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Nice idea, why did you choose to build it in typescript/node out of interest?

• williamcotton 4y ago
  Because I wanted to run it in the browser and have a document object in context of the LLM response being eval’d! Also, having the exemplars typed has saved me from sending broken few-shots many times! That is, I keep all of the few-shot exemplars in TypeScript and then compile them into an array of system/user/assistant message strings at some point before making any calls to an LLM.
  • james-revisoai 4y ago
    Nice, although you did that so you can avoid having an API when running some web application you make for yourself or am I misunderstanding you sorry? Because the other distribution paradigms are... - sharing your key with the user on client side is risky, so you have API side requests. - one day LLMs might be local and can then run off-browser
    • williamcotton 4y ago
      The approach I’ve been using is to keep the API requests server-side and to expose a client interface, thus keeping the keys safe, but the response is eval’d client-side so when OpenAI starts referencing document.body in a completion it affects the browser runtime directly.
      • james-revisoai 3y ago
        Yeah it's a smart idea I see now, you can use it as a universal database as such for all clients, like having a Python dict for all the outputs or something, but you can also easily spin up the UIs enabling your cool examples.