Politician who investigated spyware abuses had his phone hacked with Pegasus spyware

Security researchers have confirmed that a European politician had his phone hacked with the Pegasus spyware while serving on an investigatory committee probing abuses of the notorious surveillance tool. This has reigniting fresh controversy over governments abusing spyware to collect information about their critics. The researchers at the University of Toronto’s digital rights unit The…

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Interfaze Ships diffusion-gemma-asr-small, an Open-Source Diffusion ASR Model Transcribing Six Languages via DiffusionGemma’s Parallel Denoising Decoder

Interfaze, a young YC’s startup, has open-sourced a new speech recognition model. It is called diffusion-gemma-asr-small. The model transcribes audio through a diffusion decoder, not an autoregressive one. It is described as the first multilingual audio diffusion ASR model. One adapter handles six languages. The research team trained only about 42M parameters on top of…

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Thiel Capital’s Jack Selby nabs stakes in sizzling startups like Etched by way of Arizona connections

Thiel Capital’s Jack Selby nabs stakes in sizzling startups like Etched by way of Arizona connections

Nvidia competitor Etched introduced this week that TSMC had manufactured its first chip earlier this 12 months. Whereas the four-year-old startup valued at $5 billion is on the brink of ship methods powered by that chip to prospects later this summer time, scaling manufacturing might show difficult. Like different chip designers, Etched should compete for…

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Meet Alibaba’s Page Agent: A JavaScript In-Page GUI Agent That Controls Web Interfaces With Natural Language Through the DOM

Most browser automation runs from the outside. Playwright, Puppeteer, Selenium, and browser-use all drive a browser from an external process. They read the page through screenshots or the Chrome DevTools Protocol. Alibaba’s Page Agent takes the opposite path. The agent lives inside the webpage as plain JavaScript. It reads the live DOM as text and…

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RAG-Anything Tutorial: Build a Multimodal Retrieval Pipeline for Text, Tables, Equations, and Images in Colab

print(“\n[5/10] Creating a synthetic multimodal report…”) monthly_data = pd.DataFrame( { “Month”: [“Jan”, “Feb”, “Mar”, “Apr”, “May”, “Jun”], “Query Volume”: [1200, 1700, 2100, 2600, 3300, 4100], “Hybrid Accuracy”: [0.71, 0.74, 0.79, 0.83, 0.87, 0.91], “Average Latency ms”: [980, 920, 850, 790, 760, 730], } ) table_md = monthly_data.to_markdown(index=False) plt.figure(figsize=(8, 4.8)) plt.plot(monthly_data[“Month”], monthly_data[“Query Volume”], marker=”o”, label=”Query Volume”)…

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