labs-OO-Agents

NVIDIA Object Oriented Agents: the Pythonic way to build AI Agents.

https://github.com/nvidia-nemo/labs-OO-Agents

Django 6.1 released

Django 6.1 is now available with features including model field fetch modes, database-level ForeignKey delete options, and dictionary-based email settings. Django 6.0 has ended mainstream support and will receive only security and data-loss fixes until April 2027.

https://www.djangoproject.com/weblog/2026/aug/05/django-61-released/

Categorization with NLP

A practical look at building grocery categorization without machine learning, using NLP techniques such as stemming, n-grams, syllable splitting, and spell checking. The author shows how a hand-crafted Python algorithm handles messy real-world inputs and edge cases when training data is scarce.

https://softwaremaniacs.org/blog/2026/07/30/categorization-with-nlp/en/

How to Conquer Concurrency in Python

A practical guide to Python concurrency that builds from OS fundamentals, processes, threads, race conditions, and the GIL to choosing between asyncio, threading, and multiprocessing. It also explains concurrency vs. parallelism, CPU vs. GPU tradeoffs, and how profiling and strong mental models help avoid common performance mistakes.

https://www.youtube.com/watch?v=chrOym38pw4

Wheels, Bottles and Images

Simon Willison packaged a Go CLI inside Python wheels, showing that PyPI can distribute platform-specific binaries containing no Python at all. The article compares wheels with Homebrew bottles and OCI images, highlighting their shared model of immutable artifacts and client-side platform selection, along with growing convergence in registry infrastructure and Sigstore attestations.

https://nesbitt.io/2026/07/30/wheels-bottles-images.html

arc53 / DocsGPT

Private AI platform for agents, assistants and enterprise search. Built-in Agent Builder, Deep research, Document analysis, Multi-model support, and API connectivity for agents.

https://github.com/arc53/DocsGPT

Gleam for Python Programmers

A practical guide to Gleam for Python programmers, explaining its syntax, static typing, immutability, pattern matching, and functional programming model through Python comparisons. It gives Python developers a quick way to understand how Gleam differs while building on concepts they already know.

https://third-bit.com/gl4py/

Python: how time-machine is O(1) where freezegun is O(n)

A benchmark shows Python’s time-machine library stays O(1) when mocking time, while freezegun scales O(n) with the number of loaded module attributes and becomes dramatically slower as projects grow. The difference comes from time-machine swapping CPython function pointers directly, while freezegun scans loaded modules to replace references to date and time functions.

https://adamj.eu/tech/2026/08/03/python-time-machine-o1-freezegun-on/

Celery: from first task to advanced recipes

The article introduces Celery, a distributed task queue for Python, then walks through practical patterns for running, routing, batching, timing out, and retrying asynchronous tasks. It also covers advanced recipes such as preventing parallel execution with Redis locks and integrating Celery tasks with Python’s async/await workflows.

https://sgolev.github.io/blog/2026-07-28-celery-recipes/

SkyRL

A Modular Full-stack RL Library for LLMs.

https://github.com/NovaSky-AI/SkyRL

Building an Advanced Agentic Harness

From a single pilot to an air campaign: planning, parallelism, memory, verification, and observability for production-shaped agents.

https://data4sci.com/blog/building-an-advanced-agentic-harness

Introducing Flex: Let the Model Write the Code

DSPy’s new Flex module allows optimizers like GEPA to improve programs by rewriting both their instructions and underlying Python code. This lets the optimizer route easy cases to fast deterministic code while reserving model calls for ambiguity, significantly reducing cost and latency while improving accuracy.

https://www.cmpnd.ai/blog/let-the-model-write-the-code.html

Agent Memory Guard

Agent Memory Guard is an OWASP Incubator Project that prevents AI agents from being weaponized through their own memory. It implements MITRE ATLAS mitigation AML.M0031 (Memory Hardening) to defend against context poisoning attacks (AML.T0080).

https://github.com/OWASP/www-project-agent-memory-guard

Model Genome: Fingerprinting Whether an LLM Was Trained From Scratch or Derived

Outsiders can assess whether a foundation model was truly built from scratch by analyzing architecture configurations, tokenizer overlap, and weight embeddings using a reproducible fingerprinting pipeline. While architecture and tokenizer artifacts provide the strongest evidence, weight analysis has limitations and cannot cleanly distinguish continued pretraining from training from scratch.

https://huggingface.co/blog/mayafree/model-dna

semantica

Graph-Native Infrastructure for Context and Accountable AI Systems.

https://github.com/semantica-agi/semantica

Python: introducing emojet, a fast emoji lookup library

emojet is an emoji library for Python: it converts between emoji and their names, in both directions, plus the searching and lookup functions that go with that. It covers the core API of the emoji package, a library that been available for this job since 2014, using the same names and the same data. The difference is that emojet does the work in Rust, running 3.5x faster for conversion, ...

https://adamj.eu/tech/2026/08/12/python-introducing-emojet/

earthtojake / text-to-cad

A library of agent skills for CAD, CAE and CAM

https://github.com/earthtojake/text-to-cad

A quick look at zero-knowledge proofs

In this post, the authors break down non-cryptocurrency zero-knowledge proofs (ZKPs) using graph theory and Python. By implementing Protocol 4 from Goldreich, Micali, and Wigderson, they show how a prover uses randomized color permutations, nonces, and cryptographic hashes to iteratively prove they hold a valid 3-coloring for a graph without revealing the actual solution to the verifier.

https://bernsteinbear.com/blog/zkp/