DynamoDB crash course: part 1 – philosophy

This is part one of a series covering core DynamoDB concepts and patterns, all the way up to single-table design; the goal is to get you to understand idiomatic usage and trade-offs in under an hour. Today, we're looking at what DynamoDB is and why it is that way.

https://death.andgravity.com/dynamodb

mini-swe-agent

The 100 line AI agent that solves GitHub issues or helps you in your command line. Radically simple, no huge configs, no giant monorepo—but scores >74% on SWE-bench verified!

https://github.com/SWE-agent/mini-swe-agent

Creating Reddit Summaries with URL Context and Gemini

Raymond Camden demonstrates using the Gemini API's "URL Context" tool to scrape and summarize Reddit threads without relying on Reddit's restricted APIs. He also shares a "double-prompting" workaround to convert the unstructured URL summaries into a precise JSON format, since Gemini currently disables structured output when using the URL tool.

https://www.raymondcamden.com/2026/02/09/creating-reddit-summaries-with-url-context-and-gemini

distil-text2sql

Query your data in plain English with a fine-tuned Text2SQL model.

https://github.com/distil-labs/distil-text2sql

ACE-Step-1.5

The most powerful local music generation model that outperforms most commercial alternatives

https://github.com/ace-step/ACE-Step-1.5

knowledge-work-plugins

Open source repository of plugins primarily intended for knowledge workers to use in Claude Cowork.

https://github.com/anthropics/knowledge-work-plugins

How the Self-Driving Tech Stack Works

The article breaks down the core components of an autonomous driving stack, including sensing (cameras, lidar, radar), perception (object detection and tracking), planning (behavior and trajectory), and control (actuation and safety). It explains how these layers interact in real time, the trade-offs between different sensors and algorithms, and practical considerations for building and ...

https://cardog.app/blog/autonomous-driving-stack-technical-guide

PyNote

A lightweight, browser-based Python notebook editor that runs entirely client-side using WebAssembly (via Pyodide), so it needs no server or installation.

https://github.com/bouzidanas/pynote-notebook-editor

copier-astral

Fast Python project template with Astral's toolchain (uv, ruff, ty) + pytest, MkDocs, Typer, GitHub Actions, Docker

https://github.com/ritwiktiwari/copier-astral

nanobot

nanobot is an ultra-lightweight personal AI assistant inspired by Clawdbot. Delivers core agent functionality in just ~4,000 lines of code — 99% smaller than Clawdbot's 430k+ lines.

https://github.com/HKUDS/nanobot

So... Postgres is now basically a search engine? (And it's free)

This video demonstrates how to turn PostgreSQL into a powerful, personalized search engine using the open-source PG text search extension (based on the BM25 algorithm). It provides a technical walkthrough on using Claude Code and UV to build a Python-based system for scraping web content, tracking links, and managing a private knowledge base.

https://www.youtube.com/watch?v=zrokmIR9Ytg

Expanse

Expanse is a modern Python web application framework focused on developer experience that offers features like transparent dependency injection, a SQLAlchemy-powered database component, and conventions to help build scalable web apps quickly.

https://github.com/expanse-framework/expanse

qrt

This project explores data transfer using a screen-to-camera approach. The idea is simple: encode information into a sequence of QR codes, display them as a video on a screen, and then use a camera to capture and decode the video frames to retrieve the original data.

https://github.com/smyrgeorge/qrt

PentestAgent

PentestAgent is an AI agent framework for black-box security testing, supporting bug bounty, red-team, and penetration testing workflows.

https://github.com/GH05TCREW/pentestagent

10 Python Features You’re Not Using (But Really Should)

The video highlights lesser-known but powerful Python features that can significantly improve code readability, safety, and performance. Through practical before-and-after examples, it shows experienced Python developers how small language features can meaningfully level up real-world code.

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

Django: profile memory usage with Memray

The article shows how to profile Python-based Django apps using Memray to find memory leaks and inefficient allocations by capturing allocation snapshots and comparing them over time. It walks through setup, running profiles locally, analyzing results, and using traces to make targeted improvements that reduce memory usage and improve performance.

https://adamj.eu/tech/2026/01/29/django-profile-memray/