# Python Projects at AI Tinkerers

> Canonical HTML: https://aitinkerers.org/technologies/python?page=28
> Markdown URL: https://aitinkerers.org/technologies/python.md?page=28
> Technology record last updated: 2026-09-18T15:13:57Z
> Generated: 2026-09-21T00:45:42Z

Python: The high-level, general-purpose language built for readability, powering everything from web backends to advanced machine learning models.

Python is the high-level, general-purpose language prioritizing clear, readable syntax (via significant indentation), ensuring rapid development for any team . Its ecosystem is massive: use it for robust web development with frameworks like Django and Flask, or leverage its power in data science with libraries such as Pandas and NumPy . The Python Package Index (PyPI) provides thousands of community-contributed modules, offering immediate solutions for tasks from network programming to GUI creation . The language is actively maintained by the Python Software Foundation (PSF), with the stable release currently at Python 3.14.0 (as of November 2025) .

- Official technology site: https://python.org
- Public AI Tinkerers demos and talks: 662
- Result page: 28 of 28

## Recent Public Talks and Demos

### [Federated Learning with Flame](https://seattle.aitinkerers.org/talks/rsvp_HniMKJNpKf8)

Traditional machine learning depends on the centralization of data, but that comes with privacy and computational concerns. A reality with billions of edge devices diminish those issues, especially with the advent of federated machine learning. Training may be performed on edge devices directly, keeping datasets decentralized and private. Additionally, offloading work to different nodes means less computation per device. Projects like GBoard, Siri, and even the medical and military fields already use federated learning. One current open-source framework for federated machine learning is Project Flame (maintained by Cisco Systems). Flame uses object-oriented programming to implement different graphs between edge devices for a federated learning network. Flame can be extended to different kinds of topologies and executed across multiple devices that run Python code using P2P communication.

- Event context: AI Tinkerers Seattle - February 2024 Meetup — 2024-02-29 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_HniMKJNpKf8

### [Standard Draft - AI POC Demo](https://la.aitinkerers.org/talks/rsvp_glIhwojRa74)

Showing the flow of our jupyter notebooks (and live product, if ready!) in preparing contracts using AI

- Event context: Feb 8th - AI Tinkerers Meetup — 2024-02-09 — Los Angeles
- Public talk page: https://la.aitinkerers.org/talks/rsvp_glIhwojRa74

### [Extraction: Making Using Tools With OpenAI Clean And Simple](https://la.aitinkerers.org/talks/rsvp_Sq2DRFV4644)

We recently implemented some functionality that enabled you to define tools as Pydantic models so that you get validation. Additionally, you can use functions directly as tools with no additional work if documented properly. Furthermore, you can extract information directly into a Pydantic model, built using this functionality

- Event context: Feb 8th - AI Tinkerers Meetup — 2024-02-09 — Los Angeles
- Public talk page: https://la.aitinkerers.org/talks/rsvp_Sq2DRFV4644

### [Fantail](https://seattle.aitinkerers.org/talks/rsvp_oWX_Mon1IuY)

Fantail is a generative AI powered brainstorming, story development, and visualization platform for filmmakers. It provides an integrated workspace with built in gen-AI that makes a script come alive with storyboards, emotional text to speech, and much more!

- Event context: AI Tinkerers Seattle - January Meetup — 2024-01-25 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_oWX_Mon1IuY

### [CorpusKeeper - Talk To Data](https://seattle.aitinkerers.org/talks/rsvp_xEKgjcarlb8)

using LLMs, RAG, functions, and prompt/configuration to talk to airplane assembly robotic controller programs

- Event context: AI Tinkerers Seattle - January Meetup — 2024-01-25 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_xEKgjcarlb8

### [FINN – Parsing complex invoices with Vision API and GPT4](https://munich.aitinkerers.org/talks/rsvp_9d12SMNubpU)

FINN has 20.000 cars on the road that all want to be serviced and inspected. This happens in hundreds of different workshops all across the country. Every month, we receive more than 1,000 invoices that tell us what car was serviced, at which mileage, at which date, and with which costs. All invoices must be read out for this data. Because we have hundreds of different invoice layouts, conventional invoice parsing software failed. With the GPT Vision API preview and GPT 4, however, we were able to extract this unstructured data with ~85% accuracy automatically, saving us 15-20 working hours a month.

- Event context: AI Tinkerers Munich - January 18 — 2024-01-18 — Munich
- Public talk page: https://munich.aitinkerers.org/talks/rsvp_9d12SMNubpU

### [The most pythonic LLM application building experience](https://la.aitinkerers.org/talks/rsvp_OzizrJU1JuU)

This library intends to give developers a better way to build with LLMs that integrates seamlessly into your workflow, starting with better prompt management and core LLM interaction calls. We’re building the de facto pythonic prompt templating and LLM-interaction library.

- Event context: Jan 10 - AI Tinkerers Meetup - FOR BUILDERS — 2024-01-11 — Los Angeles
- Public talk page: https://la.aitinkerers.org/talks/rsvp_OzizrJU1JuU

### [Self-healing code from Docs](https://berlin.aitinkerers.org/talks/rsvp_V3U7W4z5oaw)

The idea is basic: generate python code based on library documentation, on which the LLM hasn't been trained. But with the current state of LLMs, this turns out to be overly optimistic. So we relocate the effort of the task to the environment engineering. How can the environment, with which the agent interacts, maximally contribute to the agent's evolution and success? Fortunately, the task of code generation offers a rich feedback loop, from static code analysis tools, to run time execution logs, to code base knowledge graphs. In this talk I will make a demo of the coding assistant and provide a sneak peak into the ongoing process of enriching the environment/agent interaction for optimal agent performance.

- Event context: AI Tinkerers Berlin - November 24 — 2023-11-24 — Berlin
- Public talk page: https://berlin.aitinkerers.org/talks/rsvp_V3U7W4z5oaw

### [LingoMind: Where AI Meets Fun Learning](https://chicago.aitinkerers.org/talks/rsvp_oE3jntWpelU)

Ever thought of leveling up your learning game? Meet "LingoMind"! Imagine a world where flashcards aren't just static cards, but dynamically generated challenges tailored just for you, thanks to our nifty AI algorithms. But that's not all. Share, swap, and collaborate with friends, turning the learning process into an exciting multiplayer game. Dive into quizzes, race against the clock, and challenge your peers. With LingoMind, education isn't just about absorbing facts – it's about strategy, competition, and having a blast while you're at it. Ready to play?

- Event context: AI Tinkerers Chicago - Inaugural Meetup — 2023-10-04 — Chicago
- Public talk page: https://chicago.aitinkerers.org/talks/rsvp_oE3jntWpelU

### [Fine-tune your own Llama 2 to replace GPT-3.5/4](https://seattle.aitinkerers.org/talks/rsvp_i9TfjPwwdKk)

I recently put together a set of Python code files that walk you through how to replace GPT-3.5 with your own fine-tuned Llama 2 model. The replacement model is 1/50th the cost and often more accurate. You can see the pretty active thread where I shared it with HN here: https://news.ycombinator.com/item?id=37484135 This isn't a pitch for our company or anything -- in fact the project only uses a small piece of OpenPipe functionality for convenience and is mostly standalone.

- Event context: AI Tinkerers Seattle - September Meetup — 2023-09-20 — Seattle
- Public talk page: https://seattle.aitinkerers.org/talks/rsvp_i9TfjPwwdKk

### [BookNote](https://sf.aitinkerers.org/talks/rsvp_fytFeb3PERc)

-- Summary -- BookNote is a new computing abstraction for end-user programming - allowing anyone to quickly build & deploy micro-tools and long-tail software to get things done. This includes stuff like python scripts for data wrangling, cleaning & visualization. Chaining a bunch of different APIs together to get stuff done. Being able to quickly build frontends for the above tasks. BookNote does this by taking the free-form NL to Code abilities of LLMs and makes it visual, auditable & shareable. BookNote generates both JS frontends and python backends. The super cool part is that the python code runs in the browser (via pyodide) right away AND the js frontend is able to talk to the python backend. So, the tools/apps are ready to go with no deployment time/effort. The JS frontend generation also make the whole process a lot more visual, fun & auditable! For the talk, we'd love to demo our prototype and also give attendees a look into how it works under the hood :) -- Sample BookNotes -- These are very much hacky WIP prototypes so pardon us if they bug out :) Gallery https://www.booknote.cloud/editor/265z8pof5w6 Chaining Multiple APIs https://www.booknote.cloud/editor/oykst6b9pw Chuck Norris Joke Generator https://www.booknote.cloud/editor/djfy7a3kvia Data Enrichment + Plotting https://www.booknote.cloud/editor/dbr9kvrlicg Data Cleaning + Plotting https://www.booknote.cloud/editor/85sx8iq4fal Interactive Plots https://www.booknote.cloud/editor/1ondtnicclc

- Event context: 🤖🔄🧠 AI Tinkerers SF - August Meetup — 2023-08-10 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_fytFeb3PERc

### [Embedchain - Flask for LLM powered bots](https://sf.aitinkerers.org/talks/rsvp_-gevJ9M6hTI)

Embedchain(3.8K GitHub stars, 750 forks) is a simple framework to create LLM powered bots. Problem: Developers want to create ChatGPT like bots over their continuously updating data sets, but most of them are struggling to achieve good results. Developers face following problems: * Given a data set, how to chunk, which embedding model and vector database to use * How to retrieve relevant documents and what retrieval strategy to apply (semantic search or lexical or both, metadata filtering, re-ranking, HYDE) * How to create and manage embeddings of dataset which is updating? Langchain and LLamaIndex provide extensive tools to interface with an LLM but they are hard for most software developers to understand and use. Embedchain solution: Python and JS packages to add a dataset and query/chat over it. Built on top of Langchain for quick GTM, Embedchain abstracts out: • Loading, chunking, creating vectors and storing in a vector database • Query formation and document retrieval strategies • Creating and managing embeddings. https://embedchain.ai

- Event context: 🤖🔄🧠 AI Tinkerers SF - August Meetup — 2023-08-10 — San Francisco
- Public talk page: https://sf.aitinkerers.org/talks/rsvp_-gevJ9M6hTI

### [Explaining Machine Learning with Llama-LIME: A Novel Approach to AI Explainability](https://nyc.aitinkerers.org/talks/rsvp_sgOAGxpAXPc)

As machine learning models become increasingly complex, understanding their decision-making process can be a challenging task. How can we "peek under the hood" and understand why a particular prediction was made? Enter Llama-LIME, a Python library that utilizes the power of large language models to generate intuitive, human-readable explanations for machine learning predictions. In this talk, we will take a deep dive into Llama-LIME, exploring how it integrates with existing machine learning models and leverages language models to create explanations. We'll also walk through a live demonstration, showcasing Llama-LIME's capabilities using real-world data. Whether you're a data scientist looking to better understand your models or a developer interested in the intersection of AI and explainability, this talk will provide insights into a novel approach to making AI more transparent and understandable. Intended Audience This talk is suitable for machine learning practitioners, data scientists, AI enthusiasts, and developers interested in AI explainability. Basic knowledge of Python and familiarity with machine learning concepts will be helpful, but not necessary.

- Event context: AI Tinkerers NYC Inaugural Meetup - August — 2023-08-01 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_sgOAGxpAXPc

### [Einblick Prompt AI](https://nyc.aitinkerers.org/talks/rsvp_egOXGJglcEc)

Tell your data notebook what to do. Meet Einblick, the AI-native notebook that writes and fixes code, plots beautiful charts, builds models, and much more. Provide a prompt and Einblick’s AI agent builds workflows using Python, SQL, and interactive components.

- Event context: AI Tinkerers NYC Inaugural Meetup - August — 2023-08-01 — New York City
- Public talk page: https://nyc.aitinkerers.org/talks/rsvp_egOXGJglcEc

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## More Results

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