VectorSmith

VectorSmith turns vector database collections into LLM tools and exposes them through an MCP server. Using the existing collection schemas and a simple YAML configuration, you can make your vector DB data available to AI agents without writing custom tool and MCP integrations from scratch.

https://github.com/kjgpta/vectorsmith

Fuzzy String Matching in Django and PostgreSQL

Django and PostgreSQL can handle fuzzy name matching with Soundex, Daitch-Mokotoff, Levenshtein distance, and trigram similarity, each suited to different kinds of misspellings and phonetic variation. The article shows how to expose these techniques through Django ORM expressions and pair them with the right PostgreSQL indexes for efficient search at scale.

https://www.caktusgroup.com/blog/2026/08/21/fuzzy-string-matching-django-postgresql/

IPython is All You Need

Nathan Cooper shows how to turn IPython into a full terminal shell that runs Bash commands without !, displays images, and retains rich execution history. He then adds an AI assistant that can understand shell context, outputs, images, and errors, and safely execute Python and Bash commands through allowlisted tools.

https://nathancooper.io/blog/2026-08-10-ipython-is-all-you-need

Training and Finetuning Multi-Vector Embedding Models with Sentence Transformers

Sentence Transformers 6.0 introduces MultiVectorEncoder for training and fine-tuning ColBERT-style retrieval models in Python. The tutorial walks through the full training stack and shows a domain-tuned medical model outperforming over 50 retrieval model configurations on the author’s evaluation.

https://huggingface.co/blog/train-multi-vector-encoder

Tech-OA-Interview-Questions

Daily updated list of Tech Company OAs and Interview Problems. Save your time from finding them all over the internet.

https://github.com/perixtar/Tech-OA-Interview-Questions

linecast

Weather, tides, the sun, the moon, and maps, in your terminal. The Old Farmer's Almanac meets Minitel.

https://github.com/ashuttl/linecast

microduck_rl

RL training environments for Microduck (mjlab)

https://github.com/pollen-robotics/microduck_rl

Securo

Open-source personal finance manager. Self-hosted, privacy-first.

https://github.com/securo-finance/securo

FlightScnr_Pi

Desktop flight and marine radar: a real-time aircraft and marine vessel tracker powered by a Raspberry Pi and 4" round screen.

https://github.com/yashmulgaonkar/FlightScnr_Pi

Unit testing with wrapture

Wrapture takes a different approach to Python unit testing by wrapping real code instead of replacing it with mocks, preserving real arguments, return values, call structure, and signatures. It makes it easier to test internal calls, inject failures, verify call order, and assert what did or did not happen without losing the behavior of the code under test.

https://grahamdumpleton.me/posts/2026/09/unit-testing-with-wrapture/

You Think This Is Good OOP… It’s Not

Six common OOP practices can make code harder to maintain, including overusing inheritance, building unnecessary class hierarchies, breaking the Liskov Substitution Principle, and abstracting too early. The video explains why these patterns backfire and shows simpler alternatives that keep object-oriented code more flexible, understandable, and maintainable.

https://www.youtube.com/watch?v=RqcEK7sWesQ

AutoSaddler

Automatic Harness Optimization with Durable Updates from Agent Execution Traces.

https://github.com/microsoft/AutoSaddler

Django API Overview - DRF, django-ninja, django-bolt and django-modern-rest

In this video, we'll look at Django API options, exploring the best modern packages for API-driven development in Django. We will take a look at Django REST Framework, django-ninja, django-bolt, and django-modern-rest - looking at the pros/cons of each.

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

When to use NotImplemented

When should you return NotImplemented from a dunder method? Why not return False or raise an exception instead?

https://www.pythonmorsels.com/when-to-use-notimplemented/

Tracing np.add, all the way down

The author traces a simple np.add(a, b) call through NumPy’s internals, from Python argument handling and dtype dispatch to iteration strategy and the final SIMD-optimized C loop. Along the way, the post explores ufunc overrides, type promotion, dispatch caching, memory layout, GIL release, and CPU-specific optimizations that determine how NumPy actually performs an addition.

https://blog.veitheller.de/numpy.html