Introducing Retrace: Record Production Python. Debug It Backwards.
Retrace introduces deterministic record-replay debugging for Python, allowing production executions to be recorded with very low overhead and replayed locally in VS Code with reverse debugging support. Instead of relying on logs or reproducing failures, it records the boundary between application code and nondeterministic systems like APIs, databases, threads, and file I/O, enabling deve...
https://retracesoftware.com/blog/introducing-retrace/
Retrace introduces deterministic record-replay debugging for Python, allowing production executions to be recorded with very low overhead and replayed locally in VS Code with reverse debugging support. Instead of relying on logs or reproducing failures, it records the boundary between application code and nondeterministic systems like APIs, databases, threads, and file I/O, enabling deve...
https://retracesoftware.com/blog/introducing-retrace/
Retrace | Explore your production, distributed application in real-time
Retrace: Deterministic Record-Replay for Python
Record production crashes, replay them locally in VS Code. <1% overhead, open source.
The Simplest MCP Example Possible in Python
The post shows the simplest Python example of MCP (Model Context Protocol) for connecting a local LLM like Llama 3.2 to tools. It uses fastmcp and ollama packages with two scripts: mcpserver.py for time/date tools and ollamaclient.py to run the chat loop.
https://inventwithpython.com/blog/basic-mcp-python-example.html
The post shows the simplest Python example of MCP (Model Context Protocol) for connecting a local LLM like Llama 3.2 to tools. It uses fastmcp and ollama packages with two scripts: mcpserver.py for time/date tools and ollamaclient.py to run the chat loop.
https://inventwithpython.com/blog/basic-mcp-python-example.html
Inventwithpython
The Simplest MCP Example Possible in Python
Labb - UI components for Django
This video looks at labb - a UI component library for Django apps that builds on top of TailwindCSS, DaisyUI, django-cotton and Alpine.js. This library has over 70 components that can be dropped into Django projects, and makes it easy to add custom themes, for example using DaisyUI Theme Generator.
https://www.youtube.com/watch?v=ZZd7cvbJ-1w
This video looks at labb - a UI component library for Django apps that builds on top of TailwindCSS, DaisyUI, django-cotton and Alpine.js. This library has over 70 components that can be dropped into Django projects, and makes it easy to add custom themes, for example using DaisyUI Theme Generator.
https://www.youtube.com/watch?v=ZZd7cvbJ-1w
YouTube
Labb - UI components for Django (built on Tailwind, django-cotton, Alpine.js!)
▶ Django & HTMX FULL COURSE: https://www.udemy.com/course/django-htmx-hypermedia-web-apps/?couponCode=BUGBYTES-MAY
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☕️ 𝗕𝘂𝘆 𝗺𝗲 𝗮 𝗰𝗼𝗳𝗳𝗲𝗲:
To support the…
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DRY Often Makes Your Code Worse
A DRY refactor can make code worse when it removes duplication by hiding different behaviors behind flags and shared abstractions. Better refactoring focuses on separating responsibilities and making behavior clear, not just making the code look smaller.
https://www.youtube.com/watch?v=GmlZBdKhl9Y
A DRY refactor can make code worse when it removes duplication by hiding different behaviors behind flags and shared abstractions. Better refactoring focuses on separating responsibilities and making behavior clear, not just making the code look smaller.
https://www.youtube.com/watch?v=GmlZBdKhl9Y
YouTube
DRY Often Makes Your Code Worse
🧱 Build software that lasts. Join the Software Design Mastery waiting list → https://arjan.codes/mastery.
In this video, I show how a “clean” DRY refactor can actually make your code worse. Instead of improving the design, it introduces hidden complexity…
In this video, I show how a “clean” DRY refactor can actually make your code worse. Instead of improving the design, it introduces hidden complexity…
aidlc-workflows
AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI coding agents.
https://github.com/awslabs/aidlc-workflows
AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI coding agents.
https://github.com/awslabs/aidlc-workflows
GitHub
GitHub - awslabs/aidlc-workflows: AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI coding agents
AI-Driven Life Cycle (AI-DLC) adaptive workflow steering rules for AI coding agents - awslabs/aidlc-workflows
❤1
In-Kernel Broadcast Optimization: Co-Designing Kernels for RecSys Inference
In-Kernel Broadcast Optimization (IKBO) eliminates redundant user-embedding replication by fusing broadcast logic directly into interaction kernels, significantly reducing memory bandwidth and compute waste. This co-design approach delivers up to a two-thirds reduction in latency across Meta's recommendation stack, optimized for high-performance hardware like NVIDIA H100 and Meta’s MTIA.
https://pytorch.org/blog/in-kernel-broadcast-optimization-co-designing-kernels-for-recsys-inference/
In-Kernel Broadcast Optimization (IKBO) eliminates redundant user-embedding replication by fusing broadcast logic directly into interaction kernels, significantly reducing memory bandwidth and compute waste. This co-design approach delivers up to a two-thirds reduction in latency across Meta's recommendation stack, optimized for high-performance hardware like NVIDIA H100 and Meta’s MTIA.
https://pytorch.org/blog/in-kernel-broadcast-optimization-co-designing-kernels-for-recsys-inference/
Numexpr: Fast numerical array expression evaluator for Python, NumPy, Pandas
https://github.com/pydata/numexpr
https://github.com/pydata/numexpr
GitHub
GitHub - pydata/numexpr: Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more
Fast numerical array expression evaluator for Python, NumPy, Pandas, PyTables and more - pydata/numexpr
nb-cli: A Command-Line Interface for AI Agents and Notebook Automation
The article introduces nb-cli, a Rust-built command-line interface engineered to let developers and AI agents programmatically read, write, and execute Jupyter notebooks without needing a running server. It highlights an AI-optimized Markdown format that exposes structured token-efficient code and cell metadata, enabling language models and automated CI/CD pipelines to manipulate noteboo...
https://blog.jupyter.org/nb-cli-a-command-line-interface-for-ai-agents-and-notebook-automation-996ad7edacd9
The article introduces nb-cli, a Rust-built command-line interface engineered to let developers and AI agents programmatically read, write, and execute Jupyter notebooks without needing a running server. It highlights an AI-optimized Markdown format that exposes structured token-efficient code and cell metadata, enabling language models and automated CI/CD pipelines to manipulate noteboo...
https://blog.jupyter.org/nb-cli-a-command-line-interface-for-ai-agents-and-notebook-automation-996ad7edacd9
Medium
nb-cli: A Command-Line Interface for AI Agents and Notebook Automation
The rise of AI coding agents has transformed how we think about developer tools. Large language models like Claude, GPT, and others are…
semble
Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read.
https://github.com/MinishLab/semble
Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read.
https://github.com/MinishLab/semble
GitHub
GitHub - MinishLab/semble: Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read
Fast and Accurate Code Search for Agents. Uses ~98% fewer tokens than grep+read - MinishLab/semble
django-completion
Project-aware tab completion for Django's manage.py.
https://github.com/soldatov-ss/django-completion
Project-aware tab completion for Django's manage.py.
https://github.com/soldatov-ss/django-completion
GitHub
GitHub - soldatov-ss/django-completion: Tab completion for Django's manage.py — commands, app labels, options, and migration targets…
Tab completion for Django's manage.py — commands, app labels, options, and migration targets in bash and zsh - soldatov-ss/django-completion
CodeBoarding
Interactive architecture diagrams for codebases.
https://github.com/CodeBoarding/CodeBoarding
Interactive architecture diagrams for codebases.
https://github.com/CodeBoarding/CodeBoarding
GitHub
GitHub - CodeBoarding/CodeBoarding: Interactive architecture diagrams for codebases
Interactive architecture diagrams for codebases. Contribute to CodeBoarding/CodeBoarding development by creating an account on GitHub.
geo-seo-claude
GEO-first, SEO-supported. Optimize websites for AI-powered search engines.
https://github.com/zubair-trabzada/geo-seo-claude
GEO-first, SEO-supported. Optimize websites for AI-powered search engines.
https://github.com/zubair-trabzada/geo-seo-claude
GitHub
GitHub - zubair-trabzada/geo-seo-claude: GEO-first SEO skill for Claude Code. Comprehensive AI search optimization for any website…
GEO-first SEO skill for Claude Code. Comprehensive AI search optimization for any website — citability scoring, AI crawler analysis, brand authority, schema markup, platform-specific optimization, ...
django-q2 - Background Tasks in Django (Celery alternative!)
The video explores django-q2, a lightweight alternative to Celery for handling background jobs and asynchronous task processing in Django applications. It highlights how django-q2 simplifies setup and integration while providing built-in support for async workers, scheduled tasks, and Django Admin management features.
https://www.youtube.com/watch?v=0hRDCrxfHug
The video explores django-q2, a lightweight alternative to Celery for handling background jobs and asynchronous task processing in Django applications. It highlights how django-q2 simplifies setup and integration while providing built-in support for async workers, scheduled tasks, and Django Admin management features.
https://www.youtube.com/watch?v=0hRDCrxfHug
YouTube
django-q2 - Background Tasks in Django (Celery alternative!)
▶ Django & HTMX FULL COURSE: https://www.udemy.com/course/django-htmx-hypermedia-web-apps/?couponCode=BUGBYTES-MAY
🙏 Join our channel to get access to perks:
https://www.youtube.com/channel/UCTwxaBjziKfy6y_uWu30orA/join
☕️ 𝗕𝘂𝘆 𝗺𝗲 𝗮 𝗰𝗼𝗳𝗳𝗲𝗲:
To support the…
🙏 Join our channel to get access to perks:
https://www.youtube.com/channel/UCTwxaBjziKfy6y_uWu30orA/join
☕️ 𝗕𝘂𝘆 𝗺𝗲 𝗮 𝗰𝗼𝗳𝗳𝗲𝗲:
To support the…
Python 3.15: features that didn't make the headlines
https://blog.changs.co.uk/python-315-features-that-didnt-make-the-headlines.html
https://blog.changs.co.uk/python-315-features-that-didnt-make-the-headlines.html
blog.changs.co.uk
Python 3.15: features that didn't make the headlines
It's that time of the year again, a new version of Python is just around the corner. With the Python 3.15.0b1 feature freeze, we know what's...
Watching for file changes on macOS
This post details how to build a lightweight tool for monitoring file system activity on macOS without relying on third-party dependencies. It explains how to implement a Swift script that interfaces directly with Apple's native FSEvents API and forwards those live change notifications to a Python environment via stdout.
https://alexwlchan.net/2026/watch-files-on-macos/
This post details how to build a lightweight tool for monitoring file system activity on macOS without relying on third-party dependencies. It explains how to implement a Swift script that interfaces directly with Apple's native FSEvents API and forwards those live change notifications to a Python environment via stdout.
https://alexwlchan.net/2026/watch-files-on-macos/
alexwlchan.net
Watching for file changes on macOS
I've written a Swift script that uses the FSEvents API to get notified of file changes, then I'm using stdout as a bridge to forward those notifications to Python.
Agent Hooks: Deterministic Control for Agent Workflows
The post is about agent hooks as a control layer that makes AI behavior more deterministic by enforcing rules at specific lifecycle points, instead of relying on prompts alone. It emphasizes using hooks for policy enforcement, validation, and observability so teams can block bad actions, add guardrails, and make agent workflows more production-safe.
https://nader.substack.com/p/agent-hooks-deterministic-control
The post is about agent hooks as a control layer that makes AI behavior more deterministic by enforcing rules at specific lifecycle points, instead of relying on prompts alone. It emphasizes using hooks for policy enforcement, validation, and observability so teams can block bad actions, add guardrails, and make agent workflows more production-safe.
https://nader.substack.com/p/agent-hooks-deterministic-control
Substack
Agent Hooks: Deterministic Control for Agent Workflows
Also available as Markdown on GitHub. Example code available here.
Is UV still worth learning/switching to now that it's owned by OpenAI?
https://www.reddit.com/r/Python/comments/1tjonyf/is_uv_still_worth_learningswitching_to_now_that/
https://www.reddit.com/r/Python/comments/1tjonyf/is_uv_still_worth_learningswitching_to_now_that/
Reddit
From the Python community on Reddit
Explore this post and more from the Python community
Feloxi
Self-hosted monitoring for Python task queues. Live dashboards, searchable task history, and alerting that fires.
https://github.com/thesaadmirza/feloxi
Self-hosted monitoring for Python task queues. Live dashboards, searchable task history, and alerting that fires.
https://github.com/thesaadmirza/feloxi
GitHub
GitHub - thesaadmirza/feloxi: Real-time Celery task queue monitoring — Rust/Axum backend, Next.js dashboard, ClickHouse analytics
Real-time Celery task queue monitoring — Rust/Axum backend, Next.js dashboard, ClickHouse analytics - thesaadmirza/feloxi
HKUDS / ViMax
"ViMax: Agentic Video Generation (Director, Screenwriter, Producer, and Video Generator All-in-One)"
https://github.com/HKUDS/ViMax
"ViMax: Agentic Video Generation (Director, Screenwriter, Producer, and Video Generator All-in-One)"
https://github.com/HKUDS/ViMax
GitHub
GitHub - HKUDS/ViMax: "ViMax: Agentic Video Generation (Director, Screenwriter, Producer, and Video Generator All-in-One)"
"ViMax: Agentic Video Generation (Director, Screenwriter, Producer, and Video Generator All-in-One)" - HKUDS/ViMax