Show HN: Roboco-op, a computational blackboard for efficient human/AI collab
observablehq.com
observablehq.com
For a slightly different take focused more on that kind of user, eg, connected to their operational databases like Splunk, elastic, neo4j/Neptune, & databricks, and want the choice to use directly or embedded in their existing tools & automations, we are getting ready for more cohorts on louie.ai cloud, and would welcome folks curious for their team. Early enterprise & gov teams on self-hosted louie.ai face the same thing every day.. it really changes how I feel about programming.
While a lot more to do, half a year of working with LLM-augmented querying & coding already feels like a weight has been lifted from my shoulders that's been there for decades yet I never realized things can be different. Ex: "Try that last cell again, except this time trim the IP columns to the first 2 octals, and then visualize how servers connect together in a GPU graph network viz." I can in theory code the sql/spl, pandas, and python viz for that... and figure out the precise syntax/lib/code tweaks to get there... Or just write that high-level edit as if I was pairing with any reasonable person!
Goes from something was trained by ai to markdown to visualisations to react stuff.
AI and notebooks sounds like it could be a powerful concept - just can't tell what's going on here in this article.
Which parts of this is AI generated and what's the user input. maybe I'm looking at llm output? Though the cells seem to contain input and output combined with && somehow. Maybe that's the llm echoing back the input? Is the ai generating the cells or interpreting them? Both?
The cells were written by AI, but the metadata data that generated the cell is mixed in (thats the {...} && bit).
So now the full chat context can be derived from the notebook. Because the notebook is editable, you can fully edit the chat context in an ergonomic way. Both the prompt, the AI code output. So when the AI makes a mistake (it often does), you can correct it. Your ability to detect mistakes is accelerated by the fact it's a computation based conversation. Because the chat context is derived from the editable state, your corrections are internalised into the chat conversation. It continues as if it answered that for real.
So in the notebook you see that the AI is rapidly become an Observable notebook expert, because I have manually coarse corrected its chain-of-thought. An out-of-the box chatGPT session cannot do what is in that notebook. My expertise have been transferred. Once it knows how to solve something in the chat context, its very easy for it to repeat the pattern for new situations. Having it as a standalone forkable self-enclosed notebook artifact is useful. The existing notebook infra allows you to copy and paste cells across notebook too. So I am thinking we can curate stand alone skill memes. The examples are all over the shop just to see what kinda of things I could get it to solve, I imagine a real use would concentrate on upskilling just enough to solve a specific class of problem.
Ah right. That makes more sense to me. Thanks for explaining
Is there more to it than that?
Cells can depend on each other, so you can build up. Cells can be prose, so you can plan. You can change the plan halfway. You can edit the AIs plan. You can forget the plan and do something else without affecting what happened after. You can keep the dialogue and context tight and information dense.
When generating a response, the LLM considers it's previous responses, which you can edit. So you get very fine control over its chain-of-thought. No need to use system prompt to train.
It's simple, but the emergent properties enable much more powerful collaboration.
The cells become manipulatable knowledge memes. That they are computer checked and editable totally changes the speed you can teach by example, and therefore how quickly you can upskill it to do what you actually want it to do.