#adaptive software development

[sohojware]

The software landscape is a churning sea of innovation and change. Businesses that rely on rigid, monolithic software applications struggle to keep pace with evolving market demands and user expectations. This is where Adaptive Software Development (ASD) steps in, offering a lifeline for businesses seeking to build software that bends with the times.

ASD in Action: Embracing Change is the New Norm

ASD is an iterative and flexible software development methodology that prioritizes continuous learning and adaptation. Imagine a sculptor meticulously shaping clay, constantly refining their creation based on their vision and the feedback they receive. This is the essence of ASD. Requirements are not set in stone; instead, they are treated as fluid guidelines that can evolve as the project progresses and new information emerges. This adaptability ensures your software remains relevant and competitive in a dynamic marketplace.

Why Choose Adaptive Software Development?

Here's why businesses across the United States are embracing the power of ASD:

Unmatched Agility and Responsiveness: ASD empowers you to respond swiftly to market shifts and customer feedback. New features and functionalities can be readily incorporated through continuous iteration, ensuring your software stays ahead of the curve.

Reduced Risk, Enhanced Confidence: Traditional development methodologies often lead to costly surprises down the road. ASD mitigates this risk by prioritizing early and frequent feedback loops. Potential issues are identified and addressed early in the development cycle, fostering a sense of confidence and control throughout the project.

Happy Stakeholders, Happy Business: ASD fosters a collaborative environment where stakeholders are actively involved throughout the development process. This ongoing communication ensures the final product aligns with their expectations and addresses their specific needs. Satisfied stakeholders translate to a successful project and a thriving business.

Cost-Effective Development: The iterative nature of ASD allows for course correction throughout the development lifecycle. This reduces the risk of costly rework and ensures resources are allocated efficiently. You get the most out of your development budget.

Built to Last: ASD promotes the creation of modular and well-documented software. This translates to easier maintenance, updates, and future scaling, reducing long-term costs and ensuring your software remains a valuable asset for years to come.

How Does Adaptive Software Development Work?

ASD follows a cyclical process that emphasizes continuous learning, feedback, and adaptation. Here's a breakdown of the core principles that guide this dynamic approach:

Continuous Delivery: Frequent releases of working software functionalities are a hallmark of ASD. This allows for early feedback and identification of potential issues, ensuring your software is constantly evolving and improving.

Embrace Change: ASD acknowledges that requirements are likely to fluctuate throughout the development process. The methodology is designed to accommodate these changes seamlessly, ensuring your software stays relevant and meets the ever-evolving needs of your users.

Collaboration is Key: ASD thrives in a collaborative environment where stakeholders, developers, and testers work together as a cohesive unit. This open communication fosters a deeper understanding of project goals and ensures everyone is on the same page.

Refine the Backlog: The project backlog, a prioritized list of features, is continuously refined based on new information and feedback. This ensures development efforts are focused on the most critical functionalities and user needs.

Short Iterations: ASD breaks down the development process into short, manageable iterations. This allows for faster feedback loops, quicker time to market, and a more controlled development process.

Building Software for the Future with Sohojware

Sohojware, a leading software development company with a proven track record of success, is your trusted partner in the world of ASD. Here's why choosing Sohojware for your Adaptive Software Development project is the smart move:

A Team of Seasoned Professionals: Sohojware boasts a team of highly skilled and experienced developers who are well-versed in ASD methodologies. Their expertise ensures your project is navigated smoothly and efficiently.

The Agile Advantage: Sohojware's agile development approach aligns perfectly with the core principles of ASD, ensuring a flexible and responsive development process that adapts to your changing needs.

A History of Success: Sohojware has a well-deserved reputation for delivering successful ASD projects for clients across various industries. Their proven track record gives you peace of mind knowing your project is in capable hands.

Cost-Effective Solutions: Sohojware understands the importance of budget optimization. They offer competitive rates and work closely with clients to develop cost-effective solutions that meet their specific needs.

Your Satisfaction is Our Priority: Sohojware is committed to exceeding client expectations and delivering software that meets their specific needs. Their focus on clear communication and collaboration ensures a successful and rewarding development experience.

Is Adaptive Software Development Right for You?

ASD is a powerful approach for businesses that operate in dynamic and fast-changing markets. Here are some key indicators that ASD might be the perfect fit for your next project:

Market Agility is Paramount: If your industry is subject to rapid shifts and fluctuations, ASD's adaptability allows you to respond quickly and effectively to changing market demands.

Flexibility is Key: ASD embraces change and thrives in environments where requirements are not set in stone. If you anticipate your project needs evolving throughout the development process, ASD can accommodate those changes seamlessly.

Early Feedback is Invaluable: ASD prioritizes early and frequent feedback, allowing you to course-correct and refine your software based on real-time user insights. This is ideal for businesses that value an iterative approach and continuous improvement.

Risk Mitigation is a Priority: Traditional development methodologies can be fraught with unforeseen challenges. ASD's focus on early issue identification and proactive problem-solving minimizes development risks and ensures a smoother project journey.

Building for the Long Haul: ASD promotes the creation of well-documented and modular software. This translates to easier maintenance and future scaling, ensuring your software remains a valuable asset for years to come.

Sohojware Can Help You Navigate the World of ASD

If you're considering Adaptive Software Development for your next project, Sohojware is here to guide you every step of the way. Contact us today for a free consultation! Our experienced team will assess your specific needs and help you determine if ASD is the right approach for you. We can also provide you with a custom quote tailored to your project requirements.

In Conclusion

The business landscape is constantly evolving, and the software you use needs to evolve with it. Adaptive Software Development offers a compelling solution for businesses seeking to build software that is flexible, responsive, and adaptable to change. With its focus on continuous learning and collaboration, ASD empowers you to stay ahead of the curve and deliver exceptional value to your customers.

Sohojware is your trusted partner in the world of ASD. Our team of experts, combined with our agile development approach and commitment to client satisfaction, ensures a successful and rewarding development experience. Let's work together to build software that thrives in the face of change and propels your business forward.

FAQ’s

What are the key differences between Adaptive Software Development and traditional methodologies?

Traditional methodologies often follow a linear development process, where requirements are defined upfront and changes are discouraged. ASD, on the other hand, is an iterative and flexible approach that embraces change and prioritizes continuous learning.

How can Sohojware help me implement Adaptive Software Development?

Sohojware's experienced team will guide you through the entire ASD process, from initial planning and requirement gathering to development, testing, and deployment. We will work closely with you to ensure your project is successful and meets your specific needs.

What are the benefits of working with Sohojware for my Adaptive Software Development project?

Sohojware offers a combination of experience, expertise, and a commitment to client satisfaction. Our agile approach and focus on continuous communication ensure that your project stays on track and delivers the results you expect.

How much does Adaptive Software Development cost?

The cost of Adaptive Software Development can vary depending on the size and complexity of your project. However, Sohojware offers competitive rates and works closely with clients to develop a cost-effective solution that meets their budget.

What are the next steps to get started with Adaptive Software Development at Sohojware?

Contact Sohojware today for a free consultation! Our team will discuss your project requirements and help you determine if Adaptive Software Development is the right approach for you. We can also provide you with a custom quote based on your specific needs.

[block-verse-infotech-solution]

Unraveling the Essentials of Software Development

In today’s digital era, software development stands as the backbone of technological advancement. From powering intricate systems to simplifying everyday tasks, the significance of software in modern life cannot be overstated. Whether you’re an aspiring developer or a business owner venturing into the digital realm, understanding the fundamentals of software development is paramount.

Software development encompasses a myriad of processes, methodologies, and tools aimed at creating functional and user-friendly applications. From conceptualization to deployment, each phase demands meticulous planning, coding prowess, and rigorous testing. Here are some key aspects to delve into:

Planning and Analysis: Every successful software project commences with thorough planning and analysis. This phase involves identifying requirements, understanding user needs, and outlining the project scope.

Design and Architecture: Crafting a robust architecture lays the foundation for a scalable and efficient software solution. Design principles such as modularity, scalability, and maintainability are pivotal in this stage.

Development and Coding: Armed with a solid plan and design, developers embark on coding, breathing life into the software. Proficiency in programming languages, frameworks, and development methodologies is indispensable here.

Testing and Quality Assurance: Rigorous testing ensures that the software meets predefined standards of functionality, performance, and security. Various testing techniques like unit testing, integration testing, and user acceptance testing are employed to identify and rectify defects.

Deployment and Maintenance: Once the software clears the testing phase, it’s ready for deployment. Continuous monitoring, updates, and bug fixes ensure smooth operation and longevity of the software.

In today’s fast-paced digital landscape, Agile methodologies have revolutionized the software development process. Agile emphasizes adaptive planning, iterative development, and close collaboration between cross-functional teams. By embracing Agile principles, organizations can respond swiftly to changing requirements, mitigate risks, and deliver high-quality software in a timely manner.

Software development is a dynamic and multifaceted domain that continues to evolve with technological advancements. By understanding its intricacies and embracing best practices, businesses can leverage software to streamline operations, enhance user experiences, and stay ahead of the competition.

For unrivaled software development solutions tailored to your unique needs, look no further than Blockverse Infotech Solutions. With a team of seasoned developers and a proven track record of delivering cutting-edge software solutions, Blockverse Infotech stands ready to transform your ideas into reality. Whether you’re envisioning a bespoke mobile app, a scalable web platform, or enterprise-grade software, trust Blockverse Infotech to exceed your expectations.

Blockverse Infotech Solutions — Your Partner in Software Development Excellence.

[zennaxxtech]

Adaptive Software Development (ASD) thrives on embracing change as a core principle, enabling teams to respond dynamically to evolving requirements. By fostering flexibility and collaboration, ASD empowers organizations to stay ahead in a rapidly changing environment, delivering value to customers faster and more effectively. Check complete guide on this.

[blockverse-infotech]

Software Engineer Explores: Software Design Patterns for Enhancing Maintainability and Scalability

In the fast-paced world of software engineering, crafting robust, maintainable, and scalable solutions is paramount. At Blockverse Infotech Solutions, our team of software engineers is constantly striving to push the boundaries of innovation while ensuring that our products remain reliable and adaptable. One of the key strategies we employ to achieve this is the utilization of software design patterns. In this article, we will delve into the importance of software design patterns in enhancing the maintainability and scalability of software systems, exploring how they enable us to tackle the evolving challenges of modern software development.

Software design patterns are recurring solutions to common problems encountered in software design. They provide a structured approach to solving design issues and promote code reusability, flexibility, and maintainability. By following established design patterns, developers can leverage proven solutions to address specific concerns within their software architecture.

Maintainability refers to the ease with which a software system can be modified, updated, or repaired over time. Software design patterns play a crucial role in enhancing maintainability by promoting modularization and separation of concerns. For example, the Model-View-Controller (MVC) pattern facilitates the separation of user interface logic, business logic, and data manipulation, making it easier to modify one component without affecting others.

Scalability is the ability of a system to handle increasing workload or growth without compromising performance. Design patterns contribute to scalability by enabling developers to design systems that can efficiently adapt to changing requirements and accommodate increased demand. For instance, the Singleton pattern ensures that only one instance of a class exists throughout the application, making it easier to manage shared resources and scale the system horizontally.

Several design patterns are commonly used in software development to address various design challenges. Some of the most widely recognized patterns include:

Factory Method Pattern: Facilitates the creation of objects without specifying the exact class of the object to be created.

Observer Pattern: Defines a one-to-many dependency between objects, ensuring that changes to one object trigger updates in its dependents.

Decorator Pattern: Allows behavior to be added to individual objects dynamically, providing a flexible alternative to subclassing.

In conclusion, software design patterns are invaluable tools for enhancing the maintainability and scalability of software systems. By adopting proven solutions to common design challenges, developers can create software that is more adaptable, resilient, and easier to maintain over time. At Blockverse Infotech Solutions, we recognize the importance of incorporating design patterns into our development practices, enabling us to deliver high-quality solutions that meet the evolving needs of our clients and stakeholders.

[maxlearnmicrolearning]

The MaxLearn Method: A Methodology for Effective Microlearning Platform

Introduction

In the rapidly evolving landscape of corporate training and education, microlearning has emerged as a powerful tool for delivering targeted, engaging, and efficient learning experiences. At MaxLearn, we have embraced microlearning not just as a trend but as a cornerstone of our methodology for enhancing learning effectiveness and engagement. This article explores the MaxLearn Method, focusing on key aspects such as AI for training, learning personalization, gamification in LMS, and the role of adaptive learning platforms and tools.

AI for Training

Artificial Intelligence (AI) has revolutionized various industries, and its impact on training and development is profound. AI-powered systems can analyze learner data, personalize content delivery, and provide real-time feedback, enhancing the efficiency and effectiveness of training programs. At MaxLearn, we integrate AI into our platform to create adaptive learning experiences that cater to individual learner needs and preferences.

Learning Personalization

Personalization is crucial for engaging learners and maximizing learning outcomes. By tailoring content based on learner profiles, preferences, and progress, MaxLearn ensures that each learner receives a customized learning path. This approach not only boosts engagement but also improves knowledge retention and application.

Gamified LMS Microlearning

Gamification in Learning Management Systems (LMS) adds an element of fun and motivation to the learning process. MaxLearn incorporates gamified elements into microlearning modules to make learning enjoyable and interactive. Leaderboards, badges, and rewards incentivize participation and drive learner engagement.

LMS with Gamification

A Learning Management System with gamification features enhances learner motivation and encourages active participation. MaxLearn’s gamified LMS transforms traditional training into immersive learning experiences, fostering a competitive spirit among learners while reinforcing key concepts and skills.

Gamified Learning Management System

A Gamified Learning Management System combines gaming mechanics with educational content to create an engaging and effective learning environment. MaxLearn’s gamified LMS promotes learning through challenges, achievements, and progress tracking, making learning more enjoyable and memorable.

Artificial Intelligence in Learning and Development

AI technologies optimize learning experiences by analyzing learner behavior, preferences, and performance data. MaxLearn utilizes AI to deliver personalized recommendations, adaptive content delivery, and automated assessments, enhancing learning efficiency and effectiveness.

Adaptive Learning

Adaptive learning adjusts the pace and content of training based on individual learner performance and understanding. MaxLearn’s adaptive learning approach ensures that learners receive targeted interventions and support, optimizing learning outcomes and minimizing time spent on irrelevant content.

Gamification of Learning

Gamification transforms learning into a game-like experience by incorporating game elements such as points, levels, and rewards. MaxLearn leverages gamification to motivate learners, promote healthy competition, and increase engagement and retention rates.

Gamification and Learning

The integration of gamification principles into educational contexts enhances learner motivation, engagement, and knowledge retention. MaxLearn’s approach to gamification focuses on aligning game mechanics with learning objectives to foster a positive and productive learning environment.

Learner Experience

Learner experience encompasses every aspect of a learner's interaction with the training environment. MaxLearn prioritizes learner experience by designing intuitive interfaces, personalized learning paths, and engaging content that cater to diverse learning styles and preferences.

Adaptive Learning Platforms

Adaptive learning platforms dynamically adjust learning content and pace in response to learner performance and needs. MaxLearn’s adaptive learning platforms empower learners to progress at their own pace, receive personalized feedback, and achieve mastery of learning objectives.

Learning Experience Platforms

Learning Experience Platforms (LXPs) focus on delivering a holistic learning experience that goes beyond traditional LMS functionalities. MaxLearn’s LXP integrates social learning, content curation, and personalized learning paths to create engaging and impactful learning journeys.

Gamified Learning Platforms

Gamified learning platforms combine educational content with game-like elements to enhance motivation and engagement. MaxLearn’s gamified learning platforms leverage interactive features, rewards, and competitions to make learning enjoyable and effective.

Training Tools for Employees

Effective training tools are essential for equipping employees with the skills and knowledge needed to succeed in their roles. MaxLearn offers a suite of training tools that support diverse learning formats, including videos, simulations, assessments, and interactive modules.

Adaptive Learning Software

Adaptive learning software adjusts learning experiences in real-time based on learner performance data. MaxLearn’s adaptive learning software uses AI and machine learning algorithms to deliver personalized content recommendations and assessments, maximizing learning outcomes.

Training and Development Software

Training and development software facilitates the creation, delivery, and management of training programs. MaxLearn’s comprehensive software solutions empower organizations to streamline training processes, track learner progress, and assess training effectiveness.

AI Powered Authoring Tool

AI-powered authoring tools automate content creation processes, making it easier and faster to develop high-quality training materials. MaxLearn’s AI-powered authoring tool enhances content creation efficiency, scalability, and customization, enabling rapid deployment of engaging learning experiences.

Risk-Focused Training

Risk-focused training prepares employees to identify, assess, and mitigate risks within their roles and organizational contexts. MaxLearn integrates risk management principles into training programs to enhance organizational resilience, compliance, and decision-making.

Personalization of Learning

Personalizing learning experiences improves learner engagement and knowledge retention. MaxLearn’s approach to personalization involves adapting content, assessments, and learning paths to align with individual learner needs, preferences, and goals.

Personalized Learning

Personalized learning tailors educational experiences to meet the unique needs and interests of individual learners. MaxLearn’s personalized learning solutions enhance learner satisfaction, motivation, and skill development through adaptive content delivery and feedback mechanisms.

[max-learn]

Unleashing the Potential of Adaptive Microlearning for Frontline Workforce | Maxlearn

Introduction:

In today's rapidly evolving business landscape, frontline employees play a crucial role in driving customer satisfaction, operational efficiency, and business growth. To equip frontline workforce with the skills and knowledge they need to excel in their roles, organizations are turning to innovative learning solutions like adaptive microlearning. Adaptive Microlearning combines the power of microlearning with adaptive technologies to deliver personalized, targeted learning experiences that meet the unique needs of each learner. In this article, we'll explore the concept of adaptive microlearning, its benefits, and how it can transform training for frontline employees. With a focus on keywords like adaptive learning, adaptive learning platforms, and adaptive learning examples, we'll delve into the key features and advantages of this cutting-edge approach to workforce development.

Understanding Adaptive Learning:

Adaptive learning is a pedagogical approach that leverages technology to deliver personalized learning experiences tailored to the individual needs and preferences of each learner. Unlike traditional one-size-fits-all training programs, adaptive learning platforms use data analytics, artificial intelligence, and machine learning algorithms to assess learners' knowledge, skills, and learning patterns. Based on this information, adaptive learning platforms dynamically adjust the content, pace, and difficulty level of learning materials to optimize learning outcomes.

Benefits of Adaptive Microlearning for Frontline Workforce:

For frontline employees who often have limited time and busy schedules, adaptive microlearning offers several advantages:

Personalized Learning: Adaptive microlearning platforms analyze learners' performance and preferences to deliver personalized learning paths and content recommendations. This ensures that frontline employees receive training that is relevant, engaging, and tailored to their individual needs.

Flexibility and Accessibility: Microlearning's bite-sized format makes it ideal for frontline employees who may have limited time for training between their shifts or customer interactions. Adaptive microlearning platforms offer anytime, anywhere access to learning materials, allowing frontline employees to learn at their own pace and convenience.

Targeted Skill Development: Adaptive microlearning enables organizations to target specific skills or competencies that are critical for frontline roles. By focusing on high-impact topics and providing immediate feedback and reinforcement, adaptive microlearning helps frontline employees build and reinforce essential skills quickly and effectively.

Continuous Improvement: Adaptive microlearning platforms track learners' progress and performance over time, allowing organizations to identify areas for improvement and provide targeted interventions as needed. This promotes continuous learning and skill development among frontline employees, ensuring that they remain up-to-date with industry trends and best practices.

Examples of Adaptive Microlearning in Action:

To illustrate the concept of adaptive microlearning, let's consider a few examples:

Customer Service Training: In a retail setting, frontline employees may receive adaptive microlearning modules on topics such as effective communication, product knowledge, and conflict resolution. Based on learners' performance and feedback, the platform adjusts the difficulty level and content of subsequent modules to address areas where additional support is needed.

Safety and Compliance Training: In industries such as healthcare or manufacturing, frontline workers must adhere to strict safety and compliance regulations. Adaptive microlearning platforms deliver targeted training modules on topics such as workplace safety procedures, regulatory requirements, and emergency response protocols, ensuring that frontline employees have the knowledge and skills to perform their jobs safely and effectively.

Product Knowledge Training: In a sales environment, frontline employees may receive adaptive microlearning modules to familiarize themselves with new products or services. The platform tracks learners' progress and proficiency levels, providing additional support or advanced training modules as needed to ensure that frontline employees have the product knowledge they need to engage customers effectively.

Conclusion:

Adaptive microlearning represents a game-changer for frontline workforce training, offering personalized, flexible, and targeted learning experiences that meet the unique needs of each learner. By leveraging Adaptive Technologies and microlearning principles, organizations can empower frontline employees to acquire and apply the skills and knowledge they need to succeed in their roles. As the business landscape continues to evolve, adaptive microlearning will play an increasingly important role in driving performance, productivity, and success for frontline teams.

[shayenaxcrino]

https://www.xcrino.com/gym-management-software-in-delhi-ncr-india

Smart Gym Management Documentation by Xcrino

Smart Gym Management Documentation by Xcrino is a comprehensive guide that provides detailed information on how to effectively manage a gym or fitness center using the Xcrino gym management software. This documentation covers various aspects of gym management, including member management, sales and marketing, financial management, and facility management.

[phantomrose96]

The conversation around AI is going to get away from us quickly because people lack the language to distinguish types of AI--and it's not their fault. Companies love to slap "AI" on anything they believe can pass for something "intelligent" a computer program is doing. And this muddies the waters when people want to talk about AI when the exact same word covers a wide umbrella and they themselves don't know how to qualify the distinctions within.

I'm a software engineer and not a data scientist, so I'm not exactly at the level of domain expert. But I work with data scientists, and I have at least rudimentary college-level knowledge of machine learning and linear algebra from my CS degree. So I want to give some quick guidance.

What is AI? And what is not AI?

So what's the difference between just a computer program, and an "AI" program? Computers can do a lot of smart things, and companies love the idea of calling anything that seems smart enough "AI", but industry-wise the question of "how smart" a program is has nothing to do with whether it is AI.

A regular, non-AI computer program is procedural, and rigidly defined. I could "program" traffic light behavior that essentially goes { if(light === green) { go(); } else { stop();} }. I've told it in simple and rigid terms what condition to check, and how to behave based on that check. (A better program would have a lot more to check for, like signs and road conditions and pedestrians in the street, and those things will still need to be spelled out.)

An AI traffic light behavior is generated by machine-learning, which simplistically is a huge cranking machine of linear algebra which you feed training data into and it "learns" from. By "learning" I mean it's developing a complex and opaque model of parameters to fit the training data (but not over-fit). In this case the training data probably includes thousands of videos of car behavior at traffic intersections. Through parameter tweaking and model adjustment, data scientists will turn this crank over and over adjusting it to create something which, in very opaque terms, has developed a model that will guess the right behavioral output for any future scenario.

A well-trained model would be fed a green light and know to go, and a red light and know to stop, and 'green but there's a kid in the road' and know to stop. A very very well-trained model can probably do this better than my program above, because it has the capacity to be more adaptive than my rigidly-defined thing if the rigidly-defined program is missing some considerations. But if the AI model makes a wrong choice, it is significantly harder to trace down why exactly it did that.

Because again, the reason it's making this decision may be very opaque. It's like engineering a very specific plinko machine which gets tweaked to be very good at taking a road input and giving the right output. But like if that plinko machine contained millions of pegs and none of them necessarily correlated to anything to do with the road. There's possibly no "if green, go, else stop" to look for. (Maybe there is, for traffic light specifically as that is intentionally very simplistic. But a model trained to recognize written numbers for example likely contains no parameters at all that you could map to ideas a human has like "look for a rigid line in the number". The parameters may be all, to humans, meaningless.)

So, that's basics. Here are some categories of things which get called AI:

"AI" which is just genuinely not AI

There's plenty of software that follows a normal, procedural program defined rigidly, with no linear algebra model training, that companies would love to brand as "AI" because it sounds cool.

Something like motion detection/tracking might be sold as artificially intelligent. But under the covers that can be done as simply as "if some range of pixels changes color by a certain amount, flag as motion"

2. AI which IS genuinely AI, but is not the kind of AI everyone is talking about right now

"AI", by which I mean machine learning using linear algebra, is very good at being fed a lot of training data, and then coming up with an ability to go and categorize real information.

The AI technology that looks at cells and determines whether they're cancer or not, that is using this technology. OCR (Optical Character Recognition) is the technology that can take an image of hand-written text and transcribe it. Again, it's using linear algebra, so yes it's AI.

Many other such examples exist, and have been around for quite a good number of years. They share the genre of technology, which is machine learning models, but these are not the Large Language Model Generative AI that is all over the media. Criticizing these would be like criticizing airplanes when you're actually mad at military drones. It's the same "makes fly in the air" technology but their impact is very different.

3. The AI we ARE talking about. "Chat-gpt" type of Generative AI which uses LLMs ("Large Language Models")

If there was one word I wish people would know in all this, it's LLM (Large Language Model). This describes the KIND of machine learning model that Chat-GPT/midjourney/stablediffusion are fueled by. They're so extremely powerfully trained on human language that they can take an input of conversational language and create a predictive output that is human coherent. (I am less certain what additional technology fuels art-creation, specifically, but considering the AI art generation has risen hand-in-hand with the advent of powerful LLM, I'm at least confident in saying it is still corely LLM).

This technology isn't exactly brand new (predictive text has been using it, but more like the mostly innocent and much less successful older sibling of some celebrity, who no one really thinks about.) But the scale and power of LLM-based AI technology is what is new with Chat-GPT.

This is the generative AI, and even better, the large language model generative AI.

(Data scientists, feel free to add on or correct anything.)

[april]

TVs and monitors are separate species with common features, but which features these are have drastically changed over time. the two used to be similar sizes with very similar behaviour, and were differentiated primarily by which connection ports they had.

over time, their courses of evolution brought them closer together in that regard, with both species' survival becoming contingent on how well they could digest HDMI. as their analogue prey, such as VGA and Component, died out, the two display species were faced with the same choice: adapt, or die.

despite this newfound similarity, though, the two species still fill different ecological niches, and the way they adapted to these new environmental situations resulted in further physical distinction in other areas. for example, it is now almost impossible to find a modern TV that is a comparable size to a monitor; while the modern monitor is still limited in dimensions by the desktops where it prefers to nest, the modern TV has an almost unbounded adult size.

another strange new differentiation is that the TV seems to have developed a dependence on internet connectivity and software updates. while this benefits them in the short term, having more selling points than a monitor at first glance, it is working against them in the long-term, with each one's effective lifespan being cut dramatically.

the "dumb TV" that, quite intelligently, does not have any big software features, is nearing extinction, with very few members still producing offspring. and while we may feel sorrow for these displays, it is only natural that they are dying off - they are simply being outcompeted by the once-humble monitor. at the same size, and without the advantage of a wider variety of ports, the dumb TV cannot keep up with the monitor's much more refined adaptations for the same niche.

however, one mystery remains: why did the dumb TV never grow to the same impressive dimensions as its smart siblings? some observations suggests that the larger smart TVs have become overly territorial as a result of their decreased longevity, to the point that they will kill an infant dumb TV if they feel that it could grow to compete with them. it seems cruel to us, but in the wild, it's all a matter of survival. if you win the evolutionary race - you fight to keep first place.

[colleendoran]

Great Big Good Omens Graphic Novel Update

AKA A Visit From Bildad the Shuhite.

The past year or so has been one long visit from this guy, whereupon he smiteth my goats and burneth my crops, woe unto the woeful cartoonist.

Gaze upon the horror of Bildad the Shuhite.

You kind of have to be a Good Omens fan to get this joke, but trust me, it's hilarious.

Anyway, as a long time Good Omens novel fan, you may imagine how thrilled I was to get picked to adapt the graphic novel.

 Go me!  

This is quite a task, I have to say, especially since I was originally going to just draw (and color) it, but I ended up writing the adaptation as well. Tricky to fit a 400 page novel into a 160-ish page graphic novel, especially when so much of the humor is dependent on the language, and not necessarily on the visuals.

Not complainin', just sayin'.

Anyway, I started out the gate like a herd of turtles, because  right away I got COVID which knocked me on my butt. 

And COVID brain fog? That's a thing. I already struggle with brain fog due to autoimmune disease, and COVID made it worse.

Not complainin' just sayin'.

This set a few of the assignments on my plate back, which pushed starting Good Omens back. 

But hey, big fat lead time! No worries!

Then my computer crawled toward the grave.

My trusty MAC Pro Tower was nearly 15 years old when its sturdy heart ground to a near-halt with daily crashes. I finally got around to doing some diagnostics; some of its little brain actions were at 5% functionality. I had no reliable backups.

There are so many issues with getting a new computer when you haven't had a new computer or peripherals in nearly fifteen years and all of your software, including your Photoshop program is fifteen years old.

At the time, I was still on rural internet...which means dial-up speed.

Whatever you have for internet in the city, roll that clock back to about 2001.

That's what I had. I not only had to replace almost all of my hardware but I had to load and update all programs at dial-up speed.

Welcome to my gigabyte hell.

The entire process of replacing the equipment and programs took weeks and then I had to relearn all the software.

All of this was super expensive in terms of money and time cost.

But I was not daunted! Nosirree!

I still had a huge lead time! I can do anything! I have an iron will!

And boy, howdy, I was going to need it.

At about the same time, a big fatcat quadrillionaire client who had hired me years ago to develop a big, major transmedia project for which I was paid almost entirely in stock, went bankrupt leaving everyone holding the bag, and taking a huge chunk of my future retirement fund with it.

I wrote a very snarky almost hilarious Patreon post about it, but am not entirely in a position to speak freely because I don't want to get sued. Even though I had to go to court over it, (and I had to do that over Zoom at dial-up speed,) I'm pretty sure I'll never get anything out of this drama, and neither will anyone else involved, except millionaire dude and his buddies who all walked away with huge multi-million dollar bonuses weeks before they declared bankruptcy, all the while claiming they would not declare bankruptcy.

Even the accountant got $250,000 a month to shut down the business, while creators got nothing.

That in itself was enough drama for the year, but we were only at February by that point, and with all those months left, 2023 had a lot more to throw at me.

Fresh from my return from my Society of Illustrators show, and a lovely time at MOCCA, it was time to face practical medical issues, health updates, screening, and the like. I did my adult duty and then went back to work hoping for no news, but still had a weird feeling there would be news.

I know everyone says that, but I mean it. I had a bad feeling.

Then there was news.

I was called back for tests and more tests. This took weeks. The ubiquitous biopsy looked, even to me staring at the screen in real time, like bad news. 

It also hurt like a mofo after the anesthesia wore off. I wasn't expecting that.

Then I got the official bad news.

Cancer which runs in my family finally got me. Frankly, I was surprised I didn't get it sooner.

Stage 0, and treatment would likely be fast and complication-free. Face the peril, get it over with, and get back to work. 

I requested surgery months in the future so I could finish Good Omens first, but my doc convinced me the risk of waiting was too great. Get it done now.

"You're really healthy," my doc said. Despite an auto-immune issue which plagues me, I am way healthier than the average schmoe of late middle age. She informed me I would not even need any chemo or radiation if I took care of this now.

So I canceled my appearance at San Diego Comic Con. I did not inform the Good Omens team of my issues right away, thinking this would not interfere with my work schedule, but I did contact my agent to inform her of the issue. I also contacted a lawyer to rewrite my will and make sure the team had access to my digital files in case there were complications.

Then I got back to work, and hoped for the best.

Eff this guy.

Before I could even plant my carcass on the surgery table, I got a massive case of ocular shingles.

I didn't even know there was such a thing. 

There I was, minding my own business. I go to bed one night with a scratchy eye, and by 4 PM the next day, I was in the emergency room being told if I didn't get immediate specialist treatment, I was in big trouble.

I got transferred to another hospital and got all the scary details, with the extra horrid news that I could not possibly have cancer surgery until I was free of shingles, and if I did not follow a rather brutal treatment procedure - which meant super-painful  eye drops every half hour, twenty-four hours a day and daily hospital treatment - I could lose the eye entirely, or be blinded, or best case scenario, get permanent eye damage.

What was even funnier (yeah, hilarity) is the drops are so toxic if you don't use the medication just right, you can go blind anyway.

Hi Ho.

Ulcer is on the right. That big green blob.

I had just finished telling my cancer surgeon I did not even really care about getting cancer, was happy it was just stage zero, had no issues with scarring, wanted no reconstruction, all I cared about was my work. 

Just cut it out and get me back to work.

And now I wondered if I was going to lose my ability to work anyway.

Shingles often accompanies cancer because of the stress on the immune system, and yeah, it's not pretty. This is me looking like all heck after I started to get better.

The first couple of weeks were pretty demoralizing as I expected a straight trajectory to wellness. But it was up and down all the way. 

Some days I could not see out of either eye at all. The swelling was so bad that I had to reach around to my good eye to prop the lid open. Light sensitivity made seeing out of either eye almost impossible. Outdoors, even with sunglasses, I had to be led around by the hand.

I had an amazing doctor. I meticulously followed his instructions, and I think he was surprised I did. The treatment is really difficult, and if you don't do it just right no matter how painful it gets, you will be sorry. 

To my amazement, after about a month, my doctor informed me I had no vision loss in the eye at all. "This never happens," he said.

I'd spent a couple of weeks there trying to learn to draw in the near-dark with one eye, and in the end, I got all my sight back.

I could no longer wear contact lenses (I don't really wear them anyway, unless I'm going to the movies,) would need hard core sun protection for awhile, and the neuralgia and sun sensitivity were likely to linger. But I could get back to work.

I have never been more grateful in my life.

Neuralgia sucks, by the way, I'm still dealing with it months later.

Anyway, I decided to finally go ahead and tell the Good Omens team what was going on, especially since this was all happening around the time the Kickstarter was gearing up.

Now that I was sure I'd passed the eye peril, and my surgery for Stage 0 was going to be no big deal, I figured all was a go. I was still pretty uncomfortable and weak, and my ideal deadline was blown, but with the book not coming out for more than a year, all would be OK. I quit a bunch of jobs I had lined up to start after Good Omens, since the project was going to run far longer than I'd planned.

Everybody on the team was super-nice, and I was pretty optimistic at this time. But work was going pretty slow during, as you may imagine.

But again...lots of lead time still left, go me.

Then I finally got my surgery.

Which was not as happy an experience as I had been hoping for.

My family said the doc came out of the operating room looking like she'd been pulled backwards through a pipe, She informed them the tumor which looked tiny on the scan was "...huge and her insides are a mess."

Which was super not fun news.

Eff this guy.

The tumor was hiding behind some dense tissue and cysts. After more tests, it was determined I'd need another surgery and was going to have to get further treatments after all.

The biopsy had been really painful, but the discomfort was gone after about a week, so no biggee. The second surgery was, weirdly, not as painful as the biopsy, but the fatigue was big time.

By then, the Good Omens Kickstarter had about run its course, and the record-breaker was both gratifying and a source of immense social pressure.

I'd already turned most of my social media over to an assistant, and I'm glad I did.

But the next surgery was what really kicked me on my keister.

All in all, they took out an area the size of a baseball. It was  hard to move and wiped me out for weeks and weeks. I could not take care of myself. I'd begun losing hair by this time anyway, and finally just lopped it off since it was too heavy for me to care for myself. The cut hides the bald spots pretty well.

After about a month, I got the go-ahead to travel to my show at the San Diego Comic Con Museum (which is running until the first week of April, BTW). I was very happy I had enough energy to do it. But as soon as I got back, I had to return to treatment.

Since I live way out in the country, going into the city to various hospitals and pharmacies was a real challenge. I made more than 100 trips last year, and a drive to the compounding pharmacy which produced the specialist eye medicine I could not get anywhere else was six hours alone.

Naturally, I wasn't getting anything done during this time.

But at least my main hospital is super swank.

The oncology treatment went smoothly, until it didn't. The feels don't hit you until the end. By then I was flattened.

So flattened that I was too weak to control myself, fell over, and smashed my face into some equipment.

Nearly tore off my damn nostril.

Eff this guy.

Anyway, it was a bad year.

Here's what went right.

I have a good health insurance policy. The final tally on my health care costs ended up being about $150,000. I paid about 18% of that, including insurance. I had a high deductible and some experimental medicine insurance didn't cover. I had savings,  enough to cover the months I wasn't working, and my Patreon is also very supportive. So you didn't see me running a Gofundme or anything.

Thanks to everyone who ever bought one of my books.

No, none of that money was Good Omens Kickstarter money. I won't get most of my pay on that for months, which is just as well because it kept my taxes lower last year when I needed a break.

So, yay.

My nose is nearly healed. I opted out of plastic surgery, and it just sealed up by itself. I'll never be ready for my closeup, but who the hell cares.

I got to ring the bell.

I had a very, VERY hard time getting back to work, especially with regard to focus and concentration. My work hours dropped by over 2/3. I was so fractured and weak, time kept slipping away while I sat in the studio like a zombie. Most of the last six months were a wash.

I assumed focus issues were due (in part) to stress, so sought counseling. This seemed like a good idea at first, but when the counselor asked me to detail my issues with anxiety, I spent two weeks doing just that and getting way more anxious, which was not helpful.

After that I went EFF THIS NOISE, I want practical tools, not touchy feelies (no judgment on people who need touchy-feelies, I need a pragmatic solution and I need it now,) so tried using the body doubling focus group technique for concentration and deep work.

Within two weeks, I returned to normal work hours.

I got rural broadband, jumping me from dial up speed to 1 GB per second.

It's a miracle.

Massive doses of Vitamin D3 and K2. Yay.

The new computer works great.

The Kickstarter did so well, we got to expand the graphic novel to 200 pages. Double yay.

I'm running late, but everyone on the Good Omens team is super supportive. I don't know if I am going to make the book late or not, but if I do, well, it surely wasn't on purpose, and it won't be super late anyway. I still have months of lead time left.

I used to be something of a social media addict, but now I hardly ever even look at it, haven't been directly on some sites in over a year, and no longer miss it. It used to seem important and now doesn't.

More time for real life.

While I think the last year aged me about twenty years, I actually like me better with short hair. I'm keeping it.

OK. Rough year. 

Not complainin', just sayin'.

Back to work on The Book.

And only a day left to vote for Good Omens, Neil Gaiman, and Sandman in the Comicscene Awards. Thanks. 

[snapscube]

what's up with GShade what kind of drama could a Shader have

to the best of my memory: the lead developer of gshade started to get very controlling and arrogant about their software and how people used it. one common annoyance was that when there was an update released, the software would very intrusively bug you about updating right then and there and it was a headache to have to constantly manually remove the notice from your screen. i believe the crux of the drama was that a young modder took it upon themselves to mod and release a version of Gshade, or just some kind of additional software, that bypassed the annoying update notifications. and when the lead gshade dev found this out they sneakily implemented new code into gshade that would force shutdown someone’s computer if gshade detected that they were using this mod.

after the initial wave of controversy their reputation basically tanked overnight once people found this out. understandably so, as this was in very literal terms implementing hidden malware into the software. a bunch of shader preset creators started working on adapting their presets for reshade immediately after. i know i personally switched over to reshade immediately myself. it was such an easily avoidable mess haha.

also if i’ve gotten any of these details incorrect please feel free to correct me or elaborate in the replies, it’s been a while since i’ve looked into this drama and i’m literally just recounting it off the dome :)

[poipoipoi-2016]

Apropos of nothing

If you are the techiest person in the house (and for many of you, this is not techy at all), today is a good day to build a pihole thanks to Google's new TLDs.

For the record, this straight up stopped Dad from getting computer viruses when coupled with the Ublock browser extension, so I will volunteer my time to get you set up. We will find an evening and do a Zoom call. I am serious.

Prerequisities:

Before you start, this will be way way easier if your router has a magic way to:

Set static IP addresses

Set a custom DNS server

If you can't do this, I'm not saying you're stuck, but there's some non-obvious failure modes and maybe it's time to buy a better router.

Parts:

Raspberry Pi 4B. 2GB if you just want to set and forget, 8GB if you want to do more things on this than just your pihole (Coughs in a MarioKart box) -> https://www.raspberrypi.com/products/raspberry-pi-4-model-b/

Spare USB-C charger if you don't have one already. I'm a fan of https://www.amazon.com/Argon-USB-C-Power-Supply-Switch/dp/B0919CQKQ8/ myself

A microSD card at least UHS class 3 or better. 32 is fine for just a pihole, I have a 512 in some of mine that I use for more stuff. https://www.tomshardware.com/best-picks/raspberry-pi-microsd-cards

Some method of flashing the card if you don't have one (Some come with SD to micro-SD adapters, if not a USB to SD/micro-SD adapter is about $10 off Amazon)

If you really feel like going nuts, go buy yourself an Argon case and then very very carefully never ever install the software for the fan that does nothing. The value is entirely in having a big giant brick that is self-cooling. If you want to play MarioKart, I would consider this a requirement. https://www.amazon.com/Argon-Raspberry-Aluminum-Heatsink-Supports/dp/B07WP8WC3V

Setup:

Do yourself a favor and ignore all the signs telling you to go get Raspbian and instead go grab an ISO of Ubuntu 64-bit using RPi Imager. Because Raspbian cannot be upgraded across version WHY U DO THIS

Download Rpi Imager, plug the microSD card into your computer,

Other General Purpose OS -> Ubuntu -> Ubuntu 22.04 LTS

So now you have an operating system on an SD card.

Assemble the case if you bought one, plug in the SD card, power supply, ethernet cable if you have one or mouse and (mini) HDMI cable if you don't. If you bought that Argon case, you can just plug a keyboard (server OS means no mouse gang; In this house, we use the Command Line) and HDMI cable into the Pi. Turn it on.

Gaining access

The end state of this is that your pi is:

Connected to the internet by cable or wifi

You can SSH to it (Also not scary)

If you plugged in an ethernet cable, once it's done booting (1-2 minutes?), you should be able to ssh to "ubuntu@<the IP of the system>". Look it up in your router. It may make sense to give the static IP NOW to keep it stable.

If you've never used SSH before, I think the standard is Putty on Window or you can just open a terminal in Mac. (And if you know enough Linux to have a Linux computer, why are you reading this?)

If you didn't plug it in, and need to setup the wifi, there's magic incantations to attach it to the wifi and to be quite blunt, I forget what they are.

Your username is ubuntu, your password is ubuntu and then it will ask you to make a new password. If you know the meaning of the phrase "keypair-based access", it may make sense to run `ssh-copy-id` at this point in time.

Router settings (part 1)

Give your new Pi a static IP address, and reboot your pi (as simple as typing in `sudo reboot`).

Open a new SSH session to the pihole on the new address.

Installing pihole

Open up an SSH session and

curl -sSL https://install.pi-hole.net | bash

This is interactive. Answer the questions

When it's done, on your other computer, navigate to <the ip>/admin

Login with the password you just set. Router settings part 2

Give your new Pi a static IP address then point your router at that address

Set the DNS servers to the static IP

Then ensure you're blocking something. Anything.

Then do what you want to do. You'll probably need to whitelist some sites, blacklist some more, but the main thing is going to be "Adding more list of bad sites". Reddit has some lists.

And... enjoy.

/But seriously, there's some stuff to do for maintenance and things. I wasn't joking about the pair setup.

[maxlearnmicrolearning]

The MaxLearn Method: Redefining Microlearning from the CEO's Desk | Adaptive Learning

In the fast-paced landscape of modern education and professional development, the traditional one-size-fits-all approach to learning is rapidly becoming outdated. Recognizing the need for more effective and personalized learning solutions, MaxLearn has pioneered a revolutionary methodology known as the MaxLearn Method. In this article, we'll delve into the intricacies of this method from the CEO's perspective, exploring how it leverages cutting-edge technologies, such as AI and gamification, to create an unparalleled microlearning experience. We'll also examine the role of personalization, adaptive learning, and gamified elements in the MaxLearn Method, and how it's shaping the future of learning and development.

The MaxLearn Method: A CEO's Vision

As the CEO of MaxLearn, I am proud to introduce the MaxLearn Method, a groundbreaking approach to microlearning that reflects our commitment to innovation and excellence. At its core, the MaxLearn Method is about delivering highly personalized, engaging, and effective learning experiences that empower individuals and organizations to reach their full potential. By combining the latest advancements in technology with proven pedagogical principles, the MaxLearn Method ensures that every learner receives the support and guidance they need to succeed.

AI for Training: Personalized Learning at Scale

Central to the MaxLearn Method is the integration of artificial intelligence (AI) into the learning process. AI enables us to analyze vast amounts of data about each learner, including their learning preferences, strengths, and areas for improvement. By harnessing the power of AI, we can personalize the learning experience for each individual, delivering content that is tailored to their unique needs and learning style. Whether it's recommending relevant courses, adapting the difficulty level of exercises, or providing real-time feedback, AI allows us to create a truly personalized learning journey for every learner.

Learning Personalization: Putting Learners in the Driver's Seat

In the MaxLearn Method, learning personalization is not just a buzzword – it's a fundamental principle. We believe that learners should have control over their learning experience, with the ability to choose what, when, and how they learn. Our platform offers a wide range of microlearning courses across various disciplines and industries, giving learners the freedom to pursue their interests and career goals. With personalized recommendations, progress tracking, and flexible learning paths, we empower learners to take ownership of their learning journey and achieve their full potential.

Gamified LMS Microlearning: Making Learning Fun and Engaging

One of the key features of the MaxLearn Method is the integration of gamified elements into our learning management system (LMS). By gamifying the learning experience, we transform mundane tasks into fun and engaging activities, motivating learners to actively participate and progress through their training. From earning badges and points to competing with peers on leaderboards, gamification adds an element of excitement and friendly competition to the learning process, making it more enjoyable and rewarding for learners.

Adaptive Learning: Tailoring Content to Individual Needs

Adaptive learning is another cornerstone of the MaxLearn Method, allowing us to dynamically adjust content and delivery methods based on each learner's progress and performance. Through continuous assessment and feedback, our platform identifies areas where learners may be struggling and provides additional support and resources to help them succeed. Whether it's adapting the difficulty level of quizzes, providing personalized recommendations for further study, or offering remedial exercises, adaptive learning ensures that every learner receives the assistance they need to master the material.

Gamification and Learning: A Winning Combination

In the MaxLearn Method, gamification isn't just a gimmick – it's a powerful tool for enhancing engagement and motivation. By incorporating game-like elements such as rewards, challenges, and competition, we create an immersive learning experience that captivates learners' attention and encourages them to stay focused and committed to their goals. Gamification also fosters a sense of camaraderie and community among learners, as they collaborate with peers, share achievements, and celebrate success together.

The Role of Learner Experience in the MaxLearn Method

Learner experience is a top priority in the MaxLearn Method, with a focus on creating intuitive, user-friendly interfaces that enhance engagement and satisfaction. Our platform is designed with the learner in mind, with features such as seamless navigation, interactive multimedia content, and personalized recommendations. By prioritizing learner experience, we ensure that every interaction with our platform is enjoyable, efficient, and conducive to learning.

The Future of Learning: Adaptive Learning Platforms and Learning Experience Platforms

Looking ahead, the MaxLearn Method is poised to shape the future of learning and development. As technology continues to evolve, we are committed to staying at the forefront of innovation, leveraging emerging technologies such as AI, machine learning, and augmented reality to enhance the learning experience even further. With adaptive learning platforms and learning experience platforms, we aim to provide learners with a truly immersive and personalized learning experience that prepares them for success in an ever-changing world.

Conclusion: Embracing the MaxLearn Method

In conclusion, the MaxLearn Method represents a paradigm shift in the way we approach microlearning. By combining AI, gamification, and personalized learning, we are revolutionizing the learning experience and empowering individuals and organizations to achieve their goals. As we continue to refine and expand our methodology, we remain committed to our mission of providing innovative, effective, and accessible learning solutions that transform lives and drive success. Join us on this journey as we redefine the future of learning with the MaxLearn Method.

[max-learn]

Unveiling the Power of Microlearning with MaxLearn's Methodology

Introduction:

Microlearning has emerged as a game-changer in the realm of training and development, offering organizations a flexible and effective approach to learning. At the forefront of this revolution stands MaxLearn, a pioneering platform that employs a unique methodology to deliver powerful microlearning experiences. In this article, we'll delve into the intricacies of MaxLearn's Methodology, exploring how it leverages microlearning, adaptive techniques, AI technologies, and gamification to transform training initiatives. With a focus on keywords like microlearning, adaptive learning, AI for training, and gamified learning management systems, we'll uncover the secrets behind MaxLearn's success in empowering organizations to unlock the full potential of their workforce.

Microlearning: The Cornerstone of Learning Efficiency:

MaxLearn's methodology revolves around the concept of Microlearning, which involves the delivery of bite-sized learning modules that cater to the modern learner's preferences. By breaking down complex topics into digestible chunks, MaxLearn ensures maximum engagement, retention, and application of knowledge among learners. Whether it's delivering quick tutorials, quizzes, or interactive simulations, MaxLearn's microlearning approach enables organizations to deliver targeted and impactful training experiences.

Adaptive Learning: Tailoring Learning to Individual Needs:

One of the key pillars of MaxLearn's methodology is adaptive learning, which involves dynamically adjusting learning content and assessments based on each learner's performance and progress. By leveraging data analytics and AI technologies, MaxLearn personalizes the learning experience, ensuring that learners receive content that is tailored to their individual needs and learning preferences. This adaptive approach enhances engagement, promotes mastery of key concepts, and maximizes learning outcomes.

AI for Training: Enhancing Learning Efficiency:

MaxLearn harnesses the power of artificial intelligence to enhance the effectiveness and efficiency of training initiatives. AI algorithms analyze learner data to provide personalized recommendations, automate administrative tasks, and deliver real-time insights into learner performance. From intelligent content recommendations to automated assessments, AI technologies optimize the learning process, enabling organizations to achieve greater efficiency and effectiveness in their training programs.

Learning Personalization: Catering to Diverse Learner Needs:

MaxLearn prioritizes learning personalization, recognizing that each learner has unique preferences, skills, and learning styles. Through advanced algorithms and user-centric design, MaxLearn delivers personalized learning pathways, content recommendations, and assessments. By catering to individual learner needs, MaxLearn promotes greater engagement, motivation, and retention, ultimately driving better learning outcomes for organizations.

Microlearning Platforms: Empowering Training Initiatives:

MaxLearn stands out as a leading Microlearning Platform, offering organizations a comprehensive suite of tools and resources to implement successful microlearning initiatives. From content creation and delivery to performance tracking and reporting, MaxLearn's platform empowers organizations to deliver impactful training that resonates with learners and drives tangible results. With features like gamification, AI-powered tools, and adaptive learning capabilities, MaxLearn enables organizations to optimize their training initiatives and achieve their learning objectives effectively.

Gamified LMS: Making Learning Fun and Engaging:

MaxLearn's gamified learning management system (LMS) combines the effectiveness of microlearning with the engagement of gamification. By integrating features like badges, leaderboards, and rewards, MaxLearn transforms training into a fun and interactive experience. Gamification fosters healthy competition, encourages participation, and motivates learners to achieve their learning goals, ultimately driving better learning outcomes for organizations.

Artificial Intelligence in Learning and Development:

Artificial intelligence plays a pivotal role in MaxLearn's methodology, enabling organizations to harness the power of data-driven insights and personalized learning experiences. From AI-powered content recommendations to automated assessments and performance analytics, AI technologies enhance the efficiency and effectiveness of training initiatives. By leveraging AI for training, MaxLearn empowers organizations to deliver personalized, adaptive, and engaging learning experiences that drive meaningful results.

Conclusion:

MaxLearn's methodology for powerful microlearning represents a paradigm shift in the way organizations approach training and development. By leveraging microlearning, adaptive techniques, AI technologies, and gamification, MaxLearn empowers organizations to unlock the full potential of their workforce. With a focus on personalization, engagement, and efficiency, MaxLearn enables organizations to deliver impactful training experiences that drive performance, productivity, and growth.

[mostlysignssomeportents]

"Open" "AI" isn’t

Tomorrow (19 Aug), I'm appearing at the San Diego Union-Tribune Festival of Books. I'm on a 2:30PM panel called "Return From Retirement," followed by a signing:

https://www.sandiegouniontribune.com/festivalofbooks

The crybabies who freak out about The Communist Manifesto appearing on university curriculum clearly never read it – chapter one is basically a long hymn to capitalism's flexibility and inventiveness, its ability to change form and adapt itself to everything the world throws at it and come out on top:

https://www.marxists.org/archive/marx/works/1848/communist-manifesto/ch01.htm#007

Today, leftists signal this protean capacity of capital with the -washing suffix: greenwashing, genderwashing, queerwashing, wokewashing – all the ways capital cloaks itself in liberatory, progressive values, while still serving as a force for extraction, exploitation, and political corruption.

A smart capitalist is someone who, sensing the outrage at a world run by 150 old white guys in boardrooms, proposes replacing half of them with women, queers, and people of color. This is a superficial maneuver, sure, but it's an incredibly effective one.

In "Open (For Business): Big Tech, Concentrated Power, and the Political Economy of Open AI," a new working paper, Meredith Whittaker, David Gray Widder and Sarah B Myers document a new kind of -washing: openwashing:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4543807

Openwashing is the trick that large "AI" companies use to evade regulation and neutralizing critics, by casting themselves as forces of ethical capitalism, committed to the virtue of openness. No one should be surprised to learn that the products of the "open" wing of an industry whose products are neither "artificial," nor "intelligent," are also not "open." Every word AI huxters say is a lie; including "and," and "the."

So what work does the "open" in "open AI" do? "Open" here is supposed to invoke the "open" in "open source," a movement that emphasizes a software development methodology that promotes code transparency, reusability and extensibility, which are three important virtues.

But "open source" itself is an offshoot of a more foundational movement, the Free Software movement, whose goal is to promote freedom, and whose method is openness. The point of software freedom was technological self-determination, the right of technology users to decide not just what their technology does, but who it does it to and who it does it for:

https://locusmag.com/2022/01/cory-doctorow-science-fiction-is-a-luddite-literature/

The open source split from free software was ostensibly driven by the need to reassure investors and businesspeople so they would join the movement. The "free" in free software is (deliberately) ambiguous, a bit of wordplay that sometimes misleads people into thinking it means "Free as in Beer" when really it means "Free as in Speech" (in Romance languages, these distinctions are captured by translating "free" as "libre" rather than "gratis").

The idea behind open source was to rebrand free software in a less ambiguous – and more instrumental – package that stressed cost-savings and software quality, as well as "ecosystem benefits" from a co-operative form of development that recruited tinkerers, independents, and rivals to contribute to a robust infrastructural commons.

But "open" doesn't merely resolve the linguistic ambiguity of libre vs gratis – it does so by removing the "liberty" from "libre," the "freedom" from "free." "Open" changes the pole-star that movement participants follow as they set their course. Rather than asking "Which course of action makes us more free?" they ask, "Which course of action makes our software better?"

Thus, by dribs and drabs, the freedom leeches out of openness. Today's tech giants have mobilized "open" to create a two-tier system: the largest tech firms enjoy broad freedom themselves – they alone get to decide how their software stack is configured. But for all of us who rely on that (increasingly unavoidable) software stack, all we have is "open": the ability to peer inside that software and see how it works, and perhaps suggest improvements to it:

https://www.youtube.com/watch?v=vBknF2yUZZ8

In the Big Tech internet, it's freedom for them, openness for us. "Openness" – transparency, reusability and extensibility – is valuable, but it shouldn't be mistaken for technological self-determination. As the tech sector becomes ever-more concentrated, the limits of openness become more apparent.

But even by those standards, the openness of "open AI" is thin gruel indeed (that goes triple for the company that calls itself "OpenAI," which is a particularly egregious openwasher).

The paper's authors start by suggesting that the "open" in "open AI" is meant to imply that an "open AI" can be scratch-built by competitors (or even hobbyists), but that this isn't true. Not only is the material that "open AI" companies publish insufficient for reproducing their products, even if those gaps were plugged, the resource burden required to do so is so intense that only the largest companies could do so.

Beyond this, the "open" parts of "open AI" are insufficient for achieving the other claimed benefits of "open AI": they don't promote auditing, or safety, or competition. Indeed, they often cut against these goals.

"Open AI" is a wordgame that exploits the malleability of "open," but also the ambiguity of the term "AI": "a grab bag of approaches, not… a technical term of art, but more … marketing and a signifier of aspirations." Hitching this vague term to "open" creates all kinds of bait-and-switch opportunities.

That's how you get Meta claiming that LLaMa2 is "open source," despite being licensed in a way that is absolutely incompatible with any widely accepted definition of the term:

https://blog.opensource.org/metas-llama-2-license-is-not-open-source/

LLaMa-2 is a particularly egregious openwashing example, but there are plenty of other ways that "open" is misleadingly applied to AI: sometimes it means you can see the source code, sometimes that you can see the training data, and sometimes that you can tune a model, all to different degrees, alone and in combination.

But even the most "open" systems can't be independently replicated, due to raw computing requirements. This isn't the fault of the AI industry – the computational intensity is a fact, not a choice – but when the AI industry claims that "open" will "democratize" AI, they are hiding the ball. People who hear these "democratization" claims (especially policymakers) are thinking about entrepreneurial kids in garages, but unless these kids have access to multi-billion-dollar data centers, they can't be "disruptors" who topple tech giants with cool new ideas. At best, they can hope to pay rent to those giants for access to their compute grids, in order to create products and services at the margin that rely on existing products, rather than displacing them.

The "open" story, with its claims of democratization, is an especially important one in the context of regulation. In Europe, where a variety of AI regulations have been proposed, the AI industry has co-opted the open source movement's hard-won narrative battles about the harms of ill-considered regulation.

For open source (and free software) advocates, many tech regulations aimed at taming large, abusive companies – such as requirements to surveil and control users to extinguish toxic behavior – wreak collateral damage on the free, open, user-centric systems that we see as superior alternatives to Big Tech. This leads to the paradoxical effect of passing regulation to "punish" Big Tech that end up simply shaving an infinitesimal percentage off the giants' profits, while destroying the small co-ops, nonprofits and startups before they can grow to be a viable alternative.

The years-long fight to get regulators to understand this risk has been waged by principled actors working for subsistence nonprofit wages or for free, and now the AI industry is capitalizing on lawmakers' hard-won consideration for collateral damage by claiming to be "open AI" and thus vulnerable to overbroad regulation.

But the "open" projects that lawmakers have been coached to value are precious because they deliver a level playing field, competition, innovation and democratization – all things that "open AI" fails to deliver. The regulations the AI industry is fighting also don't necessarily implicate the speech implications that are core to protecting free software:

https://www.eff.org/deeplinks/2015/04/remembering-case-established-code-speech

Just think about LLaMa-2. You can download it for free, along with the model weights it relies on – but not detailed specs for the data that was used in its training. And the source-code is licensed under a homebrewed license cooked up by Meta's lawyers, a license that only glancingly resembles anything from the Open Source Definition:

https://opensource.org/osd/

Core to Big Tech companies' "open AI" offerings are tools, like Meta's PyTorch and Google's TensorFlow. These tools are indeed "open source," licensed under real OSS terms. But they are designed and maintained by the companies that sponsor them, and optimize for the proprietary back-ends each company offers in its own cloud. When programmers train themselves to develop in these environments, they are gaining expertise in adding value to a monopolist's ecosystem, locking themselves in with their own expertise. This a classic example of software freedom for tech giants and open source for the rest of us.

One way to understand how "open" can produce a lock-in that "free" might prevent is to think of Android: Android is an open platform in the sense that its sourcecode is freely licensed, but the existence of Android doesn't make it any easier to challenge the mobile OS duopoly with a new mobile OS; nor does it make it easier to switch from Android to iOS and vice versa.

Another example: MongoDB, a free/open database tool that was adopted by Amazon, which subsequently forked the codebase and tuning it to work on their proprietary cloud infrastructure.

The value of open tooling as a stickytrap for creating a pool of developers who end up as sharecroppers who are glued to a specific company's closed infrastructure is well-understood and openly acknowledged by "open AI" companies. Zuckerberg boasts about how PyTorch ropes developers into Meta's stack, "when there are opportunities to make integrations with products, [so] it’s much easier to make sure that developers and other folks are compatible with the things that we need in the way that our systems work."

Tooling is a relatively obscure issue, primarily debated by developers. A much broader debate has raged over training data – how it is acquired, labeled, sorted and used. Many of the biggest "open AI" companies are totally opaque when it comes to training data. Google and OpenAI won't even say how many pieces of data went into their models' training – let alone which data they used.

Other "open AI" companies use publicly available datasets like the Pile and CommonCrawl. But you can't replicate their models by shoveling these datasets into an algorithm. Each one has to be groomed – labeled, sorted, de-duplicated, and otherwise filtered. Many "open" models merge these datasets with other, proprietary sets, in varying (and secret) proportions.

Quality filtering and labeling for training data is incredibly expensive and labor-intensive, and involves some of the most exploitative and traumatizing clickwork in the world, as poorly paid workers in the Global South make pennies for reviewing data that includes graphic violence, rape, and gore.

Not only is the product of this "data pipeline" kept a secret by "open" companies, the very nature of the pipeline is likewise cloaked in mystery, in order to obscure the exploitative labor relations it embodies (the joke that "AI" stands for "absent Indians" comes out of the South Asian clickwork industry).

The most common "open" in "open AI" is a model that arrives built and trained, which is "open" in the sense that end-users can "fine-tune" it – usually while running it on the manufacturer's own proprietary cloud hardware, under that company's supervision and surveillance. These tunable models are undocumented blobs, not the rigorously peer-reviewed transparent tools celebrated by the open source movement.

If "open" was a way to transform "free software" from an ethical proposition to an efficient methodology for developing high-quality software; then "open AI" is a way to transform "open source" into a rent-extracting black box.

Some "open AI" has slipped out of the corporate silo. Meta's LLaMa was leaked by early testers, republished on 4chan, and is now in the wild. Some exciting stuff has emerged from this, but despite this work happening outside of Meta's control, it is not without benefits to Meta. As an infamous leaked Google memo explains:

Paradoxically, the one clear winner in all of this is Meta. Because the leaked model was theirs, they have effectively garnered an entire planet's worth of free labor. Since most open source innovation is happening on top of their architecture, there is nothing stopping them from directly incorporating it into their products.

https://www.searchenginejournal.com/leaked-google-memo-admits-defeat-by-open-source-ai/486290/

Thus, "open AI" is best understood as "as free product development" for large, well-capitalized AI companies, conducted by tinkerers who will not be able to escape these giants' proprietary compute silos and opaque training corpuses, and whose work product is guaranteed to be compatible with the giants' own systems.

The instrumental story about the virtues of "open" often invoke auditability: the fact that anyone can look at the source code makes it easier for bugs to be identified. But as open source projects have learned the hard way, the fact that anyone can audit your widely used, high-stakes code doesn't mean that anyone will.

The Heartbleed vulnerability in OpenSSL was a wake-up call for the open source movement – a bug that endangered every secure webserver connection in the world, which had hidden in plain sight for years. The result was an admirable and successful effort to build institutions whose job it is to actually make use of open source transparency to conduct regular, deep, systemic audits.

In other words, "open" is a necessary, but insufficient, precondition for auditing. But when the "open AI" movement touts its "safety" thanks to its "auditability," it fails to describe any steps it is taking to replicate these auditing institutions – how they'll be constituted, funded and directed. The story starts and ends with "transparency" and then makes the unjustifiable leap to "safety," without any intermediate steps about how the one will turn into the other.

It's a Magic Underpants Gnome story, in other words:

Step One: Transparency

Step Two: ??

Step Three: Safety

https://www.youtube.com/watch?v=a5ih_TQWqCA

Meanwhile, OpenAI itself has gone on record as objecting to "burdensome mechanisms like licenses or audits" as an impediment to "innovation" – all the while arguing that these "burdensome mechanisms" should be mandatory for rival offerings that are more advanced than its own. To call this a "transparent ruse" is to do violence to good, hardworking transparent ruses all the world over:

https://openai.com/blog/governance-of-superintelligence

Some "open AI" is much more open than the industry dominating offerings. There's EleutherAI, a donor-supported nonprofit whose model comes with documentation and code, licensed Apache 2.0. There are also some smaller academic offerings: Vicuna (UCSD/CMU/Berkeley); Koala (Berkeley) and Alpaca (Stanford).

These are indeed more open (though Alpaca – which ran on a laptop – had to be withdrawn because it "hallucinated" so profusely). But to the extent that the "open AI" movement invokes (or cares about) these projects, it is in order to brandish them before hostile policymakers and say, "Won't someone please think of the academics?" These are the poster children for proposals like exempting AI from antitrust enforcement, but they're not significant players in the "open AI" industry, nor are they likely to be for so long as the largest companies are running the show:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4493900

I'm kickstarting the audiobook for "The Internet Con: How To Seize the Means of Computation," a Big Tech disassembly manual to disenshittify the web and make a new, good internet to succeed the old, good internet. It's a DRM-free book, which means Audible won't carry it, so this crowdfunder is essential. Back now to get the audio, Verso hardcover and ebook:

http://seizethemeansofcomputation.org

If you'd like an essay-formatted version of this post to read or share, here's a link to it on pluralistic.net, my surveillance-free, ad-free, tracker-free blog:

https://pluralistic.net/2023/08/18/openwashing/#you-keep-using-that-word-i-do-not-think-it-means-what-you-think-it-means

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

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