Reddit and GitHub are littered with people launching new projects and appear to be way more feature-rich than new tool/app launches from previous years. I think it is a lot harder to get noticed with a new tool/app new because of this increase in volume of launches.
Also weekend hackathon events have completely/drastically changed as an experience in the last 2-3 years (expectations and also feature-set/polish of working code by the end of the weekend).
Was the on-device local LLM stack that you tried llama.cpp or something like MLC? I've seen better performance with MLC than llama.cpp in the past - but it has been probably at a least a year since I tested iphones and androids for local inference
Depends on what benchmarks/reports you trust I guess (and how much hardware you have for local models either in-person or in-cloud). https://aider.chat/docs/leaderboards/ has Deepseek v3 scoring higher than most closed LLMs on coding (but it is a huge local model). And https://livebench.ai has QwQ scoring quite high in the reasoning category (and that is relatively easy to run locally but it doesn't score super high in other categories).
My gut feeling is that there may be optimization you can do for faster performance (but I could be wrong since I don't know your setup or requirements). In general on a 4090 running between Q6-Q8 quants my tokens/sec have been similar to what I see on cloud providers (for open/local models). The fastest local configuration I've tested is Exllama/TabbyAPI with speculative-decoding (and quantized cache to be able to fit more context)
Also weekend hackathon events have completely/drastically changed as an experience in the last 2-3 years (expectations and also feature-set/polish of working code by the end of the weekend).
And as another example, you see people producing CUDA kernels and MLX ports as an individual (with AI) way more these days (compared to 1-2 years ago), like this: https://huggingface.co/blog/custom-cuda-kernels-agent-skills