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ARC Prize@arcprizeOfficialAI score31AIARC Prize published full Grok 4.7 results from the xAI model on its public leaderboard. The post links to the leaderboard, a GitHub repository for reproducing the results, and the ARC Prize testing policy.
ARC Prize@arcprizeOfficialAI score28AIGrok 4.7 scored 1.8% on ARC-AGI-3 in the standard harness, which lets models carry notes between turns, slightly below the 2.1% reported for Grok 4.7 in that setting. In a new provider adapter harness that preserves opaque reasoning and enables auto compaction, the score rose to 10.0%.
ARC Prize@arcprizeOfficialAI score29AIOn ARC-AGI-2, Grok 4.7's best score is 61.4% at $2.01 per task, down from Grok 4.6's 67.1% at $0.76 per task. The post notes cost per task rose 166% despite identical input and output token pricing.
ARC Prize@arcprizeOfficialAI score22AIGrok 4.7 used more reasoning tokens on average than Grok 4.6 on ARC-AGI-2 semi-private tasks at medium, high, and xhigh reasoning levels, raising its cost per task. Per test-pair attempt, medium used 136% more tokens, high 125% more, and xhigh 173% more, while low used 27% fewer. A chart compares the two models at xhigh on the 20 public tasks where Grok 4.7 increased token use the most.
ARC Prize@arcprizeOfficialAI score38AISpaceXAI's Grok 4.7 scored 90.2% on ARC-AGI-1 at $0.64 per task, higher than Grok 4.6, according to ARC Prize. It reached 61.4% on ARC-AGI-2 at $2.01 per task and 1.8% on ARC-AGI-3 under the standard harness ($2.7k), or 10.0% with the provider adapter harness ($4.8k), both lower than Grok 4.6 on those two benchmarks.
👩💻 Paige Bailey@DynamicWebPaigeXAI score54AIGoogle's EmbeddingGemma 2 is an open embeddings model for on-device use that covers code, image, video, audio, and text. It comes in modular sizes from 270M text/code to 740M full multimodal, supports Matryoshka truncation down to 128 dimensions, and reports a 14% gain on MTEB Code over v1 under an Apache 2.0 license. The author's post highlights the release and a Hugging Face demo, while the benchmark table compares it with several models.
lauren@potetoXAI score39AILauren Tan describes using Grok Bot to connect apps like Slack, email, and CRM so it can gather context and decide what work to do. She also explains scheduled and event-triggered routines, plus handing engineering tasks to Cursor cloud agents, with a daily feedback-to-merge loop example.
Krea@krea_aiOfficialAI score36AIKrea has made Nano Banana 2.1 available on its platform, promising better prompt understanding, sharper edits, and stronger subject consistency. The model now supports generations up to 4K resolution.
Khazix@Khazix0918XAI score32AIThe author spent two days building an internal enterprise platform on all Feishu data and a self-built MCP, replacing Feishu's native workbench to handle Vibe Coding app deployment, security, permissions, and app and skill circulation. A custom configuration interface is planned so employees can use their own Agents to modify their homepages and data pages.
Santiago@svpinoXAI score34AIAmpersand lets developers connect an AI agent to a customer's Salesforce by configuring object and field mappings. The platform then handles API calls, authentication, token refreshes, and retries, which the post presents as a major advantage given the difficulty of managing multiple Salesforce accounts.
AIAt the Machine Earning AI Summit in San Francisco, Town co-founder Jean-Denis Greze said personal AI agent purchases will initially require human approval, with mistakes budgeted in like credit card fraud. Lead Bank CEO Jackie Reses raised liability questions, asking "If a model hallucinates, whose responsibility is that?" Chime co-founder Ryan King said AI agents will make switching banks easier, though regular people are not yet ready to let AI manage their money.
Joshua Achiam@jachiam0XAI score26AIJoshua Achiam argues that many in Silicon Valley wrongly define success as controlling the largest share of matter and energy in the universe, a goal beyond human limits that can drive them toward successionism. He contends that success instead comes from inner lives, relationships, creativity, cooperation, and striving to overcome human limitations, which could make them less pessimistic.
Dongxi NLP@dongxi_nlpXAI score34AIMistral AI has released Mistral Large 4, a model nicknamed "Le Chonk," according to a post by Dongxi NLP. The post also highlights "sovereign AI" as a keyword, tying the release to the theme of national or independent AI capability. No specifications, benchmarks, or pricing are given in the post itself.
Google Flow@FlowbyGoogleOfficialAI score20AIGoogle Flow says it partnered with Divine to create the music video for "Tequila Dance," framing the project as a showcase of how technology can amplify human expression. The post provides no further details about the tools or production process used.
👩💻 Paige Bailey@DynamicWebPaigeXAI score60AIGoogle's Nano Banana 2.1, model gemini-nano-banana-2.1, is now available and is said to outperform the previous Pro model at about a quarter of the price, $0.034 per image versus $0.134. The quoted post lists improved instruction following, better in-image text rendering, grounding with Google Image Search, and up to 5 characters of consistency plus 14 reference images. It is available in Google AI Studio, the Gemini API, Google Cloud, the Gemini app, and Flow. The author's own post is a playful reaction praising its design ability and shows a generated vegan basketball food truck poster.
AIMicrosoft Research podcast host Chad Atalla interviews Jennifer Neville, a partner research manager at Microsoft, about her path into AI and her work on how evaluation exposes surprising failures in models tested beyond traditional benchmarks. The conversation also covers practical guidance for working with current AI systems and why examining underlying data matters when results defy expectations.
Unsloth AI@UnslothAIOfficialAI score62AIGoogle released EmbeddingGemma 2, a 740M-parameter open embedding model under Apache 2.0 that combines a 270M text model with vision (170M) and audio (300M) encoders. The 270M text model can run locally with 0.5GB of RAM, and the full multimodal model with 1GB, and Unsloth provides GGUF files and fine-tuning support.
SGLang@sgl_projectOfficialAI score46AISGLang now supports EmbeddingGemma 2 from Google DeepMind on day zero. The multimodal embedding model maps text, code, images, video, and audio into one shared 768d space, with 8K context and 100+ languages. Its modular encoders range from a 270M text-only footprint to 740M for all modalities, with Matryoshka embeddings.
Google@GoogleOfficialAI score38AIGoogle has made EmbeddingGemma 2 available to download on Hugging Face and Kaggle. Availability in the Gemini Enterprise Agent Platform Model Garden is coming soon.
Google@GoogleOfficialAI score43AIGoogle's EmbeddingGemma 2 is a 740M-parameter embedding model that outperforms some models more than twice its size while using about 191MB to 567MB of active RAM. It offers an 8K context window, 4x larger than the first generation, and can process up to 5.5 minutes of audio, 29 images, or 58 video frames in one pass.
Google@GoogleOfficialAI score44AIGoogle says EmbeddingGemma 2, paired with Gemma 4, enables efficient on-device retrieval-augmented generation with a lower memory footprint. In this setup, EmbeddingGemma 2 retrieves local files and Gemma 4 reasons over them to produce grounded answers while keeping data private.
Tibo@thsottiauxXAI score23AICodex now offers an "Approve for me" permission mode, which automatically reviews actions instead of requiring manual approval. To enable it, open the permissions menu below the composer and select "Approve for me."
Gergely Orosz@GergelyOroszXAI score37AIAccording to Gergely Orosz, GitHub now records more AI-generated pull requests per month than human-written ones. He says the volume of AI-generated PRs is about three times what it was in December 2023.
ElevenLabs@ElevenLabsOfficialAI score45AIElevenLabs has introduced ElevenAgents Architect, which lets team members manage agents from within ElevenAgents or from Claude, Claude Code, ChatGPT, Cursor, and Grok Bot. The post presents this as a way for any team member to manage agents wherever they work.
ElevenLabs@ElevenLabsOfficialAI score34AIElevenLabs introduces ElevenAgents Architect, which creates a new agent from a user's description and attached context files. The tool is said to configure the agent end to end, covering the prompt, guardrails, and knowledge base.
ElevenLabs@ElevenLabsOfficialAI score30AIElevenAgents Architect can reason across thousands of your conversations to identify opportunities you might otherwise miss. Each opportunity is returned as a proposal with the fix already built and validated in simulation.
ElevenLabs@ElevenLabsOfficialAI score20AIElevenAgents Architect is a tool that answers questions about your agents, such as why customers asked for a human agent on refund calls and how to improve resolution rates on account queries. It analyzes your transcripts, suggests improvements, implements them, and can build a test set to keep your agent on brand.
ElevenLabs@ElevenLabsOfficialAI score40AIElevenLabs introduced ElevenAgents Architect, an expert built into ElevenAgents that helps teams launch and improve AI agents through voice or text. The post describes it as a conversational way to create and refine agents without further technical detail provided.
👩💻 Paige Bailey@DynamicWebPaigeXAI score38AIGoogle has released Nano Banana 2.1, its latest image generation and editing model, which it says outperforms previous versions with gains in visual design, mask-based editing, and subject consistency. Paige Bailey shared a pug smile example made with the model and invited users to try it in Google AI Studio.
Philipp Schmid@_philschmidXAI score62AIGoogle's Nano Banana 2.1 (gemini-nano-banana-2.1) is now available and outperforms the previous Pro model at $0.034 per image, versus $0.134 before. It adds improved instruction following, better in-image text rendering, grounding with Google Image Search, and consistency for up to 5 characters with 14 reference images. It is available in Google AI Studio, the Gemini API, Google Cloud, the Gemini app, and Flow by Google.
fal@falOfficialAI score38AIGoogle's Gemini Nano Banana 2.1 is now available on fal, offering significantly faster generation than Nano Banana 2. The model adds major gains in visual design, mask-based editing, and subject consistency.
Microsoft Copilot@MSFTCopilotOfficialAI score22AIMicrosoft's Copilot has a new identity designed around how work actually happens, bringing apps, tools, and context into one connected experience. The post offers a behind-the-scenes look at the redesign but gives no further specifics.
Google Gemma@googlegemmaOfficialAI score62AIGoogle Gemma announces EmbeddingGemma 2, a lightweight embedding model that maps text, code, images, video, and audio into a single unified embedding space. The model has a 740M parameter form factor with modular encoders, Matryoshka Representation Learning dimensions from 768 down to 128, and an 8K context window that is 4x larger than the text-only EmbeddingGemma. It is released under the commercially permissive Apache 2.0 license.
Google Gemma@googlegemmaOfficialAI score42AIGoogle Gemma says EmbeddingGemma 2 delivers best-in-size-class performance on MTEB (Code) and MIEB (Lite). The post highlights local codebase indexing, semantic code search, and coding agent retrieval, with a 9.92-point code performance gain over EmbeddingGemma.
Google for Developers@googledevsOfficialAI score48AIGoogle has released EmbeddingGemma 2, a compact 740M-parameter model built for on-device and edge applications. It natively embeds text, images, audio, and video into one shared space, enabling cross-format search without manual organization, translation, or labeling.
Google for Developers@googledevsOfficialAI score36AIGoogle has released the weights for EmbeddingGemma 2 on Hugging Face and Kaggle. The release includes out-of-the-box support for LiteRT, MediaPipe, LangChain, and LlamaIndex.
Google for Developers@googledevsOfficialAI score40AIGoogle's new multimodal embedding setup processes image, audio, and video entirely offline with zero server calls. Its modular design lets developers drop unused vision and audio components to save memory, and flexible dimension sizes cut local database storage by up to 6x. It can also pair with Gemma 4 to build RAG pipelines with minimal memory and processing requirements.
Google DeepMind@GoogleDeepMindOfficialAI score58AIGoogle DeepMind released EmbeddingGemma 2, a 740M-parameter embedding model, under an Apache 2.0 license. The post says it is competitive across benchmarks and outperforms some specialist models more than twice its size, and that developers can use it for multimodal search or pair it with Gemma 4 for on-device RAG. Weights are available on Hugging Face and Kaggle.
Google DeepMind@GoogleDeepMindOfficialAI score62AIGoogle DeepMind introduced EmbeddingGemma 2, its first natively multimodal open model for on-device embeddings. The model expands beyond text to unify code, images, audio, and video in a shared embedding space.
Sundar Pichai@sundarpichaiXAI score62AIGoogle introduces EmbeddingGemma 2, its first open, natively multimodal embedding model, covering text, code, image, video, and audio tasks. It has a 740M parameter form factor, is positioned for offline, privacy-first RAG when paired with Gemma 4, and the post claims it outperforms some specialist models more than twice its size. Weights are available now on Hugging Face.
