caveman
mito
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caveman | mito | |
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10 | 18 | |
757 | 2,215 | |
- | 3.1% | |
0.0 | 10.0 | |
over 1 year ago | 10 days ago | |
Common Lisp | Python | |
- | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
caveman
- How do you think about version number management?
- I want to pursue this web app project - advice using CL?
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Mito: An ORM for Common Lisp
We are going to walk through the examples by building an online Warehouse management system using Caveman
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Using SVGs in Common Lisp web apps with Djula
Djula is a port of Python's Django template engine to Common Lisp. It's the default templating engine used by the framework Caveman for building web applications
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Is Woo still "beta quality" or prod ready?
Appreciate it. Can I ask one last thing. Between Snooze and Caveman2, which is the more current project?
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Practical? Common Lisp on the JVM: A quick intro to ABCL for modern web apps
This is interesting from a "look what we can do!" perspective, but practically speaking, I'm not sure there's a good reason for doing it this way. For all practical purposes, it would be better to use one of the "native" Common Lisp libraries for doing this, such as Caveman: http://8arrow.org/caveman/
Even as a big Common Lisp fan, I would really question using it in a situation where the project has strict requirements to use a particular framework for another language.
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Building Common Lisp web apps with Tailwind CSS
In this post, I am going to walk you through to setup Tailwind CSS for a Common Lisp web application using Caveman. If you want to know more about creating web applications using Common Lisp and Caveman, please check my previous posts on the topic.
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Building a Rentals Listing web application in Common Lisp
We are going to use Caveman for scaffolding this project. Caveman is a lightweight web application framework created by Eitaro Fukamachi for Common lisp. Caveman is available on Quicklisp, so you can install it with:
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Lisp for the Web - 5
Hence I chose Caveman for this project. After having been played around with and without Caveman for building web applications in Common Lisp, I found that it is the best framework out there for developing web apps in Lisp. Caveman is a lightweight web application framework created by Eitaro Fukamachi for Common lisp. Fukamachi has got some serious tools for doing web development in Lisp. Please feel free to check out his Github profile for more useful tools.
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How to deploy Caveman applications to Heroku?
I have been trying to come up with a standard template using Caveman to deploy on Heroku. But I am struck with these issues, not quite getting it to work with the available buildpacks. Lot of the related articles are hopelessly outdated. Appreciate any help or pointers? https://github.com/fukamachi/caveman/issues/126 https://gitlab.com/duncan-bayne/heroku-buildpack-common-lisp/-/issues/6
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 – 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 — 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
What are some alternatives?
lisp-for-the-web - Code for lisp for the web post
qgrid - An interactive grid for sorting, filtering, and editing DataFrames in Jupyter notebooks
slime - The Superior Lisp Interaction Mode for Emacs
Mage - 🧙 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
cl-super-rentals - Super rentals in Common Lisp
appsmith - Platform to build admin panels, internal tools, and dashboards. Integrates with 25+ databases and any API.
heroku-buildpack-common-lisp
dtale - Visualizer for pandas data structures
clack - Web server abstraction layer for Common Lisp
budibase - Budibase is an open-source low code platform that helps you build internal tools in minutes 🚀
easy-routes - Yet another routes handling utility on top of Hunchentoot
lux - Automatically visualize your pandas dataframe via a single print! 📊 💡