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Why scaling AI requires a new economic strategy

Moving past simple model routing to fix enterprise AI's scaling and budget challenges. [...]

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venturebeat
Train-to-Test scaling explained: How to optimize your end-to-end AI compute budget for inference

The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use inference-tim [...]

Match Score: 74.24

venturebeat
Phi-4 proves that a 'data-first' SFT methodology is the new differentiator

AI engineers often chase performance by scaling up LLM parameters and data, but the trend toward smaller, more efficient, and better-focused models has accelerated. The Phi-4 fine-tuning methodology [...]

Match Score: 59.84

blogspot
How I Get Free Traffic from ChatGPT in 2025 (AIO vs SEO)

Three weeks ago, I tested something that completely changed how I think about organic traffic. I opened ChatGPT and asked a simple question: "What's the best course on building SaaS with Wor [...]

Match Score: 56.72

venturebeat
New memory framework builds AI agents that can handle the real world's unpredictability

Researchers at the University of Illinois Urbana-Champaign and Google Cloud AI Research have developed a framework that enables large language model (LLM) agents to organize their experiences into a m [...]

Match Score: 54.78

venturebeat
Google’s new framework helps AI agents spend their compute and tool budget more wisely

In a new paper that studies tool-use in large language model (LLM) agents, researchers at Google and UC Santa Barbara have developed a framework that enables agents to make more efficient use of tool [...]

Match Score: 45.33

venturebeat
Researchers automated LLM reasoning strategy design and cut token usage by 69.5%

Test-time scaling (TTS) has emerged as a proven method to improve the performance of large language models in real-world applications by giving them extra compute cycles at inference time. However, TT [...]

Match Score: 44.23

venturebeat
Moonshot’s Kimi K2.5 is 'open,' 595GB, and built for agent swarms — Reddit wants a smaller one

Two days after releasing what analysts call the most powerful open-source AI model ever created, researchers from China's Moonshot AI logged onto Reddit to face a restless audience. The Beijing-b [...]

Match Score: 39.88

venturebeat
Thinking Machines challenges OpenAI's AI scaling strategy: 'First superintelligence will be a superhuman learner'

While the world's leading artificial intelligence companies race to build ever-larger models, betting billions that scale alone will unlock artificial general intelligence, a researcher at one of [...]

Match Score: 36.52

venturebeat
Why Weibo’s tiny VibeThinker-3B has the AI world arguing over benchmarks again

On Sunday, a team of nine researchers at Sina Weibo — the Chinese social media giant better known for its microblogging platform than for cutting-edge artificial intelligence — quietly posted a 14 [...]

Match Score: 32.77