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The Montecito was built in 1935 with 95 units at a cost of $1 million. Set on a hill overlooking the city, the Montecito is the highest building in Hollywood. It has a private swimming pool, two subterranean garages and a parking lot.The building is a classic Art Deco design with Mayan influences and windows arranged in vertical blinds. In 1946, it was sold for $600,000. In 1954, it was sold again, this time by Isadore and Libby Teacher to Howard Fox and Harry Wyatt.

The Montecito was home to several future movie stars, especially New York based actors while working in Hollywood. It was Ronald Reagan’s first residence when he moved to Hollywood; Reagan lived at the Montecito from June 1937 to late 1938. Reagan was said to have been roommates at the Montecito with Mickey Rooney. Other celebrities who have lived at the Montecito include James Cagney, George C. Scott, Montgomery Clift, Geraldine Page, Don Johnson, Sal Mineo and Ben Vereen.

Thomas Hawk posted a photo:

Thomas Hawk posted a photo:

date stamped on slide April 1963

Thomas Hawk posted a photo:

Major publishers Hachette, Cengage, Elsevier, and author Scott Turow have sued Google, accusing it of using millions of copyrighted books to train Gemini without permission or payment, in "one of the most prolific infringements of copyrighted materials in history." The Guardian reports: The publishers argue that Google repurposed books that had been supplied for limited services such as Google Books, Google Play Books and Google Scholar. Those services allowed Google to use the works in specific ways -- for example, to display searchable snippets or sell ebooks -- but not, the lawsuit claims, to copy them for training commercial AI products. "Desperate to maintain its online dominance, Google abandoned its early motto of 'Don't be evil' and engaged in one of the most prolific infringements of copyrighted materials in history," the suit states (PDF).

According to the complaint, the tech company made copies of copyrighted books to train Gemini without permission or payment, despite internal discussions acknowledging the legal risks. The filing claims Google flagged internally that it could face "$10Bs-$100Bs in potential fines" for using texts provided by publishers for Google Play Books. The publishers say Google's actions are harming authors and the wider publishing industry, arguing that AI-generated content could negatively impact book sales.

It notes that, for example, Gemini could generate "a 100-page murder mystery set in a quiet seaside town filled with secrets, that substitutes for an original copyrighted murder mystery on which Gemini trained" in 20 minutes for 39 cents. "No publisher or author can compete with that." The lawsuit names a number of specific books that the publishers allege were among the copyrighted works used without permission, including NK Jemisin's The Fifth Season, and Lemony Snicket's Who Could That Be at This Hour?

Read more of this story at Slashdot.

Spotify is testing a new "Talk to Spotify" AI feature for Premium subscribers that will let them chat with an AI assistant to explore music, podcasts, and audiobooks. The feature can answer questions about what users are listening to, adjust playback through follow-up prompts, and offer more personalized recommendations. The Verge reports: Amazon Music introduced a similar feature last year when it integrated Alexa Plus into the service. Spotify's chatbot goes a step beyond providing AI-powered recommendations and general trivia, however, because it references your playlists, favorite artists, repeat listens, and listening data when responding to requests. That means you can ask questions about your own listening history to check when you first heard a specific song, or see what genres you've been into lately if you can't hold out for the annual Wrapped insights.

The updated AI capabilities are more conversational than older features like Prompted Playlist, which automatically builds playlists based on descriptions. Now, you can ask the Spotify chatbot to "play some songs I haven't heard before," and control what's being played with further instructions like requesting specific artists or asking to make it "more upbeat." Spotify says the new conversational experience aims to make the platform "more personal and useful for every listener," making this one of several ways that the company is trying to address complaints about its algorithm.

You can also ask the Spotify AI general questions about whatever you're listening to, making the feature feel similar to using chatbot services like Google's Gemini or OpenAI's ChatGPT. That includes asking for when a song was released, exploring other titles an author has written when listening to one of their audiobooks, or checking if a podcast guest has appeared on other audio shows.

Read more of this story at Slashdot.

Tot vijf minuten voor tijd stonden de Engelsen met 1-0 voor, en lonkte de eerste finale in zestig jaar tijd. Door twee assists sneuvelde die droom in de slotfase. Argentinië speelt de finale van het WK voetbal komende zondag tegen Spanje.

Op de veerboot van Terschelling naar Harlingen is een groep meisjes van ongeveer vijftien jaar bij ons komen zitten .

Je krijgt een paar cijfers cadeau, maar het grid van 9x9 moet foutloos ingevuld worden.

Een Precies Vier bestaat uit 16 woorden, begrippen of namen, die moeten worden verdeeld in precies vier groepen van vier. Er is telkens maar één oplossing mogelijk. Welke woorden vormen een connectie?

iain.davidson100 has added a photo to the pool:

iain.davidson100 has added a photo to the pool:

iain.davidson100 has added a photo to the pool:

If you’re in the market for a frontier-class open weights model, your options are few and far between outside of the Chinese model houses. With the Wednesday release of a new model code-named "Inkling", an outfit called Thinking Machines Lab aims to change that. Founded in early 2025 by former OpenAI CTO Mira Murati, Thinking Machines' first model is a big one. Weighing in at 975 billion parameters, the model requires more than two terabytes of GPU memory — a quantity present in around eight of Nvidia's B300 accelerators, or sixteen H200s — to run at its native 16-bit precision. If that’s asking too much of your hardware, Thinking Machines has also released a NVFP4 quantized version of the model capable of running on half the GPUs. This makes it the largest American open weights model to date, and comparable to Chinese models like DeepSeek V4, GLM 5.2, and Kimi K2.6 in terms of size and capabilities. Take these claims with a grain of salt — gaming AI benchmarks isn’t exactly difficult — but Thinking Machines says Inkling is competitive with these models in a variety of workloads, although its benchmark charts also show it trailing proprietary models like Anthropic’s Claude and OpenAI’s GPT. Thinking Machines describes the model as being highly adaptable, intended for use by developers building AI apps, but suitable for general purpose applications like chat bots. And because it’s being released under a highly permissive Apache 2.0 license, end users are free to fine tune it for their specific use case. The company's Tinker platform offers tools to do just that. In fact, Thinking Machines boasts that the model is capable of writing its own fine tuning scripts to refine its behavior, teach itself new skills, and evaluate its abilities. Other notable features include support for a million-token context, which you can think of as the model’s short-term memory. This should help it wrangle large code bases and needle-in-the-haystack type search problems. While Thinking Machines admits the model’s mixture of experts (MoE) architecture was inspired by DeepSeek-V3, the company says it trained Inkling from scratch using Nvidia GB300 NVL72 systems and 45 trillion tokens worth of text, images, audio, and video. In total, the model features 256 routed exports and two shared ones. The model generates each token by six experts, totaling about 41 billion parameters. So, in spite of its size, the model should be able to churn out tokens at about the same rate as DeepSeek V4 when running on the same hardware. Like most LLMs today, Inkling is a “reasoning model” which is to say it’s been trained using reinforcement learning (RL) to use chain of thought to “think” through requests before responding. The model developer claims to have tuned the model to use these thinking tokens more efficiently and that Inkling therefore matches Nvidia’s Nemotron 3 Ultra, up to now the largest and most capable American open weights model out there at 550 billion parameters, on Terminal Bench 2.1 using roughly a third the tokens. Thinking tokens may make models more capable and less likely to hallucinate, but the capability comes at a cost. Those tokens are billed like any other and so the longer the model thinks, the larger users' bills become. Speaking of APIs, Inkling is available starting today on Thinking Machines’ Tinker platform, which in addition to model access also offers tools for customization and fine tuning. The company is also working to bring the model to 3rd-party API services including TogetherAI, Fireworks, Modal, Databricks, and Baseten. If you prefer to evaluate the model on your own hardware, it’s available for download on popular model repos like Hugging Face. At launch, the model claims support for a broad range of inference engines including vLLM, SGLang, Miles, TokenSpeed, and Llama.cpp. Inkling is the first of several new models under development by Thinking Machines. Alongside its flagship model, the company is also previewing Inkling-Small, a 276-billion-parameter MoE model with 12 billion active parameters for those prioritizing latency over throughput and quality. Thinking Machines — which shares its name with the fictional supercomputer maker immortalized in 1993's Jurassic Park — is currently in the process of finalizing the model and plans to release its weights once testing is complete. ®