claude-ads
Claude-first, portable paid-media operations for agencies, consultants, and in-house performance teams.
https://github.com/AgriciDaniel/claude-ads
Claude-first, portable paid-media operations for agencies, consultants, and in-house performance teams.
https://github.com/AgriciDaniel/claude-ads
GitHub
GitHub - AgriciDaniel/claude-ads: Claude-first paid-media operations skill for Claude Code across 12 ad platforms (Google, Meta…
Claude-first paid-media operations skill for Claude Code across 12 ad platforms (Google, Meta, YouTube, LinkedIn, TikTok, Microsoft, Apple, Amazon, Reddit, Pinterest, Snapchat, X): source-grounded ...
SimplifyJobs / Summer2027-Internships
Summer 2027 software engineering, data science, AI, quant, product management, and hardware internship postings. Updated daily by Simplify and Pitt CSC.
https://github.com/SimplifyJobs/Summer2027-Internships
Summer 2027 software engineering, data science, AI, quant, product management, and hardware internship postings. Updated daily by Simplify and Pitt CSC.
https://github.com/SimplifyJobs/Summer2027-Internships
GitHub
GitHub - SimplifyJobs/Summer2027-Internships: Summer 2027 software engineering, data science, AI, quant, product management, and…
Summer 2027 software engineering, data science, AI, quant, product management, and hardware internship postings. Updated daily by Simplify and Pitt CSC. - SimplifyJobs/Summer2027-Internships
Tencent / AI-Infra-Guard
A full-stack AI Red Teaming platform securing AI ecosystems via Agent Scan, Skills Scan, MCP scan, AI Infra scan and LLM jailbreak evaluation.
https://github.com/Tencent/AI-Infra-Guard
A full-stack AI Red Teaming platform securing AI ecosystems via Agent Scan, Skills Scan, MCP scan, AI Infra scan and LLM jailbreak evaluation.
https://github.com/Tencent/AI-Infra-Guard
GitHub
GitHub - Tencent/AI-Infra-Guard: A full-stack AI Red Teaming platform securing AI ecosystems via Agent Scan, Skills Scan, MCP scan…
A full-stack AI Red Teaming platform securing AI ecosystems via Agent Scan, Skills Scan, MCP scan, AI Infra scan and LLM jailbreak evaluation. - Tencent/AI-Infra-Guard
Origintracer
A low-level asynchronous web framework observability system. The system can trace nginx, gunicorn, Uvicorn, django and celery.
https://origintracer.app/docs/
A low-level asynchronous web framework observability system. The system can trace nginx, gunicorn, Uvicorn, django and celery.
https://origintracer.app/docs/
When str.lower() is a security vulnerability in Python
A Python security vulnerability caused IDNA 2003 handling to use the interpreter’s current Unicode case-folding rules instead of the Unicode 3.2 rules required by the specification.The fix makes Python’s case-folding behavior match Unicode 3.2 for StringPrep, restoring standards-compliant IDNA encoding and addressing CVE-2026-17084.
https://sethmlarson.dev/when-str-lower-is-a-security-vulnerability
A Python security vulnerability caused IDNA 2003 handling to use the interpreter’s current Unicode case-folding rules instead of the Unicode 3.2 rules required by the specification.The fix makes Python’s case-folding behavior match Unicode 3.2 for StringPrep, restoring standards-compliant IDNA encoding and addressing CVE-2026-17084.
https://sethmlarson.dev/when-str-lower-is-a-security-vulnerability
sethmlarson.dev
When str.lower() is a security vulnerability in Python
Some internet standards only support ASCII characters, but the world uses
much more than the Latin alphabet. Thus, a mapping from Unicode to
ASCII for use in domain names is required.
NamePrep was...
much more than the Latin alphabet. Thus, a mapping from Unicode to
ASCII for use in domain names is required.
NamePrep was...
The Mullet Stack - JavaScript in the front, Python in the back.
This is a practical guide to building a modern full-stack application with FastAPI and Pydantic on the Python backend and React and TypeScript on the frontend. It explores how their different type systems work together and shows how generating frontend types from FastAPI’s OpenAPI schema prevents backend and frontend models from drifting apart.
https://seanhelvey.com/mullet-stack/guide/
This is a practical guide to building a modern full-stack application with FastAPI and Pydantic on the Python backend and React and TypeScript on the frontend. It explores how their different type systems work together and shows how generating frontend types from FastAPI’s OpenAPI schema prevents backend and frontend models from drifting apart.
https://seanhelvey.com/mullet-stack/guide/
What's missing to have reproducible builds on PyPI
The article explores what Python packaging is missing to support reproducible builds on PyPI, allowing third parties to verify that distributions match their source code and haven't been tampered with. It proposes recording source information and build tools, using SBOMs, and having trusted verifiers report successfully reproduced packages back to PyPI.
https://snarky.ca/whats-missing-to-have-reproducible-builds-on-pypi/
The article explores what Python packaging is missing to support reproducible builds on PyPI, allowing third parties to verify that distributions match their source code and haven't been tampered with. It proposes recording source information and build tools, using SBOMs, and having trusted verifiers report successfully reproduced packages back to PyPI.
https://snarky.ca/whats-missing-to-have-reproducible-builds-on-pypi/
Tall, Snarky Canadian
What's missing to have reproducible builds on PyPI
While writing the section of my 2026 Python Packaging Council (PPC) nomination on secure supply chain, I realized that one thing related to having a secure supply chain that we lack is a defined way to perform reproducible builds. The reason I like the idea…
AIPerf
AIPerf is a comprehensive benchmarking tool that measures the performance of generative AI models served by your preferred inference solution.
https://github.com/ai-dynamo/aiperf
AIPerf is a comprehensive benchmarking tool that measures the performance of generative AI models served by your preferred inference solution.
https://github.com/ai-dynamo/aiperf
GitHub
GitHub - ai-dynamo/aiperf: AIPerf is a comprehensive benchmarking tool that measures the performance of generative AI models served…
AIPerf is a comprehensive benchmarking tool that measures the performance of generative AI models served by your preferred inference solution. - ai-dynamo/aiperf
EuroPython 2026 Videos
The videos from the EuroPython 2026 talks are now available.
https://www.youtube.com/playlist?list=PLd3Y9yzyC5Uo
The videos from the EuroPython 2026 talks are now available.
https://www.youtube.com/playlist?list=PLd3Y9yzyC5Uo
JupyterGIS 0.16: New visualization capabilities, collaborative Story Maps, and more
JupyterGIS 0.16 adds richer geospatial visualization with collaborative Story Maps, openEO layers, lazy Xarray rendering, new symbology, and support for GeoZarr and GeoPackage. The release also expands real-time collaboration to vector editing and introduces an R API alongside the existing Python workflow.
https://blog.jupyter.org/jupytergis-0-16-new-visualization-capabilities-collaborative-story-maps-and-more-03e6b78bacc0
JupyterGIS 0.16 adds richer geospatial visualization with collaborative Story Maps, openEO layers, lazy Xarray rendering, new symbology, and support for GeoZarr and GeoPackage. The release also expands real-time collaboration to vector editing and introduces an R API alongside the existing Python workflow.
https://blog.jupyter.org/jupytergis-0-16-new-visualization-capabilities-collaborative-story-maps-and-more-03e6b78bacc0
Medium
JupyterGIS 0.16: New visualization capabilities, collaborative Story Maps, and more
Read this article in Notebook.link, as a live story-map! https://notebook.link/@martinRenou/jupytergis-announcement
Reinforcement Learning for LLMs: The Complete Guide
This is a comprehensive guide to reinforcement learning for LLMs, starting from first principles and building through the policy-gradient algorithms used in modern LLM training. It covers RLHF, verifiable rewards, REINFORCE, PPO, and the newer RL methods being used to improve reasoning, agents, reliability, and efficiency.
https://cameronrwolfe.substack.com/p/llm-rl
This is a comprehensive guide to reinforcement learning for LLMs, starting from first principles and building through the policy-gradient algorithms used in modern LLM training. It covers RLHF, verifiable rewards, REINFORCE, PPO, and the newer RL methods being used to improve reasoning, agents, reliability, and efficiency.
https://cameronrwolfe.substack.com/p/llm-rl
Substack
Reinforcement Learning for LLMs: The Complete Guide
Tracing the evolution of RL from first principles to the frontier of modern AI research...
DBLift
DBLift is a Python-native database migration toolkit that brings application-style safety to schema changes with validation, dry runs, SQL previews, rollback support, and CI/CD integration.
https://github.com/dblift/dblift
DBLift is a Python-native database migration toolkit that brings application-style safety to schema changes with validation, dry runs, SQL previews, rollback support, and CI/CD integration.
https://github.com/dblift/dblift
GitHub
GitHub - dblift/dblift: Database changes deserve the same safety as application code.
Database changes deserve the same safety as application code. - dblift/dblift
👍1
How to Set Up Python for Machine Learning (The Right Way)
AI-generated ML code can run successfully while still containing subtle mistakes that waste hours or days of training. The video shows how to set up a reliable Python ML workflow with the right tools, validation steps, and verification habits to catch problems before expensive training runs.
https://www.youtube.com/watch?v=cAtKtvmds1o
AI-generated ML code can run successfully while still containing subtle mistakes that waste hours or days of training. The video shows how to set up a reliable Python ML workflow with the right tools, validation steps, and verification habits to catch problems before expensive training runs.
https://www.youtube.com/watch?v=cAtKtvmds1o
YouTube
How I Set Up Python for Machine Learning With AI
Doing Machine Learning? Try PyCharm free and set it up like I showed you: https://jb.gg/tt3
AI-generated machine learning code that runs is not the same as code that actually works. If you spend three days training a model on bad code, you've wasted three…
AI-generated machine learning code that runs is not the same as code that actually works. If you spend three days training a model on bad code, you've wasted three…
Nifty Django Feature: Counting on Multiple Columns
Django’s Count expression only works on a single column, which makes counting distinct combinations such as unique pet-vet pairs awkward. A custom CountSubquery shows how Django’s expression system can generate the needed SQL while staying inside the ORM instead of dropping to raw SQL.
https://www.better-simple.com/django/2026/08/19/nifty-feature-counting-on-multiple-columns/
Django’s Count expression only works on a single column, which makes counting distinct combinations such as unique pet-vet pairs awkward. A custom CountSubquery shows how Django’s expression system can generate the needed SQL while staying inside the ORM instead of dropping to raw SQL.
https://www.better-simple.com/django/2026/08/19/nifty-feature-counting-on-multiple-columns/
Better Simple
Nifty Django Feature: Counting on Multiple Columns
Part of the Nifty Django features series: The Count expression only works on a single column, but you can use Subquery to count on multiple columns!
ai-job-search
The job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.
https://github.com/MadsLorentzen/ai-job-search
The job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it.
https://github.com/MadsLorentzen/ai-job-search
GitHub
GitHub - MadsLorentzen/ai-job-search: The job search that runs on your machine. AI job application framework built on Claude Code:…
The job search that runs on your machine. AI job application framework built on Claude Code: evaluate postings, tailor CVs, write cover letters, prep interviews. Fork it and own it. - MadsLorentzen...
Speculative Programmatic Tool Calling
Speculative programmatic tool calling (sPTC) speeds up AI agent harnesses by pre-launching tool calls from partially generated code instead of waiting for the model to finish generating. The approach overlaps tool execution with token generation and can also parallelize independent calls, acting like a lightweight JIT compiler for agent-generated programs.
https://alexzhang13.github.io/blog/2026/spec-ptc/
Speculative programmatic tool calling (sPTC) speeds up AI agent harnesses by pre-launching tool calls from partially generated code instead of waiting for the model to finish generating. The approach overlaps tool execution with token generation and can also parallelize independent calls, acting like a lightweight JIT compiler for agent-generated programs.
https://alexzhang13.github.io/blog/2026/spec-ptc/
Alex L. Zhang
Speculative Programmatic Tool Calling
Speculative programmatic tool calling is a class of techniques for overlapping tool call computation with the code being generated by a harness.
pentestkit
Autonomous multi-agent pentest framework that plans, exploits, verifies with required proof, assigns CVSS scores, and generates client-ready reports. It achieved 104/104 (100%) on the XBOW validation benchmarks and is powered by Kimi K3.
https://github.com/lordx64/pentestkit
Autonomous multi-agent pentest framework that plans, exploits, verifies with required proof, assigns CVSS scores, and generates client-ready reports. It achieved 104/104 (100%) on the XBOW validation benchmarks and is powered by Kimi K3.
https://github.com/lordx64/pentestkit
GitHub
GitHub - lordx64/pentestkit: Autonomous multi-agent pentest framework — plans, exploits, verifies (proof-required), CVSS-scores…
Autonomous multi-agent pentest framework — plans, exploits, verifies (proof-required), CVSS-scores and writes client-ready reports. 104/104 (100%) on the XBOW validation benchmarks, powered by Kimi...
Learn Vectorized Thinking in Python Through Examples
In this article, you will learn how to think in terms of vectorized operations using NumPy, replacing slow Python loops with efficient array-level computations.
https://machinelearningmastery.com/learn-vectorized-thinking-in-python-through-examples/
In this article, you will learn how to think in terms of vectorized operations using NumPy, replacing slow Python loops with efficient array-level computations.
https://machinelearningmastery.com/learn-vectorized-thinking-in-python-through-examples/
VoiceStudio
VoiceStudio is the open-source, fully local ElevenLabs alternative for voice cloning, voice design, video dubbing, dictation, transcription, and audiobook creation in 646 languages.
https://github.com/debpalash/VoiceStudio
VoiceStudio is the open-source, fully local ElevenLabs alternative for voice cloning, voice design, video dubbing, dictation, transcription, and audiobook creation in 646 languages.
https://github.com/debpalash/VoiceStudio
GitHub
GitHub - debpalash/VoiceStudio: VoiceStudio is the open-source, fully-local ElevenLabs alternative — voice cloning, voice design…
VoiceStudio is the open-source, fully-local ElevenLabs alternative — voice cloning, voice design, video dubbing, dictation, transcription & audiobook creation in 646 languages. - debpalash/...