: took the original model, pruned its expert system from 512 down to 256 (basically removing redundant parts), fine-tuned it on ~270 competition math problems using a technique called GRPO, then compressed it with AWQ and FP8 quantization. The result? It now scores 90%+ on AIME 2026 (a notoriously hard math competition benchmark) and fits on a single H100 GPU. This is the kind of thing that matters if you're running local AI—smaller model, same or better performance, way cheaper to run. The benchmarks are solid, and they've released multiple versions (BF16 full weights, quantized versions) so you can pick what fits your hardware.
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.