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/...
How do you actually turn Python skills into freelance income?
https://www.reddit.com/r/learnpython/comments/1w1m8fg/how_do_you_actually_turn_python_skills_into/
https://www.reddit.com/r/learnpython/comments/1w1m8fg/how_do_you_actually_turn_python_skills_into/
Reddit
From the learnpython community on Reddit
Explore this post and more from the learnpython community
Time complexity of operations on Python's built-in types
https://docs.python.org/3.16/library/time-complexity.html
https://docs.python.org/3.16/library/time-complexity.html
Python documentation
Time complexity of operations on built-in types
This page documents the time complexity of various operations on built-in types in CPython. Other Python implementations may have different performance characteristics. Additionally, the listed cos...
Geolocating Random Islet Image Using Geometry & CUDA GPU Programming
The author solved Sofia Santos’s Gralhix 004 OSINT challenge using a custom geometric pipeline and CUDA GPU acceleration to filter 80.7 million candidate landmass triplets down to 26 locations. The analysis successfully geolocated the image to Oan Resort in Micronesia at 7°21'48.4"N, 151°45'20.7"E, with the camera facing northwest.
https://yassa9.github.io/osint/gralhix-004/
The author solved Sofia Santos’s Gralhix 004 OSINT challenge using a custom geometric pipeline and CUDA GPU acceleration to filter 80.7 million candidate landmass triplets down to 26 locations. The analysis successfully geolocated the image to Oan Resort in Micronesia at 7°21'48.4"N, 151°45'20.7"E, with the camera facing northwest.
https://yassa9.github.io/osint/gralhix-004/
yassa9.github.io
gralhix #004
just a blog
fastaddress
Fastaddress is a Python package that keeps the familiar usaddress API while moving its CRF runtime to Rust for much faster US address parsing. It delivers 11.3x higher single-core throughput, scales to 360K+ addresses per second on eight threads, and matches usaddress output across 20,738 real addresses.
https://github.com/vinvomero/fastaddress
Fastaddress is a Python package that keeps the familiar usaddress API while moving its CRF runtime to Rust for much faster US address parsing. It delivers 11.3x higher single-core throughput, scales to 360K+ addresses per second on eight threads, and matches usaddress output across 20,738 real addresses.
https://github.com/vinvomero/fastaddress
GitHub
GitHub - vinvomero/fastaddress: fastaddress runs the usaddress CRF model in Rust with the same Python API. 11.3x faster single…
fastaddress runs the usaddress CRF model in Rust with the same Python API. 11.3x faster single-core (89,653 vs 7,941 addr/sec) with identical output across 20,738 real county addresses. Confidence ...
VectorSmith
VectorSmith turns vector database collections into LLM tools and exposes them through an MCP server. Using the existing collection schemas and a simple YAML configuration, you can make your vector DB data available to AI agents without writing custom tool and MCP integrations from scratch.
https://github.com/kjgpta/vectorsmith
VectorSmith turns vector database collections into LLM tools and exposes them through an MCP server. Using the existing collection schemas and a simple YAML configuration, you can make your vector DB data available to AI agents without writing custom tool and MCP integrations from scratch.
https://github.com/kjgpta/vectorsmith
GitHub
GitHub - kjgpta/vectorsmith: Your vector database, as typed tools. Write a tools.yaml, then load_tools in Python or serve over…
Your vector database, as typed tools. Write a tools.yaml, then load_tools in Python or serve over MCP. - kjgpta/vectorsmith