Machine Learning

1051 articles found

OmniParse Launches Open-Source Platform Converting 20+ Unstructured Data Formats Into AI-Ready Markdown

OmniParse Launches Open-Source Platform Converting 20+ Unstructured Data Formats Into AI-Ready Markdown

May 29, 2026
GitHub

OmniParse launches as a fully local, open-source platform capable of converting over 20 unstructured data formats — including documents, images, audio, video, and web pages — into AI-ready markdown, deployable via Docker on a single T4 GPU and designed to power GenAI applications like RAG and fine-tuning.

Anthropic Raises $65 Billion, Launches Claude Opus 4.8 With Improved Honesty and Coding Capabilities

Anthropic Raises $65 Billion, Launches Claude Opus 4.8 With Improved Honesty and Coding Capabilities

May 29, 2026
The Deep View

Anthropic raises $65 billion in a Series H funding round at a near-trillion dollar valuation while simultaneously launching Claude Opus 4.8, a more honest and capable AI model that is four times less likely to miss its own coding flaws, with new agentic features and a teased next-gen model class …

Corporate AI Spending Faces Reckoning as Companies Struggle to Justify Soaring Costs and Poor Returns

Corporate AI Spending Faces Reckoning as Companies Struggle to Justify Soaring Costs and Poor Returns

May 29, 2026
Axios

Corporate AI spending faces a major reckoning as companies like Microsoft and Uber struggle to justify soaring costs and poor returns, with experts warning that unfocused deployment, high token costs, and poor use case selection are driving a correction away from AI overuse toward smarter, revenue-focused applications.

Anthropic Launches Claude Opus 4.8 With Smarter Error Transparency, Adjustable Effort Levels, and Parallel AI Subagents

Anthropic Launches Claude Opus 4.8 With Smarter Error Transparency, Adjustable Effort Levels, and Parallel AI Subagents

May 28, 2026
The Verge

Anthropic launches Claude Opus 4.8, a powerful AI upgrade that is 4x more transparent about its own errors, lets users control response effort levels to manage rate limits, and introduces dynamic workflows that can split complex tasks across hundreds of parallel AI subagents simultaneously.

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