ouroboros
Ouroboros is an open-source AI coding framework that turns vague ideas into verified code through structured interviews, specification-driven execution, automated evaluations, and iterative improvement across multiple coding agents
https://github.com/Q00/ouroboros
Ouroboros is an open-source AI coding framework that turns vague ideas into verified code through structured interviews, specification-driven execution, automated evaluations, and iterative improvement across multiple coding agents
https://github.com/Q00/ouroboros
GitHub
GitHub - Q00/ouroboros: Agent OS: the agent gets smarter on its own. We just hold the line: Interview-gated, staged evaluation…
Agent OS: the agent gets smarter on its own. We just hold the line: Interview-gated, staged evaluation, budgeted evolution loop. MCP server, 14 runtimes: Claude Code, Codex CLI, Gemini CLI, OpenCod...
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apowerb
The open-source agentic framework to build, orchestrate, and operate production AI agents.
https://github.com/apowerb/apowerb
The open-source agentic framework to build, orchestrate, and operate production AI agents.
https://github.com/apowerb/apowerb
GitHub
GitHub - apowerb/apowerb: The open-source agentic framework to build, orchestrate, and operate production AI agents.
The open-source agentic framework to build, orchestrate, and operate production AI agents. - apowerb/apowerb
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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
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
DEV Community
Speeding Up a Python Service with CinderX: JIT and Static Typing
Every Python optimizer has a number: how many times faster it is. It is measured on kernels, sorting,...
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
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
GitHub
GitHub - MohammadHijjawi97/since-cutoff: Find which APIs of your pinned Python dependencies changed after your coding model's training…
Find which APIs of your pinned Python dependencies changed after your coding model's training cutoff, and give the agent short AGENTS.md / CLAUDE.md notes from a static API diff. No model c...
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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
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
YouTube
Python 3.15 Is About to Change Python 🤯
Python 3.15 is almost here! 🐍🚀 In this video, we break down 10 of the most interesting new features and changes coming to Python 3.15, using real Python code and visual explanations.
Python 3.15.0rc2 was released on September 1, 2026, and the final Python…
Python 3.15.0rc2 was released on September 1, 2026, and the final Python…
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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
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
YouTube
How to Build AI Agents in Python - 3 Ways
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Download the code from this video here: https://www.aiagentbuilders.co/yt-resources/three-ways-agents
Everyone talks about "AI…
Download the code from this video here: https://www.aiagentbuilders.co/yt-resources/three-ways-agents
Everyone talks about "AI…
💯3
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
Which of your dependencies block a Django upgrade, and in which order to upgrade them.
https://github.com/derblub/django-upgrade-report
GitHub
GitHub - derblub/django-upgrade-report: Which of your dependencies block a Django upgrade, and in which order to upgrade them.
Which of your dependencies block a Django upgrade, and in which order to upgrade them. - 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
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
YouTube
LangSmith Crash Course: LLMOps in Python
Check out Arcade: https://arcade.dev.plug.dev/fU177V4
💻️ Need some help with a project or some consulting? Contact me here: https://www.neuralnine.com/services
🐍 The Python Bible Book: https://www.neuralnine.com/books/
💻 The Algorithm Bible Book: https…
💻️ Need some help with a project or some consulting? Contact me here: https://www.neuralnine.com/services
🐍 The Python Bible Book: https://www.neuralnine.com/books/
💻 The Algorithm Bible Book: https…
SQLite in Production: Why WAL Mode, busy_timeout, and 1-Writer Pools
https://www.reddit.com/r/Python/comments/1wowf4q/sqlite_in_production_why_wal_mode_busy_timeout/
https://www.reddit.com/r/Python/comments/1wowf4q/sqlite_in_production_why_wal_mode_busy_timeout/
Reddit
From the Python community on Reddit
Explore this post and more from the Python community
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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
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
GitHub
GitHub - asgeirtj/system_prompts_leaks: Documented system prompts from Anthropic - Claude Fable 5.1, Opus 5.5, Claude Design, Claude…
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, ...
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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/
Build data pipelines visually, run locally, preview intermediate data, and export standalone Python scripts. Open-source alpha, AGPL-3.0.
https://ciaren.com/
Ciaren
Ciaren: open-source visual ETL for pandas and Polars
Ciaren is an open source visual ETL tool. Build data flows on a local canvas, preview each step, and export readable pandas or Polars code.
❤1
livenerf
A long-running, deterministic-as-possible benchmark for detecting whether a frontier model gets quietly worse after launch.
https://github.com/ninjahawk/livenerf
A long-running, deterministic-as-possible benchmark for detecting whether a frontier model gets quietly worse after launch.
https://github.com/ninjahawk/livenerf
GitHub
GitHub - ninjahawk/livenerf: Benchmark for tracking model capability after release.
Benchmark for tracking model capability after release. - 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
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
YouTube
Python Algorithmic Trading Course – Massive, SnapTrade & Alpaca Integrations
Learn how to build a complete algorithmic paper trading system from the ground up using Python and Django. This hands-on course guides you through configuring a pipeline that connects market data from Massive, manages portfolios securely via SnapTrade, and…