A release said the award signifies the station’s unwavering commitment to maintain the highest standards in quality seed production and has emerged as a front runner setting an example for excellence in the field of seed production

Government allocates ₹1.52 lakh crore for agriculture and allied sectors in the Union Budget 2024-25; Finance Minister announces that the Centre will work with States to promote digital public infrastructure for agriculture

When working through the early stages of a product design, it's common that labels for objects and actions emerge organically. No one is overly concerned about making these labels consistent (yet). But if this proliferation of terms doesn't get reined in early, both product design and strategy get harder.

Do we call it a library, a folder, a collection, a workspace, a section, a category, a topic? How about a document, page, file, entry, article, worksheet? And.. what's the difference? While these kinds of decisions might not be front and center when working out designs for a product or feature, they can impact a lot.

For starters, having clear definitions for concepts helps keep teams on the same page. When engineering works on implementing a new object type, they're aligned with what design is thinking, which is what the sales team is pitching potential customers on. Bringing a product to life is hard enough, why complicate things by using different terms for similar things or vice versa?

Inconsistent terms are obviously also a comprehension issue for the people using our products. "Here's it's called a Document, there it's called an Article. Are those the same?" Additionally, undefined terms often lead to miscellaneous bins in our user interfaces. "What's inside Explore?" When the definition of objects and actions isn't clear, what choice do we have but to drop them into vague sounding containers like Discover?

The more a product gets developed (especially by bigger teams) the more things can diverge because people's mental model of what terms mean can vary a lot. So it's really useful to proactively put together a list of the objects and actions that make up an application and draft some simple one-liner definitions for each. These lists almost always kick off useful high-level discussions within teams on what we're building and for who. Being forced to define things requires you to think them through: what is this feature doing and why?

And of course, consistent labels also ease comprehension for users. Once people learn what something means, they'll be able to apply that knowledge elsewhere -instead of having to contend with mystery meat navigation.

As the functionality and scope of Web sites and applications has grown over the years, so has the prevalence of Help pages. Nearly every feature has an explanatory article outlining how to use it and why. But most Help pages are walls of text making them hard to act on. So a few years ago, we tried something different.

First let's look at the status quo. This Help page from Amazon is both pretty typical and by those standards, pretty good. It's specific to one topic, brief, outlines steps clearly, and includes links to help people accomplish their intended task. Companies iterated to these kinds of Help pages because they mostly work and because they're less work.

Keeping Help text up to date and accurate is less labor-intensive than updating images or videos with the same information. But as the old saying goes, a picture is worth a lot of words and there's a reason many people turn to video tutorials to learn how to do things instead of reading about how to do them.

When building Polar several years ago, we wanted a more approachable and fun way of helping people learn how to use our product. And while you might say "the best Help pages are no Help pages -just make your app easy to use" not all Help pages are smearing over usability issues. Some introduce higher level concepts, others outline capabilities, and some serve as marketing for specific features.

So with those goals in mind, we iterated to a simple formula. Each concept or feature gets a Help page that has a title alongside 1-2 sentences and as many sections consisting of a title, 1-2 sentences, plus a graphic as needed.

This approach meant people primarily relied on images (or their alt tags if visually impaired) to figure out how to get things done. So we iterated a fair amount on the images to find the right balance of detail and abstraction. Make the UI too realistic and it becomes hard to focus on the relevant elements. Realistic UI images also need updating anytime the actual product UI changes. Conversely, make the image too simplistic and it doesn't provide enough detail for people to actually learn how to do things.

Of course, not all Help topics are well suited to an image but the process of trying to create one often triggers ideas on how to simplify the actual UI or concepts within a product. So it's worth the iteration.

But is a visual approach to Help pages able to scale? Assuming it works, can companies invest the time and effort needed to generate all these images and keep them up to date? Perhaps in a time of image generation AI models, it's increasingly possible through automated or supervised pipelines. Time will tell!

In her How to Use AI to Build Accessible Products presentation at Smashing Conf New York, Carie Fisher discussed using AI coding tools to test and suggest fixes for accessibility issues in Web pages. Here's my notes on her talk.

• AI is everywhere. You can use it to write content, code, create images, and more. It impacts how everyone will work.
• But ultimately, AI is just a tool but it might not always be the right one. We need to find the tasks where it has the potential to add value.
• Over 1 billion people on the planet identify as having a disability. Accessible code allows them to access digital experiences and helps companies be complaint with emerging laws requiring accessible Web pages and apps. Businesses also get SEO, brand, and more benefits from accessible code.
• AI tools like Github Copilot can find accessibility issues in seconds consistently, especially compared to the manual checks currently being done by humans. AI can also spot patterns across a codebase and suggest solutions.
• Existing AI coding tools like Github Copilot are already better than Linters for finding accessibility issues.
• AI can suggest and implement code fixes for accessibility issues. It can also be added to CI/CD pipelines to check for accessibility issues at the point of each commit. AI can also serve as an accessibility mentor for developers by providing real-time suggestions.
• More complex accessibility issues especially those that need user context may go unfound when just using AI. Sometimes AI output can be incomplete or hallucinate solutions that are not correct. As a result, we can't over rely on just AI to solve all accessibility problems. We still need human review today.
• To improve AI accessibility, provide expanded prompts that reference or include specifications. Code reviews can double check accessibility suggestions from AI-based systems. Regularly test and refine your AI-based solutions to improve outcomes.
• Combing AI and human processes and values can help build a culture of accessibility.

New device could boost telecommunications and be adapted for photonics

As a programmer, your desk is the platform to everything you do. Every line of code you write, CSS you minify or updates you push out is done right there. See how to set up your desk for maximum productivity.

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The screenings at two other multiplexes in Kochi were cancelled

Sashi Kiran Tikka, director of ‘Goodachari’ and ‘Major’, looks back at the indie approach to filmmaking, collaborative work atmosphere and his days at the New York Film Academy

Amala Akkineni, actor and director of Annapurna College of Film and Media, on why aspiring filmmakers need formal training and how her recent acting assignments have helped her observe changes in the industry at close quarters

Covid-19 themed attacks continue to be used by hacker groups to make quick money

Zenbook 14 (UX425) is priced at ₹82,990; VivoBook Ultra K15 (K513) at ₹42,990; VivoBook Ultra 15 (X513) at ₹43,990; VivoBook Ultra 14 (X413) at ₹59,990

Prices start at $699 for Apple Silicon 'M1' based Mac Mini, $999 for Macbook Air, and $1299 for the 13-Inch MacBook Pro.

Product aimed at small business owners; prices start at ₹74,999

The main design change in testing for the iPad Pro is a switch to a glass back from the current aluminum enclosure

The new MacBook Pros are likely to use a higher-end version of the M1 chip, with increased performance

Apple introduced two chips -the M1 Pro and the M1 Max in the new MacBook Pro 14 and 16-inch models

Also launches Vivobooks starting ₹75,000 and will be sold across online channels beginning December 14.

The Galaxy Book2 series includes the Galaxy Book2 Pro 360, Book2 Pro, Book2 and Book2 360.

Two flagship products — the laptop and micro-PC — have been designed in association with Intel Corporation

UAE, Russia, Bangladesh, the US, and Sri Lanka import laptops worth $102.6 million from India.

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