sketch

more recently switched to just using the raw requests ourselves and writing out the logic ourselves in the product. For our main product, the code just lives in our next app, and deploys on vercel.

lumns parsed as datetimes? Are columns that contain integers typed as integers (a single string in the colum will force the whole column to type object). Clean the types with safeint/dropna... 3. Filter to a subset of data based on criteria. Row wise filtering. 4. Perform some type of analysis. Group by with different aggregations, a plot... Something more complex Other operations that I regularly perform 5. Concattenating similar dataframes. pd.concat is simple, knowing whether your columns match up, along with their types is difficult. I want a UI that shows where they match and where they dont. This normally will require an extra data cleaning step 6. Joining tables/dataframe. I want a UI that tells me Is there a natural key to join on? What percentage of rows are joined vs not matched. Buckaroo currently does an initial version 1,2, and 4. Buckaroo allows these steps to be done in a single notebook cell, instead of many separate cells iteratively built up. There are planned features that address all of these use cases. In addition Buckaroo has been engineered to enable easy extension by users. Plug your own functions in, and make them quickly accessible. In my view a point and click gui is the best way to quickly accomplish these tasks as opposed to a conversational AI approach. The conversational AI approach doesn't provide enough structure around common tasks. PS: This morning I added a "Related Projects" [3] Section to the Buckaroo docs. If Buckaroo doesn't solve your problem, look at one of the other linked projects (like Mito). [1] https://github.com/approximatelabs/sketch [2] https://github.com/paddymul/buckaroo [3] https://buckaroo-data.readthedocs.io/en/latest/FAQ.html

/github.com/approximatelabs/sketch Core things it does: Uses jinja templates, does sync and async, and most importantly treats LLM completion endpoints as "function calls", which you can compose and build structures around just with simple python. I also combined it with fastapi so you can just serve up any templates you want directly as rest endpoints. It also offers callback hooks so you can log & trace execution graphs. All together its only ~600 lines of python. I haven't had a chance to really push all the different examples out there, so I think it hasn't seen much adoption outside of those that give it a try. I hope to get back to it sometime in the next week to introduce local-mode (eg. all the open source smaller models are now available, I want to make those first-class)

n see being really valuable, as I believe there has been some traction with these style integrations with data catalog systems recently. People like to ask data questions to other people in slack, adding a bot that tries to answer might short-circuit a lot of this! We built a prototype where we applied similar techniques to the pandas-code-writing part of the stack, trying to help keep data scientists / data analysts "in flow", integrating the code answers in notebooks (similar to how co-pilot puts suggestions in-line) -- and released https://github.com/approximatelabs/sketch a little while ago.

in, that checks google, summarizes multiple results, then synthesizes a final result Ideas I've been sitting on, that I think would be fun to prototype: - An iterative "large document" editor: storing global intent, instructions, outline, and the raw text, and each iteration of the prompt works on the these objects to build a large document. - A "research this topic for me", similar to the above, but include the google searching, summarizing, and such - A code-repository "AI agent" that takes `Issues` and `Pull requests` as input, and writes and edits code for you, and by adding feedback in github, it uses that to modify the branch and act as a developer. (Code via github interface, rather than an IDE)