PythonHub

Python Workers redux: fast cold starts, packages, and a uv-first workflow https://blog.cloudflare.com/python-workers-advancements/

Learn NLP Research: 7 Papers Implemented This video traces the evolution of neural machine translation from RNNs and LSTMs to attention mechanisms, Transformers, and multilingual models like GNMT. It includes PyTorch implementations of 7 landmark papers, mathematical explanations, and tools like Transformer Playground for hands-on learning. https://www.youtube.com/watch?v=kRv2ElPNAdY

We Got Claude to Fine-Tune an Open Source LLM We gave Claude the ability to fine-tune language models using a new tool called Hugging Face Skills. Not just write training scripts, but to actually submit jobs to cloud GPUs, monitor progress, and push finished models to the Hugging Face Hub. This tutorial shows you how it works and how to use it yourself. https://huggingface.co/blog/hf-skills-training

facebookresearch / sam3 The repository provides code for running inference and finetuning with the Meta Segment Anything Model 3 (SAM 3), links for downloading the trained model checkpoints, and example notebooks that show how to use the model. https://github.com/facebookresearch/sam3

FunAudioLLM / CosyVoice Multi-lingual large voice generation model, providing inference, training and deployment full-stack ability. https://github.com/FunAudioLLM/CosyVoice

kubesdk — async-first, fully typed Python SDK for Kubernetes Open-source Python SDK with fully typed models, async client, and multi-cluster support for Kubernetes automation. https://github.com/puzl-cloud/kubesdk

embar A Python ORM with types. https://github.com/carderne/embar

LightX2V Light Video Generation Inference Framework. https://github.com/ModelTC/LightX2V

Python Hub Weekly Digest for 2025-12-14 https://pythonhub.dev/digest/2025-12-14/

Tacopy – Tail Call Optimization for Python https://github.com/raaidrt/tacopy

PyTogether: Collaborative lightweight real-time Python IDE for teachers/learners https://github.com/SJRiz/pytogether

Context Data Platform for Self-learning Agents One Place for Agents to Store, Observe, and Learn. Designed to simplify context engineering, improve agent reliability and task success rates. https://github.com/memodb-io/Acontext

Stop Hardcoding Everything: Use Dependency Injection The video explains Dependency Injection (DI) in Python with a practical data pipeline example, showing how DI improves code flexibility, testability, and separation of concerns by injecting dependencies like loaders, transformers, and exporters rather than hardcoding them. It covers manual DI with functions and classes, abstraction with protocols, building a simple DI container, and DI u... https://www.youtube.com/watch?v=Xhzn1eAxoXk

python-injection Fast and easy dependency injection framework. https://github.com/100nm/python-injection

Can Google's ADK Replace LangChain and MCP? Christina Lin (Google) demos Agent Development Kit (ADK), open-source Python framework for agentic pipelines: assemble LLMs + tools (via MCP servers/function calling) + prompts for complex workflows like version control or Friday night bookings, with grounding for cited real-time data to cut hallucinations/token costs. https://www.youtube.com/watch?v=nMnQ63YkftE

Django 6.0 released Django 6.0 introduces major new features: built-in support for template partials (for cleaner, reusable templates), a native background-task framework, a built-in Content Security Policy (CSP) system, and a more modern, Unicode-friendly email API. This release marks the end of mainstream support for Django 5.2; developers are encouraged to upgrade to 6.0 to benefit from the new features ... https://www.djangoproject.com/weblog/2025/dec/03/django-60-released/

vllm-omni A framework for efficient model inference with omni-modality models. https://github.com/vllm-project/vllm-omni

Modernising Django Packages Without Breaking Everything To successfully modernize a mature Django package without breaking user code, the maintainer should phase in new tools to consolidate configuration into a single pyproject.toml file. Key strategies involve streamlining the developer experience with fast tools like uv and Ruff, using a Justfile for memorable commands, and automating releases with Towncrier for clean changelog management. https://lincolnloop.com/blog/modernising-django-packages-without-breaking-everything/

Can LLMs give us AGI if they are bad at arithmetic? Wes McKinney's post questions whether large language models (LLMs) can achieve artificial general intelligence (AGI) given their persistent struggles with basic arithmetic tasks like adding single-digit numbers, even in top models. Through experiments and analysis, he shows that while LLMs perform inconsistently on simple math (e.g., summing ~10 numbers), this reveals deeper limitations ... https://wesmckinney.com/blog/llms-arithmetic/

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