26 ms·

Show HN: Probly – Spreadsheets, Python, and AI in the browser

171 points by tobiadefami 2y ago

Probly was built to reduce context-switching between spreadsheet applications, Python notebooks, and AI tools. It’s a simple spreadsheet that lets you talk to your data. Need pandas analysis? Just ask in plain English, and the code runs right in your browser. Want a chart? Just ask. While there are tools available in this space like TheBricks, Probly is a minimalist, open-source solution built with React, TypeScript, Next.js, Handsontable, Hyperformula, Apache Echarts, OpenAI, and Pyodide. It's still a work in progress, but it's already useful for my daily tasks.

deleted 2y ago
[deleted]
yawnxyz 2y ago
Very cool!! Do y'all have an use-your-own-key example deployment to try it?
- tobiadefami 2y ago
  Thanks you! Right now, you can clone the repo and follow the instructions to run it locally with your own OpenAI key. I'm working on a hosted demo that will let you try it out directly without any setup. stay tuned :)
kippinitreal 2y ago
Amazing name for this tool.
- tobiadefami 2y ago
  Glad you like it!
  - arthurcolle 2y ago
    From the Miami colloquialism, "Supposably" could be good for an advanced stats add-on!;)
    - tobiadefami 2y ago
      haha! i'll consider it
librasteve 2y ago
How about a screen video?
- deleted 2y ago
  [deleted]
- tobiadefami 2y ago
  posted one about a week or so ago on my LinkedIn https://www.linkedin.com/posts/oluwatobiadefami_vibe-coding-let-me-show-you-how-you-can-activity-7295456140669128704-hSr3?utm_source=share&utm_medium=member_desktop&rcm=ACoAACbjOKEBSxJCgF5Gh0steixdgzAB1tc4ZtA https://www.linkedin.com/posts/oluwatobiadefami_vibe-coding-...
  - librasteve 2y ago
    excellent - thanks!
westurner 2y ago
TIL that Apache Echarts can generate WAI-ARIA accessible textual descriptions for charts and supports WebGL. https://echarts.apache.org/en/feature.html#aria https://echarts.apache.org/en/feature.html#aria apache/echarts: https://github.com/apache/echarts https://github.com/apache/echarts Marimo notebook has functionality like rxpy and ipyflow to auto-reexecute input cell dependencies fwiu: https://news.ycombinator.com/item?id=41404681#41406570 https://news.ycombinator.com/item?id=41404681#41406570 .. https://github.com/marimo-team/marimo/releases/tag/0.8.4 https://github.com/marimo-team/marimo/releases/tag/0.8.4 : > With this release, it's now possible to create standalone notebook files that have package requirements embedded in them as a comment, using PEP 723's inline metadata marimo-team/marimo: https://github.com/marimo-team/marimo https://github.com/marimo-team/marimo ipywidgets is another way to build event-based UIs in otherwise Reproducible notebooks. datasette-lite doesn't yet work with jupyterlite and emscripten-forge yet FWIU; but does build SQLite in WASM with pyodide. https://github.com/simonw/datasette-lite https://github.com/simonw/datasette-lite pygwalker: https://github.com/Kanaries/pygwalker https://github.com/Kanaries/pygwalker .. https://news.ycombinator.com/item?id=35895899 https://news.ycombinator.com/item?id=35895899 How do you record manual interactions with ui controls and spreadsheet grids to code for reproducibility? > "Generate code from GUI interactions; State restoration & Undo" https://github.com/Kanaries/pygwalker/issues/90 https://github.com/Kanaries/pygwalker/issues/90 > The Scientific Method is testing, so testing (tests, assertions, fixtures) should be core to any scientific workflow system. ipytest has a %%ipytest cell magic to run functions that start with test_ and subclasses of unittest.TestCase with the pytest test runner. https://github.com/chmp/ipytest https://github.com/chmp/ipytest How can test functions with assertions be written with Probly?
- tobiadefami 2y ago
  Probly doesn't have built-in test assertion functionality yet, but since it runs Python (via Pyodide) directly in the browser, you can write test functions with assertions in your Python code. The execute_python_code tool in our system can run any valid Python code, including test functions. This is something we're considering for future development, so this is a great shout!
  - 2y ago
szajbus 2y ago
Interesting choice of a screenshot in the README... Manchester United in top four, clearly a hallucination produced by the AI.
- deleted 2y ago
  [deleted]
- tobiadefami 2y ago
  Or the AI is a man united fan and is hopeful for a top 4 finish this season :D
tankster 2y ago
[flagged]
- deleted 2y ago
  [deleted]
smjburton 2y ago
Any plans to add a config for a Dockerfile/docker-compose.yml? This could be really useful in a self-hosted environment. If you go down this route, the ability to use something like Ollama in place of OpenAI would be a nice feature as well.
- tobiadefami 2y ago
  Docker config is already implemented—you can check out the repo now. Adding support for other LLM providers like Ollama is something to consider for future development. Thanks for the suggestion!
Onavo 2y ago
Can you package it as a standalone npm component library for embedding?
jimbokun 2y ago
One of the things that has seemed suboptimal to me is having AI “write code”. Doesn’t it make more sense to ask AI a question, and the AI figures out what code is needed to answer the question, run it, and report the answer? From the description sounds like this project is a step in that direction.
- hathawsh 2y ago
  OTOH, what is "code"? In a general sense, I think of "code" as the "codification of a process." If we want to know what steps the AI is following to complete a process, then having an AI write code seems like a correct and necessary part of the solution.
librasteve 2y ago
I have a pressing need to come up with a household budget and had already decided to try using LLMs to help on this task since learning LLMs/prompt engineering is more fun than just writing a dumb script to do accounts. Thought i would try this tool - and here's a quick review of the experience: - the quickstart instructions are very clear and I was up and running on my localhost (a mac - but I think this will work well on windows and linx too) - the UX is good ... slight wrinkle is that the upload button has a down arrow ... also Ctrl+Shift+/ doesn't work on a mac - took me a while to find the speech bubble icon in the bottom right - love the import / export, love the chat box - worked well with my existing OpenAI account So - this is a fantastic concept and a well executed MLP - thanks. That said - and I highly encourage you to keep going - there are a couple of caveats: 1. The task I set is realworld - "please categorize my bank transactions into household expense groups" - and proved too much for my ChatGPT o1 account - most lines were labelled as 'other', bank charges were labelled 'fuel', etc, etc - so the underlying AI engine is not yet ready for this sadly (I am happy to be corrected if others know the recipe) 2. I wonder if using a tool like this, a set of LLM prompts to set up the query and to comb the response would help to chip away at [1] ... so I suggest that having a way for my config to accumulate my prompts maybe a nice feature. Please do not take this f/back as negative to your work ... it is more my getting to grips with the AI sweet spot.
- tobiadefami 2y ago
  Thanks for taking the time to try it out and share your thoughts. I really appreciate the detailed feedback from a real-world use case. Glad to hear the setup was smooth and that the chat box + import/export features worked well for you. Noted on the UI tweaks, I'll look into making them more intuitive. On the categorization issue, yeah, LLMs can struggle with nuanced transaction labeling, especially without proper context or examples. Structured prompting could help, which ties into your second point -- having a library of refined prompts that can be reused for repetitive tasks would be really valuable. I love your feedback -- it's exactly what helps improve the tool. And again, thanks for testing it out!
  - librasteve 2y ago
    having thought about this a bit - my new conjecture is that if I had a way to feed in an example map of transaction payee => category, as one of the prompts, and a way to incrementally add prompts for outliers, then the AI _might_ be able to do a reasonable job - I am planning to mess with raku LLM::Functions to see if I can get this to work
swyx 2y ago
any comparisons with https://github.com/quadratichq/quadratic https://github.com/quadratichq/quadratic ? *necessary disclosure, i'm a small angel investor in it but genuinely open to see new approaches
- tobiadefami 2y ago
  I'm actually a fan of what the team at Quadratic is building. It's definitely the more mature product with a robust Python implementation and a well-designed interface that bridges spreadsheets and code. Their stack appears to be built on Rust, which likely gives them performance advantages. Probly is earlier stage and more minimalist, but we're tackling the same fundamental problem. Our specific focus is on making data analysis a fully autonomous process powered by AI - where you can describe what you want to learn from your data and have the system figure out the rest.
canadiantim 2y ago
Any possibility for google sheets support?
- thrdbndndn 2y ago
  Second this. I'd consider myself the target audience since I frequently dance around spreadsheets and scripts, but being able to use my tool of choice is a must. A simple table or even a database can't fully replace a full-featured spreadsheet application. They are just not the same thing.
anonu 2y ago
I can see ChatGPT including a spreadsheet component like this in their chat one day.
gamer_545 2y ago
This is nice, is there a limit to the data set been provided?
linwangg 2y ago
This looks interesting! How does Probly handle complex Pandas operations compared to something like Deepnote or Jupyter AI plugins? Does it support custom Python scripts, or is it more of a prompt-based solution?
- tobiadefami 2y ago
  Great question, Unlike Jupyter or Deepnote where you write code directly, Probly is primarily prompt-based - you describe what analysis you want in natural language, and the AI generates and executes the appropriate Python code behind the scenes using Pyodide. The key difference has to be that Probly runs python entirely in your browser using WASM, while jupyter/Deepnote run code on servers.
  - linwangg 2y ago
    [dead]
  - mfdupuis 2y ago
    Disclosure, I'm a founder in the data space[1] Have you thought about how you would handle much larger datasets? Or is the idea that since this is a spreadsheet, the 10M cell limit is plenty sufficient? I find WASM really interesting, but I can't wrap my head around how this scales in the enterprise. But I figure it probably just comes down to the use cases and personas you're targeting. [1] https://www.fabi.ai/ https://www.fabi.ai/
    - revenga99 2y ago
      I am also very deeply invested in this question. It seems like the goto path for huge large data sets is text to sql (clickhouse, snowflake) etc. But all these juicy python data science libraries require code execution based on off the much small data payloads from the sql results. Feel free to reach out, what you are trying to achieve seems very similar to what I am trying to do in a completely different industry/usecase.

171 points by tobiadefami 2y ago

deleted 2y ago

[deleted]

yawnxyz 2y ago

Very cool!! Do y'all have an use-your-own-key example deployment to try it?

tobiadefami 2y ago
Thanks you! Right now, you can clone the repo and follow the instructions to run it locally with your own OpenAI key. I'm working on a hosted demo that will let you try it out directly without any setup. stay tuned :)

kippinitreal 2y ago

Amazing name for this tool.

tobiadefami 2y ago
Glad you like it!
- arthurcolle 2y ago
  From the Miami colloquialism, "Supposably" could be good for an advanced stats add-on!;)
  - tobiadefami 2y ago
    haha! i'll consider it

librasteve 2y ago

How about a screen video?

deleted 2y ago
[deleted]
tobiadefami 2y ago
posted one about a week or so ago on my LinkedIn https://www.linkedin.com/posts/oluwatobiadefami_vibe-coding-let-me-show-you-how-you-can-activity-7295456140669128704-hSr3?utm_source=share&utm_medium=member_desktop&rcm=ACoAACbjOKEBSxJCgF5Gh0steixdgzAB1tc4ZtA https://www.linkedin.com/posts/oluwatobiadefami_vibe-coding-...
- librasteve 2y ago
  excellent - thanks!

westurner 2y ago

TIL that Apache Echarts can generate WAI-ARIA accessible textual descriptions for charts and supports WebGL. https://echarts.apache.org/en/feature.html#aria https://echarts.apache.org/en/feature.html#aria apache/echarts: https://github.com/apache/echarts https://github.com/apache/echarts Marimo notebook has functionality like rxpy and ipyflow to auto-reexecute input cell dependencies fwiu: https://news.ycombinator.com/item?id=41404681#41406570 https://news.ycombinator.com/item?id=41404681#41406570 .. https://github.com/marimo-team/marimo/releases/tag/0.8.4 https://github.com/marimo-team/marimo/releases/tag/0.8.4 : > With this release, it's now possible to create standalone notebook files that have package requirements embedded in them as a comment, using PEP 723's inline metadata marimo-team/marimo: https://github.com/marimo-team/marimo https://github.com/marimo-team/marimo ipywidgets is another way to build event-based UIs in otherwise Reproducible notebooks. datasette-lite doesn't yet work with jupyterlite and emscripten-forge yet FWIU; but does build SQLite in WASM with pyodide. https://github.com/simonw/datasette-lite https://github.com/simonw/datasette-lite pygwalker: https://github.com/Kanaries/pygwalker https://github.com/Kanaries/pygwalker .. https://news.ycombinator.com/item?id=35895899 https://news.ycombinator.com/item?id=35895899 How do you record manual interactions with ui controls and spreadsheet grids to code for reproducibility? > "Generate code from GUI interactions; State restoration & Undo" https://github.com/Kanaries/pygwalker/issues/90 https://github.com/Kanaries/pygwalker/issues/90 > The Scientific Method is testing, so testing (tests, assertions, fixtures) should be core to any scientific workflow system. ipytest has a %%ipytest cell magic to run functions that start with test_ and subclasses of unittest.TestCase with the pytest test runner. https://github.com/chmp/ipytest https://github.com/chmp/ipytest How can test functions with assertions be written with Probly?

tobiadefami 2y ago
Probly doesn't have built-in test assertion functionality yet, but since it runs Python (via Pyodide) directly in the browser, you can write test functions with assertions in your Python code. The execute_python_code tool in our system can run any valid Python code, including test functions. This is something we're considering for future development, so this is a great shout!
- 2y ago

szajbus 2y ago

Interesting choice of a screenshot in the README... Manchester United in top four, clearly a hallucination produced by the AI.

deleted 2y ago
[deleted]
tobiadefami 2y ago
Or the AI is a man united fan and is hopeful for a top 4 finish this season :D

tankster 2y ago

[flagged]

deleted 2y ago
[deleted]

smjburton 2y ago

Any plans to add a config for a Dockerfile/docker-compose.yml? This could be really useful in a self-hosted environment. If you go down this route, the ability to use something like Ollama in place of OpenAI would be a nice feature as well.

tobiadefami 2y ago
Docker config is already implemented—you can check out the repo now. Adding support for other LLM providers like Ollama is something to consider for future development. Thanks for the suggestion!

Onavo 2y ago

Can you package it as a standalone npm component library for embedding?

jimbokun 2y ago

One of the things that has seemed suboptimal to me is having AI “write code”. Doesn’t it make more sense to ask AI a question, and the AI figures out what code is needed to answer the question, run it, and report the answer? From the description sounds like this project is a step in that direction.

hathawsh 2y ago
OTOH, what is "code"? In a general sense, I think of "code" as the "codification of a process." If we want to know what steps the AI is following to complete a process, then having an AI write code seems like a correct and necessary part of the solution.

librasteve 2y ago

I have a pressing need to come up with a household budget and had already decided to try using LLMs to help on this task since learning LLMs/prompt engineering is more fun than just writing a dumb script to do accounts. Thought i would try this tool - and here's a quick review of the experience: - the quickstart instructions are very clear and I was up and running on my localhost (a mac - but I think this will work well on windows and linx too) - the UX is good ... slight wrinkle is that the upload button has a down arrow ... also Ctrl+Shift+/ doesn't work on a mac - took me a while to find the speech bubble icon in the bottom right - love the import / export, love the chat box - worked well with my existing OpenAI account So - this is a fantastic concept and a well executed MLP - thanks. That said - and I highly encourage you to keep going - there are a couple of caveats: 1. The task I set is realworld - "please categorize my bank transactions into household expense groups" - and proved too much for my ChatGPT o1 account - most lines were labelled as 'other', bank charges were labelled 'fuel', etc, etc - so the underlying AI engine is not yet ready for this sadly (I am happy to be corrected if others know the recipe) 2. I wonder if using a tool like this, a set of LLM prompts to set up the query and to comb the response would help to chip away at [1] ... so I suggest that having a way for my config to accumulate my prompts maybe a nice feature. Please do not take this f/back as negative to your work ... it is more my getting to grips with the AI sweet spot.

tobiadefami 2y ago
Thanks for taking the time to try it out and share your thoughts. I really appreciate the detailed feedback from a real-world use case. Glad to hear the setup was smooth and that the chat box + import/export features worked well for you. Noted on the UI tweaks, I'll look into making them more intuitive. On the categorization issue, yeah, LLMs can struggle with nuanced transaction labeling, especially without proper context or examples. Structured prompting could help, which ties into your second point -- having a library of refined prompts that can be reused for repetitive tasks would be really valuable. I love your feedback -- it's exactly what helps improve the tool. And again, thanks for testing it out!
- librasteve 2y ago
  having thought about this a bit - my new conjecture is that if I had a way to feed in an example map of transaction payee => category, as one of the prompts, and a way to incrementally add prompts for outliers, then the AI _might_ be able to do a reasonable job - I am planning to mess with raku LLM::Functions to see if I can get this to work

swyx 2y ago

any comparisons with https://github.com/quadratichq/quadratic https://github.com/quadratichq/quadratic ? *necessary disclosure, i'm a small angel investor in it but genuinely open to see new approaches

tobiadefami 2y ago
I'm actually a fan of what the team at Quadratic is building. It's definitely the more mature product with a robust Python implementation and a well-designed interface that bridges spreadsheets and code. Their stack appears to be built on Rust, which likely gives them performance advantages. Probly is earlier stage and more minimalist, but we're tackling the same fundamental problem. Our specific focus is on making data analysis a fully autonomous process powered by AI - where you can describe what you want to learn from your data and have the system figure out the rest.

canadiantim 2y ago

Any possibility for google sheets support?

thrdbndndn 2y ago
Second this. I'd consider myself the target audience since I frequently dance around spreadsheets and scripts, but being able to use my tool of choice is a must. A simple table or even a database can't fully replace a full-featured spreadsheet application. They are just not the same thing.

anonu 2y ago

I can see ChatGPT including a spreadsheet component like this in their chat one day.

gamer_545 2y ago

This is nice, is there a limit to the data set been provided?

linwangg 2y ago

This looks interesting! How does Probly handle complex Pandas operations compared to something like Deepnote or Jupyter AI plugins? Does it support custom Python scripts, or is it more of a prompt-based solution?

tobiadefami 2y ago
Great question, Unlike Jupyter or Deepnote where you write code directly, Probly is primarily prompt-based - you describe what analysis you want in natural language, and the AI generates and executes the appropriate Python code behind the scenes using Pyodide. The key difference has to be that Probly runs python entirely in your browser using WASM, while jupyter/Deepnote run code on servers.
- linwangg 2y ago
  [dead]
- mfdupuis 2y ago
  Disclosure, I'm a founder in the data space[1] Have you thought about how you would handle much larger datasets? Or is the idea that since this is a spreadsheet, the 10M cell limit is plenty sufficient? I find WASM really interesting, but I can't wrap my head around how this scales in the enterprise. But I figure it probably just comes down to the use cases and personas you're targeting. [1] https://www.fabi.ai/ https://www.fabi.ai/
  - revenga99 2y ago
    I am also very deeply invested in this question. It seems like the goto path for huge large data sets is text to sql (clickhouse, snowflake) etc. But all these juicy python data science libraries require code execution based on off the much small data payloads from the sql results. Feel free to reach out, what you are trying to achieve seems very similar to what I am trying to do in a completely different industry/usecase.