How AI is driving up consumer prices
CBS MoneyWatch reporter Megan Cerullo takes a look at the data on why artificial intelligence is raising costs.
We haven't written up this one. CBS News has the full story — the link below goes straight to it.
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US to tell partners they must pick sides in AI race with China
"Signature of the Pax Silica Declaration is not merely a membership subscription, but a commitment," the draft letter says, referring to the United States-led AI coalition launched last year.
Toast 1: A New Embedding Model That Rivals OpenAI at a Fraction of the Cost
Toast 1: A New Embedding Model That Rivals OpenAI at a Fraction of the Cost Mixedbread AI announced Toast 1, a new embedding model that claims to match or exceed OpenAI's text-embedding-3-large on…
- Toast 1 is a new embedding model from Mixedbread AI
- It rivals OpenAI's text-embedding-3-large at lower cost
- Toast 1 supports 50+ languages with variable dimensionality
I Ran 4,200 Trials Testing LLM Agent Reliability. Here’s What Broke.
We know when an AI agent gets a response from a tool, getting a response back doesn’t necessarily mean that response should be trusted.
- I conducted 4,200 trials to test LLM agent reliability.
- ReliAgent identified signals for cautious response treatment.
- Distinction between model behavior and detector failure crucial.
Anthropic Q2 revenue jumps to over $11.5b
The figure refers to revenue in Anthropic’s latest completed quarter rather than an annualized run rate.
Why your AI coding agent should never see your API keys
Your AI coding agent needs your API keys. It needs them to call services, to test integrations, to run your stack. So you give it .env files, or you export keys into the environment, or you paste them…
- API keys must remain secure when using AI coding agents
- Developers often expose keys via .env files or environment exports
- AI agents can leak secrets through context, output echo, and prompt injection
Open Discovery Challenge: How to Build a Verifiable Judge for AI-Designed Malaria Drugs
Open Discovery Challenge: How to Build a Verifiable Judge for AI-Designed Malaria Drugs Generative models can propose thousands of plausible molecules in a day.
- Open Discovery Challenge aims to create a verifiable judge for AI-designed malaria drugs
- Successful drug must inhibit PfDHODH enzyme without affecting human counterpart
- Scoring system validation revealed toxicity gate errors, size bias, and novelty scoring issues