rsloop

An event loop for asyncio written in Rust.

https://github.com/RustedBytes/rsloop

justx

A TUI command launcher built on top of just. Define recipes once, run them anywhere.

https://github.com/fpgmaas/justx

agentscope-ai / ReMe

ReMe: Memory Management Kit for Agents - Remember Me, Refine Me.

https://github.com/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

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

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

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

diffly

Utility package for comparing polars data frames.

https://github.com/Quantco/diffly

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

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

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/

NumPy as Synth Engine

NumPy can be used as a real time sound synthesis engine, generating all audio directly from mathematical functions like waves, noise, and filters without any pre recorded samples. The broader idea is that powerful general purpose tools like NumPy can be pushed far beyond their intended use, enabling complex systems like music generation through pure computation.

https://kennethreitz.org/essays/2026-03-29-numpy_as_synth_engine

vectorize-io / hindsight

Hindsight: Agent Memory That Learns

https://github.com/vectorize-io/hindsight

Building a Navier-Stokes Solver in Python from Scratch: Simulating Airflow

A hands-on guide to implementing CFD with NumPy, from discretization to airflow simulation around a bird's wing

https://towardsdatascience.com/building-a-navier-stokes-solver-in-python-from-scratch-simulating-airflow/

django-modern-rest

Modern REST framework for Django with types and async support!

https://github.com/wemake-services/django-modern-rest

Autograd and Mutation

How does PyTorch autograd deal with mutation? In particular, what happens when a mutation occurs on a view, which aliases with some other tensor? In 2017, Sam Gross implemented support for in-place operations on views, but the details of which have never been described in plain English… until now.

https://blog.ezyang.com/2026/03/autograd-and-mutation/

ATLAS

Adaptive Test-time Learning and Autonomous Specialization.

https://github.com/itigges22/ATLAS

From zero to a RAG system: successes and failures

Building a production RAG system is far more about data pipelines, indexing strategy, and infrastructure tradeoffs than model choice, with most failures coming from scaling, retrieval quality, and compute constraints. The key lesson is that RAG success depends on iterative engineering and system design discipline, not just plugging in an LLM, with real-world performance shaped by bottlen...

https://en.andros.dev/blog/aa31d744/from-zero-to-a-rag-system-successes-and-failures/

OpenSpace

Make Your Agents: Smarter, Low-Cost, Self-Evolving.

https://github.com/HKUDS/OpenSpace