At the start of each school year, teachers need to compile student information into biographical records. Here's what's interesting: AI can now handle this tedious task automatically. With Gemini's help, educators can transform a standard biographical form template into a dynamic Google Form that parents and students can fill out online. The best part? All responses automatically populate into a Google Sheet for easy review and organization.
This workflow streamlines what used to be a manual, time-consuming process. Instead of manually organizing scattered forms and data, everything flows into one central spreadsheet. Let's walk through exactly how to set this up.
Creating a Student Biographical Form with Gemini
Step 1:
Start by preparing your student biographical form template as either a PDF or Word document. Once ready, upload it to Gemini and enter this prompt:
Based on the attached file, create a Google Forms template to collect all the information outlined in the document
Gemini will extract all the required information fields from your uploaded template. Copy the entire output—you'll need it for the next step.
Step 2:
Head over to Google Forms and create a new blank form. In the Gemini prompt box at the top, paste all the content you just extracted. Then add an instruction requesting Vietnamese with proper diacritical marks. Finally, click Create to let Gemini generate the student information form.
Step 3:
Gemini will display a preview of the biographical data collection form for your review. All fields are based on your original template. When you're satisfied with the layout, click Create Form to finalize it.
Step 4:
Your completed student biographical information form is now ready to use.
Feel free to customize the form as needed—change question types, delete unnecessary fields, or adjust wording. When you're done, copy the form link and distribute it to parents or students for completion.
Step 5:
Once responses start coming in, the real value emerges. Simply click "Link to Sheets" to view all submitted data in a comprehensive spreadsheet format—no manual data entry required.
Description: Use Gemini AI to automatically generate Google Forms for collecting student biographical data. Perfect for teachers managing enrollment.
While tech industry leaders continue debating the merits and risks of open-weight AI models, developers are pushing forward anyway. This Tuesday, LTX unveiled LTX-2.5, the latest iteration of their world model.
So what exactly is a world model? It's fundamentally different from most AI systems we're familiar with. While large language models excel at generating text, world models are sophisticated systems designed to convert physical reality into a digital representation that generative AI can work with. The goal is to build models that truly "understand" the real world—reducing hallucinations and enabling more realistic outputs. Imagine robots navigating a room without bumping into furniture, or AI-generated videos where people naturally walk around obstacles instead of phasing through them like ghosts. World models can simulate digital environments, generate content, and guide robot programming.
The new version tackles image artifacts and inconsistencies through an improved pixel diffusion process, delivering smoother results across multiple generations. Here's what's interesting: because LTX-2.5 is open-weight, companies can download and customize it for their own needs—protecting their intellectual property in the process. A film studio could use LTX-2.5 to create and edit videos featuring their own characters. The model also supports local inference on Nvidia RTX GPUs, meaning developers can run it directly on their hardware rather than uploading sensitive data to cloud AI services.
LTX-2.5 is now available for download through platforms like Hugging Face and ComfyUI.
LTX ranks among today's most prominent open-weight AI model developers. Their models have been downloaded over 33 million times on Hugging Face, with users creating custom versions based on LTX's designs. OpenAI and Google have their own open-weight lines—GPT-OSS and Gemma 4 respectively—though both companies still prioritize closed models. Anthropic, notably, hasn't released Claude in open-weight form at all.
Open-source advocates have long argued that AI develops best when tech companies publicly share how their tools work. This debate has only intensified in the AI era. Several popular Chinese models are launching as open-weight, including Kimi 3 from Moonshot, giving developers, researchers, and cybersecurity experts deeper visibility into how these systems operate. When the Trump administration reportedly considered banning Chinese and certain international AI models last July over security concerns, many industry leaders pushed back—arguing that such restrictions could actually harm cybersecurity itself.
Despite industry consensus against banning open-weight models, major companies have been cautious about releasing them. Meta CEO Mark Zuckerberg is aggressively championing open AI development. On Monday, he posted a lengthy essay arguing that open-weight models will bring AI benefits to more people. Meta recently released Muse Glimmer, another open-weight model, and committed to releasing weights for Spark 1.2 soon.
LTX shares this philosophy. CEO and co-founder Zeev Farbman believes neither governments nor private corporations are the right stewards for proprietary AI models. One side, he argues, follows shifting political agendas. The other chases profit motives. The implication? Open-weight models answer to neither.
Description: LTX releases LTX-2.5, an open-weight world model designed to improve video generation and robot control with better consistency and local GPU support.
You can enlarge or shrink any subject, reposition it anywhere you'd like, and the AI intelligently fills in the background without compromising composition or color balance. Here's everything you need to know about adjusting objects with Google Photos' AI tools.
Moving Objects Within Your Photos Using Google Photos AI
Step 1:
Open the photo you want to edit and tap the edit icon at the bottom. Once the editor loads, tap the Magic Editor icon in the lower left corner.
Step 2:
Now draw a selection around the object you want to move. For this example, we're repositioning a subject on the right side of the frame.
Step 3:
Hold down on the selected object and drag it to your desired location. The subject moves instantly from its original position. Once you've positioned it where you want, tap the arrow icon in the lower right to generate a new version of the image.
Step 4:
You'll get a photo with the object repositioned. Here's where the AI really shines—the original location is automatically filled in by the AI, creating a seamless, natural-looking result without visible gaps.
Google Photos generates multiple variations for you to choose from. Swipe left and right to browse different results. Not satisfied? Scroll down and select View new results to see additional variations. When you've found the one you like, tap the checkmark to save your edited photo.
Enlarging or Shrinking Objects Using Google Photos AI
Step 1:
Go to the AI editing mode in Google Photos and draw a selection around your subject. Need to adjust your selection? Tap Adjust, then choose Add or Remove to fine-tune the selected area.
Step 2:
After confirming your selection, hold the object and pinch in or out with your fingers to shrink or enlarge the subject as needed. Then tap the arrow to generate a new image.
Step 3:
Your enlarged (or shrunk) subject now looks naturally integrated into the image without affecting surrounding details. Swipe left to explore additional variations, or request more results if you want to see other options. Finally, save your final result and you're done.
Description: Master Google Photos' AI-powered object editing tools. Learn to move, resize, and reposition subjects while maintaining natural image composition.
Google AI Studio has just rolled out a real-time speech translation feature powered by the Gemini Live API and Gemini 3.5 Live Translate model. The tool translates spoken audio instantly across more than 70 languages, converting input speech into translated audio with minimal latency. What's interesting here is that this goes beyond simple subtitle translation—you get actual audio output in your target language.
By configuring the Live API with translation mode enabled, you can stream audio in one language and receive translated audio output in another, creating seamless speech-to-speech translation in real time. This means you can follow along with video content in any language you choose, without relying on YouTube's built-in captions. Let's walk through how to set it up.
How to Use Real-Time Translation in Google AI Studio
Step 1:
Open Google AI Studio and look for the Real-time section.
Once you're in the new interface, select Gemini 3.5 Live Translate Preview to get started.
Step 2:
In the translation setup interface, start by choosing your target language from the sidebar options.
Next, tap Share Audio From Tab to capture audio from your current browser tab while playing video. Alternatively, you can hit the Talk button to translate your own speech in real time.
Click to confirm and activate the translation tool.
Step 3:
You'll need to select which screen or window you want to share with Google AI Studio.
After selecting your screen, hit the Share button below to proceed.
Step 4:
You'll immediately see the translated text appearing on screen, complete with audio output in your chosen language. Keep in mind there will be some delay between the original audio and the translated version—that's normal.
In practice, Google AI Studio's translations are quite accurate. The system automatically selects a male or female voice to match the speaker in the original video, which feels natural.
To stop the translation, simply click the Stop Sharing icon.
Pros and Cons of Google AI Studio's Real-Time Translation
Advantages:
Speech translation happens nearly instantly.
Supports over 70 languages.
Outputs actual spoken audio, not just text.
Perfect for watching videos or following conversations in foreign languages.
The synthesized voice sounds natural and human-like.
Limitations:
There's still noticeable delay between the source audio and translation.
Translation quality depends heavily on input audio clarity.
This tool is focused purely on translation, lacking the broader conversational features of Gemini Live.
Description: Google AI Studio now offers instant speech translation across 70+ languages using Gemini 3.5 Live Translate. Here's how to use it.
Microsoft's latest Windows 11 update packed Edge with a bunch of AI features. You've got Copilot sitting right there in the sidebar, plus writing assistance tools and various other AI-powered helpers designed to boost your productivity. Here's the thing though—not everyone wants these features turned on by default. If you'd rather work without them, the good news is disabling them takes just a few clicks.
The process is straightforward. All you need to do is adjust your browser's default settings. Let's walk through it step by step.
How to Disable AI Features in Edge
Step 1:
Open Edge and navigate to Settings. Look for Copilot and AI in the left sidebar and select it.
On the right side, toggle off the "Show Copilot button in toolbar" option. This removes the Copilot button from your toolbar entirely.
Step 2:
Next, disable the following options: Copilot in new tabs and Allow Copilot to take actions on your behalf, as shown below.
Step 3:
Find the Browsing with Copilot option and select it. Turn off Browsing with Copilot to complete this step.
Step 4:
Finally, click on the Writing tools section to adjust how Edge handles AI-assisted writing features.
On the right, toggle off the "Use writing assistant on the web" option to disable AI writing help.
Once you've completed all these steps, Copilot and its related AI features will be fully disabled throughout your browser.
Common Questions About Disabling AI in Microsoft Edge
Can you turn off all AI features in Microsoft Edge?
Yes. Microsoft Edge lets you disable virtually every AI-powered feature, including Copilot Mode, the Copilot button, the sidebar integration, and writing assistance. You can manage all of this directly from your browser settings.
Can you completely uninstall or remove AI components from Edge?
No. You can disable or hide AI features, but you cannot fully uninstall the underlying AI components from Edge itself.
What happens when you turn off Copilot Mode?
Disabling Copilot Mode removes the AI-enhanced new tab experience. Your new tab page will revert to Edge's traditional layout.
Does turning off AI features affect browsing performance?
No. Disabling AI features simply removes the AI integrations and won't impact your browsing speed or overall performance.
Can you re-enable Copilot after turning it off?
Absolutely. You can turn Copilot or any other AI feature back on anytime by returning to settings and flipping the toggles back on.
Description: Step-by-step guide to disable Copilot and all AI-powered features in Microsoft Edge browser settings.
Over the past two years, people have gotten better at squeezing quality results out of large language models by writing clearer prompts: adding context, defining roles, including examples, specifying output formats, and constantly iterating. These techniques still work. But as AI moves beyond chatbots into coding agents, research assistants, data science tools, and automated workflows, good prompts alone aren't cutting it anymore.
The emerging skill is called Specification Engineering—essentially the discipline of building detailed requirements for AI systems. It's the ability to clearly define goals, constraints, desired outputs, edge cases, testing methods, success criteria, and failure scenarios for AI-assisted tasks. Here's the key difference: Prompt Engineering is how you ask the question, while Specification Engineering is how you define what "done" actually looks like.
Why Prompts Stop Being Enough
A prompt can produce an answer that sounds entirely convincing. A specification is what actually determines whether that answer meets requirements.
This distinction matters increasingly because modern AI doesn't just generate text. It writes SQL queries, edits codebases, analyzes spreadsheets, structures JSON output, builds applications, and executes multi-step decision chains. In scenarios like these, you can't just ask "Can the model answer this?" You need to ask: Does it meet the actual requirements? Does it respect constraints? Does it handle edge cases? Can you verify the results? Can other systems use the output? Is the AI optimizing for the wrong thing?
That last question is particularly critical. AI safety researchers have long studied specification gaming—when an AI system technically completes the stated goal but ignores what humans actually wanted. What's interesting here is this isn't some theoretical risk anymore. It's happening in everyday work. A coding agent asked to "fix bugs" might create a patch that passes visible tests but breaks hidden system assumptions. An AI summarizing a report might produce coherent text while completely missing the metrics leadership cares about. When asked to "clean data," it might silently remove rows that should have been preserved for investigation.
The prompt did its job. The specification is what failed.
What Specification Engineering Actually Is
Specification Engineering is the process of turning a vague task into a set of executable, testable, and evaluable instructions.
A simple prompt might read:
"Analyze this customer churn dataset and tell me what stands out."
A complete specification would require:
"Analyze this customer churn dataset. Identify missing values, class imbalance, data leakage risks, and the highest-predictive features. Split data into train and test sets before preprocessing. Compare logistic regression, random forest, and XGBoost. Report accuracy, precision, recall, F1, ROC-AUC, PR-AUC, and confusion matrices. Do not draw causality conclusions. Provide three business recommendations linked only to observed correlations."
The second version isn't just a better prompt. It defines the entire job.
A solid specification typically includes eight core components:
Objective: What must the model accomplish?
Context: What information does the AI need to know?
Inputs: What data, files, tools, or assumptions are allowed?
Output Format: How must the final result be presented?
Constraints: What must the AI avoid?
Evaluation Criteria: How do you know the result is correct?
Edge Cases: What scenarios could cause failure?
Verification Steps: What tests or checks must be run?
This explains why Specification Engineering has so much in common with product management, software testing, data validation, and research design—not traditional prompt-writing technique.
A 2024 study on Requirement-Oriented Prompt Engineering found that most current prompt training programs focus on techniques like role-playing or asking AI to "think step by step." But actually using LLMs for complex tasks depends far more on clearly articulating requirements. In a randomized study with 30 inexperienced users, the ROPE-trained group improved their requirement-writing skills by 20%, while the traditionally trained prompt engineering group improved just 1%. The researchers also found a direct link between input request quality and LLM output quality.
We're seeing similar trends in real-world AI tools. OpenAI's Structured Outputs feature lets developers constrain model responses to specific JSON schemas—a capability emphasized in OpenAI's own benchmarks. This is Specification Engineering at the API level: instead of hoping the model returns valid JSON, you predefine the structure all results must follow.
OpenAI's Model Spec and Anthropic's Constitution express the same idea at the model behavior level. Model Spec documents how OpenAI's models should behave in ChatGPT and APIs. Constitutional AI uses explicitly written principles to guide model behavior. The real concern is: the AI industry itself is gradually shifting from prompts to specifications.
From Vibe Coding to Spec-Driven Development
This difference shows up most clearly in AI-assisted programming.
A prompt-engineering approach might just ask:
"Build me a simple expense tracker app."
A specification-engineering approach would be far more precise:
"Build an expense tracker in React with add, edit, delete, category filtering, monthly totals, and local storage. Validate that amounts are positive, transaction dates are required, and users must select a category. Write unit tests for add, delete, filter, and total functions. Don't use paid external APIs. Return the file structure first, then implement each file."
The second version gives the AI less room to make decisions that create problems later.
This matters increasingly because software engineering benchmarks are shifting toward real-world problems, test coverage, and verifiability. SWE-bench evaluates how well models handle actual GitHub issues by editing real codebases, not just generating isolated code snippets. OpenAI's SWE-bench Verified is a human-validated subset designed to measure real-world software problem-solving more reliably.
But here's the thing: even passing tests isn't perfect. Research on patches generated by agents on SWE-bench Verified found that passing patches still differed significantly from human-written ones—revealing benchmark blindspots. Another study, SWT-Bench, showed that well-designed test suites act as effective filters for proposed fixes, doubling SWE-Agent's accuracy.
The New AI Workflow
The future workflow with AI won't look like this:
prompt → result → manual fix
Instead, it'll look more like:
specification → generate result → validate → refine → audit
The process might include: writing a specification for the task; asking the AI to identify missing requirements; asking it to generate a solution; running tests or checks; asking the AI to fix only the detected issues; finally documenting assumptions and limitations.
This approach is especially critical for agentic systems. OpenAI's own practical guidance for agents recommends breaking complex resources or tasks into smaller, clearer steps—each tied to a specific action or output. That's Specification Engineering applied to workflows.
Google's DORA research reinforces this trend. A survey of nearly 5,000 tech professionals concluded that AI acts as an amplifier—for both organizational strengths and weaknesses. Strong foundations and quality processes help businesses extract more value from AI. Weak processes get amplified too.
This is what organizations need to understand: AI doesn't eliminate the need for technical discipline. It increases the value of having good technical processes.
Prompt Engineering isn't disappearing. It's becoming part of something larger.
The early era of AI rewarded people who knew how to get chatbots to generate better answers. The next era will favor people who can design trustworthy AI processes—with clear requirements, structured outputs, evaluation mechanisms, and defined boundaries.
The critical skill is no longer:
"How do I get the model to answer?"
It's now:
"How do I define the task so the model, users, and evaluators all agree on what a correct answer looks like?"
That's Specification Engineering. As AI systems become increasingly autonomous, this could become one of the most important technical skills after Prompt Engineering.
Description: Why prompt engineering alone isn't enough anymore. Learn specification engineering—the emerging discipline shaping how teams build reliable AI systems
Ask Photos is a new AI-powered search feature in Google Photos, backed by Google's Gemini technology. It lets you find images and information using natural language queries instead of traditional keywords. What's interesting here is that Google has embedded Gemini directly into Photos, fundamentally changing how you interact with your library.
Google Photos already had solid search capabilities, but Ask Photos takes things to another level. The AI-driven approach gives you more options and better results overall. Instead of typing keywords, you can now describe what you're looking for in plain English—and Gemini understands the context behind your query to surface exactly what you need. Let's walk through how to get started with Ask Photos.
How Does Ask Photos Work?
Ask Photos is Google Photos' new AI search tool that understands descriptive queries and returns relevant results. Under the hood, it uses Google's Gemini AI models to analyze context and deliver smarter search results.
The feature understands a ton of information stored in your Google Photos library—important people in your life, favorite meals, locations you've visited, hobbies, and much more. You can query it with natural questions or descriptions, adding as much detail as you want.
Because it grasps context, Ask Photos can surface images that traditional keyword searching would struggle to find. The real power comes from its ability to connect the dots across your entire photo collection.
How to Use Ask Photos AI on Google Photos
Step 1:
Open Google Photos and you'll see a notification about the new AI search tool. Accept it to enable the feature. Next, tap the search icon to activate Ask Photos. You'll now see a search bar where you can enter your description.
Step 2:
Type in a description of the image you're trying to find. Ask Photos understands natural language, so it will process your query and return related results. The more descriptive you are, the better your results.
You can continue entering new search queries to find different images. Gemini scans your photos and identifies matches based on the details in your description. The results align closely with your search terms, so specificity pays off.
The results Ask Photos delivers will closely match your search criteria. That's why detailed descriptions yield better outcomes.
Step 3:
Gemini has its own settings panel within Google Photos so you can tweak how it operates. In the Ask Photos search interface, tap the three-dot menu in the top right corner and select Gemini in Photos.
You'll now see Gemini settings for Google Photos. Review these options and disable any features you don't want to use.
What Can You Do With Ask Photos?
Ask Photos, powered by Gemini in Google Photos, lets you search for images using anything from simple to highly specific questions. The AI grasps context to locate the right photos. Here are some practical search examples:
Find travel photos: Ask "Where did I go to [location]?" or "Show me photos from my trip to [location] in [year]." Ask Photos pulls from multiple data points to find the right images.
Find food photos: Forgotten what you ate? Try "What did I eat at the resort in [location]?" or "Show me all the desserts I had this year."
Find specific details: You can ask hard-to-remember questions like "What's my license plate number?" Ask Photos searches through related images to provide the answer.
Find particular photos: Short queries like "Recent selfies" or "Landscape photos" work too, helping you quickly locate specific image types.
Find photos with specific people: Ask "When was the last time I saw my sister?" or "Photos of me and my brother at the restaurant." Thanks to its face recognition capabilities, Google Photos pinpoints relevant images with impressive accuracy.
Description: Learn how to search your photo library using natural language with Ask Photos, Google's new AI-powered search feature powered by Gemini.
