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Capitalism Doesn’t Just Need Workers — It Needs Buyers Too
Here s a thought that doesn t cross most people s minds: companies don t hire people just to get work done. They hire them because, at the Continue reading on Medium »
The AI Gold Rush’s Weirdest Side Effect: Everyone Now Builds the Same App
A Thousand Startups, One Idea, Infinite Slight VariationsContinue reading on The Haven »
Ford picks Apple Maps for its next EV platform and updated BlueCruise
Ford plans to embed Apple Maps directly into its Universal Electric Vehicle Platform, which will start with a $30,000 midsize pickup truck set for release in 2027. In an announcement on Thursday, Ford says it will use Apple Maps to offer "turn-by-turn directions using natural language, real-time traffic and incident information, intuitive search featuring detailed place cards, and routing options." The vehicle will also have an intelligent EV routing feature and battery preconditioning. Since the Apple Maps system will be integrated into Ford's upcoming platform, drivers won't need an iPhone to use it - though Ford notes that CarPlay will s … Read the full story at The Verge.
Lessons Learned After 8.5 Years of ML
Patience, Optimism, Discipline, Projects, Teams The post Lessons Learned After 8.5 Years of ML appeared first on Towards Data Science.
Securing Services with Rootless Containers
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Alphabet's cash burn raises alarm for Big Tech as AI spending climbs
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OpenAI and Anthropic unite against open-weight AI risks to their bottom line
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Show HN: I simulated closing the Strait of Hormuz on real oil trade data
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Prompt Compression Techniques: How to Reduce LLM Costs Without Losing Important Context
Large language models often receive more information than they need. A prompt may include long instructions, retrieved documents, chat history, examples, and tool descriptions. This increases token usage, cost, and response time. It can also make important details harder for the model to identify. Prompt compression reduces the prompt while keeping the key meaning, instructions, [ ] The post Prompt Compression Techniques: How to Reduce LLM Costs Without Losing Important Context appeared first on Analytics Vidhya.
The right-wing boomers protesting data centers have a lot in common with the left
A person holds signs during a nationwide protest against AI data center expansion outside Peace Hall in New Port Richey, Florida. | AFP via Getty Images On a gray, humid Saturday morning in central Florida, a little under a dozen people gathered outside the Spring Hill Branch Library to protest the construction of a hyperscale data center in their community. There was no immediate threat - the Hernando County commission had unanimously approved a one-year moratorium on such developments in June - but the organizers weren't satisfied. A temporary pause wouldn't be enough. They wanted a ban. "We are going to fight, fight, fight," said a 71-year-old woman named Carol, who was manning a table with flyers and a petition the group, No Data Centers Hernando County, planned on presenting to the loc … Read the full story at The Verge.
Why Adding More AI Agents Made Our System Slower
The hidden cost of asynchronous systems, how tiny CPU tasks quietly became our biggest bottleneck while scaling hundreds of LLM agents. The post Why Adding More AI Agents Made Our System Slower appeared first on Towards Data Science.
I Spent Six Months Watching People Waste ChatGPT.
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