advisory-database
Flask
advisory-database | Flask | |
---|---|---|
5 | 135 | |
238 | 66,538 | |
0.4% | 0.6% | |
7.3 | 8.7 | |
5 days ago | 11 days ago | |
Python | ||
Creative Commons Attribution 4.0 | BSD 3-clause "New" or "Revised" License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
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.
advisory-database
- LangChain Arbitrary Command Execution - CVE-2023-34541
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pyscan v0.1.0: A python dependency vulnerability scanner, written in Rust.
source
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Auditing your python environment
The second tool I want to introduce to you is pip-audit. It is maintained by folks at Trails of Bit with some Google support. It uses the Pypa Advisory Database via the PyPI JSON API as a source of vulnerability reports.
- Adding Auditing to Pip
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Google's unified vulnerability schema for open source supports Rust on launch
Today, weβre excited to announce a new milestone in expanding OSV to several key open-source ecosystems: Go, Rust, Python, and DWF.
Flask
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Ask HN: High quality Python scripts or small libraries to learn from
I'd suggest Flask or some of the smaller projects in the Pallets ecosystem:
https://github.com/pallets/flask
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Rapid Prototyping with Flask, Bootstrap and Secutio
#!/usr/bin/python # # https://flask.palletsprojects.com/en/3.0.x/installation/ # from flask import Flask, jsonify, request contacts = [ { "id": "1", "firstname": "Lorem", "lastname": "Ipsum", "email": "[email protected]", }, { "id": "2", "firstname": "Mauris", "lastname": "Quis", "email": "[email protected]", }, { "id": "3", "firstname": "Donec Purus", "lastname": "Purus", "email": "[email protected]", } ] app = Flask(__name__, static_url_path='', static_folder='public',) @app.route("/contact//save", methods=["PUT"]) def save_contact(id): data = request.json contacts[id - 1] = data return jsonify(contacts[id - 1]) @app.route("/contact/", methods=["GET"]) @app.route("/contact//edit", methods=["GET"]) def get_contact(id): return jsonify(contacts[id - 1]) @app.route('/') def root(): return app.send_static_file('index.html') if __name__ == '__main__': app.run(debug=True)
- Microdot "The impossibly small web framework for Python and MicroPython"
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Why do all the popular projects use relative imports in __init__ files if PEP 8 recommends absolute?
I was looking at all the big projects like numpy, pytorch, flask, etc.
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10 Github repositories to achieve Python mastery
Explore here.
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Ask HN: What would you use to build a mostly CRUD back end today?
I may use Flask-Admin initially to offload the "CRUD" operations to have an initial prototype fast but then drop it ASAP because I don't want to write a "flask-admin application" to fight against later on. If the application is mainly "CRUD", then Flask-Admin is suitable.
Now...
Would you do a breakdown/list of all the jobs you've done by sector/vertical and by function/role and by application functionality?
- [0]: https://flask.palletsprojects.com
- [1]: https://flask-admin.readthedocs.io/en/latest
- [2]: https://flask.palletsprojects.com/en/2.3.x/patterns/celery
- [3]: https://sentry.io
- [4]: https://posthog.com
- [5]: https://www.docker.com
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Implementing continuous delivery pipelines with GitHub Actions
In the lab to follow, we will be setting up an end-to-end DevOps workflow for a Flask microservice with GitHub Actions, using a self-managed custom runner for maximal control over the pipeline execution environment and automating deployments to a local Kubernetes cluster. Furthermore, we will construct separate pipelines for our "development" and "production" environments to further elaborate on the concepts of continuous deployment and delivery.
- How do you iterate on a library built locally?
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Flask Application Load Balancing using Docker Compose and Nginx
Flask Micro web Framework: You will use Flask to build a Flask web application.
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Open Source Flask-based web applications
In an earlier post I mentioned a bunch of Open Source web applications. Let's now focus on the ones written in Python using Flask the light-weight web framework.
What are some alternatives?
pyscan - python dependency vulnerability scanner, written in Rust.
fastapi - FastAPI framework, high performance, easy to learn, fast to code, ready for production
vulndb - [mirror] The Go Vulnerability Database
Django - The Web framework for perfectionists with deadlines.
advisory-db - Security advisory database for Rust crates published through crates.io
AIOHTTP - Asynchronous HTTP client/server framework for asyncio and Python
dwflist - The DWF IDs
starlette - The little ASGI framework that shines. π
publications - Publications from Trail of Bits
quart - An async Python micro framework for building web applications.
langchain - π¦π Build context-aware reasoning applications
Tornado - Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed.