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Using AI For Neurodiversity And Building Inclusive Tools

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In 1998, Judy Singer, an Australian sociologist working on biodiversity, coined the term “neurodiversity.” It means every individual is unique, but sometimes this uniqueness is considered a deficit in the eyes of neuro-typicals because it is uncommon. However, neurodiversity is the inclusivity of these unique ways of thinking, behaving, or learning.

Humans have an innate ability to classify things and make them simple to understand, so neurodivergence is classified as something different, making it much harder to accept as normal.

“Why not propose that just as biodiversity is essential to ecosystem stability, so neurodiversity may be essential for cultural stability?”

— Judy Singer

Culture is more abstract in the context of biodiversity; it has to do with values, thoughts, expectations, roles, customs, social acceptance, and so on; things get tricky.

Discoveries and inventions are driven by personal motivation. Judy Singer started exploring the concept of neurodiversity because her daughter was diagnosed with autism. Autistic individuals are people who are socially awkward but are very passionate about particular things in their lives. Like Judy, we have a moral obligation as designers to create products everyone can use, including these unique individuals. With the advancement of technology, inclusivity has become far more important. It should be a priority for every company.

As AI becomes increasingly tangled in our technology, we should also consider how being more inclusive will help, mainly because we must recognize such a significant number. AI allows us to design affordable, adaptable, and supportive products. Normalizing the phenomenon is far easier with AI, and it would help build personalized tools, reminders, alerts, and usage of language and its form.

We need to remember that these changes should not be made only for neurodiverse individuals; it would help everyone. Even neurotypicals have different ways of grasping information; some are kinesthetic learners, and others are auditory or visual.

Diverse thinking is just a different way of approaching and solving problems. Remember, many great minds are neurodiverse. Alan Turing, who cracked the code of enigma machines, was autistic. Fun fact: he was also the one who built the first AI machine. Steve Jobs, the founder and pioneer design thinker, had dyslexia. Emma Watson, famously known for her role as Hermione Granger from the Harry Potter series, has Attention-Deficit/Hyperactivity Disorder (ADHD). There are many more innovators and disruptors out there who are different.

Neurodivergence is a non-medical umbrella term.) used to classify brain function, behavior, and processing, which is different from normal. Let’s also keep in mind that these examples and interpretations are meant to shed some light on the importance of the neglected topic. It should be a reminder for us to invest further and investigate how we can make this rapidly growing technology in favor of this group as we try to normalize neurodiversity.

Types Of Neurodiversities
  • Autism: Autism spectrum disorder (ASD) is a neurological and developmental disorder that affects how people interact with others, communicate, learn, and behave.
  • Learning Disabilities
    The common learning disabilities:
  • Attention-Deficit/Hyperactivity Disorder (ADHD): An ongoing pattern of inattention and/or hyperactivity-impulsivity that interferes with functioning or development.
Making AI Technology More Neuro-inclusive

Artificial Intelligence (AI) enables machines to think and perform tasks. However, this thinking is based on algorithmic logic, and that logic is based on multiple examples, books, and information that AI uses to generate the resulting output. The network of information that AI mimics is just like our brains; it is called a neural network, so data processing is similar to how we process information in our brains to solve a problem.

We do not need to do anything special for neurodiversity, which is the beauty of AI technology in its current state. Everything already exists; it is the usage of the technology that needs to change.

There are many ways we could improve it. Let’s look at four ways that are crucial to get us started.

Workflow Improvements

For: Autistic and ADHD
Focus: Working memory

Gartner found that 80% of executives think automation can be applied to any business decision. Businesses realized that a tactical approach is less successful than a strategic approach to using AI. For example, it can support business decisions that would otherwise require a lot of manual research.

AI has played a massive role in automating various tasks till now and will continue to do so in the future; it helps users reduce the time they spend on repetitive aspects of their jobs. It saves users a lot of time to focus their efforts on things that matter. Mundane tasks get stacked in the working memory; however, there is a limit: humans can keep up to 3–5 ideas simultaneously. If there are more than five ideas at play, humans ought to forget or miss something unless they document it. When completing these typical but necessary tasks, it becomes time-consuming and frustrating for users to focus on their work. This is especially troublesome for neurodivergent employees.

Autistic and ADHD users might have difficulty following through or focusing on aspects of their work, especially if it does not interest them. Straying thoughts is not uncommon; it makes it even harder to concentrate. Autistic individuals are hyper-focused, preventing them from grasping other relevant information. On the contrary, ADHD users lose focus quickly as their attention span is limited, so their working memory takes a toll.

AI could identify this and help users overcome it. Improving and automating the workflow will allow them to focus on the critical tasks. It means less distractions and more direction. Since they have trouble with working memory, allowing the tool to assist them in capturing moments to help recall later would benefit them greatly.

Example That Can Be Improved

Zoom recently launched its AI companion. When a user joins a meeting as a host, they can use this tool for various actions. One of those actions is to summarize the meeting. It auto-generates meeting notes at the end and shares them. AI companion is an excellent feature for automating notes in the meeting, allowing all the participants to not worry about taking notes.

Opportunity: Along with the auto-generated notes, Zoom should allow users to take notes in-app and use them in their summaries. Sometimes, users get tangent thoughts or ideas that could be useful, and they can create notes. It should also allow users to choose the type of summary they want, giving them more control over it, e.g., short, simplified, or list. AI could also personalize this content to allow participants to comprehend it in their own way. Autistic users would benefit from their hyper-focused attention in the meeting. ADHD users can still capture those stray thoughts, which the AI will summarize in the notes. Big corporations usually are more traditional with incremental improvements. Small tech companies have less to lose, so we often see innovation there.

Neurodivergent Friendly Example

Fireflies.ai is an excellent example of how neuro-inclusivity can be considered, and it covers all the bases Zoom falls short of. It auto-generates meeting notes. It also allows participants to take notes, which are then appended to the auto-generated summary: this summary can be in a bullet list or a paragraph. The tool can also transcribe from the shared slide deck within the summary. It shares audio snippets of important points alongside the transcription. The product can support neurodivergent users far better.

Natural Language Processing

For: Autistic, Learning Disabilities, and ADHD
Focus: Use simple words and give emotional assistance

Words have different meanings for all. Some might understand the figurative language, but others might get offended by the choice of it. If this is so common with a neurotypical, imagine how tricky it will be for a neurodivergent. Autistic users have difficulty understanding metaphorical language and empathizing with others. Learning disabilities will have trouble with language, especially figurative language, which perplexes them. ADHD users have a short attention span, and using complex sentences would mean they will lose interest.

Using simple language aids users far better than complex sentence constructions for neurodivergent. Metaphors, jargon, or anecdotal information might be challenging to interpret and frustrate them. The frustration could avert them from pursuing things that they feel are complex. Providing them with a form of motivation by allowing them to understand and grow will enable them to pursue complexities confidently. AI could help multifold by breaking down the complex into straightforward language.

Example That Can Be Improved

Grammarly is a great tool for correcting and recommending language changes. It has grammatical and Grammarly-defined rules based on which the app makes recommendations. It also has a feature that allows users to select the tone of voice or goals, casual or academic style, enhancing the written language to the expectation. Grammarly also lets organizations define style guides; it could help the user write based on the organization’s expectations.

Opportunity: Grammarly still needs to implement a gen AI assistive technology, but that might change in the future. Large learning models (LLM) can further convert the text into inclusive language considering cultural and regional relevance. Most presets are specific to the rules Grammarly or the organization has defined, which is limiting. Sentimental analysis is still not a part of their rules. For example, if the write-up is supposed to be negative, the app recommends changing or making it positive.

Neurodivergent Friendly Example

Writer is another beautiful product that empowers users to follow guidelines established by the organization and, obviously, the grammatical rules. It provides various means to rewrite sentences that make sense, e.g., simplify, polish, shorten, and so on. Writers also assist with sentence reconstruction and recommendation based on the type of content the user writes, for instance, an error or a tooltip. Based on those features and many more under the gen AI list, Writer can perform better for neurodivergent users.

Cognitive Assistance

For: Autistic, Learning Disabilities, and ADHD
Focus: Suggestive technology

Equality Act 2010 was established to bring workplace equality with legislation on neurodiversity. Employers need to understand the additional needs of neurodivergent employees and make amendments to existing policies to incorporate them. The essence of the Equality Act can be translated into actionable digital elements to bring equality of usage of products.

Neurodiverse or not, cognitive differences are present in both groups. The gap becomes more significant when we talk about them separately. Think about it: all AI assistive technologies are cognition supplements.

Cognoassist did a study to understand cognition within people. They found that less than 10% of them score within a typical range of assessment. It proves that the difference is superficial, even if it is observable.

Cognition is not just intelligence but a runway of multiple mental processes, irrespective of the neural inclination. It is just a different way of cognition and reproduction than normal. Nonetheless, neurodivergent users need assistive technologies more than neuro-typicals; it fills the gap quickly. This will allow them to function at the same level by making technology more inclusive.

Example That Can Be Improved

ClickUp is a project management tool that has plenty of automation baked into it. It allows users to automate or customize their daily routine, which helps everyone on the team to focus on their goals. It also lets users connect various productivity and management apps to make it a seamless experience and a one-stop shop for everything they need. The caveat is that the automation is limited to some actions.

Opportunity: Neurodivergent users sometimes need more cognitive assistance than neuro-typicals. Initiating and completing tasks is difficult, and a push could help them get started or complete them. The tool could also help them with organization, benefiting them greatly. Autistic individuals prefer to complete a task in one go, while ADHD people like to mix it up as they get the necessary break from each task and refocus. An intelligent AI system could help users by creating more personalized planned days and a to-do list to get things started.

Neurodivergent Friendly Example

Motion focuses on planning and scheduling the user’s day to help with their productivity goals. When users connect their calendars to this tool, they can schedule their meetings with AI by considering heads-down time or focused attention sessions based on each user’s requirement. The user can personalize their entire schedule according to their liking. The tool will proactively schedule incoming meetings or make recommendations on time. This AI assistive technology also aids them with planning around deadlines.

Adaptive Onboarding

For: Learning Disabilities and ADHD
Focus: Reduce Frustration

According to Epsilon, 80% of consumers want a personalized experience. All of these personalization experiences are to make the user’s workflow easier. These personalized experiences start from the introduction to the usage of the product. Onboarding helps users learn about the product, but learning continues after the initial product presentation.

We cannot expect users to know about the product once the onboarding has been completed and they need assistance in the future. Over time, if users have a hard time comprehending or completing a task, they get frustrated; this is particularly true for ADHD users. At the same time, users with learning disabilities do not remember every step either because they are too complex or have multiple steps.

Adaptive onboarding will allow everyone to re-learn when needed; it would benefit them more since help is available when needed. This type of onboarding could be AI-driven and much more generative. It could focus on different learning styles, either assistive, audio, or video presentation.

Example That Can Be Improved:

Product Fruits has a plethora of offerings, including onboarding. It offers personalization and the ability to tailor the onboarding to cover the product for new users. Allowing customization with onboarding gives the product team more control over what needs attention. It also provides the capability to track product usage based on the onboarding.

Opportunity: Offering AI interventions for different personas or segments will give the tool an additional layer of experience tailored to the needs of individuals. Imagine a user with ADHD who is trying to figure out how to use the feature; they will get frustrated if they do not identify how to use it. What if the tool intuitively nudges the user on how to complete the task? Similarly, if completing the task is complex and requires multiple steps, users with learning disabilities have difficulty following and reproducing it.

Neurodivergent Friendly Example

Onboarding does not always need to be at the start of the product introduction. Users always end up in situations where they need to find a step in the feature of completing a task but might have difficulty discovering it. In such cases, they usually seek help by asking colleagues or looking it up on the product help page.

Chameleon helps by offering features that let users use AI more effectively. Users can ask for help anytime, and the AI will generate answers to help them.

Considerations

All the issues I mentioned are present in everyone; the difference is the occurrence and intensity between neurotypical and neurodiverse individuals. Everyday things, discussions, conclusions, critical thinking, comprehension, and so on, are vastly different. It is like neurodiverse individuals’ brains are wired differently. It becomes more important to build tools that solve problems for neurodiverse users, which we inadvertently solve for everyone.

An argument that every human goes through those problems is easy to make. But, we tend to forget the intensity and criticality of those problems for neurodiverse individuals, which is far too complex than shrugging it off like neuro-typicals who can adapt to it much more quickly. Similarly, AI too has to learn and understand the problems it needs to solve. It can be confusing for the algorithm to learn unless it does not have multiple examples.

Large Language Models (LLM) are trained on vast amounts of data, such as ChatGPT, for example. It is accurate most of the time; however, sometimes, it hallucinates and gives an inaccurate answer. That might be a considerable problem when no additional guidelines exist except for the LLM. As mentioned above, there is still a possibility in most cases, but having the company guidelines and information would help give correct results.

It could also mean the users will be more dependent on AI, and there is no harm in it. If neurodiverse individuals need assistance, there cannot be a human present all the time carrying the patience required every time. Being direct is an advantage of AI, which is helpful in the case of their profession.

Conclusion

Designers should create efficient workflows for neurodivergent users who are having difficulty with working memory, comprehending complex language, learning intricate details, and so on. AI could help by providing cognitive assistance and adaptive technologies that benefit neurodivergent users greatly. Neurodiversity should be considered in product design; it needs more attention.

AI has become increasingly tied in every aspect of the user’s lives. Some are obvious, like conversational UI, chatbots, and so on, while others are hidden algorithms like recommendation engines.

Many problems specific to accessibility are being solved, but are they being solved while keeping neurodiverse issues in mind?

Jamie Diamon famously said:

“Problems don’t age well.”

— Jamie Diamon (CEO, JP Morgan)

This means we have to take critical issues into account sooner. Building an inclusive world for those 1.6 billion people is not a need for the future but a necessity of the present. We should strive to create an inclusive world for neurodiverse users; it is especially true because AI is booming, and making it inclusive now would be easy as it will scale into a behemoth set of features in every aspect of our lives in the future.



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C# 13 Params Collections

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With the version 17.10.0 Preview 3.0 of Visual Studio Preview you can test some new C# 13 features. In this blog I will explain the params Collection feature as documented in this proposal. To use this feature you have to set the LangVersion in your csproj file to preview.

<Project Sdk="Microsoft.NET.Sdk">
	<PropertyGroup>
		<OutputType>Exe</OutputType>
		<TargetFramework>net8.0</TargetFramework>
		<ImplicitUsings>enable</ImplicitUsings>
		<Nullable>enable</Nullable>
		<LangVersion>preview</LangVersion>
	</PropertyGroup>
</Project>

In C#, the `params` keyword allows a method to accept a variable number of arguments, providing flexibility in how many parameters you pass without needing to define multiple method overloads. This can make your code more concise and easier to maintain. For instance, it's particularly useful when the exact number of inputs is not known in advance or can vary. Moreover, it simplifies the calling code, as you can pass an array or a comma-separated list of arguments that the method will interpret as an array.

From C# 1.0 till 12.0 params parameter must be an array type. However, it might be beneficial for a developer to be able to have the same convenience when calling APIs that take other collection types. For example, an ImmutableArray<T>, ReadOnlySpan<T>, or plain IEnumerable. Especially in cases where compiler is able to avoid an implicit array allocation for the purpose of creating the collection (ImmutableArray<T>, ReadOnlySpan<T>, etc). This saves Heap memory allocation which improves the performance. The Garbage collector doesn't have to free this memory.

C# 1.0 params array

Lets start with an example of an old-school params array parameter in a Sum method. 

internal class Program {

    static void Main(string[] args) {
        Console.WriteLine(Sum(1, 2, 3, args.Length));
    }

    private static Sum(params int[] values) {
        int sum = 0;
        foreach (var item in values) {
            sum += item;
        }
        return sum;
    }
}

When you decompile this code using a tool like ILSpy or the SharpLab.io website you see that an array of int[4] is created in the Main() method (line 5). This is heap allocation which is something you can/should avoid.

internal class Program
{
    private static void Main(string[] args)
    {
        int[] array = new int[4];
        RuntimeHelpers.InitializeArray(array, (RuntimeFieldHandle)/*OpCode not supported: LdMemberToken*/);
        array[3] = args.Length;
        Console.WriteLine(Sum(array));
    }

    private static int Sum(params int[] values)
    {
        int num = 0;
        int num2 = 0;
        while (num2 < values.Length)
        {
            int num3 = values[num2];
            num += num3;
            num2++;
        }
        return num;
    }
}

C# 13 params Collections

In the next example the Sum() method is using a params ReadOnlySpan<int> parameter in line 7. Nothing else is changed. 

internal class Program {

    static void Main(string[] args) {
        Console.WriteLine(Sum(1, 2, 3, args.Length));
    }

    private static int Sum(params ReadOnlySpan<int> values) {
        int sum = 0;
        foreach (var item in values) {
            sum += item;
        }
        return sum;
    }
}

When you decompile this code you see a  <>y__InlineArray4<int> value is used in the Main() method (line 6). This is a struct which is created by the compiler. It uses the Inline Arrays feature of C# 12. Structs are allocated on the Stack so this code doesn't allocate any heap memory.

internal class Program
{
    [NullableContext(1)]
    private static void Main(string[] args)
    {
        <>y__InlineArray4<int> buffer = default(<>y__InlineArray4<int>);
        <PrivateImplementationDetails>.InlineArrayElementRef<<>y__InlineArray4<int>, int>(ref buffer, 0) = 1;
        <PrivateImplementationDetails>.InlineArrayElementRef<<>y__InlineArray4<int>, int>(ref buffer, 1) = 2;
        <PrivateImplementationDetails>.InlineArrayElementRef<<>y__InlineArray4<int>, int>(ref buffer, 2) = 3;
        <PrivateImplementationDetails>.InlineArrayElementRef<<>y__InlineArray4<int>, int>(ref buffer, 3) = args.Length;
        Console.WriteLine(Sum(<PrivateImplementationDetails>.InlineArrayAsReadOnlySpan<<>y__InlineArray4<int>, int>(ref buffer, 4)));
    }

    private static int Sum([ParamCollection] ReadOnlySpan<int> values)
    {
        int num = 0;
        ReadOnlySpan<int> readOnlySpan = values;
        int num2 = 0;
        while (num2 < readOnlySpan.Length)
        {
            int num3 = readOnlySpan[num2];
            num += num3;
            num2++;
        }
        return num;
    }
}

}

[StructLayout(LayoutKind.Auto)]
[InlineArray(4)]
internal struct <>y__InlineArray4<T>
{
    [CompilerGenerated]
    private T _element0;
}

Benchmark

To compare the performance between the two I have created this Benchmark using BenchmarkDotNet. It compares the Sum of 5 decimals using old-school params arrays and params ReadOnlyCollection<decimal>.

using BenchmarkDotNet.Attributes;
using BenchmarkDotNet.Running;

BenchmarkRunner.Run<BM>();

[MemoryDiagnoser(false)]
[HideColumns("RatioSD", "Alloc Ratio")]
//[ShortRunJob]
public class BM {

    private decimal _value = 500m;

    [Benchmark]
    public decimal CallSumArray() => SumArray(1m, 100m, 200m, 300m, 400m, _value);

    [Benchmark(Baseline = true)]
    public decimal CallSumSpan() => SumSpan(1m, 100m, 200m, 300m, 400m, _value);

    private static decimal SumArray(params decimal[] values) {
        decimal sum = 0;
        foreach (var item in values) {
            sum += item;
        }
        return sum;
    }

    private static decimal SumSpan(params ReadOnlySpan<decimal> values) {
        decimal sum = 0;
        foreach (var item in values) {
            sum += item;
        }
        return sum;
    }  
}

The CallSumSpan method is 28% faster and doesn't allocate any heap memory. The CallSumArray method allocated 120 bytes.

Benchmark summary

Don't want to wait for C# 13

If you don't want to wait for C# 13 you can already use a solution with simular results in C# 12. You can use Collection Expression with a normal ReadOnlySpan<T> parameter. A collection expression contains a sequence of elements between [ and ] brackets, see line 4. 

internal class Program {

    static void Main(string[] args) {
        Console.WriteLine(Sum([1, 2, 3, args.Length]));
    }

    private static int Sum(ReadOnlySpan<int> values) {
        int sum = 0;
        foreach (var item in values) {
            sum += item;
        }
        return sum;
    }
}

When you decompile this code you see the same code you saw when you used the params ReadOnlyCollection<decimal>.

Closure

In this blog post I showed you the new 'params Collection' feature in C# 13, available in Visual Studio Preview 17.10.0 Preview 3.0. It explains how to enable the feature by setting 'LangVersion' to preview in the project file and delves into the benefits of using 'params' with collection types other than arrays, like 'ReadOnlySpan<T>'. This enhancement aims to improve performance by reducing heap memory allocations, thus easing the workload on the garbage collector. The post includes an example of the traditional 'params array' in a `Sum` method to illustrate the concept.

Hopefully Microsoft will add in .NET 9 (and later) more overloads with 'params ReadOnlySpan<T>' to the methods which are using 'params arrays'. For example the String.Split method.

 

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New memory variants for the Raspberry Pi Compute Module family

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Since 2014, we have provided the power of Raspberry Pi in a flexible form factor designed specifically for industrial and embedded applications, and we’ve been surprised and delighted to see the incredible variety of ways in which our customers use Raspberry Pi Compute Modules. Today, we are pleased to announce that we have expanded our Compute Module 4S offering: these industrial boards are now available with additional SDRAM, and as well as the original 1GB variant, you can now choose from 2GB, 4GB, and 8GB options.


This is an image of a Raspberry Pi compute module. It's a compact, single-board computer with various electronic components visible. The green circuit board has multiple chips and connectors soldered onto it, and you can see the Raspberry Pi logo as well as other certification marks. At the center is a large, square processor chip, and there's a smaller, rectangular chip likely for memory. There are also various smaller components and connectors that are part of the board's circuitry. The module is designed to be integrated into other devices as a computing core.
Raspberry Pi Compute Module 4S

Compute Module 4S is based on the Raspberry Pi 4 architecture. We designed it for industrial customers who are migrating from Compute Module 3 or Compute Module 3+, and who are looking to retain the same form factor but would like greater computing power and more memory.

We will keep Compute Module 4S in production until at least January 2034, and we have produced a transition document specially to help users migrate to it from Compute Module 1, Compute Module 3, or Compute Module 3+. A full product brief and datasheet are also available.


This is an image of a Raspberry Pi compute module. It's a compact, single-board computer with various electronic components visible. The green circuit board has multiple chips and connectors soldered onto it, and you can see the Raspberry Pi logo as well as other certification marks. At the center is a large, square processor chip, and there's a smaller, rectangular chip likely for memory. There are also various smaller components and connectors that are part of the board's circuitry. The module is designed to be integrated into other devices as a computing core.
Ask your Approved Reseller if our Compute Module 4S Lite variant may better suit your needs

Compute Module 4S boards are in stock and available now from our Approved Resellers for industry, with a maximum lead time of six weeks. These modules are supplied only in bulk 200-unit boxes, with prices per unit starting from $25 US. Find your local Raspberry Pi Approved Reseller for industry and contact them directly to discuss sales.

What is Compute Module 4S used for?

Our industrial customers have used these boards in everything from electric vehicle charging stations to self-pour beer taps and coffee machines. We’ve also seen specialised medical monitoring devices built around our Compute Modules. A market research customer of ours used the modules to develop a system that understands the types of TV programmes different people enjoy watching, and Kunbus has developed an entire industrial product line, Revolution Pi, around our Compute Module.

Browse our customer success stories for plenty more examples of all types of Raspberry Pi product in use in business and industry. You’ll find everything from farming and factory automation to digital signage and earthquake monitoring. And to make sure you don’t miss any news about Raspberry Pi for industry, sign up to receive our quarterly updates for business customers.

The post New memory variants for the Raspberry Pi Compute Module family appeared first on Raspberry Pi.

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Jeremy Sinclair

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Jeremy is a Windows Developer MVP, Avalonia MVP, and Arm Ambassador who mostly focuses on all things Windows on Arm. He is a member of the .NET Foundation Project Committee and is also a member of the selection committee for .NET on AWS FOSS Fund.

He enjoys making contributions to OSS to help move projects forward and to ensure that Arm support is a first-class citizen.

You can find Jeremy on the following sites:

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Coffee and Open Source is hosted by Isaac Levin

--- Support this podcast: https://podcasters.spotify.com/pod/show/coffeandopensource/support



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Windows 11 Start menu ads are now rolling out to everyone

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Start menu ads in Windows 11
The app recommendations in the Windows 11 Start menu. | Image: Microsoft

Microsoft is starting to enable ads inside the Start menu on Windows 11 for all users. After testing these briefly with Windows Insiders earlier this month, Microsoft has started to distribute update KB5036980 to Windows 11 users this week, which includes “recommendations” for apps from the Microsoft Store in the Start menu.

“The Recommended section of the Start menu will show some Microsoft Store apps,” says Microsoft in the update notes of its latest public Windows 11 release. “These apps come from a small set of curated developers.” The ads are designed to help Windows 11 users discover more apps, but will largely benefit the developers that Microsoft is trying to tempt into building more Windows apps.

Microsoft only started testing...

Continue reading…

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Get Better at Using Prompts With Deliberate Practice: One technical writer's little experiment -- by Diana Cheung

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In this guest post, Diana Cheung explores how to learn AI by using deliberate practice to enhance her prompting skills. In her deliberate practice, she emphasizes the importance of intentional, systematic practice rather than mindless repetition, similar to how one would learn coding or other skills.
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