Data science
fromInfoWorld
2 days agoHow to halve Claude output costs with a markdown tweak
A markdown file can reduce Claude's token output by over 50%, aiding enterprises in managing AI costs during production.
#token-efficiency
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#toon #ai #cost-reduction #data-analysis #markdown #gpt-54-release #ai-model-capabilities #error-reduction
Artificial intelligence
fromTechCrunch
4 weeks agoOpenAI launches GPT-5.4 with Pro and Thinking versions | TechCrunch
OpenAI released GPT-5.4, a more capable and efficient frontier model available in standard, reasoning, and pro versions with up to 1 million token context windows and improved accuracy.
Marketing tech
fromInfoQ
4 weeks agoCloudflare Debuts Markdown for Agents and Content Signals to Guide AI Crawlers
Cloudflare introduced Markdown for Agents to reduce token consumption for AI crawlers and proposed Content Signals mechanism allowing publishers to declare content usage permissions for AI training, search indexing, and inference.
Artificial intelligence
fromInfoQ
2 months agoAI-Powered Code Editor Cursor Introduces Dynamic Context Discovery to Improve Token-Efficiency
Dynamic context discovery reduces LLM token usage by storing and retrieving only necessary information from files, enabling agents to fetch context and tools on demand.
fromZDNET
4 months agoOpenAI's Codex Max solves one of my biggest AI coding annoyances - and adds dramatically faster performance
Following a week of major AI programming announcements from Microsoft and Google, OpenAI has joined in the fun. Today, OpenAI is announcing a new version of Codex, its programming-focused AI model. While the announcement was today, the actual GPT-5.1-Codex-Max capability will be available tomorrow in Codex for ChatGPT Plus, Pro, Business, Edu, and Enterprise users. API access is "coming soon." OpenAI says the new Max model "replaces GPT-5.1-Codex as the recommended model for agentic coding tasks in Codex and Codex-like environments."
Tech industry
fromTomasz Tunguz
6 months agoHow AI Tools Differ from Human Tools
Now that we've compressed nearly all human knowledge into large language models, the next frontier is tool calling. Chaining together different AI tools enables automation. The shift from thinking to doing represents the real breakthrough in AI utility. I've built more than 100 tools for myself, & they work most of the time, but not all the time. I'm not alone. Anthropic's Economic Index report reveals that 77% of business use of Claude centers on full-task automation, not co-piloting.
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