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interlock
Circuit breaker for Python: sync + async in one class, sliding-window rate, slow-call detection, Polly-style resilience pipeline, type-safe API.
From Routing Checks to Trajectory Testing: Evaluating an Agentic Chatbot
Traditional chatbot testing that checks only final responses misses many agent failures, so this article builds an evaluation framework that progresses from routing checks and task completion to full trajectory testing of tool use, conversations, and intermediate actions. It also shows how to combine LLM-as-a-judge evals, multi-turn testing, and telemetry to debug agent behavior and vali...
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.
earthtojake / text-to-cad
A library of agent skills for CAD, CAE and CAM
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, ...
💖 Most Popular in the Last Two Weeks
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.
dottxt-ai / outlines
Structured Outputs
Self-contained highly-portable Python distributions
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.
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.
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