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Doubao rolls out new work features, including support for local Office document editing, browser recording, and playback.

1 hours ago

Beating AI News Flash: Doubao Work launches three new features: local Office editing, browser recording and playback, and free switching of task execution environments. Currently, Doubao Work supports synchronous editing of local PPT and Excel files, with Word editing functionality set to roll out later. Additionally, users can switch between different "local computer" instances during task execution without creating a new conversation, and also toggle to the "cloud computer" execution environment at any time to sync tasks to the cloud.

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Bitcoin rebounds to break through $79,000, while Ethereum rebounds to surpass $2,500.

According to HTX market data, Bitcoin has rebounded to break through $79,000, while Ethereum has also rebounded above $2,500.

13 minutes ago

US journalist: Apple's foldable iPhone will be named iPhone Duo, with a starting price of $2,000.

US tech journalist Mark Gurman has revealed that Apple (AAPL.O) has finalized the name of its first foldable iPhone as iPhone Duo, with a starting price of $2,000 — a figure that accounts for additional memory costs. The device will offer a maximum storage capacity of 2TB, corresponding to a price tag of around $3,000. The iPhone Duo is slated for an official launch in October, supports the Apple Pencil, and comes in two color options: dark blue and white.

13 minutes ago

Ant Bailing has officially open sourced its first native multimodal model, Ling-3.0-flash-VL.

Ant Bailing has officially open-sourced its first native multimodal model, Ling-3.0-flash-VL. The model was previously launched via API; this release includes BF16 and FP8 weights under the MIT license, with FP4 and INT4 versions to follow later. Ling-3.0-flash-VL can process images, videos, documents, and software interfaces, then call tools to complete operations, and further check and modify based on execution results. For example, when building a webpage, it can write code based on reference images, then review the actual page performance and adjust it on its own. The model uses a Mixture of Experts (MoE) architecture, with approximately 124 billion total parameters and around 5.5 billion activated parameters per inference run. Its context window can be extended up to 1 million tokens. Ling-3.0-flash-VL inherits the core advantage of Ling-3.0-flash as an efficient execution node in agent workflows, balancing output quality and operational efficiency in visual feedback loops, enabling it to complete full tasks at lower cost and in less time.

13 minutes ago

A crypto whale transfers another 209 BTC to a centralized exchange (CEX), with its total cashout over the past three months exceeding $12.9 million.

According to EmberCN’s monitoring, a crypto whale transferred 209.1 BTC to Binance roughly half an hour ago, worth about $16.44 million. Over the past three months, this whale has sold a total of 329.1 BTC at an average selling price of $75,392. The BTC were originally accumulated in 2022 at an average cost of $36,066, generating a total profit of approximately $12.94 million and a return of 109%.

13 minutes ago

LIT climbs another 10% in 24 hours, lifting its market capitalization to $1.277 billion.

According to HTX market data, LIT has risen 10% in the past 24 hours, currently trading at $5.11, with its market cap climbing to $1.277 billion and fully diluted valuation (FDV) exceeding $5.1 billion. The token has gained 43.7% over the last seven days and 108.4% over the past 30 days.

13 minutes ago

Ant Group announces open-sourcing of multimodal large model Ling-3.0-flash-VL

Ant Group’s open-source announcement has officially unveiled and open-sourced Ling-3.0-flash-VL, the first native multimodal large model in its Ling series. Derived from the MoE (Mixture of Experts) architecture of Ling-3.0-flash, the model has a total parameter count of 124 billion, with 5.5 billion parameters activated per inference. It natively supports image, text, and video inputs, and features a 256K-token context window. Focused on the theme of “how to complete real-world tasks more reliably and efficiently”, Ling-3.0-flash-VL explores key directions including: Integrating visual capabilities into large models— a common concern is that this will diminish text intelligence, but training practices have yielded the opposite result: native multimodal joint training not only expands application boundaries but also enhances text intelligence. The model introduces a visual feedback mechanism: observing execution results, comparing against targets, identifying discrepancies, and iterating corrections, shifting tasks from one-off “generation” to a closed loop of “observe → act → verify → correct” to boost execution reliability. It also inherits Ling-3.0-flash’s core strength as an efficient execution node in Agent workflows, balancing output quality and operational efficiency within the visual feedback loop, enabling full task completion at lower cost and in shorter time.

13 minutes ago

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