“Philipp Schmid launched Multi-Token Prediction for Gemma 4, tripling inference speed with zero quality loss—now available E2B/E4B under Apache 2.0.”

#13 𝕏 Philipp Schmid launched Multi-Token Prediction for Gemma 4, tripling inference speed with zero quality loss—now available E2B/E4B under Apache 2.0. #14 𝕏 Philipp Schmid outlines four subagent coordination patterns—tool calls, spawns, pools, and teams—to structure multi-agent workflows.

“Open-Source Gemma 4 Embedding Demo Available #1 𝕏 Sebastian Raschka shared a from-scratch Jupyter Notebook implementation of Gemma 4 E2B on GitHub, demonstrating how per-layer embeddings are built.”

Open-Source Gemma 4 Embedding Demo Available #1 𝕏 Sebastian Raschka shared a from-scratch Jupyter Notebook implementation of Gemma 4 E2B on GitHub, demonstrating how per-layer embeddings are built.

“Google AI spotlights fun builder projects powered by last week’s open-source Gemma 4 models.”

#9 𝕏 Google AI spotlights fun builder projects powered by last week’s open-source Gemma 4 models. Examples include automated meeting summarizers, code-generation assistants, and multilingual chatbots, each detailed with tool integrations, performance stats, and user insights.

“Google DeepMind launched Gemma 4, a lineup of 7B–196B-parameter foundation models with up to 100K-token contexts and multimodal capabilities.”

#2 𝕏 Google DeepMind launched Gemma 4, a lineup of 7B–196B-parameter foundation models with up to 100K-token contexts and multimodal capabilities. Developers can now access open-source weights, code samples, and tutorials via Vertex AI and GitHub to jumpstart building AI apps. Also covered by: @Jeff Dean

“Google DeepMind launched Gemma 4, a lineup of 7B–196B-parameter foundation models with up to 100K-token contexts and multimodal capabilities. Developers can now access open-source weights, code samples, and tutorials via Vertex AI and GitHub to jumpstart building AI apps.”

#2 𝕏 Google DeepMind launched Gemma 4, a lineup of 7B–196B-parameter foundation models with up to 100K-token contexts and multimodal capabilities. Developers can now access open-source weights, code samples, and tutorials via Vertex AI and GitHub to jumpstart building AI apps. Also covered by: @Jeff Dean

“Google DeepMind launched Gemma 4, a lineup of 7B–196B-parameter foundation models with up to 100K-token contexts and multimodal capabilities.”

Google DeepMind launched Gemma 4, a lineup of 7B–196B-parameter foundation models with up to 100K-token contexts and multimodal capabilities. Developers can now access open-source weights, code samples, and tutorials via Vertex AI and GitHub to jumpstart building AI apps.

“#6 𝕏 Sundar Pichai announces that Gemma 4 has exceeded 10 million downloads in its first week, pushing total Gemma model downloads past 500 million, and shares excitement to see what users build next. #7 ▶️ Google just casually disrupted the open-source AI narrative… Fireship Google’s Gemma 4 is a 31 billion-parameter, Apache 2.0-licensed open-source LLM that runs locally in 20 GB on an RTX 4090 by using TurboQuant and per-layer embeddings for compression.”

Today's top 25 insights for PM Builders, ranked by relevance from Blogs, X, YouTube, and LinkedIn. Anthropic Scales Managed Agents #1 📝 Anthropic Engineering Scaling Managed Agents: Decoupling the brain from the hands - This article describes an approach to scale managed agents by separating decision-making (the 'brain') from execution (the 'hands'), enabling better scalability and modularity of agentic systems. It outlines architectural patterns for building managed-agent platforms. #2 📝 OpenAI News The next phase of enterprise AI - OpenAI announces the next phase of its enterprise AI strategy, describing initiatives to accelerate adoption of advanced AI capabilities across businesses and enterprises. #3 𝕏 Sundar Pichai announced Notebooks are now rolling out in the Gemini app for Google AI Ultra, Pro, and Plus web subscribers, letting users organize conversations, notes, and project sources. The feature integrates with NotebookLM for seamless deep dives. #4 𝕏 Philipp Schmid rolled out Flex and Priority `service_tiers` for the Gemini API—Flex inference (`service_tier="flex"`) cuts costs by 50% on latency-tolerant workloads, while Priority (`service_tier="priority"`) guarantees low-latency with automatic fallback to Standard, all vi... #5 𝕏 AI at Meta unveiled Muse Spark, a multimodal model built from the ground up to integrate visual and textual data for richer AI understanding. #6 𝕏 Sundar Pichai announces that Gemma 4 has exceeded 10 million downloads in its first week, pushing total Gemma model downloads past 500 million, and shares excitement to see what users build next. Also covered by: @Santiago #7 ▶️ Google just casually disrupted the open-source AI narrative… Fireship Google’s Gemma 4 is a 31 billion-parameter, Apache 2.0-licensed open-source LLM that runs locally in 20 GB on an RTX 4090 by using TurboQuant and per-layer embeddings for compression. Gemma 4 big model (31 B parameters) downloads in 20 GB and delivers ~10 tokens/sec on a single RTX 4090, while its Edge variant can run on a phone or Raspberry Pi. TurboQuant compresses model weights by converting Cartesian data to polar coordinates and applying the Johnson–Lindenstrauss transform to quantize values to single sign bits while preserving distances. Models named E2B and E4B use “effective parameters” via per-layer embeddings, giving each transformer layer its own token embedding to introduce information exactly when needed. Also covered by: @Santiago ...

“Google's official iPhone app for running Gemma 4 models (E2B, E4B and some Gemma 3 family) works very well locally, with the E2B model a 2.54GB download; it supports image Q&A, short audio transcription and an interactive "skills" demo but conversations are ephemeral and the app lacks permanent logs.”

#2 📝 Simon Willison Google AI Edge Gallery - Google's official iPhone app for running Gemma 4 models (E2B, E4B and some Gemma 3 family) works very well locally, with the E2B model a 2.54GB download; it supports image Q&A, short audio transcription and an interactive "skills" demo but conversations are ephemeral and the app lacks permanent logs.

“Google AI Edge Gallery - Google's official app for running Gemma 4 models on iPhone provides fast, useful local inference (notably the E2B model) plus image question answering, short audio transcription, and an interesting 'skills' demo showing tool-calling via HTML widgets.”

Google Launches AI Edge Gallery App for iPhone #1 📝 Simon Willison Google AI Edge Gallery - Google's official app for running Gemma 4 models on iPhone provides fast, useful local inference (notably the E2B model) plus image question answering, short audio transcription, and an interesting 'skills' demo showing tool-calling via HTML widgets. The app works well but conversations are ephemeral and it lacks permanent logs.

“Google DeepMind launched Gemma 4, a family of Apache 2.0–licensed open models you can run on your own hardware for advanced reasoning and agentic workflows.”

Google DeepMind Releases Gemma 4 Open Models #1 𝕏 Google DeepMind launched Gemma 4, a family of Apache 2.0–licensed open models you can run on your own hardware for advanced reasoning and agentic workflows. Also covered by: @Sebastian Raschka , @Simon Willison , @Philipp Schmid , @Jeff Dean , @Google DeepMind , @Demis Hassabis , @Demis Hassabis , @Sebastian Raschka #2 𝕏 Qwen unveiled Qwen3.6-Plus, a next-gen multimodal agentic model with smarter, faster coding execution, sharper vision reasoning and a 1M-token context window by default via API, all while maintaining top-tier general performance.