mito
github-orgmode-tests
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mito | github-orgmode-tests | |
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18 | 245 | |
2,215 | 147 | |
3.1% | - | |
10.0 | 4.8 | |
9 days ago | 4 months ago | |
Python | ||
GNU General Public License v3.0 or later | - |
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mito
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The Design Philosophy of Great Tables (Software Package)
2. The report you're sending out for display is _expected_ in an Excel format. The two main reasons for this are just organizational momentum, or that you want to let the receiver conduct additional ad-hoc analysis (Excel is best for this in almost every org).
The way we've sliced this problem space is by improving the interfaces that users can use to export formatting to Excel. You can see some of our (open-core) code here [2]. TL;DR: Mito gives you an interface in Jupyter that looks like a spreadsheet, where you can apply formatting like Excel (number formatting, conditional formatting, color formatting) - and then Mito automatically generates code that exports this formatting to an Excel. This is one of our more compelling enterprise features, for decision makers that work with non-expert Python programmers - getting formatting into Excel is a big hassle.
[1] https://trymito.io
[2] https://github.com/mito-ds/mito/blob/dev/mitosheet/mitosheet...
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What codegen is (actually) good for
3. So you do want to do code-gen, does it make sense to do it in a chat interface, or can we do better?
As a Figma user, I'd answer these in the following way:
> Why is it necessary to generate code in the first place?
Because mockups aren't your production website, and your production website is written in code. But maybe this is just for now?
I'm sure some high-up PM at Figma has this as their goal - mockup the website in Figma, it generates the code for a website (you don't see this code!), and then you can click deploy _so easily_. Who wants to bet that hosting services like Vercel etc reach out to Figma once a week to try and pitch them...
In the meantime, while we have websites that don't fit neatly inside Figma constraints, while developers are easier to hire than good designers (in my experience), while no-code tools are continually thought of as limiting and a bad long-term solution -- Figma code export is good.
> Why is just writing the code by the hand not the best solution?
For the majority of us full-stack devs who have written >0 CSS but are less than masters, I'll leave this as self-evident.
> So you do want to do code-gen, does it make sense to do it in a chat interface, or can we do better?
In the case of Figma, if they were a new startup with no existing product and they were trying to "automation UI creation" -- v1 of their interface probably would be a "describe your website" and then we'll generate the code for it.
This would probably suck. What if you wanted to easily tweak the output? What if you had trouble describing what you wanted, but you could draw it (ok, OpenAI vision might help on this one)? What if you had experience with existing design tools you could use to augment the AI. A chat interface is not the best interface for design work.
ChatGPT-style code-generation is like v0.1. Github Copilot is an example of next step - it's not just a chat interface, it's something a bit more integrated into an environment that make sense in the context of the work you're doing. For design work, a canvas (literally! [2]) like Figma is well-suited as an environment for code-gen that can augment (and maybe one day replace) the programmers working on frontend. For tabular data work, we think a spreadsheet is the interface where users want to be, and the interface it makes sense to bring code-gen to.
Any thoughts appreciated!
[1] https://trymito.io, https://github.com/mito-ds/mito
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Pandas AI โ The Future of Data Analysis
I think the biggest area for growth for LLM based tools for data analysis is around helping users _understand what edits they actually made_.
I'm a co-founder of a non-AI data code-gen tool for data analysis -- but we also have a basic version of an LLM integration. The problem we see with tooling like Pandas AI (in practice! with real users at enterprises!) is that users make an edit like "remove NaN values" and then get a new dataframe -- but they have no way of checking if the edited dataframe is actually what they want. Maybe the LLM removed NaN values. Maybe it just deleted some random rows!
The key here: how can users build an understanding of how their data changed, and confirm that the changes made by the LLM are the changes they wanted. In other words, recon!
We've been experimenting more with this recon step in the AI flow (you can see the final PR here: https://github.com/mito-ds/monorepo/pull/751). It takes a similar approach to the top comment (passing a subset of the data to the LLM), and then really focuses in the UI around "what changes were made." There's a lot of opportunity for growth here, I think!
Any/all feedback appreciated :)
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The hand-picked selection of the best Python libraries and tools of 2022
Mito โ spreadsheet inside notebooks
- I made an open source spreadsheet that turns your edits into Python
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I made a tool that turns Excel into Python
You can see the open source code here.
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I made a Spreadsheet for Python beginners that writes Python for you
Here is the Github again.
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Learn Python through your Spreadsheet Skills
Mito is an open source Python package that allows the user to call an interactive spreadsheet into their Python environment. Each edit made in the spreadsheet generates the equivalent Python.
- A Spreadsheet for Data Science that Writes Python for Every Edit
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Mito lets you write Python by editing a spreadsheet
Mito is an open source Python tool that allows you to call a spreadsheet into your Python environment. Each edit you make in the spreadsheet generates the equivalent Python for you. This allows users to access Python with the spreadsheet skills they already have. Here is the Github
github-orgmode-tests
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Ask HN: Has Anyone Trained a personal LLM using their personal notes?
- or to visualize and use it as a personal partner.
There's already a ton of open-source UIs such as Chatbot-ui[3] and Reor[4]. And that's just the tip of the iceberg.
Personally, I haven't been consistent enough through the years in note-taking.
So, I'm really curious to learn more about those of you who were and implemented such pipelines.
I'm sure there's a ton of really fascinating experiences.
[1] https://orgmode.org/
- Org Mode
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From Doom to Vanilla Emacs
literate config (using ORG mode)
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My productivity app is a never-ending .txt file
Obligatory reference to Emacs Org-Mode [1].
Author's approach is basically Org-Mode with fewer helpers.
Org-mode's power is that, at core, it's just a text file, with gradual augmentation.
Then again, Org-Mode is a tool you must install, accessible through a limited list of clients (Emacs obviously, but also VSCode), and the power of OP's approach is that it requires no external tools.
[1] https://orgmode.org
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Show HN: Heynote โ A Dedicated Scratchpad for Developers
This reminds me a lot of [Org Mode](https://orgmode.org/). Do you have plans to add other org-like features, like evaluating code blocks? I don't personally see myself moving away from org-mode, but it would be nice to have something to recommend to people who are reluctant to use emacs, even if it's only for a single application.
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How to combine daily journal with general database of people, places, things, etc.
If you want to spare a couple of detours, you probably could start with Emacs Org-mode according to Greenspun's eleventh rule: "Any sufficiently complicated PIM or note-taking program contains an ad hoc, informally specified, bug-ridden, slow implementation of half of Org mode."
- github-orgmode-tests: This is a test project where you can explore how github interprets Org-mode files
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Ask HN: Local Wysiwyg HTML Editor for Mac
Wow, no one has recommended Org mode (https://orgmode.org). I started using Emacs nearly 20 years ago specifically because of Org. I use Org for all my static sites, note taking, to-do lists and calendar. Org has a lightweight markup language that has far more features than Markdown (e.g., plain text spreadsheets!), but the markup isn't visible to the extent that Markdown is in most editors. Emacs with Org files behaves almost like a WYSIWYG editor. For example, links in Org files are clickable and their URLs aren't visible unless a cursor is hovered over them. I'm an obsessive note-taker with more than 6,000 Org files in my personal knowledge base and none of the dozens of other note-taking apps that I've evaluated comes even close to Emacs with Org. But to be fair, I create content on Linux only so support for mobile devices doesn't matter to me.
By the way, I think it's hilarious that you mentioned Dreamweaver, dv35z, because I experimented with using Dreamweaver for note-taking in the 90s! I still have a few HTML files that include notes I took back then using Dreamweaver. Needless to say, I definitely prefer Emacs with Org!
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Think in Analog, Capture in Digital
Just another reason for one to get into org-mode[1] and org-roam[2].
Combine this with the concept of Zettelkasten[3] and you have a wonderful way to organize and store all your notes and writings, and even a way to know at what point you should move your idea from analog to digital (based on it's maturity, e.g. "evergreen state").
1. https://orgmode.org/
- Welche Note taking/Wiki App nutzt ihr, falls รผberhaupt?
What are some alternatives?
qgrid - An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks
logseq - A local-first, non-linear, outliner notebook for organizing and sharing your personal knowledge base. Use it to organize your todo list, to write your journals, or to record your unique life.
Mage - ๐ง The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
org-roam-ui - A graphical frontend for exploring your org-roam Zettelkasten
appsmith - Platform to build admin panels, internal tools, and dashboards. Integrates with 25+ databases and any API.
todo.txt-cli - โ๏ธ A simple and extensible shell script for managing your todo.txt file.
dtale - Visualizer for pandas data structures
marktext - ๐A simple and elegant markdown editor, available for Linux, macOS and Windows.
budibase - Budibase is an open-source low code platform that helps you build internal tools in minutes ๐
Joplin - Joplin - the secure note taking and to-do app with synchronisation capabilities for Windows, macOS, Linux, Android and iOS.
lux - Automatically visualize your pandas dataframe via a single print! ๐ ๐ก
pandoc - Universal markup converter