Can Google’s Nano-banana Make Photoshop Obsolete?

Google has released Gemini 2.5 Flash Image, its most capable AI image model to this point, designed to repair two long-standing weaknesses in generative editing: prompt precision and subject fidelity. Previously referred to as Namobananthe model is now live in Gemini, Vertex AI, AI Studio, and via API access. It will probably keep a subject’s identity intact across edits, whether shifting a pose, swapping a background, or changing lighting, a problem that also causes distortion in competitors like GPT-4o or Firefly.

The system handles multi-step instructions, blends up to a few images, and supports localized edits or style transfers. Google claims benchmark wins on prompt accuracy, a long-standing pain point in image generation. It even shows basic early “real-world reasoning,” and each output ships with SynthID watermarks, a direct response to deepfake misuse and Google’s own past controversies over AI-generated inaccuracies. At roughly 4 cents per image ($30 per million tokens), Gemini 2.5 Flash is priced for scale.

That very same push for practical AI shows up in Translatenow morphing right into a Duolingo rival with AI. The brand new beta feature, powered by Gemini, builds personalized lessons that adapt to your skill level and purpose: brushing up for a visit, prepping for study abroad, or polishing business conversations.

On top of that, Google is rolling outlast AI-powered translation for actual conversations. Available within the US, India, and Mexico, it supports over 70 languages, delivering real-time transcripts and audio translations backwards and forwards. It doesn’t clone your voice yet, unlike Pixel’s on-device version, but Google says it’s experimenting. Read more.

Anthropic is charting a cautious course in browser-controlling AI with its Claude for Chrome pilot, letting 1,000 premium users automate tasks like scheduling, inbox triage, and web navigation. Early tests exposed prompt-injection vulnerabilities, where hidden instructions tricked Claude into destructive actions with a 23.6% success rate. Safeguards reminiscent of site-level permissions, high-risk confirmations, and domain blocks cut that to 11.2%, but risk stays.

The corporate’s measured approach mirrors its legal strategy. Anthropic also reached a preliminary settlement in a high-stakes copyright lawsuit over pirated books used to coach its models, sidestepping potential statutory damages that might have exceeded $1 trillion. Read more.

OpenAI is retiring its Assistants API, giving developers until August 26, 2026, to migrate to the newer Responses API. Assistants were the corporate’s early approach to constructing AI agents, but with Responses reaching feature parity, OpenAI has folded the very best elements, including code interpreter and chronic conversations, into the brand new system.

Responses streamlines multi-step workflows across built-in tools like deep research, MCP, and computer use, while GPT-5 preserves reasoning tokens across turns. Usage already outpaces Chat Completions, and OpenAI recommends it as the first integration path going forward. Developers can follow the migration guide here: [https://platform.openai.com/docs/assistants/migration.] Read more.

Nvidia is back within the highlight as its Q2 earnings drop, and the market is watching prefer it’s a stress test for your entire AI boom. The $4 trillion chip giant drives the GPUs powering generative AI and cloud workloads. Analysts expect $46 billion in revenue, up 53% year-over-year, with data center sales near $40 billion and EPS hitting $1.01. Growth is heavily concentrated amongst a handful of cloud giants and deep-pocketed AI startups, and China stays a wildcard: limited export licenses with a 15% revenue-share deal reduce upside visibility. After a 35% stock surge since May, even a small miss could rattle the market, feeding fears of an AI bubble.

Parallel to its market influence, Nvidia is pushing AI off the cloud and into the physical world. Nvidia’s Jetson AGX Thor brings “physical AI” to the sting. Delivering 2,070 FP4 teraflops with 128GB memory at 130W, Thor runs LLMs, vision transformers, and Isaac GR00T in parallel. Robots, surgical machines, and industrial systems now perceive, reason, and act in real time without cloud latency.

Early adopters include Boston Dynamics, Amazon Robotics, Caterpillar, and Figure, with Meta, OpenAI, and Medtronic testing. Thor is basically a supercomputer you may plug right into a robot. Read more.