Meet Turbovec: A Rust Vector Index with Python Bindings, and Built on Google’s TurboQuant Algorithm

AI agents can quickly become expensive without a clear strategy for planning, skill coverage, and budgets. This article shows how to use operations research and data science to optimize AI agent cost and resource allocation. You will learn how to frame common agent problems—skill coverage, project assignment, and budgeting—as set covering, assignment, and knapsack optimization models in Python using Gurobi. The post Optimizing AI Agent Planning with Operations Research and Data Science appeared first on Towards Data Science.

Learn how to utilize Python's Mimesis library for anonymizing sensitive production data, based on a step-by-step example to try yourself.

Back in 2023, Chris Lattner, creator of LLVM, and his team at Modular unveiled a new language called Mojo. Its syntax resembled Python, but it compiled to machine-native code and offered memory-safety features akin to Rust. It also offered cross-compatibility with existing Python programs, one of many hints that Mojo aimed to capture the math, stats, and machine learning segment of Python developers. Now in 2026, the first beta version of Mojo 1.0 is out, and with that the shape of the language is far clearer than before. Most crucially: Mojo is not a drop-in replacement for Python. It still features Python-esque syntax and uses many of Python’s concepts, but is unmistakably headed in its own direction. As of 1.0 and beyond, Mojo aims to be a systems language with precise control over memory and strong types, while sporting convenience features inspired by higher-level languages. Mojo basics Mojo syntax resembles Python at first glance. The use of indents instead of braces to delineate

Want to level up your data engineering toolkit? Here are some Python libraries that'll make your pipelines faster, cleaner, and easier to maintain.

Anthropic has acquired Stainless, a startup that generates SDKs, command-line tools, and MCP servers from API specifications, in a move analysts say targets the “last mile” of developer experience. Founded in 2022 by former Stripe engineer Alex Rattray, Stainless converts API specifications into production-ready SDKs across languages, including Python, TypeScript, Kotlin, Go, and Java. Stainless does not sell primarily to enterprises, but its tools form part of the software development chain that enterprise teams may rely on. They help generate SDKs, documentation, and MCP servers that developers can use to connect AI models, cloud services, and APIs to business applications. In a statement, Stainless said it will wind down all hosted products, including its SDK generator, as the team shifts focus to Claude Platform capabilities and connecting agents to APIs. Existing customers will retain the right to modify and extend SDKs they have already generated. This could have competitive impl

This article is about the gap between what candidates prepare for and what companies actually need right now.

Most LLM evaluation systems rely on vague scoring and human judgment disguised as metrics. I built a lightweight evaluation layer in pure Python that turns LLM outputs into reproducible decisions by separating attribution, specificity, and relevance—so hallucinations are caught before they reach production. The post LLM Evals Are Based on Vibes — I Built the Missing Layer That Decides What Ships appeared first on Towards Data Science.

In this tutorial, we explore how to use Repowise to build repository-level intelligence for the itsdangerous Python project in a practical and reproducible way. We start with an already cloned repository, configure Repowise using the available LLM credentials, and initialize its indexing pipeline. We then inspect the generated .repowise artifacts, analyze the repository graph with […] The post How to Build Repository-Level Code Intelligence with Repowise Using Graph Analysis, Dead-Code Detection, Decisions, and AI Context appeared first on MarkTechPost.