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. ®

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?

Een kruiswoordpuzzel, maar dan heel klein (en snel).

Als je wel zin hebt om te sudokuen, maar het liever bij een gridje van 5x5 houdt.

A hacker who breached Suno reportedly revealed source code and training-library details showing the AI music generator scraped millions of songs and lyrics from sources including YouTube Music, Deezer, Genius, Pond5, Jamendo, Freesound, and podcast RSS feeds. "The hacked data is a rare look at exactly how AI models and tools are built," reports 404 Media. "Suno is one of the largest AI music generation tools on the internet, and has been the subject of several major lawsuits from the record industry, which accused the company of training on millions of copyrighted songs." Suno maintains that its models were trained on publicly available music files and metadata as fair use. 404 Media reports: The Recording Industry Association of America accused Suno of ripping songs directly from YouTube; the hacked data seen by 404 Media confirms this. The hacked material includes source code that appears to be from 2023 and 2024 that includes scraping instructions and details about the scope of at least some of the scraping. For example, the comments in one file note that they will pull from "genius_hq, youtube_music, freesound, jamendo, imp, deezer, ytm_tagged," and that "non-music will be filtered out." A file called "youtube_music" notes that at the time the file was last updated, it had ingested "2,013,545 music clips." Another file contains comments about different datasets Suno had created, which included "113,879 hours of youtube_music," "17,615 hours of genius_hq," "410 hours of free sound," "19,514 hours of imslp," "3,726 hours of jamendo," "62,117 hours of pond5_music," "12,287 hours of deezer," "152,162 hours of ytm_tagged," and "103 hours of musescore_lyrics." In total, this is at least decades worth of music.

Other code the hacker shared with 404 Media appeared to look specifically for vocals by searching specifically for acapella versions of songs on YouTube. The code also suggested that Suno was using proxies to scrape songs from YouTube through a company called Bright Data, which sells scraping tools, infrastructure, and data services. Additional code shows that with the help of an online tool called PodcastIndex, Suno identified 420,000 different podcasts that had at least five, 30-minute episodes and sought to download roughly 1 million hours of podcasts.

[...] The hacker, ellie.191, told 404 Media they breached the company by hacking an individual employee using the Shai-Hulud worm, a supply chain attack that allowed hackers to harvest GitHub and cloud service credentials. They said they also accessed Suno's customer list, which included customers' emails and/or phone numbers and Stripe payment details, depending on what they used to login. The hacker provided a sample of some of the customers, some of whom confirmed to 404 Media they had used their phone number to sign up for Suno and said they were never notified of a breach. The hacker told 404 Media they had no specific motivation for hacking Suno and said "I like to hack anything and everything."

Read more of this story at Slashdot.

Thomas Hawk posted a photo:

Thomas Hawk posted a photo:

Thomas Hawk posted a photo:

Thomas Hawk posted a photo:

Thomas Hawk posted a photo:

Thomas Hawk posted a photo: