As a data scientist, I’ve been frustrated that there haven’t been any impactful new Python data science tools released in the past few years other than polars. Unsurprisingly, research into AI and LLMs has subsumed traditional DS research, where developments such as text embeddings have had extremely valuable gains for typical data science natural language processing tasks. The traditional machine learning algorithms are still valuable, but no one has invented Gradient Boosted Decision Trees 2: Electric Boogaloo. Additionally, as a data scientist in San Francisco I am legally required to use a MacBook, but there haven’t been data science utilities that actually use the GPU in an Apple Silicon MacBook as they don’t support its Metal API; data science tooling is exclusively in CUDA for NVIDIA GPUs. What if agents could now port these algorithms to a) run on Rust with Python bindings for its speed benefits and b) run on GPUs without complex dependencies?
“春还草阁梅先动，月满虚庭雪未消。”颈联景中藏心，最见功夫。春回大地，梅花率先绽放，是良知在困顿中悄然觉醒。月色满庭，残雪未消，是小我与私欲尚待克制。王阳明说“心外无物”，眼前的梅与雪，都是内心的映照。顺境不骄，逆境不馁，于冷清中见生机，于残缺中守清明，这便是我们最该学习的心学定力。
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This isn't an argument against platforms like Vapi or ElevenLabs. Those systems offer far more than orchestration: APIs, observability, reliability, and deep config options that would take real effort to reproduce. For most teams, rebuilding all of that would be a mistake - being able to test and validate a voice agent app without getting to this level of technical depth is truly amazing, and that's how I first got excited about the technology.