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Google is now testing AI-generated summaries direc...
Google is now testing AI-generated summaries directly inside Search ads. Yes, ads - the one place where businesses usually assume they control the message. This has huge implications for B2B SaaS, e-commerce, financial services, education, local services, insurance and pretty much any business spending money on Google Search. Let’s go through it. By the way, you can see whether your business is appearing across Google AI, ChatGPT, Claude, Perplexity and Grok here. It’s free: https://t.co/Pn764BHwyL Search Engine Land just reported that Google is testing AI-generated summaries beneath sponsored Search results. Search Engine Roundtable covered the same test. The summaries appear directly under the ad description. Google is also adding a disclaimer that says the AI responses are generated independently and can make mistakes, which is pretty important. Because if Google’s AI is generating context under your ad, then your paid search result is no longer limited to only *your* headline, your description and your landing page. If Google is explaining your business to the customer, it needs to work off a basic level of information about your entity to format that explanation. And that explanation may influence whether they click. Google has already been moving in this direction for a while. At Google Marketing Live, Google talked about new ad formats built with Gemini: conversational discovery ads, highlighted answers, AI-powered shopping ads, business agent for leads, direct offers inside AI-assisted search experiences, etc. This is all part of the same bigger shift and Google Search is becoming more AI-driven. And Google Ads is moving with it. Obviously Google has been automating more of that for years, but the core idea was still pretty simple. Now things are shifting. If Google generates a summary under your ad, where does that summary come from? Google has not given advertisers a clean answer there yet. Businesses with clear, specific, well-structured information are going to be in a much better position than the businesses with vague landing pages and generic positioning. If you are a SaaS company, Google needs to understand what your product does, who it is for, how it compares, what it costs, what integrations it supports, what problems it solves and why a customer should trust it. If you are an e-commerce brand, Google needs to understand your product details, categories, pricing, availability, shipping, returns, reviews, use cases and comparison points. If you are a financial services company, Google needs clear service pages, current information, transparent methodology, credentials, calculators, risk context and authority outside your own website. If you are a local business, Google needs service pages, location proof, reviews, Google Business Profile strength, local citations, clear contact information and actual proof that you serve the area. If you are in education, Google needs program details, admissions information, outcomes data, faculty expertise, location-specific context and helpful resources for real students. If Google’s AI is going to summarize your business in paid results, then your website needs to give it accurate information to work with. And if your website is thin, vague, outdated or full of generic marketing language, you should probably expect generic summaries. Maybe even bad ones. This is where paid search and organic search start overlapping more. The same pages that help you show up in Google AI Overviews can help Google understand your ads. The same comparison pages that help you show up in ChatGPT can help define how your product is described. The same product data that helps with Shopping can help AI explain what you sell. The same reviews, third-party mentions and authority signals that help AI search systems trust you can help shape how your brand is understood. The businesses that will benefit from this kind of shift are probably the ones with pages that already answer the questions buyers care about. The businesses that may struggle are the ones relying on thin landing pages, vague claims, generic service pages, weak product data, missing pricing context, unclear category positioning, fake review content or no third-party validation. That stuff was already a problem for SEO and now it may become a problem for paid search too. The e-commerce angle is especially interesting. Google is already pushing AI-powered Shopping ads where Gemini can pull up relevant products and write a custom explainer for why a product may be right for the shopper. That means product data becomes even more important. If AI cannot understand the product, it cannot confidently explain it. And if it cannot confidently explain it, the product may lose at the exact moment the customer is deciding what to buy. The SaaS version is similar. If your pricing page is weak, your comparison pages are thin, your integrations are hard to understand and your documentation does not answer real buyer questions, Google has less to work with. Same with local. Same with finance. Same with education. Same with insurance. The more Google uses AI to explain businesses, the more important it becomes for businesses to explain themselves clearly first. This is where SEO Stuff (https://t.co/wKpf0EILTx) can help. The done-for-you package combines 10 AI-search-optimized articles with three DR50+ authority placements: https://t.co/yEFyM0Ze7W The content helps your business cover the questions customers ask before buying, including problems, use cases, comparisons, pricing, alternatives, industries, objections, case studies, product details and implementation. The authority placements help your business show up across trusted sources that search and AI systems use to understand categories. And if Google keeps moving this direction, it may increasingly affect how your ads are interpreted too. The companies that win from here will be the ones giving Google and AI systems the clearest, most trustworthy version of what they do and why customers should care. And again, if you're curious about whether your business is appearing across Google AI, ChatGPT, Claude, Perplexity and Grok, you can check here. It’s free: https://t.co/Pn764BHwyL
Biology classes are about to change forever. Some...
Biology classes are about to change forever. Someone built an app where you can grab a cell, rotate it in 3D, isolate every organelle, and compare structures in seconds. It feels more like a game than a textbook. Built with: • Gemini 3.1 Pro for the code • GPT Images 2 for the UI It’s getting harder to justify learning biology from static PDFs. https://t.co/wUfxynQWyL
Bro, no one believes Gemini’s benchmark scores sin...
Bro, no one believes Gemini’s benchmark scores since Gemini-2.5
Upgrading your image pipeline? ⚡ Meet Nano Banana...
Upgrading your image pipeline? ⚡ Meet Nano Banana 2 Lite! Engineered for rapid ideation and high-velocity developer pipelines, it’s the ultimate choice when speed and cost are your primary constraints. If you’re currently using the first-generation version (gemini-2.5-flash-image), this new model offers an easy way to unlock immediate latency leaps and performance gains. Explore the new model in Google AI Studio: https://t.co/wZle5zFMwQ
KID BUILT A FULL FPS GAME WITH ZERO CODE AND POCKE...
KID BUILT A FULL FPS GAME WITH ZERO CODE AND POCKETED $10,000 USING TEXT PROMPTS. No Unity tutorials. No C# scripts. He typed "add shift to run" and "make the screen shake when sprinting" into a chat box. Gemini 3 Flash and Claude Sonnet 4.5 wrote the mechanics live. Dash ability. Crosshair. Health bar. Stamina bar. Head bob on sprint. All generated from plain English in one sitting. Most devs spend six months learning engines and never ship a playable build. This kid banked ten grand before writing a single manual line. Open the tool. Type the mechanic. Ship the build. Is manual scripting officially dead, or is this a bubble?
Gemini 2.5 failing for anyone / everyone right now...
Gemini 2.5 failing for anyone / everyone right now? Getting 404s on all their models saying they're done, but their EOL says different https://t.co/Aa0siAZesv
wow! turns out Cupidly was a Gemini 2.5 Pro wrappe...
wow! turns out Cupidly was a Gemini 2.5 Pro wrapper this entire time. And to think that I thought it was a “custom trained rizz model”.
is Gemini 2.5 Flash already deprecated? Seems to b...
is Gemini 2.5 Flash already deprecated? Seems to be out of action today on api and ai studio. cc: @GoogleAIStudio any idea what's going? https://t.co/7cFfV1QgJ5
An agentic artificially intelligent X-ray scientis...
An agentic artificially intelligent X-ray scientist Getting an LLM to reliably plan, act, and adapt in a physical lab is one of the harder tests of agentic AI. The environment is noisy, actions have real consequences, and the agent has to reason over a stream of images and scan results rather than clean tabular data. Zhantao Chen and coauthors put a reasoning-capable agent through exactly this kind of test, and the setup is a nice study in operationalizing off-the-shelf models with structured tool use. The scientific problem is single-crystal sample alignment on a six-circle diffractometer. Before any scattering experiment can run, you need the orientation matrix: two reference reflections that fix how the crystal sits in reciprocal space relative to the beam. Doing this by hand means navigating six motors under hard physical limits and safety interlocks, often through slow trial-and-error. It's an ideal testbed because it demands multi-step planning under uncertainty, not a single scripted optimization. The agent connects to the beamline through the Model Context Protocol, issuing SPEC commands (move motors, scan, acquire detector images) and interpreting rendered plots and images directly, no raw arrays. The workflow is a conceptual scaffold, not a hard-coded script: the LLM decides which reflections to probe, when to switch from searching to centering, and how to configure each scan. They developed everything in a virtual beamline first, then deployed to real hardware at SLAC's SSRL. The results are convincing. On the magnetic Weyl semimetal Co3Sn2S2, benchmarked agents (Claude Sonnet 4, Gemini 2.5 Flash) aligned the sample with errors below 5 degrees in most runs. On the real beamline, a Claude Opus 4 agent found both reference reflections, and notably detected an unexpected 1.22° motor offset from the sample holder, then reused that correction in later steps. That short-term experimental memory, emerging without any fine-tuning, is the part that matters. The takeaway is practical: expensive instruments in materials development, drug discovery, and energy research often stall on tedious setup and calibration that ties up expert time. An agent that handles alignment and adapts to real-world imperfections turns off-the-shelf LLMs into a layer that lowers the barrier to autonomous experimentation, freeing scientists for the questions that actually need them. Paper: Chen et al., Nature Machine Intelligence (2026), CC BY-NC-ND 4.0 | https://t.co/ZHUKrRb3RT
Best Google model: Gemini 2.5 Pro
Best Google model: Gemini 2.5 Pro
今日の #ジェミラン 1600×6(4:25/km→5秒ずつup) ともコーチの素晴らしいペースメイ...
今日の #ジェミラン 1600×6(4:25/km→5秒ずつup) ともコーチの素晴らしいペースメイクのおかげで気持ちよく走れました。ラスト1本もいい感じに上げれて満足度高めです。 #Gemini3 #まるお製作所RC https://t.co/0T05aFjQ7z
7/7（火）60分ジョグ 🗒️12.01km 🔧Free 👟ヴェロシティニトロ4 今週末にレ...
7/7（火）60分ジョグ 🗒️12.01km 🔧Free 👟ヴェロシティニトロ4 今週末にレースがあるので今週はボリュームを押さえて 膝の調子が最近悪く痛みが（長年の付き合いなのでそこまで気にしてない） アイシングなどケアをしっかりしてレースは万全で行きたい #GeminiRunners #Gemini3 #まるお製作所RC https://t.co/YU1Xqvxvw8