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Bitcoin has fallen to ~$78K, down ~7% in 24 hours, over 12% in the past week, and down ~37% since its ATH in Oct. 2025; Ethereum dropped ~18% in the past week (Bloomberg)

Bitcoin has fallen to ~$78K, down ~7% in 24 hours, over 12% in the past week, and down ~37% since its ATH in Oct. 2025; Ethereum dropped ~18% in the past week (Bloomberg)

Bloomberg : Bitcoin has fallen to ~$78K, down ~7% in 24 hours, over 12% in the past week, and down ~37% since its ATH in Oct. 2025; Ethereum dropped ~18% in the past week —  Bitcoin fell sharply in early Saturday afternoon trading in New York, tumbling below $80,000 mark to hit levels last seen in April 2025.

OnlyFans is reportedly in talks to sell a 60 percent stake to a San Francisco investment firm

OnlyFans is reportedly in talks to sell a 60 percent stake to a San Francisco investment firm

OnlyFans is looking to cash out once again, but this time in a deal that would value it at several billion dollars less than a potential sale that previously fell through. As reported by TechCrunch , the online platform known for subscription-based pornographic content is in talks to sell a majority stake to Architect Capital, an investment firm based in San Francisco. According to the report, the proposed deal includes $3.5 billion in equity and $2 billion in debt, which values OnlyFans at $5.5 billion. TechCrunch also reported that Architect Capital and OnlyFans are currently in exclusive talks, where the website's owner can't negotiate with other potential buyers for a certain amount of time. With no set timeline yet for the deal, the deal is far from an official closing. Last year, OnlyFans' owner Leonid Radvinsky was also negotiating with another investment firm, Forest Road Company, to sell the platform . Although that deal never went through, the talks leading up to the sale valued OnlyFans at a much higher $8 billion. The London-based website, which still doesn't want to be known as just a porn site , is still growing and reported a nine percent increase in gross revenue for its 2024 fiscal year, earning more than $7.2 billion . This article originally appeared on Engadget at https://www.engadget.com/social-media/onlyfans-is-reportedly-in-talks-to-sell-a-60-percent-stake-to-a-san-francisco-investment-firm-191842666.html?src=rss

Online Apple Store makes buying a Mac more like buying an iPhone

Online Apple Store makes buying a Mac more like buying an iPhone

Apple has quietly updated the way that users buy and configure Macs on the online Apple Store, giving customers a process that's closer to buying an iPhone or iPad. The new process of buying a Mac from Apple is similar to that of an iPhone or iPad The online Apple Store guides users through configuring their new hardware before purchasing it, with some variation in the methodology depending on the product. In a very quiet change to the website, Apple has updated the way users buy a Mac. Initially spotted by Consomac , users are no longer provided with a selection of base configuration Macs to choose from before configuring them. Instead, they are taken through a process that's pretty close to what happens when they buy an iPhone or iPad from the website. Continue Reading on AppleInsider | Discuss on our Forums

Most RAG systems don’t understand sophisticated documents — they shred them

Most RAG systems don’t understand sophisticated documents — they shred them

By now, many enterprises have deployed some form of RAG. The promise is seductive: index your PDFs, connect an LLM and instantly democratize your corporate knowledge. But for industries dependent on heavy engineering, the reality has been underwhelming. Engineers ask specific questions about infrastructure, and the bot hallucinates. The failure isn't in the LLM . The failure is in the preprocessing. Standard RAG pipelines treat documents as flat strings of text. They use "fixed-size chunking" (cutting a document every 500 characters). This works for prose, but it destroys the logic of technical manuals. It slices tables in half, severs captions from images, and ignores the visual hierarchy of the page. I mproving RAG reliability isn't about buying a bigger model; it's about fixing the "dark data" problem through semantic chunking and multimodal textualization. Here is the architectural framework for building a RAG system that can actually read a manual. The fallacy of fixed-size chunking In a standard Python RAG tutorial, you split text by character count. In an enterprise PDF, this is disastrous. If a safety specification table spans 1,000 tokens, and your chunk size is 500, you have just split the "voltage limit" header from the "240V" value. The vector database stores them separately. When a user asks, "What is the voltage limit?", the retrieval system finds the header but not the value. The LLM, forced to answer, often guesses. The solution: Semantic chunking The first step to fixing production RAG is abandoning arbitrary character counts in favor of document intelligence. Using layout-aware parsing tools (such as Azure Document Intelligence), we can segment data based on document structure such as chapters, sections and paragraphs, rather than token count. Logical cohesion: A section describing a specific machine part is kept as a single vector, even if it varies in length. Table preservation: The parser identifies a table boundary and forces the entire grid into a single chunk, preserving the row-column relationships that are vital for accurate retrieval. In our internal qualitative benchmarks, moving from fixed to semantic chunking significantly improved the retrieval accuracy of tabular data, effectively stopping the fragmentation of technical specs. Unlocking visual dark data The second failure mode of enterprise RAG is blindness. A massive amount of corporate IP exists not in text, but in flowcharts, schematics and system architecture diagrams. Standard embedding models (like text-embedding-3-small) cannot "see" these images. They are skipped during indexing. If your answer lies in a flowchart, your RAG system will say, "I don't know." The solution: Multimodal textualization To make diagrams searchable, we implemented a multimodal preprocessing step using vision-capable models (specifically GPT-4o) before the data ever hits the vector store. OCR extraction: High-precision optical character recognition pulls text labels from within the image. Generative captioning: The vision model analyzes the image and generates a detailed natural language description ("A flowchart showing that process A leads to process B if the temperature exceeds 50 degrees"). Hybrid embedding: This generated description is embedded and stored as metadata linked to the original image. Now, when a user searches for "temperature process flow," the vector search matches the description , even though the original source was a PNG file. The trust layer: Evidence-based UI For enterprise adoption, accuracy is only half the battle. The other half is verifiability . In a standard RAG interface, the chatbot gives a text answer and cites a filename. This forces the user to download the PDF and hunt for the page to verify the claim. For high-stakes queries ("Is this chemical flammable?"), users simply won't trust the bot. The architecture should implement visual citation. Because we preserved the link between the text chunk and its parent image during the preprocessing phase, the UI can display the exact chart or table used to generate the answer alongside the text response. This "show your work" mechanism allows humans to verify the AI's reasoning instantly, bridging the trust gap that kills so many internal AI projects . Future-proofing: Native multimodal embeddings While the "textualization" method (converting images to text descriptions) is the practical solution for today, the architecture is rapidly evolving. We are already seeing the emergence of native multimodal embeddings (such as Cohere’s Embed 4). These models can map text and images into the same vector space without the intermediate step of captioning. While we currently use a multi-stage pipeline for maximum control, the future of data infrastructure will likely involve "end-to-end" vectorization where the layout of a page is embedded directly. Furthermore, as long context LLMs become cost-effective, the need for chunking may diminish. We may soon pass entire manuals into the context window. However, until latency and cost for million-token calls drop significantly, semantic preprocessing remains the most economically viable strategy for real-time systems. Conclusion The difference between a RAG demo and a production system is how it handles the messy reality of enterprise data. Stop treating your documents as simple strings of text. If you want your AI to understand your business, you must respect the structure of your documents. By implementing semantic chunking and unlocking the visual data within your charts, you transform your RAG system from a "keyword searcher" into a true "knowledge assistant." Dippu Kumar Singh is an AI architect and data engineer.

How the music industry is split over AI; some labels signed licensing deals for AI that mirror revenue splits they use with YouTube for user-generated content (Anna Nicolaou/Financial Times)

How the music industry is split over AI; some labels signed licensing deals for AI that mirror revenue splits they use with YouTube for user-generated content (Anna Nicolaou/Financial Times)

Anna Nicolaou / Financial Times : How the music industry is split over AI; some labels signed licensing deals for AI that mirror revenue splits they use with YouTube for user-generated content —  A folk-pop song called “I Know, You're Not Mine” recently topped Spotify's charts in Sweden.  The soft vocals …