PythonHub

How prompt caching works - Paged Attention and Automatic Prefix Caching plus practical tips Prompt caching in large language models (LLMs) is an optimization technique that stores and reuses intermediate computational states (key-value caches) of repeated prompt prefixes, significantly reducing redundant processing and speeding up responses. By breaking prompts into fixed-size token blocks and utilizing a hash-based prefix matching system, prompt caching enables multiple reques... https://sankalp.bearblog.dev/how-prompt-caching-works

Onlymaps A Python micro-ORM. https://github.com/manoss96/onlymaps

Is the 79-character limit still in actual (with modern displays)? https://www.reddit.com/r/Python/comments/1pejhny/is_the_79character_limit_still_in_actual_with/

anthropics / claude-quickstarts A collection of projects designed to help developers quickly get started with building deployable applications using the Claude API https://github.com/anthropics/claude-quickstarts

runprompt Tiny script to run .prompt files. https://github.com/chr15m/runprompt

A first look at Django's new background tasks Django 6.0 introduces django.tasks, a lightweight framework for defining and enqueuing background tasks via a standard API, but lacks built-in workers—requiring external infrastructure like custom database-backed backends. The article builds a demo notification app with a DB backend, worker, retries, and result polling, showing how to implement queuing while noting limitations like no co... https://roam.be/notes/2025/a-first-look-at-djangos-new-background-tasks/

Improve Query Performance Using Python Django QuerySets The post shows how efficient Django QuerySet usage can significantly improve database performance, reduce latency, and create faster applications. It explains that writing better queries leads to more stable, scalable, and cost-effective Django systems because the ORM can easily generate unnecessary load when used carelessly. https://blog.appsignal.com/2025/12/03/improve-query-performance-using-django-python-querysets.html

Become an AI Researcher Course – LLM, Math, PyTorch, Neural Networks, Transformers This comprehensive course on becoming an AI Researcher starts with the foundational mathematics (vectors, derivatives, gradients, matrices) and PyTorch fundamentals necessary for understanding modern AI. It then progresses through the building blocks of neural networks and culminates with an in-depth module on Transformers, the critical technology behind Large Language Models and generat... https://www.youtube.com/watch?v=wu8npoU37cI

Keras HyperParameters Tuning This is an example i provided on the Keras ecosystem. https://keras.io/examples/structured_data/class_with_grn_and_vsn_with_hyperparameters_tuning/

AI infrastructure in the "Era of experience" The article analyzes AI infrastructure needs in the "Era of Experience," where RL-trained models interact with proprietary environments, using GRPO for efficient policy optimization and LoRA adapters to enable low-cost training/inference via multi-tenancy and large-batch async RL. It predicts commoditized base models will spawn a reinforcement fine-tuning (RFT) industry for custom models... https://www.tensoreconomics.com/p/ai-infrastructure-in-the-era-of-experience

trustedsec / social-engineer-toolkit The Social-Engineer Toolkit (SET) repository from TrustedSec - All new versions of SET will be deployed here. https://github.com/trustedsec/social-engineer-toolkit

Setting up a Django project with Vite, React, and Tailwind CSS The video demonstrates setting up a modern Django project with Vite for frontend builds: create Django app with UV, configure Vite for JS/CSS bundling to Django's staticfiles, integrate django-vite for HMR dev server. https://www.youtube.com/watch?v=GztJ1h6ZXA0

Gunicorn Internals This blog is a technical case of the Gunicorn source code. https://humbulani1234.github.io/blog/

Nano-PDF A CLI tool to edit PDF slides using natural language prompts, powered by Google's Gemini 3 Pro Image ("Nano Banana") model. https://github.com/gavrielc/Nano-PDF

Building Data Visualisations in Python in Minutes The video demonstrates how to use Streamlit, a Python framework, to quickly build professional and interactive data visualizations with minimal code, showing live examples involving Pandas for data manipulation and visualization. It highlights Streamlit's simplicity, live reloading, interactivity, and caching features, making it ideal for rapid data exploration and sharing within small u... https://www.youtube.com/watch?v=lQRq4-MiAGA

llm-council LLM Council works together to answer your hardest questions. https://github.com/karpathy/llm-council

elf - Advent of Code helper CLI and Python library elf is a fast and lightweight Advent of Code helper CLI and Python library. https://github.com/cak/elf

MemoriLabs / Memori Open-Source Memory Engine for LLMs, AI Agents & Multi-Agent Systems https://github.com/MemoriLabs/Memori

200 Lines of Python beats $50M supercomputer – Navier-Stokes at Re=10⁸ [pdf] https://philpapers.org/archive/CAMIIA-3.pdf

Raptor An autonomous offensive/defensive security research framework, based on Claude Code. It empowers security research with agentic workflows and automation. https://github.com/gadievron/raptor