agentscope-ai / ReMe
ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.
https://github.com/agentscope-ai/ReMe
ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.
https://github.com/agentscope-ai/ReMe
GitHub
GitHub - agentscope-ai/ReMe: ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.
ReMe: Memory Management Kit for Agents - Remember Me, Refine Me. - agentscope-ai/ReMe
Pydantic AI - Intro to Agentic AI with Pydantic AI framework
We'll look at using Pydantic AI to build agent-based workflows, starting with simple fundamentals, and building up to more complex examples that use vector databases, RAG, multi-agent workflows and more.
https://www.youtube.com/playlist?list=PL-2EBeDYMIbSWGoDzOFm33_5W_ShO-VIi
We'll look at using Pydantic AI to build agent-based workflows, starting with simple fundamentals, and building up to more complex examples that use vector databases, RAG, multi-agent workflows and more.
https://www.youtube.com/playlist?list=PL-2EBeDYMIbSWGoDzOFm33_5W_ShO-VIi
Oxyde ORM
A type-safe, Pydantic-centric asynchronous ORM with a high-performance Rust core designed for clarity, speed, and reliability.
https://github.com/mr-fatalyst/oxyde
A type-safe, Pydantic-centric asynchronous ORM with a high-performance Rust core designed for clarity, speed, and reliability.
https://github.com/mr-fatalyst/oxyde
GitHub
GitHub - mr-fatalyst/oxyde: Oxyde ORM is a type-safe, Pydantic-centric asynchronous ORM with a high-performance Rust core designed…
Oxyde ORM is a type-safe, Pydantic-centric asynchronous ORM with a high-performance Rust core designed for clarity, speed, and reliability. - mr-fatalyst/oxyde
Fixed Python autocomplete
The post suggests that heavy LSP and static analysis approaches are unnecessary for many common autocomplete scenarios. It shows a lightweight, pattern-based approach can deliver faster, more responsive suggestions without full semantic analysis.
https://matan-h.com/better-python-autocomplete
The post suggests that heavy LSP and static analysis approaches are unnecessary for many common autocomplete scenarios. It shows a lightweight, pattern-based approach can deliver faster, more responsive suggestions without full semantic analysis.
https://matan-h.com/better-python-autocomplete
Matan-h
Fixed Python autocomplete
I fixed autocomplete sorting
Smello
A developer tool that captures outgoing HTTP requests from your code and displays them in a local web dashboard.
https://github.com/smelloscope/smello
A developer tool that captures outgoing HTTP requests from your code and displays them in a local web dashboard.
https://github.com/smelloscope/smello
GitHub
GitHub - smelloscope/smello: A developer tool that captures outgoing HTTP requests from your code and displays them in a local…
A developer tool that captures outgoing HTTP requests from your code and displays them in a local web dashboard - smelloscope/smello
claude-howto
A visual, example-driven guide to Claude Code - from basic concepts to advanced agents, with copy-paste templates that bring immediate value.
https://github.com/luongnv89/claude-howto
A visual, example-driven guide to Claude Code - from basic concepts to advanced agents, with copy-paste templates that bring immediate value.
https://github.com/luongnv89/claude-howto
GitHub
GitHub - luongnv89/claude-howto: A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy…
A visual, example-driven guide to Claude Code — from basic concepts to advanced agents, with copy-paste templates that bring immediate value. - luongnv89/claude-howto
How Clean Code Turns Into Overengineering
This video is about how code that looks clean can still hide a bad design, and why overusing tiny abstractions can make a program harder to understand and change. It refactors a Python reporting example by simplifying the structure, making the pipeline explicit, and focusing on cohesion over smallness.
https://www.youtube.com/watch?v=U4sPMwAiXco
This video is about how code that looks clean can still hide a bad design, and why overusing tiny abstractions can make a program harder to understand and change. It refactors a Python reporting example by simplifying the structure, making the pipeline explicit, and focusing on cohesion over smallness.
https://www.youtube.com/watch?v=U4sPMwAiXco
YouTube
Why “Clean Code” Often Creates Worse Designs
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In this video, I refactor a small Python program that looks clean on the surface but hides a messy design underneath. Step by step, I remove unnecessary…
In this video, I refactor a small Python program that looks clean on the surface but hides a messy design underneath. Step by step, I remove unnecessary…
Why pylock.toml includes digital attestations
Including digital attestations in pylock.toml allows developers to verify the origin and integrity of dependencies, not just their versions and hashes, improving protection against supply chain attacks. The broader point is that modern package security requires provenance, not just reproducibility, so lock files are evolving from “what to install” into “what can be trusted to install.”
https://snarky.ca/why-pylock-toml-includes-digital-attestations/
Including digital attestations in pylock.toml allows developers to verify the origin and integrity of dependencies, not just their versions and hashes, improving protection against supply chain attacks. The broader point is that modern package security requires provenance, not just reproducibility, so lock files are evolving from “what to install” into “what can be trusted to install.”
https://snarky.ca/why-pylock-toml-includes-digital-attestations/
Tall, Snarky Canadian
Why pylock.toml includes digital attestations
A Python project got hacked where malicious releases were directly uploaded to PyPI. I said on Mastodon that had the project used trusted publishing with digital attestations, then people using a pylock.toml file would have noticed something odd was going…