Super Bowl - How excited are you about this year's Entertainment Lineup?
228 members have voted
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1. Are you going to watch Super Bowl?
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Hell Yeah!!!49
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No31
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2. Which event are you Most excited for other than the game?
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Alicia Keys is Awesome!5
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I like Tom Petty!11
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I can't wait to hear New Paula Abdul new song2
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I am a Huge Jordin Spark fan!0
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Ironman Trailer (Adage Confirmed)17
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Possible Batman Trailer29
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Possible Narnia Trailer (Adage likely candidate)8
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Possible Star Trek Trailer18
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Possible X-Files 2 Trailer19
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Possible Incredible Hulk Trailer8
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Possible Indy 4 Trailer21
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Possible Hellboy 2 Trailer9
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Possible Mummy 3 Trailer13
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Possible 10,000 BC Trailer(Adage likely candidate)8
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Wanted Trailer(Confirmed)5
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Semi-Pro(Adage Confirmed)3
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You Don't mess with Zohan ((Adage Confirmed)4
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Godaddy Ad13
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Victoria Secret Ad23
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Other - Write in Post12
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3. Who will Win?
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New England Patriots38
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New York Giants42
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Posts
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By Karthik Mudaliar · Posted
GitHub removes manual model selection from Copilot free and student plans by Karthik Mudaliar GitHub is removing the ability to manually select an AI model from its Copilot Free and Student plans, making its automatic routing system the default and only way to choose a model. This means users on these tiers will no longer be able to deliberately select a particular OpenAI, Anthropic, Google, or Microsoft model for a task. In its announcement, GitHub said Copilot Auto will dynamically choose what it considers the best model for each request. Free and Student accounts will retain access to models from multiple families, although the available selection will continue to depend on the restrictions attached to each plan. GitHub did not identify a fixed pool of models that Auto will always use, and its documentation warns that model availability can change over time. GitHub describes Auto as more than a random fallback system. On supported surfaces, its task-optimization technology evaluates the complexity of a request alongside real-time information about model health and availability. Straightforward prompts can be routed to faster and less expensive models, while more demanding coding tasks may be sent to higher-cost reasoning models. The company says this approach should reduce rate limiting, latency, and failed requests. Auto generally selects one model along natural prompt-caching boundaries rather than repeatedly switching models during a session, as GitHub found that mid-session changes increased costs without producing sufficient improvements in output quality. Users can still check which model generated a response. In Copilot Chat, the information appears when hovering over an answer, while Copilot CLI and the Copilot cloud agent display the selected model alongside their output. Auto is available in Copilot Chat, Copilot CLI, and the cloud agent, with the exact implementation and release status varying between supported development environments. The latest restriction follows several months of adjustments to Copilot’s individual plans. GitHub temporarily halted new Pro, Pro+, and Student subscriptions in April as it sought to manage demand and service reliability. It later introduced token-based billing and began gradually reopening individual-plan registrations on June 17. Alongside the picker change, GitHub is retiring the “Preview” label from Microsoft-developed models. It argues that the label is no longer necessary because Auto handles model routing and models are continuously updated behind the scenes. -
By thartist · Posted
Look up 'inflation' kid. Ask an AI for the numbers between both games. -
By Karthik Mudaliar · Posted
Google reportedly set to lose two key Gemini and DeepMind researchers to Anthropic by Karthik Mudaliar Google is reportedly preparing to lose two more prominent artificial intelligence researchers, with Gemini contributors Jonas Adler and Alexander Pritzel planning to join rival AI developer Anthropic. According to a report from Bloomberg, both researchers are viewed internally as important contributors to Google’s flagship Gemini model family. Adler worked on Google’s AI coding efforts, while Pritzel was involved in the process used to train AI systems. Neither company has publicly confirmed the moves. The report also does not say when the researchers will formally leave Google or what positions they will hold at Anthropic. Training a large AI model requires decisions covering its architecture, data preparation, distributed computing infrastructure, and post-training methods that shape how the finished system behaves. Researchers with experience operating at the scale of Gemini are consequently difficult to replace quickly. Both Adler and Pritzel have previously contributed to Google DeepMind’s scientific research as well. They are listed among the authors of the company’s work on expanding AlphaFold protein-structure predictions across entire proteomes, alongside AlphaFold researchers including John Jumper. The reported departures arrive shortly after another important change within Google’s Gemini organization. Gemini co-lead Noam Shazeer is leaving Google for OpenAI, after returning to the search company in 2024 through its deal with Character.AI. Shazeer is particularly well known as one of the authors of the Transformer paper, whose architecture became the foundation for most modern large language models. Anthropic, meanwhile, has been recruiting recognizable figures from other leading laboratories. OpenAI co-founder and former Tesla AI director Andrej Karpathy joined Anthropic’s pre-training team in May. His move, followed by the reported recruitment of several Google researchers, suggests Anthropic is strengthening the research teams responsible for the core capabilities of future Claude models rather than concentrating solely on product and enterprise sales. The competition is complicated by the companies’ extensive commercial relationships. Anthropic competes directly with Google’s Gemini models, but it also relies on Google as an infrastructure partner. In April, Anthropic announced an expanded agreement with Google and Broadcom covering multiple gigawatts of next-generation Tensor Processing Unit capacity. TPUs are Google-designed accelerators used to train and run large AI models. via Bloomberg -
By Primey_ · Posted
This article makes my head hurt. Lots of confusing words -
By Karthik Mudaliar · Posted
Google adds built-in computer control to Gemini 3.5 flash by Karthik Mudaliar Google has added Computer Use as a built-in tool in Gemini 3.5 Flash, giving developers a single model that can reason about a task and operate graphical interfaces across browsers, mobile devices, and desktop environments. The feature is available through the Gemini API and Google’s Gemini Enterprise Agent Platform, although it remains a preview feature for now. Computer Use enables an AI agent to examine screenshots and return actions such as mouse clicks, scrolling, and keyboard input. A developer’s application must execute those actions, capture the resulting screen, and send it back to Gemini, creating a continuous loop until the task is completed. Google says the integration can be used for activities including repetitive form filling, application testing, research across multiple websites, and longer enterprise workflows. Gemini 3.5 Flash can work with browser, mobile, and desktop environments, whereas Google’s earlier standalone Computer Use model was primarily positioned around browser interaction. The main change is consolidation. Computer control was previously offered through the separate Gemini 2.5 Computer Use preview model. As Neowin reported when that model was introduced, it was designed to interpret a visual interface and generate actions without requiring a website-specific API. Google later brought Computer Use to preview versions of Gemini 3 Pro and Gemini 3 Flash in January 2026. The latest release now incorporates the tool into the stable Gemini 3.5 Flash model rather than requiring developers to select a specialized model solely for interface automation. Gemini 3.5 Flash itself was announced in May as Google’s latest fast model for coding and multi-step agent workflows. It supports a one-million-token input context window and up to 65,000 output tokens, along with adjustable thinking levels that let developers trade additional reasoning for lower latency and cost. Google also added that Gemini 3.5 Flash received targeted adversarial training for computer-use scenarios. The company is also offering safeguards that can require user confirmation before sensitive or irreversible actions and automatically stop a workflow when suspected prompt injection is detected. Its developer documentation describes configurable protections for areas such as financial transactions and changes to sensitive records. Google isn't the first to bring Computer Use to its platform. Anthropic has made computer control available through Claude, while OpenAI has continued improving computer-use performance in its recent models. Microsoft has also applied the concept to business workflows, including a Computer Use capability for the Researcher agent in Microsoft 365 Copilot.
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