laya

Laya is a fast multilingual decision engine that produces typed classifications, scores, and probabilities in a single forward pass.

https://github.com/NandhaKishorM/laya

Django: serve a security.txt file

The post shows how to serve a standards-compliant security.txt file in Django so security researchers know how to report vulnerabilities. It also adds tests and a Django system check to validate the file and warn before its required Expires field becomes stale.

https://adamj.eu/tech/2026/10/01/django-security-txt/

ollaya

Run open decision models locally: pull and serve Laya, decider, NLI and GLiClass behind a TypeSafe-compatible API. Ollama for decision models.

https://github.com/ollaya-dev/ollaya

Language Models for Text Classification: From Bag-of-Words to Jev

A concise history of text classification, from bag-of-words and neural networks to transformers and modern LLM-based approaches. It also introduces Jev and examines where specialized classifiers can still outperform or complement general-purpose language models.

https://magazine.sebastianraschka.com/p/classifier-history-and-jev

Building an Ultra-High Throughput AI-SQL Engine

Quail is an open-source AI-SQL engine that jointly optimizes query planning and LLM inference to reduce redundant model work and keep GPUs busy when running large numbers of AI-powered database operations. In benchmarks across 29 queries, it was 1.84x faster on average than tuned vLLM baselines and up to 14.04x faster on a workload with substantial KV-cache reuse.

https://fsdatalab.github.io/blog/introducing-quail/

AI Engineering from Scratch

Build AI systems from first principles with 523 free, open-source lessons spanning math, models, tokenizers, agents, evaluation, and production.

https://aiengineeringfromscratch.com/index.html

EvoOntology: A Self-Evolving Ontology Layer for Data Agents

EvoOntology is the first self-evolving ontology layer for data agents, aiming to bridge the agent-data gap over heterogeneous tables, files, and databases.

https://github.com/ruc-datalab/EvoOntology

How to Rotate Proxies in Python with Requests (2026 Guide)

Practical Requests guide covering proxy authentication, rotation, sticky sessions, error handling, exit-IP checks, and SOCKS5 remote DNS.

https://unlimitedresidentialproxy.com/blog/how-to-rotate-proxies-python

Taste-Bench

Taste-Bench measures the taste of an LLM agent: its ability to choose the better direction at a real decision fork in a long-horizon task.

https://github.com/wbopan/tastebench

type_enforced

Fast runtime type enforcement for Python 3.11+ type annotations. Zero dependencies and uncompromising performance.

https://github.com/connor-makowski/type_enforced

Lemma

The open-source workspace where humans and AI agents work as one team.

https://github.com/lemma-work/lemma-platform

Making np.searchsorted up to 25× Faster in NumPy 2.5

The article speeds up binary search by batching independent searches with NumPy’s vectorized operations, then reformulates the algorithm to use only O(1) extra memory. After porting it to C++, the approach achieves up to a 25× speedup over NumPy 2.4’s implementation.

https://blog.scientific-python.org/numpy/searchsorted/

Building a Faster (Rust-Based) Python Driver for ScyllaDB

ScyllaDB is building a new Python driver on top of its Rust driver using PyO3, with most serialization, deserialization, request execution, and concurrency handled in Rust for better performance and maintainability. Benchmarks show especially large gains under concurrent workloads, while the project still needs production hardening, broader feature coverage, and a compatibility layer for...

https://www.scylladb.com/2026/09/28/building-a-faster-rust-based-python-driver/

AI Development on Windows: from PyTorch and llama.cpp to Windows ML

Microsoft is expanding local AI development on Windows with experimental GGUF support in Windows ML via llama.cpp, new native inference APIs, and an OpenAI-compatible endpoint for running models locally. The broader stack also adds native PyTorch and Triton support on Windows Arm64, giving developers a path to train and optimize models with familiar tools and deploy them through Windows ...

https://devblogs.microsoft.com/foundry-on-windows/build-on-winml-oct-7-26/

ldraw-nova

Agent tooling for generative LEGO models building, built with Astra and Opus 5.5, powered by Jev

https://github.com/anteloc/ldraw-nova

Orchestrating Disposable Browser

Glazer replaced race-prone parallel startup with readiness signals between Python and Docker containers, allowing components to start concurrently without assuming that running means ready. That design cut cold starts from 8–10 seconds to about 4.5 seconds, and caching the Tor data directory brought startup down to a consistent 1.9–2.1 seconds.

https://blog.glazer.ee/posts/building-glazer-disposable-browser/

The things you own end up owning you

NumPy is being ported toward Python’s Limited API so it could eventually ship abi3 wheels that work across multiple Python versions, reducing the large per-version wheel matrix and easing support for new Python releases. The same work is also pushing NumPy toward subinterpreter support by replacing static types and global state with heap types and per-module state, though multiarray rema...

https://labs.quansight.org/blog/limited_api_numpy

Build & Train a GLM-5.3-Flash Model From Scratch with Python

This tutorial demonstrates how to build and train a compact 25-million parameter multimodal language model from scratch using modern techniques like mixture-of-experts and sparse attention. It further explores the AI researcher's role by guiding viewers through pre-training, reinforcement learning experiments, and the critical importance of designing effective data-driven environments.

https://www.youtube.com/watch?v=-gfgQfw2g_E