Spliit
sketch
Spliit | sketch | |
---|---|---|
7 | 20 | |
488 | 2,195 | |
24.4% | 0.7% | |
9.2 | 4.4 | |
4 days ago | 3 months ago | |
TypeScript | Python | |
MIT License | MIT License |
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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.
Spliit
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Ask HN: What have you built with LLMs?
For my expense sharing app [1], I added receipt scanning in a few minutes and a few lines of code by using GPT 4 with Vision. I am aware that LLMs often are a solution looking for a problem, but there are some situations where a bit of magic is just great :)
It is a Next.js application, calling OpenAI’s API using a plain API route.
[1] https://spliit.app
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TripSplit VS spliit2 - a user suggested alternative
2 projects | 15 Jan 2024
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splitio VS spliit2 - a user suggested alternative
2 projects | 15 Jan 2024
- Show HN: Spliit – Free and Open Source Alternative to Splitwise
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Show HN: Spliit v2 – Free and Open Source Alternative to Splitwise
I created the project a couple of years ago to learn Go, but I just rewrote it using a stack I am more comfortable with (Next.js). I also made it open source [1], to feel free to contribute ;)
[1] https://github.com/scastiel/spliit2
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Spliit v2 – Open Source alternative to Splitwise
I totally rewrote it (migrating the existing data of course) with a technology I am more familiar with (Next.js, React, TailwindCSS, Prisma…), and made the new version open source! Feel free to contribute by creating an issue or even a pull-request if you want 😉.
sketch
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Ask HN: What have you built with LLMs?
We've made a lot of data tooling things based on LLMs, and are in the process of rebranding and launching our main product.
1. sketch (in notebook, ai for pandas) https://github.com/approximatelabs/sketch
2. datadm (open source, "chat with data", with support for the open source LLMs (https://github.com/approximatelabs/datadm)
3. Our main product: julyp. https://julyp.com/ (currently under very active rebrand and cleanup) -- but a "chat with data" style app, with a lot of specialized features. I'm also streaming me using it (and sometimes building it) every weekday on twitch to solve misc data problems (https://www.twitch.tv/bluecoconut)
For your next question, about the stack and deploy:
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Pandas AI – The Future of Data Analysis
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
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Ask HN: What's your favorite GPT powered tool?
For GPT/Copilot style help for pandas, in notebooks REPL flow (without needing to install plugins), I built sketch. I genuinely use it every-time I'm working on pandas dataframes for a quick one-off analysis. Just makes the iteration loop so much faster. (Specifically the `.sketch.howto`, anecdotally I actually don't use `.sketch.ask` anymore)
https://github.com/approximatelabs/sketch
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RasaGPT: First headless LLM chatbot built on top of Rasa, Langchain and FastAPI
https://github.com/approximatelabs/lambdaprompt It has served all of my personal use-cases since making it, including powering `sketch` (copilot for pandas) https://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, but most "complex behaviors", so there aren't many patterns to copy. But if you're comfortable in python, then I think it offers a pretty good interface.
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)
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[D] The best way to train an LLM on company data
Please look at sketch and langchain pandas/SQL plugins. I have seen excellent results with both of these approaches. Both of these approaches will require you to send metadata to openAI.
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Meet Sketch: An AI code Writing Assistant For Pandas
👉 Understand your data through questions 👉 Create code from plain text Quick Read: https://www.marktechpost.com/2023/02/01/meet-sketch-an-ai-code-writing-assistant-for-pandas/ Github: https://github.com/approximatelabs/sketch
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Replacing a SQL analyst with 26 recursive GPT prompts
(3) Asking for re-writes of failed queries (happens occasionally) also helps
The main challenge I think with a lot of these "look it works" tools for data applications, is how do you get an interface that actually will be easy to adopt. The chat-bot style shown here (discord and slack integration) I can 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.
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FLiP Stack Weekly for 21 Jan 2023
Python AI Helper https://github.com/approximatelabs/sketch
- LangChain: Build AI apps with LLMs through composability
- Show HN: Sketch – AI code-writing assistant that understands data content
What are some alternatives?
CX_DB8 - a contextual, biasable, word-or-sentence-or-paragraph extractive summarizer powered by the latest in text embeddings (Bert, Universal Sentence Encoder, Flair)
RasaGPT - 💬 RasaGPT is the first headless LLM chatbot platform built on top of Rasa and Langchain. Built w/ Rasa, FastAPI, Langchain, LlamaIndex, SQLModel, pgvector, ngrok, telegram
Language-games - Dead simple games made with word vectors.
lmql - A language for constraint-guided and efficient LLM programming.
data-analytics - Welcome to the Data-Analytics repository
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
grand-slams-dashboard - ATP / WTA Grand Slam Tennis Dashboard. View all time major leaders, filter by tournament, analyze player major final performance
pandas-ai - Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.
langchain - ⚡ Building applications with LLMs through composability ⚡ [Moved to: https://github.com/langchain-ai/langchain]
rasa - 💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
viper - Simple, expressive pipeline syntax to transform and manipulate data with ease
ccl - Clozure Common Lisp