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Metering LLM usage goes beyond a simple token count, with hidden costs and scattered reporting across providers.

A key sample in AWS's Agent EvalKit uses the same LLM for both evaluating and generating responses, raising questions about test validity.

A developer's 48-hour experiment reveals the limitations of budget AI summarization when faced with incomplete log data.

Eliminate manual data compilation and get AI-powered market insights in seconds.

Developers find unexpected advantages in using open-source large language models, from control to cost.

Fermion Research releases Neutrino-1 8B, an open-source LLM targeting performance and efficiency for developers and researchers.

AI code generation tools flood developers with unmanageable changes, making reviews arduous. Agent-Up commits aims to fix this.

Modern AI isn't just LLMs; it's a complex engineering stack. Understand the components for the agentic era.

A small, cost-effective reinforcement learning fine-tune of a 9 billion parameter open model now leads on complex catalog review tasks.

A developer's AI project aimed at deep career insights learned a hard lesson about testing: passing isn't everything.

A user saved a peculiar exchange with Snapchat's AI during a technical glitch, revealing unusual multi-message responses and strange content.

AI companies are using hydraulic presses and industrial scanners to digitize antique books for model training, often destroying unique physical artifacts in the process.
A comprehensive MIT analysis highlights five critical AI risks, ranging from autonomous weapons to societal manipulation, demanding immediate global action.