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AIAnthropic's Claude Startup Stack combines offers from companies building with Claude, including Linear, Lovable, ElevenLabs, Granola, and Hex. Startups get discounts and credits worth up to $45,000 on tools spanning sales, design, data engineering, and more.

AIARC Prize reports that Grok 4.7's reasoning-token usage correlates with its ARC-AGI-2 public scores. The low setting averaged about 10k tokens per test-pair attempt and scored 25%, while medium through xhigh used 86k to 120k tokens and scored 57.5% to 60%. ARC Prize suggests the lower token usage may help explain the low setting's lower score.

AIOn 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.

AIGrok 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.

AISpaceXAI'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.

AIGoogle'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.

AIKrea 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.

AIGoogle 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.

AIGoogle'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.

Why it matters: The quoted announcement gives concrete changes and a price drop for image generation, useful for weighing cost against the previous Pro model.

AIGoogle 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.

Why it matters: The post pairs the model's parameter split and local memory footprint with a benchmark table, showing how the multimodal embedding model compares with other embedding models.

AISGLang 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.

AIGoogle'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.

AIGoogle 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.

AIElevenLabs 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.

AIElevenLabs 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.

AIElevenAgents 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.

AIElevenAgents 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.

AIElevenLabs 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.

AIGoogle 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.

AIGoogle'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.

Why it matters: The source gives concrete pricing and feature changes for an image model, letting developers compare cost and capability against the previous Pro version.

AIGoogle'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.

AIMicrosoft'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.

AIGoogle 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.

Why it matters: The post gives concrete specs for an on-device multimodal embedding model, including parameter count, dimension options, context window, and license, useful for judging deployment fit.

AIGoogle 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.

AIGoogle 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.

AIGoogle'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.

AIGoogle 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.

AIGoogle 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.

Why it matters: The release extends an on-device embedding model from text to code, images, audio, and video, which matters for teams building cross-modal search or retrieval.

AIGoogle 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.

Why it matters: The post gives the parameter count and modalities, and notes that weights are on Hugging Face, which helps readers assess its fit for offline RAG.