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gemini

Gemini is a digital asset trading and custody service company that allows users to buy, sell, and store cryptocurrencies.
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first_img Gemini founder's PAC invested $81,000 in advertising to support Paxton's campaign for Texas Senator

The super political action committee Digital Freedom Fund, initiated by Gemini co-founders Cameron Winklevoss and Tyler Winklevoss in 2025, submitted documents to the Federal Election Commission on Tuesday, reporting for the first time advertising expenses exceeding $81,000 to support Republican candidate Ken Paxton's campaign for the Texas Senate seat, while opposing Democratic candidate James Talarico. Early voting in Texas began on October 19, with Election Day on November 3.As of September 30, the PAC has raised approximately $34 million, and its expenditures are not limited to Texas. Documents show its involvement in the Ohio Senate race between Republican candidate Jon Husted and Democratic candidate Sherrod Brown, bringing its total support for Husted to about $7 million. Another crypto industry PAC, Fairshake, also reported over $13 million in direct mail advertising expenditures this week, with approximately $9 million supporting Husted and over $4 million opposing independent candidate Dan Osborn in the Nebraska Senate election.These expenditures indicate potential support for candidates who favor crypto policies and legislation. Prediction contracts from Kalshi and Polymarket suggest that Democrats are favored to regain majority control of the House and Senate by 2027, and the majority party in both chambers will influence the legislative process for bills like the Digital Asset Market Structure Act, which failed to pass procedural voting in the Senate last month. Paxton previously defeated incumbent Senator John Cornyn in the May Republican primary, receiving over $500,000 in advertising support from the Fellowship PAC, backed by Anchorage Digital and Cantor Fitzgerald, with the funds executed through the Nxum Group co-founded by Bo Hines.

first_img Google DeepMind's First Audit: AI Did Not Accelerate Its Own Development, 75% of Code Written by AI

Google DeepMind released the first comprehensive empirical assessment of automation in machine learning research and development in the Gemini 4 Argon security report. The conclusion is that AI has not accelerated the R&D of AI itself, despite the fact that AI has written most of the code delivered by the lab's engineers. Researcher Haydn Belfield, who participated in the assessment, stated on X that there is currently no significant end-to-end ML R&D acceleration, so the risks of AI-driven ML R&D are currently low.The team built an internal index, GCI v1, which synthesizes the scores of 123 AI models across 92 benchmarks into a single value. The cutting-edge Gemini capabilities are improving at a stable rate of about 0.1 GCI points per month, with Gemini 1.5 Pro approaching 0.5 points by the end of 2024, Gemini 3.8 Flash slightly above 2.9 points by September 2026, and Argon about 3.3 points above the trend line. However, Belfield noted that this is due to increased computing power and organizational investment, rather than AI-driven acceleration.Over 75% of the code produced by DeepMind is written by AI, and the proportion of fully delegated code merge requests increased from 7.5% to 11.9% between April and September, but about 88% of requests still require multiple rounds of human guidance or manual writing. A survey of 12 senior researchers showed that automation is highest in clearly defined, verifiable execution tasks and lowest in areas such as open science concepts, new experimental designs, and unstable diagnostic training.

first_img Google released the Nano Banana 2.1 image model, with the developer call price being about half of the previous generation

Google released its latest image generation and editing model, Nano Banana 2.1, on October 6, and it has been launched in the Gemini app, AI mode of Google Search, Google Ads, as well as developer tools like Google AI Studio, Flow, and Stitch. This model can generate images from text descriptions and edit existing photos.Google stated that this upgrade focuses on three aspects: visual design, mask-based editing (where only the marked area changes), and subject consistency, meaning that characters or objects retain their original features after multiple edits. In the overall preference test for text-to-image generation, Nano Banana 2.1 scored 1050 ELO points, higher than Nano Banana 2's 990 points and Nano Banana Pro's 935 points. The model can handle up to 14 reference images simultaneously, track 4 characters and 10 objects, and output up to 4K resolution with an aspect ratio of up to 8:1. Developers can also set the thinking time before generation and enable fact-checking based on Google Search and Google Image Search.When called through the Google Developer API, the standard 1K image is priced at $0.0336, about half of Nano Banana 2's $0.067; the 4K image is $0.0756, while the previous generation was $0.151, and bulk tasks can enjoy a 50% discount. The above performance data comes from Google's own testing.
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