A peek into Reddit's anti-spam internals

How Reddit accidentally leaked its spamurai system.

https://lyra.horse/blog/2026/06/reddit-spam-internals/

Celery on AWS ECS - Complete Guide

The post explains why running Celery on AWS ECS can cause lost, delayed, duplicated, or never-completed tasks due to ECS shutdown behavior, Celery defaults, prefetching, and timeout edge cases. It recommends safer production settings, shorter/idempotent task design, fan-out or batching, RabbitMQ on Amazon MQ, and Redis-based locking to make Celery workers more reliable on ECS.

https://jangiacomelli.com/blog/celery-on-aws-ecs/

TabFM

TabFM (Tabular Foundation Model) is a scikit-learn compatible tabular foundation model. It allows you to perform zero-shot classification and regression on tabular datasets with mixed column types out-of-the-box.

https://github.com/google-research/tabfm

Needle

26m function call model that runs on incredibly small devices.

https://github.com/cactus-compute/needle

magic-extractor

Universal Windows extraction tool that detects unknown files and routes them to the right bundled extractor.

https://github.com/xchwarze/magic-extractor

Mellea

Mellea is a library for writing generative programs.

https://github.com/generative-computing/mellea

An Intro to Spiel – Creating Presentations in Your Terminal with Python

The post introduces Spiel, an archived Python package that uses Rich to create and run slide presentations directly inside the terminal. It walks through installing Spiel, running the demo, and building simple terminal-based slides with Deck, Slide, decorators, and keyboard navigation.

https://blog.pythonlibrary.org/2026/07/10/an-intro-to-spiel-creating-presentations-in-your-terminal-with-python/

neuro-san-studio

A playground for neuro-san.

https://github.com/cognizant-ai-lab/neuro-san-studio

6× faster binary search: from compiled code to mechanical sympathy

The post shows how a compiled binary-search bucketization routine used from Python can get much faster by aligning the algorithm with how modern CPUs actually execute code. It walks through branchless execution, removing bounds-check overhead, and enabling SIMD-friendly auto-vectorization to make the final version about 6x faster than the original.

https://pythonspeed.com/articles/branchless-binary-search/

LLM-as-a-Verifier: A General-Purpose Verification Framework

The paper introduces LLM-as-a-Verifier, a training-free framework that improves agentic task evaluation by using continuous scores from scoring-token logits instead of discrete judge ratings. It shows that scaling verification through finer score granularity, repeated evaluation, and criteria decomposition improves accuracy across coding, robotics, and medical benchmarks, while also prov...

https://arxiv.org/pdf/2607.05391

codex-lb

Load balancer for ChatGPT accounts. Pool multiple accounts, track usage, manage API keys, view everything in a dashboard.

https://github.com/Soju06/codex-lb

Stop Checking for None Everywhere

The video explains why overusing None makes code harder to reason about by forcing checks, validation, and defensive handling throughout the system. It shows practical alternatives like better defaults, fail-fast validation, explicit state modeling, and the Null Object pattern to keep uncertainty at the edges and simplify core logic.

https://www.youtube.com/watch?v=h8ZwhU3PpVw

GeoSQL

Turn Claude/Codex into geospatial analytics agent.

https://github.com/dekart-xyz/geosql

Django: introducing django-orjson

The post introduces django-orjson, a new package that gives Django and Django REST Framework drop-in replacements powered by the faster Rust-based orjson library. It also points to a Django proposal for pluggable JSON backends, which could make faster JSON serialization and deserialization easier to adopt across Django itself.

https://adamj.eu/tech/2026/07/15/introducing-django-orjson/

How Airflow is using AI to make data engineering more resilient, not more complex

The post explains how Airflow is adding AI-assisted reliability features that detect schema drift, resume long-running jobs from saved state, and classify failures using team runbooks. It shows AI being used inside data infrastructure rather than as an app layer, making pipelines more self-healing without forcing data engineers to rebuild their workflows around agents.

https://blog.dataengineerthings.org/how-airflow-is-using-ai-to-make-data-engineering-more-resilient-not-more-complex-36ff44fd8df7