Vimmerse

vimmerse

For some Vimmers, their statusline is like the Lightsaber of a Jedi. Each one similar, yet unique — the aesthetic cherry revealing a lurking identity. This notion has led to projects like , , and . While all interesting, this tutorial is about doing it the old school way. We’ll be traveling to a metaphorical to craft our statusline using the methods of the force — script. Powerline Airline Lightline Dagobah Vim Before we get going, here is a preview of the syntax. The screenshot below is my current statusline, alongside the used to generate it. code Code below The Basics As with any exploration into Vim, we need to start from the ground floor — . :help statusline When nonempty, this option determines the content of the statusline. The option consists of printf style ‘%’ items interspersed with normal text. Each statusline item is of the form: %-0{minwid}.{maxwid}{item} All fields except the {item} are optional. A single percent sign can be given as “%%”. Up to 80 items can be specified. This may seem equal parts confusing and non-intuitive, but fear not…it’s not that bad. The first thing is to tell Vim we want to see the statusline. You can do this by putting the following code in your (vim) or (neovim). .vimrc init.vim set laststatus=2 Now that we have told Vim we are interested, we need to give it something to display! For a very basic example, type the following below: set statusline=lightsaber! When you reload Vim you should see: Clearly, this statusline isn’t incredibly useful, but baby steps! Syntax Within the galaxy of statuslines, a special syntax exists. Every line is derived from individual items, with each corresponding to a particular piece of information. For instance, will give the relative path to the file currently present in the buffer. %f echo "Hello, this is a .jedi file" >> lightsaber.jedi Insert the following into your Vim config. set statusline=%f Open the file we created earlier. You should see your statusline with the name of the file printed on it. This is, by and large, the formula for statuslines. You’ll be using these predefined mappings to create your weapon. There is one thing to note: how to combine multiple items. The full list of mapings: A minimal example that displays the file name, current line, total lines, and buffer number: Formatting We’re starting to get somewhere, a padawan no more. However, there are a few more tips, which are largely formatting quirks, to cover. The first of these deals with controlling which side our items reside on. Specifically, the symbol. An denotes a separation point between sections. = = Another formatting must-have is spacing. Spaces are escaped using backslashes — . The following example has one other symbol of note - . The is used to tell Vim where to truncate the line if it is too long. \ < < With the knowledge you now posess you should be able to throw together something half-decent. But the journey is not over yet! Expressions There may come a time when you’re interested in including something that isn’t previously defined. This is where expressions will come into play. Within Vim’s statusline syntax anything between is evaluated as an expression, and substituted with the result. So, let’s say you’re too cool for and want it directly on your line, this is how you would achieve such a task. %{...} set showmode This is your first step towards the darkside. You can now add just about anything your heart desires to your line. For instance, if you’re a crypto-nut, you could add the prices of Bitcoin and ETH — the possibilities are endless. For a great introduction to Vim Script . I’ve also included a few more examples below. head here File Size Read Only File It is also worth noting that you can utilize Vim’s internal functions within expressions. Color The time has come. Which side will you pick? The life of a gritty minimalist accepting what the Vim gods have to offer. Or, will you indulge in the lush ecstasy of the rainbow? Personally, I could care less…but, luckily, the choice is yours! There are two ways to go about adding color to your statusline. One is by piggy backing off of your colorscheme. The other is by defining the colors yourself. Highlight Groups The first method for coloring borrows from your colorscheme. For example, will utilize the defined highlighting for the function keyword. The next image is my statusline using this technique: #function# The accompanying code below User1..9 Colors The second method relies on colors that you’ll define. If you want a high degree of control, this is your choice. We’ll briefly dive back into Vim’s help to get an overview before preceeding. Set highlight group to User{N}, where {N} is taken from the minwid field, e.g. %1. Restore normal highlight with % or %0*. The difference between User{N} and StatusLine will be applied to StatusLineNC for the statusline of non-current windows. The number N must be between 1 and 9. See |hl-User1..9| That may be slightly confusing, so let’s hop into an example. The first thing to do is specify our colors of interest. The syntax is akin to creating a custom colorscheme. hi User1 ctermbg=green ctermfg=red   guibg=green guifg=redhi User2 ctermbg=red   ctermfg=blue  guibg=red   guifg=bluehi User3 ctermbg=blue  ctermfg=green guibg=blue  guifg=green In order to apply it, you designate the User number followed by a - User1 colors. Below is a full blown example. * %1* The code for this line is below A lightline-esque version: Your options are only limited by your imagination and time. Next Steps Despite all that has been covered, there is still a lot to learn. I’ll briefly cover a few more topics before suggesting additional resources. Special Characters The on my line are solely for aesthetics. You can go to to find interesting characters. ‹‹ ›› unicode-table Different Colors Per Mode If you are determined to do this without using a plugin it is possible. The easiest path will be using and . I’d start with a . Then you’ll set up an for your user defined colors and switch them based off the event. I’m not going to give a solution because I’m guessing it’s the sort of thing that would incite some motivation. Thus, it would be a worthwhile task to start to learn some Vim script. It’s honestly not too bad. There are other ways, however at some point you end up re-inventing the wheel. augroup Events :help Events augroup For more about Vim, head to my blog! Other Resources DIY Vim Statusline Vim pr0n: Making Statuslines That Own A Simpler Vim Statusline Build Your Own Vim Statusline My Custom Statusline

The Last Statusline For Vim

The Contribution of US Broadband Infrastructure to GDP: Acknowledgements & References

Hey there, fellow DevOps geeks! I'm yours truly, the DevOps Guru. I've been immersed in the wonderful world of DevOps for a cool 10 years now, helping all kinds of organizations dish out software faster, better, and safer. is a dynamic duo of software development and IT operations, joining forces to keep that value train chugging to the end users. But hey, it's not just about gadgets and gizmos; it's also a vibe and a way of thinking. DevOps , I discussed the perils I faced in code deployment and how I learned to adopt Continuous Integration to stop worrying. Today, I'm going to spill the beans on some of my personal mistakes. These are the hiccups and blunders I encountered on my journey through DevOps-land. I'll let you in on how I'd tackle things differently if I could hop in a time machine and start fresh. My hope is that you'll pick up a few tricks from my bloopers, find that motivation to level up your DevOps game, and maybe even have a few laughs along the way. Let's dive in! 🚀💡 In our previous meeting Confession 1 - Principles Trump Tools One of the major blunders I made on my DevOps journey was getting too starry-eyed about tools and not giving principles their due. I mean, who could blame me? The DevOps toolbox is like a treasure trove of goodies – Docker, Kubernetes, Jenkins, Ansible, you name it. I wanted to be the master of them all, thinking that wielding these tools would instantly make me a DevOps wizard. But guess what? Reality check time! It turned out that this approach was a bit like getting behind the wheel of a car without a clue about the rules of the road. I could use those tools, sure, but I couldn't apply DevOps effectively in different situations. Problems? Oh, I had my fair share, and those tools alone couldn't always bail me out. Plus, I missed out on the golden opportunities that DevOps was ready to serve up – things like quicker feedback, higher quality, cheaper costs, and happier customers. Hindsight is 20/20, right? Looking back, I wish I had cozied up to DevOps principles first, then hand-picked the tools based on each project's needs and goals. Those principles are like the DevOps compass, guiding the way. Some of the MVP principles are: : It's the art of regularly and automatically merging code changes from different devs into a shared treasure chest to keep code quality in check and avoid messy integration hiccups. Continuous integration This one's all about feeding your software to production in bite-sized portions, keeping the feedback loop tight and risks low. Continuous delivery: : It's like sculpting infrastructure (servers, networks, storage, etc.) with code and config files, ensuring uniformity and reliability. Infrastructure as code : Who doesn't love when software tools tackle the boring, repetitive stuff, like testing, deployment, and monitoring? It's like having a trusty sidekick, making work more efficient and error-free. Automation : Teamwork makes the dream work. DevOps is all about playing nice with folks from different squads (devs, testers, operators, customers) to swap knowledge, feedback, and responsibilities. Collaboration : It's a never-ending story of growth and innovation – measure, crunch the data, try new stuff, and learn from your slip-ups. Learning If I could turn back time, I'd cozy up to these principles first and really grasp why DevOps rocks. Armed with that wisdom, I'd pick and use the right tools for the job, whether it's Docker for containers, Kubernetes for orchestration, Jenkins for code juggling, Ansible for config magic, and so on. Flexibility would be my middle name, adapting my toolset as projects morphed and grew. So, if you're diving into the DevOps ocean, kindly do yourself a favor: don't get too tool-happy. Principles are what makes DevOps sizzle. Confession 2: Overlooking the Human Side of DevOps Now, here's another goof-up I made on my DevOps journey. I totally brushed off the whole " " and " " thing in DevOps. You know, all that warm and fuzzy stuff like teamwork, talking, feedback, trust, and empathy. I was all about the tech wizardry and thought I could just handle everything solo. People and relationships? It's not really my jam. people culture But surprise, surprise – that was a big no-no. Ignoring the human and cultural side of DevOps just led to problems galore. Think silos, conflicts, misunderstandings, and delays. Picture this: - Developers and operators doing their own thing, like they're on different planets, not sharing a word or cooperating. - Customers and users left out in the cold, missing from feedback and testing loops. - Managers and bosses setting impossible deadlines or demands, having zero clue about the tech hurdles and trade-offs. - Team members pointed fingers when stuff went south, but no one stepped up to take responsibility or learn from the hiccups. All this jazz put the brakes on quality, speed, and everyone's happiness in the software delivery game. It hit me like a ton of bricks – DevOps isn't just about coding and gadgets; it's a human and cultural puzzle, too. If I could rewind time, I'd be all about building a DevOps culture that champions teamwork, transparency, learning, and all the warm and fuzzies. A DevOps culture is like a secret sauce of shared values and behaviors that make folks play nice and talk to each other. Here's how: - Smash those silos, put together teams that do everything together, and chase a common goal. - Set up chit-chat lines between different teams and peeps, so they can grab and share stuff, whether it's through chat, email, or carrier pigeons. 😉 Confession 3: Learning Stuff I'll Never Use Okay, here's the final fumble in my DevOps Rumble (Feel the rhyme 😂…..NVM) – I spent a bunch of time learning things that are about as useful to me as a chocolate teapot in the desert. Think of it as taking a crash course in cooking when your job's taste-testing ice cream. Silly, right? Let me lay it out. I dove headfirst into learning some tools and tricks that are now fossils or just don't vibe with my DevOps gig. Case in point: I busted my hump learning , a config tool that had its moment in the sun a while back. But, oops, most of my projects rock with , a simpler and newer cousin that does the same job. Oh, and I went all in on , a language that's like Chef's BFF, but Ansible's like, " ." Chef Ansible Ruby Nah, I'm good Then there's the whole saga. It's all about creating and bossing around infrastructure with code. But, plot twist: most of the places I work at swear by , and they've got their own cool toys for that job, like and . I even spent hours learning advanced Terraform stuff, like modules, variables, and state files. But, in the real world? Crickets. I never got to use those skills. What a waste! Terraform Azure Azure Resource Manager Azure DevOps All this jazz had me doing somersaults to adapt, dealing with tech incompatibilities, and going through the "unlearn and relearn" dance. I also missed out on the real game – mastering essential DevOps skills and knowledge, the stuff that's like gold for my career. I'm talking: - : Like the holy grail of DevOps principles and practices. Think continuous integration, continuous delivery, automation, and all that good stuff. The DevOps Basics - : Understanding the ABCs of software development – programming languages, algorithms, testing, and debugging. Coding Know-How - : The lowdown on IT ops – networking, security, monitoring, and all that IT wizardry. Ops Insights - : Using the rockstars of the DevOps world, like Docker, Kubernetes, Jenkins, Ansible, and good old Git. Must-Have Tools - : Keeping an eye on the latest trends, like cloud computing, microservices, and all that futuristic jazz. Staying Current If I could rewind the clock, I'd have gone straight for the good stuff – those essential skills and knowledge that set the stage for a killer DevOps career. With those in my toolkit, I could easily pick up new skills as they popped up, and I'd be an all-around DevOps champ, no matter what twist the job threw at me. So, learn smart, friends – it's the name of the game. 🚀💡 In a nutshell So, there you have it, folks! I've spilled the beans on my DevOps blunders and how I'd tackle 'em differently if I could hit the rewind button. I hope my little confessions have been a light bulb moment for you, helping you sidestep the same traps and giving you that oomph to supercharge your DevOps game. To wrap it all up, here's the crib notes: : I got all starry-eyed over tools and left the principles behind. Basically, I went on a tech tool shopping spree without understanding the DevOps core principles. Big mistake! My tip? Learn the principles first, then pick the tools that match your project's needs. Confession 1 : I ghosted the human side of DevOps. Yup, I treated it like a solo mission and ignored the whole teamwork, communication, and empathy bit. That led to drama, silos, and delays. I should've been all about building a DevOps culture that's all about transparency and learning together. Confession 2 : I wasted time learning stuff that's about as useful as a screen door on a submarine. Think of outdated tools and skills I'd never use. Instead, I should've been all about the essentials that rock in the DevOps world. Confession 3 So, that's the wrap on my DevOps confessions. Thanks for hanging out, and if you enjoy reading my ramblings, share this article with your friends and Mayes, too! Read Also: Netflix Secret Sauce; The DevOps Behind Your Binge-Watching Bliss Confessions of a DevOps Guru: How I Learned to Stop Worrying and Embrace Continuous Integration If you've got your own DevOps stories or some feedback to drop, hit me up in the comments below. 🚀🙌😊

Confessions of a DevOps Guru: How I Would Learn Devops If I Could Start Again 

It’s sometimes easy to forget that the age of the smartphone is barely 15 years old. The launch of the Apple iPhone 3 was a watershed moment for an industry that had experienced rapid growth as technology to accommodate more powerful handheld devices grew. In the years that followed, there were virtually no rivals to the smartphone, but according to manufacturers like Nokia, change is on the horizon. As one of the manufacturers that flourished amidst the first cellular phone boom at the turn of the century, Nokia has performed exceptionally well in creating cutting edge devices, but now the Finnish telecommunications giant is will replace the smartphone in the coming years. anticipating that the metaverse “Our belief is that this device will be overtaken by a metaverse experience in the second half of the decade,” , chief strategy and technology officer at Nokia. said Nishant Batra The notion that anything could replace our dependence on a device that many of us can’t leave the house without seems unlikely, but Batra’s thoughts are shared by many other key players in the world of technology. As more major tech firms rebrand to accommodate the age of Web 3.0 like Meta (formerly Facebook), and Block (formerly Square), it’s clear that businesses are betting big on the metaverse and the next generation of online experiences, but what does it mean for the existence of the smartphone? Could we really be set for a future without our mobile companions? ‘Living in the Metaverse’ For tech pioneers like Mark Zuckerberg, the role of the metaverse in the future will be transformative for users. “A lot of people assume the metaverse is about a location, but one description of it is a period when immersive digital worlds become the primary way we live our lives and spend our time,” in a podcast with Lex Fridman last year. "What I care about is inventing technology to enable people to engage with one another." Zuckerberg said Today, our primary way of engaging with one another is through the use of our smartphones. Here, we can communicate verbally, textually, or through an ever-expanding range of social networks and socially-focused apps. Collaboration apps can also help us to work via our smartphones and complete tasks on an entirely remote basis. However, the a different proposition entirely. When fully realized, the metaverse will act as an immersive virtual world where we can engage with other users in a far more comprehensive way. metaverse represents Through the use of augmented or virtual reality eyewear, we will be capable of placing ourselves anywhere within 3D digital locations. Whether it’s a virtual fashion retailer, interactive gaming environment, or a board room, the metaverse promises to deliver meaningful experiences as we work, play, and communicate with others. This would see the emphasis of tech giants switch to reality technology as a primary means of delivering these impactful experiences. Already, leading smartphone manufacturers like Apple have sought to create as a means of helping users to access the metaverse. augmented reality eyewear If Mark Zuckerberg expects users to ‘live’ in the metaverse in the mid-term future, how can smartphones remain relevant when we’re expected to constantly be interconnected in virtual reality? The answer is that we’re likely to see smartphones adapt to complement the metaverse over the coming years. Adapting to Change The great thing about cellular devices is that they’re highly adaptable. Although the earliest mobile phones were capable of sending SMS text messages, the feature was never meant to become a dominant form of communication that paved the way for the likes of WhatsApp and Messenger today. As technology continued to evolve, phones were quick to become more intelligent with color screens, built-in cameras, and video streaming capabilities. The metaverse will also be a new technology that the smartphone will accommodate. 2022 saw HTC launch the first ‘metaverse smartphone,’ which had the ability to access a range of Web 3.0 features for users. The is capable of offering cryptocurrency and NFT functionality as well as access to HTC’s own metaverse platform, Viverse. HTC Desire 22 Pro Likewise, we’re seeing Apple continue to make a conscious effort to in its App Store repertoire of apps. Today, the latest iPhone 14 Pro Max not only features metaverse-ready connectivity, but it also comes with , which can help to scan real-world spaces to recreate in the metaverse. accommodate more metaverse tools 5G embedded LIDAR Naturally, in its early stages, the metaverse will exist primarily through smartphone access, which will only deepen our bonds with handsets. This has already seen the development of metaverse-based , an augmented reality music and movies app. platforms like Visualax Although the future role of smartphones in the metaverse is unclear, it’s highly likely that our favorite handheld devices will evolve to support the age of Web 3.0 rather than become obsolete by the growing technology. They may even entry-points when we’re out and about and unable to fully immerse ourselves into virtual worlds. become portable metaverse We’ve become accustomed to seeing smartphones quickly embrace innovation to maintain their relevance, and we can fully expect the same trend to take place–even if we really do begin to ‘live’ in the metaverse.

Is There Still a Place for the Smartphone in a Future Dominated by the Metaverse?

মেটাভার্স দ্বারা প্রভাবিত ভবিষ্যতে স্মার্টফোনের জন্য এখনও একটি জায়গা আছে?

Gibt es in einer vom Metaversum dominierten Zukunft noch einen Platz für das Smartphone?

¿Todavía hay lugar para el teléfono inteligente en un futuro dominado por el metaverso?

Y a-t-il encore une place pour le smartphone dans un futur dominé par le métaverse ?

क्या मेटावर्स के वर्चस्व वाले भविष्य में स्मार्टफोन के लिए अभी भी कोई जगह है?

メタバースが支配する未来にスマートフォンの居場所はあるのか?

메타버스가 지배하는 미래에도 여전히 스마트폰을 위한 자리가 있을까요?

Ainda há lugar para o smartphone em um futuro dominado pelo metaverso?

Metaevrenin Hakim Olduğu Bir Gelecekte Akıllı Telefonlara Hala Yer Var mı?

Vẫn còn chỗ cho điện thoại thông minh trong tương lai bị thống trị bởi Metaverse?

元宇宙主导的未来，智能手机还有一席之地吗？

I always advise my people to read outside your field, every day something. And most people say, ‘Well, I don’t have time to read outside my field.’ I say, ‘No, you do have time, it’s far more important.’ Your world becomes a bigger world, and maybe there’s a moment in which you make connections. Nobel prize winner Oliver Smithies in dialogue with writer David Epstein in Epstein’s book Range One of the things that surprised me as a parent was how much I benefited from reading parenting books. There are some fascinating parenting books, such as or . Between Parent and Child, Peaceful Parent, Happy Kids: How to Stop Yelling and Start Connecting How to Talk So Kids Will Listen & Listen So Kids Will Talk Simply substituting the word with the word and voilà, all those parenting principles and techniques about effective conflict resolution or more compassionate communication skills apply to adults too. child human Or the basic principles of the prepared environment (materials and experiences are always at a child’s fingertips: furniture appropriately sized, shelves at children’s height, miniature everyday tools, self-correcting materials such as Matrioshka dolls – if dolls are not classified correctly, there will be one left, etc.) or uninterrupted work cycle (don’t disturb the child when they are working, as children will usually display an immense and surprising capacity for concentration when they are immersed in an activity that captivates them). Aren’t these pedagogical concepts quite similar to or ? Montessori deep work flow Another concept that blends the worlds of children’s psychology and human psychology is , a theory introduced by in the 1930s. the zone of proximal development Lev Vygotsky At that time, there were contradictory teaching theories. Some would say that traditional teacher-led instruction followed by assessment was best. In contrast, other educators such as or proposed that children learn at their highest abilities when the educational environment is self-directed. In such a setting, children would choose their materials or activities and stay involved for as long as they wanted. Maria Montessori Jean Piaget Vygotsky suggested that while curiosity-led environments were well suited for subjects such as motric skills, practical skills, etc., in other domains, the presence of a teacher or a more knowledgeable individual was needed (e.g. mathematics or writing). The zone of proximal development defines learning as a set of experiences where a novice takes on a task that sits within their skills. Another person(s) with a higher skill set assists them. Once the novice understands and completes the initial task, they can continue improving by taking assignments of greater and greater difficulty. This concept does not apply only to classrooms but in workplaces too. Although the zone of proximal development looks like common sense, we need to remember that workplaces are not classrooms where everybody follows the same curriculum but places where people work on tasks of varying difficulties. Still, some of the best schools and companies are those that, among other things, can keep children and employees engaged and stimulated by providing tasks in their zones of proximal development. Image Credit: Simply Psychology They say that money makes the world go round, but is it true? What if, instead, ideas, and not money, are the currency of life? What if theories, discoveries, or principles keep us all spinning? By digging deeper underneath our mental models, we can easily see the cross-pollination of ideas from different domains linked together by imagination, creativity, or observation. A discovery in a field might lead to innovation in a different specialization. Of course, not all ideas or discoveries evolved from other concepts, as some ideas are dramatic paradigm shifts (e.g. Einstein’s General Theory of Relativity). Nevertheless, it is the fertilization of ideas across various domains, industries, sciences that derives progress and move us idea by idea towards a better understanding of ourselves. For example, is a series of numbers in which each number is the sum of the two preceding numbers: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34, etc. The Fibonacci series has a ratio between any number and the previous one that tends towards the so-called (around 1.618). This sequence forms patterns commonly found in nature, such as , , , , , etc. the Fibonacci sequence golden ratio nautilus shells pinecones broccoli , petals bones in the human hand galaxies So, how can we move the Fibonacci sequence from nature to human domains? Simple. Just look at applications in , , or task estimation in technology projects ( ). architecture music visual arts planning poker The is a design principle originated by aeronautical and systems engineer . Variations of this principle include “keep it simple, silly”, “keep it short and simple”, “keep it small and simple”, etc. KISS (keep it simple stupid) principle Kelly Johnson According to Kelly told the designers and engineers at Lockheed (an American aerospace manufacturer, where Johnson worked as a team leader) that everything they created had to be something that could be easily repaired with basic mechanic’s skills and simple tools. Lockheed’s products were designed for war and combat settings, which would not allow for mistakes. this article , This principle stands true for designing fighter planes or raising children. Simple structures and plans are more reliable than complex ones, as simplicity makes building, maintaining, and troubleshooting systems easier. So, we should aim for clarity in writing, relationships, personal health, software development, engineering, entrepreneurship, etc. Less is more. (the 80/20 rule, or the law of the vital few) is a principle noticed by economist . It states that a low percentage of variables in any extensive system determines a high rate of effects. Pareto noticed that approximately 80% of Italy’s land was owned by 20% of the population. He discovered the 80/20 ratio held after conducting surveys on other countries. The Pareto Principle Vilfredo Pareto This principle remarks that the minority owns the majority. Although broadly applied, it does not apply to every scenario. Still, practical applications of this law are easily found. Give or take, 20% of the sales agents generate 80% of total sales, 20% of customers account for 80% of total profits, , or , where approximately 20% of infected individuals are responsible for 80% of transmissions. 20% of the most reported software bugs cause 80% of software crashes 20% of patients use 80% of health care resources , cases of super-spreading during the ongoing COVID-19 pandemic But how do we connect the Pareto Principle to our daily lives? Let’s take a look at the clothes we usually choose, the shoes we usually grab, the routes we typically take in and out of the house, the toys children usually select. And then, as a personal productivity rule, the 80/20 rule notes that completing 20% of the most critical tasks will lead to 80% of the impact we create in a day. Beneath the skin, we carry our fears and our dreams. Our night and our light. Our less and our more. We want to stay still. But our mind will eternally want to leap forward, connecting dots, thoughts, perceptions, questions as we try to make sense of the incomplete map that was given to us. And sometimes, just sometimes, this hyper-connecting mind will lead to astounding discoveries that will forever alter the way we live. In the 1930s, earned two bachelor’s degrees in mathematics and electrical engineering. By having broader knowledge, Shannon correlated the algebraic system created by George Boole in the 19th century and the telephone circuits of Shannon’s time. Claude Shannon The result? Shannon revolutionized our world because he practically created the basic units of modern computing, where values of 0 and 1 were assigned to electrical circuits. Shannon coined these units “bits”, contracted from “binary information digits”. Decades later, : Shannon said about his discovery It just happened that no one else was familiar with both those fields at the same time. Previously published here .

A Hyper-Connected World

Maybe raising a child and growing up as a person are not that different. Reading parenting books taught me more about adults – most of the time, I just substitute the word “child” with the word “human” and the meaning is not changed a bit. I learned more about and how to effectively by reading than in any self-help book I read. conflict resolution skills communicate How to Talk So Kids Will Listen & Listen So Kids Will Talk taught me how to mirror the emotions of adults and children in my life, with no judgment. As a result, they just keep on talking, revealing more of their internal landscape. Peaceful Parent, Happy Kids: How to Stop Yelling and Start Connecting And then, there is Montessori. For a parent lucky enough to be literate, have access to sanitary living conditions and opportunities, it has become something like a rite of passage to get accustomed to the . Montessori pedagogy Prepared Environment In a Montessori prepared environment, all the materials and experiences are always at a child’s fingertips: furniture appropriately sized, shelves at her height, miniature everyday tools, self-correcting materials (think of Matrioshka dolls, if they are not classified correctly, there will be one doll left). This kind of environment improves learning and independence as it enables children to carry out most tasks by themselves. My adult-prepared environment is a blend of analog and digital. The most important material for me is my daily agenda. After a few tinkerings, what works for me is putting all tasks digitally in or Google Calendar synced with in Todoist. Then, I rewrite manually some of my daily tasks in my daily notebook. I prefer to plan every day in advance, and continuously throughout the day. I think better on paper, so I journal daily, trying to be more careful with how I’m spending my time. Todoist IFTTT Digitally, I also make sure to always log out from websites after I use them. At the end of the day, I clear my tabs, either using or just close them. is enabled by default. The purpose is to have an environment that would lead to… Onetab ColdTurkey Uninterrupted Work Cycle In Montessori pedagogy, it is paramount to not disturb the child when she is working. Just sit back and watch her. If she wishes to repeat the activity, let her do it as many times as she wants (practice makes it permanent). When a child is immersed in an activity that captivates her, she displays a surprising capacity for concentration, and she can stay in that activity for prolonged periods of time. Anecdotally, I lost count of hours of watching my child immersed in transfer or water activities. It is obvious that this Montessori idea corresponds to the term coined by Cal Newport. deep work Sometimes we forget that: M "The child is not an empty being who owes whatever he knows to us who have filled him up with it. No, the child is the builder of man. There is no man existing who has not been formed by the child he once was." aria Montessori If you would like to find more about Montessori, I recommend , especially for her down to earth approach (you have been warned, there are some blogs or books pushing on “My Montessori is better than yours”). Julia Palmarola’s Practical Guide book Just as children thrive on , I noticed a vast improvement in my own non-parent life when using . One of my best ways to fight procrastination is to have the same productive routine, over and over and over. And over. There is less and less mental friction to just start on something if I managed to do the same thing repeatedly in the past. routines atomic habits Always make a . Like every parent knows, leaving the house without having a plan, is a sure recipe for disaster. This does track to what one of the most productive people said plan If you fail to plan, you are planning to fail. Benjamin Franklin Problem-solving, empathy, analytical reasoning, critical thinking, leadership, adaptability, teamwork, efficient communication, all these transferable soft skills are crucial regardless of operating in a high-performance work setting or negotiating going-out rules with a 3-year-old. Previously published on my blog .

Being a Productive Parent with the Montessori Pedagogy in Mind

2001: A Bitcoin Odyssey

2001: 'n Bitcoin Odyssey

2001: একটি বিটকয়েন ওডিসি

2001: Bitcoinová odysea

2001: Una odisea de Bitcoin

2001. Բիթքոյնի ոդիսական

2001: Sebuah Pengembaraan Bitcoin

2001年: ビットコインの旅

2001: អូឌីសស៊ី Bitcoin

2001: Одисеја на Биткоин

2001 watapi: Huk Bitcoin Odisea

2001: Bitcoin Odisseý

AI is evolving rapidly, and its influence in enterprise environments is undeniable. We’ve seen the rise of AI agents capable of handling specific tasks, from automating data entry to providing customer support. But with this explosion of specialized agents comes a new challenge: how to manage their collective actions and ensure they work together seamlessly. Enter the orchestrator agent, an emerging concept poised to shape the future of enterprise AI. These agents don’t replace task-specific bots, they elevate them, coordinating workflows, integrating systems, and ensuring collaboration between humans and machines. Let’s explore why orchestrator agents are essential and how they might transform the way businesses leverage AI in the years to come. Why Orchestrator Agents Are Emerging Now The rapid adoption of AI agents has brought incredible opportunities but also introduced new complexities. Today, most AI systems operate in silos, focusing narrowly on their tasks without understanding the broader business context. This fragmentation creates inefficiencies and limits their potential. As companies deploy more AI-driven solutions, the need for a higher-level system to manage and harmonize these agents becomes apparent. Orchestrator agents are emerging as the answer to this challenge. They act as meta-managers, ensuring that individual agents collaborate effectively while adapting dynamically to the needs of the enterprise. https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true What Orchestrator Agents Could Bring to the Table Although orchestrator agents are still in their infancy, their potential impact is enormous. Here are some of the capabilities they might offer as they develop: 1. Intelligent Workflow Management In the future, orchestrator agents will enable businesses to design and execute complex workflows that involve multiple AI agents and human operators. They will understand the sequence of tasks needed to achieve a goal and ensure that each agent contributes effectively. For example, an orchestrator agent might: Assign a customer inquiry to a chatbot for initial triage.
Escalate the case to a human representative if the chatbot cannot resolve the issue.
Follow up with a satisfaction survey sent by another specialized agent. This seamless handoff between agents and humans will optimize operations while maintaining a high level of service. 2. Advanced Integration Across Systems As AI ecosystems grow, integration will become even more critical. Orchestrator agents will serve as the central hub, connecting disparate systems, agents, and data sources. They’ll ensure that no agent operates in isolation and that all tools contribute to a unified strategy. Future orchestrator agents could: Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise.
Enable real-time data sharing between agents, preventing duplication and enhancing decision-making.
Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. 3. Enhanced Reasoning and Decision-Making Unlike task-specific agents, orchestrator agents will rely heavily on reasoning capabilities. They’ll use advanced AI models to simulate scenarios, weigh options, and make informed decisions. This ability to "think" at a higher level will allow them to adapt to changing circumstances, ensuring workflows remain efficient even as variables shift. For instance, if an AI agent encounters an unexpected error, the orchestrator could assess the situation, identify an alternative path, and trigger the next best step. 4. Human-AI Collaboration Orchestrator agents won’t just automate workflows; they’ll also make them more transparent and collaborative. By providing intuitive interfaces, these agents will allow employees to: Visualize the progress of tasks.
Understand how decisions are being made.
Provide feedback to improve the system over time. In the future, orchestrator agents might also enable natural language interactions, letting employees adjust workflows or intervene directly through simple commands. The Road Ahead While orchestrator agents hold immense promise, they are still in the early stages of development. Many companies are experimenting with basic versions of these systems, but the full realization of their potential lies ahead. Here’s what the future could look like: Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems.
The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Conclusion The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Orchestrator agents represent more than just a solution, they symbolize the next step in how humans and AI systems collaborate. By ensuring that agents, systems, and people work together harmoniously, these meta-managers could redefine efficiency and innovation across industries. The road ahead is filled with questions: How will these agents evolve? What challenges will emerge as they scale? And, most importantly, how can enterprises prepare to harness their full potential? One thing is certain: orchestrator agents are a glimpse into the future of enterprise AI, and the journey has only just begun. AI is evolving rapidly, and its influence in enterprise environments is undeniable. We’ve seen the rise of AI agents capable of handling specific tasks, from automating data entry to providing customer support. But with this explosion of specialized agents comes a new challenge: how to manage their collective actions and ensure they work together seamlessly. Enter the orchestrator agent, an emerging concept poised to shape the future of enterprise AI. These agents don’t replace task-specific bots, they elevate them, coordinating workflows, integrating systems, and ensuring collaboration between humans and machines. Let’s explore why orchestrator agents are essential and how they might transform the way businesses leverage AI in the years to come. Why Orchestrator Agents Are Emerging Now Why Orchestrator Agents Are Emerging Now The rapid adoption of AI agents has brought incredible opportunities but also introduced new complexities. Today, most AI systems operate in silos, focusing narrowly on their tasks without understanding the broader business context. This fragmentation creates inefficiencies and limits their potential. As companies deploy more AI-driven solutions, the need for a higher-level system to manage and harmonize these agents becomes apparent. Orchestrator agents are emerging as the answer to this challenge. They act as meta-managers, ensuring that individual agents collaborate effectively while adapting dynamically to the needs of the enterprise. https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true https://www.youtube.com/watch?v=YQupw1xY16I&embedable=true What Orchestrator Agents Could Bring to the Table What Orchestrator Agents Could Bring to the Table Although orchestrator agents are still in their infancy, their potential impact is enormous. Here are some of the capabilities they might offer as they develop: 1. Intelligent Workflow Management In the future, orchestrator agents will enable businesses to design and execute complex workflows that involve multiple AI agents and human operators. They will understand the sequence of tasks needed to achieve a goal and ensure that each agent contributes effectively. For example, an orchestrator agent might: Assign a customer inquiry to a chatbot for initial triage. Escalate the case to a human representative if the chatbot cannot resolve the issue. Follow up with a satisfaction survey sent by another specialized agent. Assign a customer inquiry to a chatbot for initial triage. Escalate the case to a human representative if the chatbot cannot resolve the issue. Follow up with a satisfaction survey sent by another specialized agent. This seamless handoff between agents and humans will optimize operations while maintaining a high level of service. 2. Advanced Integration Across Systems 2. Advanced Integration Across Systems As AI ecosystems grow, integration will become even more critical. Orchestrator agents will serve as the central hub, connecting disparate systems, agents, and data sources. They’ll ensure that no agent operates in isolation and that all tools contribute to a unified strategy. Future orchestrator agents could: Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise. Enable real-time data sharing between agents, preventing duplication and enhancing decision-making. Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. Integrate legacy systems with modern AI tools, ensuring compatibility across the enterprise. Enable real-time data sharing between agents, preventing duplication and enhancing decision-making. Facilitate cross-platform workflows, allowing businesses to use the best tools for each task. 3. Enhanced Reasoning and Decision-Making 3. Enhanced Reasoning and Decision-Making Unlike task-specific agents, orchestrator agents will rely heavily on reasoning capabilities. They’ll use advanced AI models to simulate scenarios, weigh options, and make informed decisions. This ability to "think" at a higher level will allow them to adapt to changing circumstances, ensuring workflows remain efficient even as variables shift. For instance, if an AI agent encounters an unexpected error, the orchestrator could assess the situation, identify an alternative path, and trigger the next best step. 4. Human-AI Collaboration 4. Human-AI Collaboration Orchestrator agents won’t just automate workflows; they’ll also make them more transparent and collaborative. By providing intuitive interfaces, these agents will allow employees to: Visualize the progress of tasks. Understand how decisions are being made. Provide feedback to improve the system over time. Visualize the progress of tasks. Understand how decisions are being made. Provide feedback to improve the system over time. In the future, orchestrator agents might also enable natural language interactions, letting employees adjust workflows or intervene directly through simple commands. The Road Ahead The Road Ahead While orchestrator agents hold immense promise, they are still in the early stages of development. Many companies are experimenting with basic versions of these systems, but the full realization of their potential lies ahead. Here’s what the future could look like: Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Greater Autonomy: Orchestrator agents will increasingly handle complex, multi-step workflows with minimal human input. Greater Autonomy: Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Deeper Learning: These agents will continuously refine their understanding of business processes, becoming more effective over time. Deeper Learning: Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems.
The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems. The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Wider Adoption: As enterprises recognize the value of orchestration, these systems will become a standard feature of AI ecosystems. Wider Adoption: The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Conclusion The rise of orchestrator agents highlights a critical evolution in enterprise AI. As businesses grapple with the complexities of integrating specialized AI tools, the need for a centralized, intelligent system to manage workflows will only grow. Orchestrator agents represent more than just a solution, they symbolize the next step in how humans and AI systems collaborate. By ensuring that agents, systems, and people work together harmoniously, these meta-managers could redefine efficiency and innovation across industries. The road ahead is filled with questions: How will these agents evolve? What challenges will emerge as they scale? And, most importantly, how can enterprises prepare to harness their full potential? One thing is certain: orchestrator agents are a glimpse into the future of enterprise AI, and the journey has only just begun.

Orchestrator Agents: The Next Evolution in Enterprise AI

I think OpenAI is not being honest about the diminishing returns of scaling AI with data and compute alone. I think they are also putting a lot of the economy, the world and this entire industry in jeopardy by not talking more openly about the topic. At first, I believed what they told us, that all you need to do is add more compute power and more data, and LLMs as well as other models will simply get better. That this relationship between the models, their compute and data could grow linearly until the end of time. The leap from GPT-3 and GPT-3.5 was immense. The leap from GPT-3.5 to GPT-4 seemed like clear evidence that this presumption was correct. But then things got weird. Instead of releasing a model called GPT-5 or even GPT-4.5, they released GPT-4-turbo. GPT-4-turbo is not as intelligent as GPT-4 but it is much faster and it's cheaper. That all makes sense. But then, this trend kept going. After GPT-4-turbo, OpenAI's next release was GPT-4o. GPt-4o is more or less just as intelligent as GPT-4-turbo, but it is even faster and even cheaper. The functionality that really sold us, however, was its ability to talk and understand things via audio and its speed. However, take note, at this point in our story, GPT-4-turbo is not more intelligent than GPT-4 and GPT-4o is not more intelligent than GPT-4-turbo. And none of them are more intelligent than GPT-4. Their next and most recent release was GPT-o1. GPT-o1 can perform better than GPT-4 onsome tasks. But that's because o1 is not really a single model. GPT-o1 is actually a black box of multiple lightweight LLM models working together. Perhaps o1 is even better described as software or middleware than it is an actual model. You give it a question, it comes up with an answer, and then it repeatedly uses other models tasked with checking the answer to make sure it’s right, and it disguises all of these operations. It does all of this very, very quickly. Why not just make an LLM that's more powerful than GPT-4? Why resort to such cloak-and-dagger techniques to achieve new releases? GPT-4 came out 2 years ago, we should be well beyond its capabilities by now. Well, Noam Brown, a researcher at OpenAI had something to say on why they went this route with o1 at TED AI. He said “It turned out that having a bot think for just 20 seconds in a hand of poker got the same boosting performance as scaling up the model by 100,000x and training it for 100,000 times longer,” Now stop and really think about what is being said there. A bot thinking for 20 seconds is as good as a bot trained 100,000 times longer with 100,000 times more computing power.  If the scaling laws are infinite, that math is impossible. Something is either wrong here or someone is lying. Why does all of this matter? OpenAI is worth 150 billion dollars and the majority of that market cap is based on projections that depend on the improvement of models over time. If AI is only as good as it is today, that’s still an interesting future, but that’s not what’s being sold to investors by AI companies whose entire IP is their model. That also changes the product roadmap of many other companies that depend on the continued advancement of their LLMs to build their own products. OpenAI’s goal and ambitions of AGI are severely delayed if this is all true. A Hypothesis The reason LLMs are so amazing is because of a higher level philosophical phenomenon that we never considered, that language inherently possesses an extremely large amount of context and data about the world within even small sections of text. Unlike pixels in a picture or video, words in a sentence implicitly describe one another. A completely cohesive sentence is by definition, “rational”. Whether or not it’s true is a very different story and a problem that transcends language alone. No matter how much text you consume, “truth” and “falsehoods” are not simply linguistic concepts. You can say something is completely rational but in no way “true”. It is at this point LLMs will consistently hit a brick wall. Over the last 12 months, I’d like to formally speculate that behind closed doors there have been no huge leaps in LLMs at OpenAI, GrokAI, or at Google. To be specific I don’t think anyone, anywhere has made any LLM that is even 1.5X better than GPT-4. At OpenAI it seems that high-level staff are quitting. Right now they’re saying it’s because of safety but I’m going to put my tinfoil hat on now and throw an idea out there. They are aware of this issue and they’re jumping ship before it’s too late. Confirmation I started discussing this concern with friends 3 months ago. I was called many names haha. But in the last 3 weeks, a lot of the press has begun to smell something fishy: OpenAI is no longer releasing Orion (GPT-5) because it did not meet expected performance benchmarks and it is seeing diminishing returns. (https://www.theinformation.com/articles/openai-shifts-strategy-as-rate-of-gpt-ai-improvements-slows)


Bloomberg reports that OpenAI, Google and Anthropic are all having struggles making more advanced AI. (https://www.bloomberg.com/news/articles/2024-11-13/openai-google-and-anthropic-are-struggling-to-build-more-advanced-ai) What can we do about it? It’s hard to recommend a single solution. The tech behind o1 is proof that even low-performance models can be repurposed to do complicated operations. But that is not a solution to the problem of AI scaling. I think there needs to be substantial investment and rapid testing of new model architectures. We also have run out of data and need new ways of extrapolating usable data for LLMs to be trained on. Perhaps using multidimensional labeling that helps guide its references for truthful information directly. Another good idea could be to simply continue fine-tuning LLMs for specific use cases like math, science, and healthcare running and using AI agent workflows, similar to o1. It might give a lot of companies wiggle room until a new architecture arises. This problem is really bad but I think that the creativity in machine learning and software development it will inspire will be immense. Once we get over this hurdle, we’ll certainly be well on schedule for AGI and perhaps ASI. I think OpenAI is not being honest about the diminishing returns of scaling AI with data and compute alone. I think they are also putting a lot of the economy, the world and this entire industry in jeopardy by not talking more openly about the topic. At first, I believed what they told us, that all you need to do is add more compute power and more data, and LLMs as well as other models will simply get better. That this relationship between the models, their compute and data could grow linearly until the end of time. The leap from GPT-3 and GPT-3.5 was immense. The leap from GPT-3.5 to GPT-4 seemed like clear evidence that this presumption was correct. But then things got weird. Instead of releasing a model called GPT-5 or even GPT-4.5, they released GPT-4-turbo. GPT-4-turbo is not as intelligent as GPT-4 but it is much faster and it's cheaper. That all makes sense. But then, this trend kept going. After GPT-4-turbo, OpenAI's next release was GPT-4o. GPt-4o is more or less just as intelligent as GPT-4-turbo, but it is even faster and even cheaper. The functionality that really sold us, however, was its ability to talk and understand things via audio and its speed. However, take note, at this point in our story, GPT-4-turbo is not more intelligent than GPT-4 and GPT-4o is not more intelligent than GPT-4-turbo. And none of them are more intelligent than GPT-4. Their next and most recent release was GPT-o1. GPT-o1 can perform better than GPT-4 on some tasks. But that's because o1 is not really a single model. GPT-o1 is actually a black box of multiple lightweight LLM models working together. Perhaps o1 is even better described as software or middleware than it is an actual model. You give it a question, it comes up with an answer, and then it repeatedly uses other models tasked with checking the answer to make sure it’s right, and it disguises all of these operations. It does all of this very, very quickly. some Why not just make an LLM that's more powerful than GPT-4? Why resort to such cloak-and-dagger techniques to achieve new releases? GPT-4 came out 2 years ago, we should be well beyond its capabilities by now. Well, Noam Brown, a researcher at OpenAI had something to say on why they went this route with o1 at TED AI. He said “It turned out that having a bot think for just 20 seconds in a hand of poker got the same boosting performance as scaling up the model by 100,000x and training it for 100,000 times longer,” Now stop and really think about what is being said there. A bot thinking for 20 seconds is as good as a bot trained 100,000 times longer with 100,000 times more computing power.  If the scaling laws are infinite, that math is impossible. Something is either wrong here or someone is lying. Why does all of this matter? OpenAI is worth 150 billion dollars and the majority of that market cap is based on projections that depend on the improvement of models over time. If AI is only as good as it is today, that’s still an interesting future, but that’s not what’s being sold to investors by AI companies whose entire IP is their model. That also changes the product roadmap of many other companies that depend on the continued advancement of their LLMs to build their own products. OpenAI’s goal and ambitions of AGI are severely delayed if this is all true. A Hypothesis The reason LLMs are so amazing is because of a higher level philosophical phenomenon that we never considered, that language inherently possesses an extremely large amount of context and data about the world within even small sections of text. Unlike pixels in a picture or video, words in a sentence implicitly describe one another. A completely cohesive sentence is by definition, “rational”. Whether or not it’s true is a very different story and a problem that transcends language alone. No matter how much text you consume, “truth” and “falsehoods” are not simply linguistic concepts. You can say something is completely rational but in no way “true”. It is at this point LLMs will consistently hit a brick wall. Over the last 12 months, I’d like to formally speculate that behind closed doors there have been no huge leaps in LLMs at OpenAI, GrokAI, or at Google. To be specific I don’t think anyone, anywhere has made any LLM that is even 1.5X better than GPT-4. At OpenAI it seems that high-level staff are quitting. Right now they’re saying it’s because of safety but I’m going to put my tinfoil hat on now and throw an idea out there. They are aware of this issue and they’re jumping ship before it’s too late. Confirmation Confirmation I started discussing this concern with friends 3 months ago. I was called many names haha. But in the last 3 weeks, a lot of the press has begun to smell something fishy: OpenAI is no longer releasing Orion (GPT-5) because it did not meet expected performance benchmarks and it is seeing diminishing returns. (https://www.theinformation.com/articles/openai-shifts-strategy-as-rate-of-gpt-ai-improvements-slows) Bloomberg reports that OpenAI, Google and Anthropic are all having struggles making more advanced AI. (https://www.bloomberg.com/news/articles/2024-11-13/openai-google-and-anthropic-are-struggling-to-build-more-advanced-ai) OpenAI is no longer releasing Orion (GPT-5) because it did not meet expected performance benchmarks and it is seeing diminishing returns. (https://www.theinformation.com/articles/openai-shifts-strategy-as-rate-of-gpt-ai-improvements-slows) OpenAI is no longer releasing Orion (GPT-5) because it did not meet expected performance benchmarks and it is seeing diminishing returns. ( https://www.theinformation.com/articles/openai-shifts-strategy-as-rate-of-gpt-ai-improvements-slows ) OpenAI is no longer releasing Orion (GPT-5) because it did not meet expected performance benchmarks and it is seeing diminishing returns. https://www.theinformation.com/articles/openai-shifts-strategy-as-rate-of-gpt-ai-improvements-slows Bloomberg reports that OpenAI, Google and Anthropic are all having struggles making more advanced AI. (https://www.bloomberg.com/news/articles/2024-11-13/openai-google-and-anthropic-are-struggling-to-build-more-advanced-ai) Bloomberg reports that OpenAI, Google and Anthropic are all having struggles making more advanced AI. ( https://www.bloomberg.com/news/articles/2024-11-13/openai-google-and-anthropic-are-struggling-to-build-more-advanced-ai ) Bloomberg reports that OpenAI, Google and Anthropic are all having struggles making more advanced AI. https://www.bloomberg.com/news/articles/2024-11-13/openai-google-and-anthropic-are-struggling-to-build-more-advanced-ai What can we do about it? It’s hard to recommend a single solution. The tech behind o1 is proof that even low-performance models can be repurposed to do complicated operations. But that is not a solution to the problem of AI scaling. I think there needs to be substantial investment and rapid testing of new model architectures. We also have run out of data and need new ways of extrapolating usable data for LLMs to be trained on. Perhaps using multidimensional labeling that helps guide its references for truthful information directly. Another good idea could be to simply continue fine-tuning LLMs for specific use cases like math, science, and healthcare running and using AI agent workflows, similar to o1. It might give a lot of companies wiggle room until a new architecture arises. This problem is really bad but I think that the creativity in machine learning and software development it will inspire will be immense. Once we get over this hurdle, we’ll certainly be well on schedule for AGI and perhaps ASI. software development

Is AI Progress Slowing? The Scaling Debate OpenAI Doesn’t Want to Have

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