apowerb

The open-source agentic framework to build, orchestrate, and operate production AI agents.

https://github.com/apowerb/apowerb

Speeding Up a Python Service with CinderX: JIT and Static Typing

Timofei Ivankov explores how CinderX’s JIT compiler and Static Python can accelerate real-world Python services, comparing their internals and performance with CPython 3.14’s experimental JIT. Benchmarks show that combining Static Python with JIT compilation increased a CPU-bound endpoint’s throughput from 140 to 250 requests per second, while NumPy-heavy workloads saw no benefit and gar...

https://dev.to/deadlovelll/speeding-up-a-python-service-with-cinderx-jit-and-static-typing-53bh

since-cutoff: which APIs of your pinned Python dependencies changed after your coding model's training cutoff

Diffs each pinned dependency's API against the release current at the model's cutoff and writes AGENTS.md notes. No model calls.

https://github.com/MohammadHijjawi97/since-cutoff

Python 3.15 Is About to Change Python

Python 3.15 is almost here! This video breaks down 10 of the most interesting new features and changes coming to Python 3.15, using real Python code and visual explanations.

https://www.youtube.com/watch?v=rxaDxyPUoSY

How to Build AI Agents in Python - 3 Ways

This video compares three Python frameworks for building more capable AI agents that can navigate codebases, edit files, and run commands: CrewAI, the OpenAI Agents SDK, and LangGraph. It walks through building an agent with each framework and compares their approaches to orchestration, tools, workflows, and choosing the right framework for a project.

https://www.youtube.com/watch?v=-RTgK6qX6A8

django-upgrade-report

Which of your dependencies block a Django upgrade, and in which order to upgrade them.

https://github.com/derblub/django-upgrade-report

LangSmith Crash Course: LLMOps in Python

This video serves as a comprehensive crash course on LangSmith, the LangChain ecosystem platform used for tracing, debugging, evaluating, and monitoring AI agents. It covers essential LLMOps workflows including tracing agent runs, performing evaluations with custom datasets, creating alerts, and managing prompts.

https://www.youtube.com/watch?v=P3kmo04DiEw

asgeirtj / system_prompts_leaks

Documented system prompts from Anthropic - Claude Fable 5.1, Opus 5.5, Claude Design, Claude Code. OpenAI - ChatGPT GPT-6-Astra, Codex. Google - Gemini 3.8 Flash, 3.1 Pro, Antigravity. xAI - Grok, Grok Bot, Cursor, Kimi and more! Updated regularly.

https://github.com/asgeirtj/system_prompts_leaks

Ciaren: open-source visual ETL exporting pandas and Polars code (alpha)

Build data pipelines visually, run locally, preview intermediate data, and export standalone Python scripts. Open-source alpha, AGPL-3.0.

https://ciaren.com/

livenerf

A long-running, deterministic-as-possible benchmark for detecting whether a frontier model gets quietly worse after launch.

https://github.com/ninjahawk/livenerf

Python Algorithmic Trading Course – Massive, SnapTrade & Alpaca Integrations

This course demonstrates how to build an algorithmic paper trading system using Python and Django to calculate momentum scores and execute trades. The project integrates Massive for market data, SnapTrade for portfolio management, and Alpaca for risk-free brokerage simulation

https://www.youtube.com/watch?v=zH2Mg782XhA

A Lean Proof Printing Python Union Find

Philip Zucker shows how a Python union-find data structure can generate Lean proof certificates, turning union and path-compression operations into machine-checkable equality proofs. He uses the experiment to explore the broader idea of treating proofs as traces of search, with applications to e-graphs and theorem-proving systems.

https://www.philipzucker.com/proof_uf/