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Path from open models to close models 1. Release data, model, code for data collection, inference and training. Also build in public. 2. Same but build privately. 3. Don't release the data, don't release the training code. Only model weight and inference code. 4. Release only an

Well written essay on AI native apps. I especially like the part explaining that we used to have only 2 choices: 1. Build the app 2. Use an existing app. Now there's a third choice which is to let the computer build one app for one user.

I was curious about Xi'an dialect today. I think it's a bit sad these dialects do not get taught to the newer generation. So I found a dictionary of the dialect, freely available on internet archive and I had chatgpt write me a script to use openai batch API (which works pretty

Yes! Apps and UIs on demand for even the need of a single person and a single use is the (short term) future. App stores will become prompt stores and will be a lot more composable. Download and connect your favorite tools and prompts and solve your problems in minutes.

Great speech. I'm really curious how we build this next generation of datasets by making these much more dynamic data engines. Not a large dataset you collect and dump once, but rather an infinite data source that the adaption engine pulls from based on needs.

If this is possible it will be the fastest way to replace 1.0 software. It will grow in usage in a decentralized way everywhere by being at the core of existing software deployments rather than the centralized way that tries to eat everything else from the outside. Really shows