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Thomas11112
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3 months ago
Thomas11112
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3 months ago
louisbrulenaudet
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an
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3 months ago
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6174
Supercharge Apple’s Shortcuts using Cloudflare Workers and Gemini within minutes (and for free, up to 1,500 requests per day) ☁️✨
Hello everyone, last week, while experimenting for fun, I created an API that allows you to easily access AI models (in this case, Google's) from the Shortcut app in order to analyze data from my apps and make the most of it thanks to the generative capabilities of advanced models.
It costs me nothing, and I think it might be good to share it so that others can build on it.
In README.md, you will find everything you need to get started and put your own microservice into production, which you can call from the app’s HTTP request features.
You will simply be asked to have a free Cloudflare account and an API key obtained from Google's AI Studio.
Feel free to take a look and get back to me if you encounter any problems during deployment.
Here is the GitHub repo where you can find all the source code and run it on your own: https://github.com/louisbrulenaudet/genai-api
Hello everyone, last week, while experimenting for fun, I created an API that allows you to easily access AI models (in this case, Google's) from the Shortcut app in order to analyze data from my apps and make the most of it thanks to the generative capabilities of advanced models.
It costs me nothing, and I think it might be good to share it so that others can build on it.
In README.md, you will find everything you need to get started and put your own microservice into production, which you can call from the app’s HTTP request features.
You will simply be asked to have a free Cloudflare account and an API key obtained from Google's AI Studio.
Feel free to take a look and get back to me if you encounter any problems during deployment.
Here is the GitHub repo where you can find all the source code and run it on your own: https://github.com/louisbrulenaudet/genai-api
louisbrulenaudet
posted
an
update
3 months ago
Post
660
Although more and more code editors are aligning themselves with the AGENTS.md file standard, some still use specific nomenclatures that can make it difficult to maintain different configuration files when several people are working on the same project with different agents.
Bodyboard addresses this by generating canonical instructions for code helpers from a single AGENTS.md file, thereby streamlining the production of adapter outputs for Gemini CLI, Copilot, Cline, Claude, Rules, Windsurf, and OpenAI Codex integrations.
You just have to:
Then run, at the root of your project:
Link to npm: https://www.npmjs.com/package/bodyboard
Link to the GitHub repo: https://github.com/louisbrulenaudet/bodyboard
It's a very simple project, but it addresses certain issues I've encountered, so why not make it available to everyone...
If you have other ideas for adapters to create, feel free to open a PR on the GitHub repo.
Bodyboard addresses this by generating canonical instructions for code helpers from a single AGENTS.md file, thereby streamlining the production of adapter outputs for Gemini CLI, Copilot, Cline, Claude, Rules, Windsurf, and OpenAI Codex integrations.
You just have to:
npm install -g bodyboardThen run, at the root of your project:
bodyboard allLink to npm: https://www.npmjs.com/package/bodyboard
Link to the GitHub repo: https://github.com/louisbrulenaudet/bodyboard
It's a very simple project, but it addresses certain issues I've encountered, so why not make it available to everyone...
If you have other ideas for adapters to create, feel free to open a PR on the GitHub repo.
louisbrulenaudet
posted
an
update
5 months ago
Post
2851
Because hackathons are often the starting point for many AI projects, I've created a Python-backend template incorporating my feedback to streamline collaboration and urgent deployments 🏎️
Within a year, I had the opportunity to participate in hackathons organized by Mistral, OpenAI, and DeepMind and this GitHub template is structured around several fundamental building blocks and recommendations I offer developers eager to participate in their first hackathon, whether as part of a team or individually. Its emphasis is on rapid setup and deployment through:
- uv as a package manager, simplifying usage via a series of pre-configured make commands.
- FastAPI for API management, structured in a modular architecture designed to minimize branch conflicts during merges to main branches (using minimal health-check and ping routes to verify Docker’s proper execution and backend accessibility on the local network).
- Pydantic for validation and type handling, which simplifies debugging and enhances understanding of data objects.
- A set of custom instructions tailored for agents (Cline and GitHub Copilot), aimed at improving overall comprehension of the application and optimizing the vibe-coding experience.
This template includes unit tests with a 100% success rate and test coverage, as well as a minimal CI file ensuring that the FastAPI application runs correctly. Thus, merging code that breaks the server into production becomes impossible ⛔️
In general, I would reiterate an essential piece of advice: your two main adversaries are branch conflicts—particularly when the same file is modified concurrently within a brief period, especially if your architecture isn’t built for scalability—and deployment issues under urgent circumstances ⏱️
Link to GitHub: https://github.com/louisbrulenaudet/hackathon-backend
Simply issue these commands and you can ship your code at the speed of light:
Within a year, I had the opportunity to participate in hackathons organized by Mistral, OpenAI, and DeepMind and this GitHub template is structured around several fundamental building blocks and recommendations I offer developers eager to participate in their first hackathon, whether as part of a team or individually. Its emphasis is on rapid setup and deployment through:
- uv as a package manager, simplifying usage via a series of pre-configured make commands.
- FastAPI for API management, structured in a modular architecture designed to minimize branch conflicts during merges to main branches (using minimal health-check and ping routes to verify Docker’s proper execution and backend accessibility on the local network).
- Pydantic for validation and type handling, which simplifies debugging and enhances understanding of data objects.
- A set of custom instructions tailored for agents (Cline and GitHub Copilot), aimed at improving overall comprehension of the application and optimizing the vibe-coding experience.
This template includes unit tests with a 100% success rate and test coverage, as well as a minimal CI file ensuring that the FastAPI application runs correctly. Thus, merging code that breaks the server into production becomes impossible ⛔️
In general, I would reiterate an essential piece of advice: your two main adversaries are branch conflicts—particularly when the same file is modified concurrently within a brief period, especially if your architecture isn’t built for scalability—and deployment issues under urgent circumstances ⏱️
Link to GitHub: https://github.com/louisbrulenaudet/hackathon-backend
Simply issue these commands and you can ship your code at the speed of light:
make init
make dev
louisbrulenaudet
posted
an
update
6 months ago
Post
1224
🌐 Clinical Trials Dataset now available on Hugging Face! 🧬
I’ve just released a comprehensive, ML-ready dataset featuring 500,000+ clinical trial records sourced directly from ClinicalTrials.gov for biomedical NLP, healthcare analytics, and clinical research applications 🤗
I wanted to produce the most complete and up-to-date dump with all raw data partially flattened to simplify extraction, self-querying and processing.
Do you have any ideas about what we can do with it? Using descriptions to enhance specialized embedding models?
louisbrulenaudet/clinical-trials
I’ve just released a comprehensive, ML-ready dataset featuring 500,000+ clinical trial records sourced directly from ClinicalTrials.gov for biomedical NLP, healthcare analytics, and clinical research applications 🤗
I wanted to produce the most complete and up-to-date dump with all raw data partially flattened to simplify extraction, self-querying and processing.
Do you have any ideas about what we can do with it? Using descriptions to enhance specialized embedding models?
louisbrulenaudet/clinical-trials
Ardent12
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model
7 months ago
Ardent12
published
a
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7 months ago
Allenbv
updated
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7 months ago
Allenbv
published
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7 months ago
huyhuung
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7 months ago
huyhuung
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7 months ago
huyhuung
updated
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7 months ago
huyhuung
published
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7 months ago
prodypanda
published
a
model
9 months ago
prodypanda
updated
a
model
9 months ago
louisbrulenaudet
posted
an
update
9 months ago
Post
1226
I’ve just released logfire-callback on PyPI, designed to facilitate monitoring of Hugging Face Transformer training loops using Pydantic Logfire 🤗
The callback will automatically log training start with configuration parameters, periodic metrics and training completion ⏱️
Install the package using pip:
First, ensure you have a Logfire API token and set it as an environment variable:
Then use the callback in your training code:
If you have any feedback, please reach out at @louisbrulenaudet
The callback will automatically log training start with configuration parameters, periodic metrics and training completion ⏱️
Install the package using pip:
pip install logfire-callbackFirst, ensure you have a Logfire API token and set it as an environment variable:
export LOGFIRE_TOKEN=your_logfire_tokenThen use the callback in your training code:
from transformers import Trainer, TrainingArguments
from logfire_callback import LogfireCallback
# Initialize your model, dataset, etc.
training_args = TrainingArguments(
output_dir="./results",
num_train_epochs=3,
# ... other training arguments
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
callbacks=[LogfireCallback()] # Add the Logfire callback here
)
trainer.train()If you have any feedback, please reach out at @louisbrulenaudet
louisbrulenaudet
posted
an
update
10 months ago
Post
3481
I am pleased to introduce my first project built upon Hugging Face’s smolagents framework, integrated with Alpaca for financial market analysis automation 🦙🤗
The project implements technical indicators such as the Relative Strength Index (RSI) and Bollinger Bands to provide momentum and volatility analysis. Market data is retrieved through the Alpaca API, enabling access to historical price information across various timeframes.
AI-powered insights are generated using Hugging Face’s inference API, facilitating the analysis of market trends through natural language processing with DuckDuckGo search integration for real-time sentiment analysis based on financial news 🦆
Link to the GitHub project: https://github.com/louisbrulenaudet/agentic-market-tool
The project implements technical indicators such as the Relative Strength Index (RSI) and Bollinger Bands to provide momentum and volatility analysis. Market data is retrieved through the Alpaca API, enabling access to historical price information across various timeframes.
AI-powered insights are generated using Hugging Face’s inference API, facilitating the analysis of market trends through natural language processing with DuckDuckGo search integration for real-time sentiment analysis based on financial news 🦆
Link to the GitHub project: https://github.com/louisbrulenaudet/agentic-market-tool
RPritam
updated
a
model
10 months ago
RPritam
published
a
model
10 months ago