Funny to think about this when he got fired releasing a well-received product. On the other hand, Google released a subpar antigravity 2.0, full of bugs, almost unusable. The tech lead of Antigravity went on X to claim users don't know what they want.
So are my DSP/Audio ML career, I spent decades acquiring the expertise. Now at 40, I don't know what to do if I lost my current job. Very sad, but from economic POV, I also don't need junior engineers in my team anymore.
Bad investment IMHO. Mistral was started by people who cheated on benchmarks with their Llama 1. It showed as they had the head start but fell far behind Gemini, DeepSeek and Qwen teams.
My go to framework. I wish we can use global metrics in DSPy, for examples, F1 score over the whole evaluation set (instead of a single query at the moment). The recent async support has been life saver.
Recent ASR models are already robust to noise due to Spec augment and large-scale data. If you use these noise reduction services to remove noise, ASR models will have harder time to recognize denoised audio. The reason is that noise reduction will create distortion which ASR didn't see during training.