This prompt works best with Nano Banana 2: Prompt: Make a photo of a cute ball-shaped version of a [animal], with big adorable over-sized eyes, it has no head, instead its face is part of the ball, its head and body are one. They are in their usual habitat. Must be almost spherical and cartoonishly cute. Its eyes are looking up in the most adorable cheeky fashion.

$GOOGL LAUNCHES “PICS,” AN AI-POWERED CANVA RIVAL FOR WORKSPACE Google is rolling out Google Pics, a new image generation and editing app built around Gemini and Nano Banana. Pics lets users: • Generate and edit images with prompts • Select and modify individual objects or text • Translate and reformat text inside images • Upscale images to 2K or 4K • Resize designs for social, web, print and digital • Collaborate with teammates Pics will work as a standalone Workspace app and inside Docs and Slides, with Drive integration coming later. The product puts Google more directly against Canva and Adobe Express for business design workflows.

Here’s this week’s shipping recap 👇 — Nano Banana 2 & Nano Banana Pro are now GA and available via the Gemini Enterprise Agent Platform, Gemini API, and in @GoogleAIStudio —Co-Scientist, our new multi-agent system for structured scientific thinking, generates and refines novel hypotheses to solve complex scientific problems — dreambeans from @GoogleLabs works overnight to curate a personalized daily collection of topics that are relevant to you based on your connected Google apps — @GoogleGemma 4 12B, our unified encoder-free model, brings powerful multimodal intelligence straight to your laptop fully offline — Gemma 4 models and their drafters are now optimized with Quantization-Aware Training (QAT) to cut memory requirements and maximize on-device performance — @GoogleMagenta RealTime 2 is our open-weights live music model that you can play like an instrument using a MIDI keyboard, text prompts, and gestures

# Elasticsearch Features and Practical Usage 🌊 The era of manually rotating ` is over. With data streams your app just keeps writing to the same name, and all the backing-index management is handled for you. 🏷️ Title: Data stream (logical stream for append-only time-series data) 🔗 URL: 📘 Overview A data stream is an abstraction layer that lets you address a set of indices, optimized for append-only time-series data, under a single name. It suits continuously flowing data like logs, events, and metrics — you read and write to one resource name without worrying about the hidden "backing indices" behind it. ⚙️ How It Works ・A data stream is made of multiple auto-generated, hidden backing indices. The naming is `.ds--<>-`, where the generation is a six-digit, zero-padded integer starting at `000001` (e.g. `.ds-logs-myapp-prod-2026.06.10-000001`). ・Only the most recent "write index" accepts new documents. You cannot write directly to older backing indices. ・Searches automatically route to all backing indices, so queries span the entire dataset. ・Every document needs an `@timestamp` field mapped as `date` or `date_nanos`. If the template omits it, a default `date` mapping is applied automatically. ・A matching index template containing a `data_stream` definition is mandatory; it holds the mappings, settings, and lifecycle policy. One template can be shared across multiple data streams. ・When an age or size threshold is hit, a "rollover" creates a new backing index and switches the write index. This is automated via ILM or data stream lifecycle. 🛠️ Practical Usage First, define an index template that includes `data_stream`. `PUT _index_template/logs-myapp-template` `{ "index_patterns": ["logs-myapp-*"], "data_stream": {}, "template": { "mappings": { "properties": { "@timestamp": { "type": "date" } } } } }` Then your app simply keeps writing to the same name. `POST logs-myapp-prod/_doc` `{ "@timestamp": "2026-06-10T09:00:00Z", "level": "INFO", "message": "started" }` 💡 Use Cases A common setup is writing application logs to a `logs-myapp-prod` data stream and letting backing-index rollover be managed automatically. The app no longer rotates date-based indices (` itself; it just POSTs to the same endpoint every time. ⚠️ Caveats ・Backing index names are an internal implementation detail. They can change during restore or shrink, so never build logic (like dates) off the names. ・It is a poor fit for frequently overwriting the same ID (last-write-wins); for that, use a regular index or an alias. ・Use dedicated APIs like `update by query` and `delete by query` for modifications, and remember you cannot write directly to anything but the write index. ・You cannot delete an index template that is in use by a data stream. #Elasticsearch# #DataStreams#

Introducing Nano Banana Pro in Ava Studio Higher-fidelity visuals, more stable characters, precise shot control, and faster renders — built for creators and marketers who need reliable, production-ready output. We’re also rolling out new subscription tiers with larger credit pools and flexible billing to support different workflow needs. Your next production starts here with Ava Studio.

We’re excited to share that @nvidia Nemotron 3 Nano Omni is now available at launch on Bitdeer AI Model Studio 🎉. As part of the NVIDIA #Nemotron# 3 family, Nemotron 3 Nano Omni is an open multimodal model that unifies reasoning across video, audio, images, and text， simplifying agentic AI development with leading efficiency and accuracy. From multimodal understanding to real-world enterprise workflows, Nemotron 3 Nano Omni helps reduce the complexity of fragmented model stacks while enabling richer cross-modal context for search, analysis, summarization, and automation. Access and run it today on Bitdeer AI Model Studio through a simple serverless #inference# API, making it easier to move from experimentation to production. 🔧 Get started today: 💡 Read the full blog: #neocloud# #AI# #ModelStudio# #NVIDIAAI#

Why choose between local performance and cloud reliability? Firebase AI Logic lets you run Gemini Nano directly in Chrome for Desktop, automatically switching to cloud models whenever needed, providing local privacy with cloud fallback. Try the setup workflow →

how to generate AI images and get different close up shots of the same character without losing consistency↓ 1 - generate your hero image on midjourney. I find that mj v8.2 has been the best model to work with for hyperrealistic portraits 2 - Use that hero image as a reference with nano banana 2 to get close up shots or new angles of the same shot. In addition to the reference image, prompt it with details of how the model looks. I find that combining a reference image + prompting descriptively helps keep it the most consistent. Here's the prompt for this one: ~Extreme macro close-up of the same woman from the reference image, keeping her exact identity, pale blue eyes, warm aged skin and silver hair. Tight crop on one eye and eyebrow, straight-on eye-level angle, serene expression. Hyperrealistic detail: pale blue iris with fine striations and a soft catchlight, natural sparse lashes, fine crepey eyelid texture, deep natural wrinkles and age spots, a soft silver brow. Soft even studio beauty light, pale gray background, 100mm macro, razor sharp, photorealistic, not airbrushed, no text.~ The gap between AI generated and real photography is closing a lot faster than most people think. A couple months ago even I thought models still weren't good enough to look human realistic, but now you have such fine control to get things like pores distributed naturally across the skin, individual eyebrow hairs and eyelashes. Natural skin creepiness and peach fuzz along the skin. Take these images and use them as references in videos for strong consistency even in complex shots