PythonHub

Defeating Nondeterminism in LLM Inference LLM inference is often nondeterministic even with temperature set to zero, primarily due to batch-size-dependent kernel behaviors that change results based on server load rather than randomness or floating-point issues. The solution is to use batch-invariant kernels, ensuring reproducible outputs even in high-concurrency environments, which is now possible but may come with some efficien... https://thinkingmachines.ai/blog/defeating-nondeterminism-in-llm-inference

Python Tutorial: Build an AI-assisted Reddit Scraping Pipeline The video provides an in-depth, hands-on tutorial for building a resilient, AI-assisted Reddit scraping pipeline in Python, covering everything from Jupyter prototyping and LangChain agents to a Django-based background worker architecture. It teaches viewers to automate web scraping, integrate Google’s Gemini LLM for query refinement, and store structured results in PostgreSQL, suitable ... https://www.youtube.com/watch?v=XI-iP-qk_Vk

Semlib Build data processing and data analysis pipelines that leverage the power of LLMs. https://github.com/anishathalye/semlib

Context Engineering - Short-Term Memory Management with Sessions from OpenAI Agents SDK The guide demonstrates how to use the OpenAI Agents SDK’s Session object to manage short-term memory in AI agents, enabling context trimming and compression for efficient, coherent, and cost-effective multi-turn conversations. Effective session memory ensures agents maintain relevant history across turns while reducing noise, latency, and error risk in longer interactions. https://cookbook.openai.com/examples/agents_sdk/session_memory

Nallely – A Python signals/MIDI processing system inspired by Smalltalk https://dr-schlange.github.io/nallely-midi/

AuthTuna The Modern Async Security Framework for FastAPI. https://github.com/shashstormer/authtuna

ApeRAG Production-ready GraphRAG with multi-modal indexing, AI agents, MCP support, and scalable K8s deployment https://github.com/apecloud/ApeRAG

Tiny LLM - LLM Serving in a Week A course of learning LLM inference serving on Apple Silicon for systems engineers: build a tiny vLLM + Qwen. https://skyzh.github.io/tiny-llm/

JiraTUI A Textual User Interface for interacting with Atlassian Jira from your shell. https://github.com/whyisdifficult/jiratui

Just for fun: animating a mosaic of 90s GIFs The post describes an experiment in animating a mosaic of vintage 90s GIFs collected from the GeoCities archive, using HTML Canvas for random, lively playback. It celebrates the playful aesthetics of early web graphics and highlights the technical and nostalgic joy of reintroducing these classic GIFs into a modern browser setting. https://alexplescan.com/posts/2025/09/15/gifs/

Sphinx Docs Instantly in Your Browser (MyST Markdown + reStructuredText) Edit and preview reStructuredText or MyST Markdown instantly in a Sphinx running in a browser. Runs entirely in Python using WebAssembly, so it’s private, fast, and ideal for learning markup. https://snippets.documatt.com

Mini-o3 Scaling Up Reasoning Patterns and Interaction Turns for Visual Search. https://mini-o3.github.io/

List of 87 Programming Ideas for Beginners (with Python implementations) https://www.reddit.com/r/Python/comments/1nitzoz/list_of_87_programming_ideas_for_beginners_with/

detroit detroit is a Python implementation of d3js. https://github.com/bourbonut/detroit

Python 3.13 is 10% slower than 3.12 for my file parser https://www.reddit.com/r/Python/comments/1nmuy7t/python_313_is_10_slower_than_312_for_my_file/

Microsoft Python Driver for SQL Server https://github.com/microsoft/mssql-python

Nvmath-Python: Nvidia Math Libraries for the Python Ecosystem https://github.com/NVIDIA/nvmath-python

Post-training 101 A hitchhiker's guide into LLM post-training. https://tokens-for-thoughts.notion.site/post-training-101

Python Hub Weekly Digest for 2025-09-21 https://pythonhub.dev/digest/2025-09-21/

Jaxformer Scaling Modern Transformers This is a zero-to-one guide on scaling modern transformers with n-dimensional parallelism. Transformers have driven much of the deep learning revolution, yet no practical guide reflects SOTA architectures and the complexities of large-scale language modelling. While excellent resources such as DeepMind’s How to Scale Your Model and HuggingFace’s Ultra Scale Playbook exist, a gap remains ... https://jaxformer.com/