Gemini API Features

🚀🤖📚🎨🧠 Gemini 3 just turned the web into a whiteboard Search used to be about finding information. Google's Gemini 3 is about generating understanding. Gemini 3 is making serious waves in the AI world. Not because of another benchmark slide. Not because of a slightly better reasoning score. The big shift sits somewhere else: AI is quietly becoming a learning and building interface. On the learning side: Gemini 3 can turn dense content into interactive experiences. Think flashcards, visualizations, small simulations, even tiny teaching games. All generated on the fly from papers, videos, or handwritten notes. That means learners don’t just receive information. They interact with it. They play with it. They test it. This opens new doors for learning UX. Interfaces can adapt to knowledge gaps in real time. Explanations, representations, and difficulty levels become fluid materials. On the web side: AI Mode in Search now uses Gemini 3 directly. Instead of ten blue links, immersive layouts appear. Interactive tools, visual flows, and simulations appear with each query. So the web shifts again. Deep explanations won’t always be found on a page. They will be composed per person, per moment, per intent. Design teams will need to rethink: What does “content strategy” mean when content is generated? What does “navigation” mean when journeys are dynamic? What does “teacher” mean when the interface itself explains? Of course, this amplification has a dark edge. The deeper the generated understanding, the higher the stakes. Hallucinations stop being annoying and start being dangerous. Trust, verification, and transparency must become core UX materials. On the building side: Gemini 3 is an impressive vibe coding partner. Zero-shot interactive prototypes become normal, not magical. Design ideas can jump straight into working UI drafts. Designers can sketch in language and behavior, not just layout. “Make this a spaced-repetition trainer.” “Turn this flow into a small simulation.” “Wrap this concept in an interactive explainer.” The official Gemini 3 announcement is worth a read. It shows how learning, building, and planning merge into one workflow. So here’s the question that keeps circling in my head: 👉 When AI starts generating understanding, not just information, who truly designs the experience — the product team, the model, or the interaction between both? For more thoughts on the Empathic Web, AI, and design, follow my profile here on LinkedIn. #Gemini3 #UXDesign #UXD #UX #Design #Google #AIInDesign #LearningExperienceDesign #ProductDesign #AIUX #VibeCoding #DesignLeadership

Excited to introduce our research and novel implementation of 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗨𝗜, coming to life today with the release of Gemini 3! Now rolling out in the Gemini app and in Google Search, starting with AI Mode. Generative UI is a new capability in which an AI model generates not only content but an entire user experience. ✨ New Design Paradigm: We introduce a novel implementation of generative UI which dynamically creates immersive, visual experiences and interactive interfaces—such as web pages, games, tools and applications—that are automatically designed and fully customized in response to any question, instruction, or prompt. 🛠️ How the generative UI implementation works  Our generative UI implementation, described in a paper we made public today, uses Google’s Gemini 3 Pro model with three important additions: tool access (e.g., image generation, web search), carefully crafted system instructions, and post-processing to manage potential common issues. 💡 Example scenarios: Generative UI is useful across a wide range of scenarios. From learning about fractals or any topic, to teaching mathematics, to getting tailored fashion advice - see more examples in the project page.  The user prompt can be as simple as a single word, or as long as needed for detailed instructions. 🚀 Our research on generative UI, comes to life today in the Gemini app through an experiment called dynamic view and in AI Mode in Search. Gemini App: In dynamic view, Gemini designs and codes a fully customized interactive response for each prompt, using Gemini’s agentic coding capabilities. It understands that explaining a complex topic like the microbiome to a child requires a different interface than explaining it to an adult. Google Search: We are integrating generative UI capabilities starting with AI Mode (for Pro and Ultra subscribers in the U.S.) unlocking dynamic visual experiences with interactive tools and simulations that are generated specifically for a user’s question. 📈 The magic cycle of generative UI research: Excited to see our foundational research on generative UI coming to life in product innovation in Search AI Mode and Gemini dynamic view.  We are still in the early days of generative UI and important opportunities for improvement remain, like improving efficiency and accuracy, extending generative UI to access a wider set of services, adapt to additional context and human feedback, and deliver increasingly more helpful visual and interactive interfaces. Read more about the research in our blog: https://goo.gle/47FdGcR  Read the new paper: https://lnkd.in/d-PxE5nG See the project page with more examples: https://lnkd.in/dzWejH2B

🔥 Inside Gemini’s AI Image Tool — Nano Banana How Multimodal Intelligence Creates Visual Precision AI image generation has evolved far beyond making “beautiful pictures.” Today, the most advanced systems understand context — across text, images, video, and even sensor data — to produce photorealistic, intention-aligned results. Gemini’s Nano Banana is a perfect example of that leap. Here’s how its end-to-end multimodal image generation pipeline works 👇 🧩 1. Input Stage Accepts multimodal inputs — text, images, video, and even real-time contextual sensor signals. 📝 2. Text Processing Multilingual datasets transform raw text into dynamic embeddings rich with nuance and context. 🖼️ 3. Image Pre-Processing Extracts lighting, materials, 3D structure, and composition to build layered feature maps. 🔗 4. Multimodal Alignment Aligns text and visual signals, learning cross-modal relationships with high efficiency. 🧠 5. Concept Understanding Builds a semantic plan and adapts to historical user preferences for personalized generation. 🌫️ 6. Noise Initialization Creates structured noise from learned distributions — forming early shapes, edges, and colors. 🔄 7. Guided Transformation Removes noise in stages, guided by real-world transformation datasets that anchor realism. 🎯 8. Attention Mechanism Focuses computation on the most relevant tokens and visual features for fine-grained accuracy. 🪄 9. Iterative Refinement Adds texture, depth, shadows, and environmental cues that mimic real-world physics. ✨ 10. Final Polishing Enhances reflections, sharpness, and micro-details using calibrated visual data. 🔐 11. Safety & Consistency Check Evaluates harmful content, style mismatches, and semantic coherence. 📤 12. Output Delivery Applies secure AI watermarks and exports multiple high-resolution formats. 🌟 Why this matters Each layer in the Nano Banana workflow represents a leap toward trustworthy, multimodal creativity — a world where AI doesn’t just render images, but truly understands them. This deep alignment between text, vision, and user intent is redefining how creators, engineers, and designers interact with AI. How close are we to achieving human-level intuition in visual AI systems? Would it change how we think about creativity, authorship, and imagination? #AI #GeminiAI #ImageGeneration #MultimodalAI #GenAI #ArtificialIntelligence #VisualComputing #Innovation #AIDesign

🧠 Gemini Embedding 2 Google DeepMind just released Gemini Embedding 2. The obvious headline is that it embeds text, images, audio, video, and documents. The more interesting shift is architectural: everything is projected into a single shared embedding space. Historically, most pipelines looked like this: text encoder → text vectors image encoder → image vectors audio encoder → audio vectors Each modality lived in its own latent space. Cross-modal retrieval required alignment tricks like projection heads or CLIP-style contrastive pipelines. Gemini Embedding 2 moves the alignment directly into the model. text → vector image → vector audio → vector video → vector PDF → vector All of these are mapped into a unified semantic manifold where vectors can be compared with cosine similarity. This makes cross-modal retrieval much simpler. A text query can retrieve a video moment. An image can retrieve documents. An audio clip can retrieve conversations. Instead of maintaining multiple modality-specific indices, you can run a single ANN index over embeddings and retrieve purely by semantic proximity. Technically, this only works if the model learns strong cross-modal alignment during training. Modern multimodal systems typically rely on combinations of: • cross-modal contrastive learning • masked multimodal modeling • generative multimodal supervision There is also interesting research emerging around how multimodal embeddings can be derived from large multimodal models. For example, a recent post from Jina AI (https://lnkd.in/gCYAVtXP) describes bootstrapping audio embeddings from multimodal LLMs by extracting hidden representations from models already trained to jointly process text, audio, and visual inputs. Since transformers internally fuse tokens from different modalities, later-layer representations often already encode cross-modal semantics. A lightweight projection layer can then convert those representations into usable embeddings. This direction highlights how the boundary between generative multimodal models and embedding systems is starting to blur. One clever component in Gemini Embedding 2 is Matryoshka Representation Learning. Instead of producing a fixed embedding dimensionality, the model learns nested representations that can be truncated: 3072 dimensions 1536 dimensions 768 dimensions The early dimensions carry the highest semantic density, allowing you to trade off storage, retrieval latency, and recall quality. ✨ The big implication is for RAG systems. When embeddings become natively multimodal, the retrieval layer stops being document retrieval and becomes knowledge retrieval across media. Your index can contain text chunks, slides, images, voice notes, and video clips and the system simply retrieves whatever is semantically closest to the query. Embeddings are increasingly becoming the universal interface between raw data and reasoning systems.

BREAKING: Google Gemini 3 might be the most powerful AI model yet! Top 5 upgrades you should know. 👇 Upgrade #1 – State-of-the-art reasoning & benchmark domination Gemini 3 Pro smashes major AI benchmarks: Tops the LMArena leaderboard with an Elo score of 1501. PhD-level reasoning: e.g., 37.5% on “Humanity’s Last Exam” without tool use; 91.9% on GPQA Diamond. Multimodal understanding: 81% on MMMU-Pro, 87.6% on Video-MMMU. Upgrade #2 – Multimodal superpowers + mega context window. Gemini 3 isn’t just text-smart. It reads images, video, audio, code. It now supports a 1 million-token context window. Example use-cases: deciphering handwritten recipes in different languages, analyzing your pickleball match video and generating training plans. If you’ve got weird formats or long-form data, this opens big new doors. Upgrade #3 – Build anything: agentic dev & vibe-coding For developers & creators: Gemini 3 is described as “the best vibe coding and agentic coding model we’ve ever built.” It achieved top marks on WebDev Arena (1487 Elo) and Terminal-Bench 2.0 (tool use via terminal) plus SWE-bench Verified. Launching with new platform: Google Antigravity — an agentic dev environment where the AI assists the editor, terminal, browser. Upgrade #4 – Plan anything: long-horizon workflows Gemini 3 isn’t just reactive — it can plan and execute complex, multi-step workflows. Example: It topped the “Vending-Bench 2” which tests managing a simulated vending machine business over a year. Google frames it as “it can take action on your behalf by navigating more complex, multi-step workflows … while under your control and guidance.” So if you’ve got big workflows (marketing campaigns, product launches, business ops) this is a serious leap. Upgrade #5 – Safety, security & responsible rollout Every power boost comes with risk. Google says Gemini 3 “is our most secure model yet,” with enhanced resistance to prompt injection, sycophancy (yes, AI flattery), and misuse. Also, roll-out is phased: Gemini 3 is available now in the Gemini app, Search (AI mode), AI Studio, Vertex AI; the “Deep Think” mode (even more capable) is coming later after extra safety review.

🚀 Google DeepMind just dropped Gemini 3, and it feels like we’re in a new era! I don’t say this lightly: what Google released today is the biggest leap forward in the Gemini lineage since the original “native multimodality” moment. Gemini 3 isn’t just a bigger model. It’s a different species of model. Here are the 6 things that blew my mind 👇 1. The model can finally “read the room”, not just the prompt Sundar Pichai, Demis Hassabis, and koray kavukcuoglu said it clearly: Gemini 3 understands intent, not just text. It scores: 1501 Elo on LMArena (new #1 after xAI's Grok 4.1 leading yesterday) This is the first Google model that feels like a thought partner, not just an autocomplete engine. 2. Deep Think mode is… wild Gemini 3 Deep Think is essentially “AGI mode on training wheels.” This is Google admitting: ➡️ We now have frontier-grade reasoning that must go through safety review before exposure. That alone is a signal. 3. Search with Gemini 3 is the biggest upgrade since PageRank For the first time ever, a Gemini model ships in Search on day one. AI Mode now gives: ✅ Dynamic visual layouts ✅ Interactive tools & simulations generated in real time ✅ A massively upgraded query fan-out engine ✅ Automatic routing to Gemini 3 for harder queries The “three-body problem → auto-generated physics simulator” example is the future of learning. Not search results, search experiences. 4. Google Antigravity might redefine how software is built This deserves its own post. Antigravity is a new agentic development platform where: ✅ Agents have direct access to editor, terminal, and browser ✅ They can plan + execute full features end-to-end ✅ Multiple agents run in parallel ✅ The developer becomes the architect, not the typist 5. Multimodality is no longer a “feature”, it’s the foundation Gemini 3 can: ✅ Parse handwritten recipes → generate a family cookbook ✅ Analyze your pickleball game from video → build a training plan ✅ Turn a single image into an interactive web app ✅ Understand OS screens, cursor movements, gestures, and intent ✅ Translate academic papers + hour-long lectures → interactive flashcards, visualizations, or full learning paths This isn’t multimodal “input.” This is multimodal thinking. 6. Developers just got a completely new toolbox Gemini 3 is now available with client-side + server-side bash tools, new “thinking level” (and thought-signature validation), and configurable multimodal fidelity (finally!) The bigger picture: Gemini 1 gave us multimodality. Gemini 2 unlocked agents. Gemini 3 combines everything into coherent intelligence. AI isn’t just answering questions anymore. It’s learning what you mean, building what you imagine, and planning what you’d do next. (official release posts are linked in comments). This is the closest Google has ever been to saying the quiet part out loud: ➡️ We’re on the AGI path, and it’s accelerating.