The Ultimate Guide to Contributing to Open Source Projects
This guide walks through what contributing to open source projects actually covers, how to pick a project that will actually respond to you, the exact git mechanics, and more.
Category: AI
Can a Local LLM Run My AI Assistant?
I replayed the same 27 real production tasks through two local models, one hardware upgrade apart, to find out what it actually takes to replace Claude as the brain behind a 90-tool personal agent. The post Can a Local LLM Run My AI Assistant? appeared first on Towards Data Science.
The Budget Split That Explains Itself
How to diversify a budget without losing the shadow prices that explain the result The post The Budget Split That Explains Itself appeared first on Towards Data Science.
Should AI Developers Make the Switch from Polars to Pandas?
Not all Python data libraries are created equal! The post Should AI Developers Make the Switch from Polars to Pandas? appeared first on Towards Data Science.
Stop Calling the First Significant Day a Win
Checking an A/B test until it crosses p < 0.05 can turn a nominal 5 percent false-positive rate into almost 28 percent. I use a seeded simulation to show how large the damage gets and compare the fixes that keep early stopping honest. The post Stop Calling the First Significant Day a Win appeared first…
3 Visual Proofs of the Central Limit Theorem to Build Your Intuition
To build your intuition, this article shows three visual proofs that the classic bell curve appears in myriad situations.
Determinization in Structure Theories: A Unified Framework via Closure, Comparability, and Joint Admissibility
arXiv:2608.07476v1 Announce Type: new Abstract: We develop a formal framework for constructing canonical interpretations from plural structure theories. A structure theory is a triple T = ({Sigma}, A, I) consisting of a signature, axioms, and an inference policy, whose admissible interpretation family collects all globally consistent assignments of structural conclusions. We distinguish three levels of…
Flow-by-Flow:Content-Judgment Bypass for Governing AI Output in High-Loss Domains
arXiv:2608.07474v1 Announce Type: new Abstract: Prior work showed that human-in-the-loop oversight becomes structurally untenable in high-loss domains when AI output velocity V exceeds human cognitive capacity C_max. The operative constraint, however, is not V alone but V x L, where L denotes per-item cognitive load. L consists of triage, judgment, and response, which respond asymmetrically…
Towards an Argumentative Foundation for Evaluative AI
arXiv:2608.07473v1 Announce Type: new Abstract: Evaluative AI (EAI) has been recently proposed as a way to support human decision-making, not by producing a single recommendation, but by presenting competing hypotheses together with evidence for and against each. In this position paper, we advocate (computational) argumentation as a particularly suitable paradigm to provide a formal, computable…
The Download: the next big thing in LLMs and how AI academic research is shifting
This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. These startups are chasing the next big thing in LLMs Nine years after Google researchers introduced the transformer, this family of neural networks has become the engine inside every major large…