The 1% Club expects 86.9 crore rupees (US$10 million) in 2025, driven by AI-driven efficiency. Source link · We strive to uphold the highest ethical standards in all of our reporting and coverage. We StartupNews.fyi want to be transparent with our readers about any potential conflicts of interest ...

Indian finance education platform eyes advisory services

A grant in excess of $4 million will enable the University of Arkansas at Little Rock to expand its efforts in preparing high school teachers to instruct on the growing field of cybersecurity.

University of Arkansas at Little Rock to expand cybersecurity education efforts with support of grant | The Arkansas Democrat-Gazette - Arkansas' Best News Source

04, 2025 (GLOBE NEWSWIRE) -- The "Plastic Waste Management Market: Analysis By Service, By Polymer, By Source, By End User, By Region Size and Trends - Forecast up to 2030" report has been added to ResearchAndMarkets.com's offering.The global plastic waste management market value in 2024 stood ...

Plastic Waste Management Market Competitive Landscape Outlook to 2030, Featuring Veolia Environnement, Clean Harbors, Biffa, Republic Services, Waste Connections, Reworld, Remondis and KW Plastics

Amazon has joined Microsoft in offering access to Chinese startup DeepSeek's low-cost, open-source artificial intelligence training models. DeepSeek is challenging Meta in open-source models.

Amazon Cloud Offers DeepSeek AI Models As Meta Open Source Rivalry Grows

Cricket Australia is revolutionising fan engagement and cricket administration through cutting-edge AI and cloud technology, leveraging Microsoft Azure.

From grassroots to elite: How Cricket Australia is scoring better digital experiences with AI and cloud technology - Source Asia

However, its widespread adoption brings heightened security challenges and evolving regulatory demands. In the coming year, we expect a rise in targeted OSS supply chain attacks, a greater reliance on AI in cybersecurity — with both positive and negative implications — and a stronger push ...

The Shifting Landscape of Open Source Security

Skip to main content · Microsoft · Source Asia · Cancel · India · Category: AI · Microsoft AI CEO Mustafa Suleyman says AI companions will create a new class of digital experience

Microsoft announces US $3bn investment over two years in India cloud and AI infrastructure to accelerate adoption of AI, skilling and innovation

Nvidia Corp. Chief Executive Officer Jensen Huang announced a raft of new chips, software and services, aiming to stay at the forefront of artificial intelligence computing. He spoke during a keynote Monday night at CES, shares rose on the news. Caroline Hyde reports. (Source: Bloomberg)

Watch Nvidia CEO Unveils New Gaming Chips, Software at CES - Bloomberg

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, Source Upon reaching the milestone of (weehee!), here's a brand new set of GIFs that might come in handy for updating your social distancing life on Snapchat or Instagram 😘 4 million views #RemoteWork QUARANTINE & CHILL . Source #WFH - WORK FROM HOME . Source STAY (THE F*CK) HOME . Source More stories on the coronavirus at https://hackernoon.com/tagged/coronavirus More awesome GIFs at https://giphy.com/hackernoon Again, stay safe and sound, and socially distant Designed by Kien Dao

#REMOTEWORK, A PIXELATED SET

( ) Source To solve a problem, you first need to understand the problem. Problems exist within systems. So, to understand the problem, you need to understand the system. By systems I mean . They are the most interesting kind, and difficult to deal with. complex adaptive systems Complex, because removing a part destroys the system. Each part is interdependent on other parts. Like removing a heart from the human body. Adaptive, because each part reacts to what the other parts do. Like the heart pumping faster when artery blockages develop. Treating complex systems like a black-box, where you figure out the problem by trying solutions is a terrible idea. Imagine going to a doctor and without listening to you, the doctor gives you aspirin. Do you feel better? No? Then paracetamol. Still no? An antacid. Still no? .. Sigh, okay, tell me what’s wrong? But that’s how we solve most surface problems. And in simple systems, it usually works. The key is, can you distinguish between simple and complex systems? If you don’t know what to call it, you can’t distinguish it. [1] An alternative is white-box thinking. Here, we dissect the system, get into its internals and figure out why things weren’t working, or how the system works. I think there are at least two ways to figure out a system. Simulate the systemExperiment with the system Simulation Simulation is either computational or thought based. One is running a computer simulation, the other is running a thought experiment. In both cases, what you get are insights into how the system works. They go hand in hand. Both need a basic understanding of how things will work — without an existing model, you can’t simulate. These methods are also used to refine your model. For example, you start with a barebones model built by experience. Then, . Then, you improve the model based on the difference in results between your model and the world. you run it One pillar example of simulation is done in 1972. It tracks the demise of humans on the current trajectory of consumption and environment degradation. The Limits to Growth Criticized in the beginning, LTG has come to be accepted as the model predicting reality, now that we have 50 years of living the model behind us. Thought experiments Take a hypothesis, theory or a principle. Think through what will happen if it were true. That’s a thought experiment. Consider an example — (1935). It presents a cat that is indeterminately alive or dead, depending on a random . Figuring out if it’s alive or not is dependent on looking in the box. Till that point, it’s both, alive and dead. Schrödinger’s cat quantum event What makes this thought experiment so great is the way it explains quantum theory. Say, instead, you had a coin inside the box. You can toss this coin by pressing a button outside the box. If it’s heads, the cat will die. Tails, and it lives. With every button press, the probability to live decreases, but at no point is that cat both alive and dead. Is your mind blowing to pieces yet? (ANDRZEJ WOJCICKI/ Science Photo Library/ Corbis) The process You begin with a set of hypothesis about what would happen. You run through them in your mind There are two things that might happen. You arrive at an outcome different from what you expect You arrive at your version of reality Both cases are a cause for concern. You confirm that’s what would happen in the real world, via experience If your model is close enough to the real world, profit. else you refine your hypothesis and restart. Experimentation Here, we enter the territory of real world experiments with real world consequences that might change the state of the system. You’re perturbing the system to try and figure out what will happen. Thus, you want to experiment with small changes first. [2] Want universal basic income? Try experimenting in a few cities first. it’s A/B testing on whatever scale works. The only caveat: things break when you scale up. Experimentation with system is needed when your imagination can’t keep up with what you’re seeing. This usually happens in complex systems. There are too many variables — you don’t know half of them, and you can’t keep the other half in your brain. [3] The main aim, using the above approaches is to build an accurate-enough model. One that behaves the same way the system behaves, given the same input. Input → System → Output Input → Model → Output Fair warning though, knowing this model isn’t the same as controlling the system. That’s the — to control a system, you need a controller at least as complex as the system. Law of Requisite Variety are an excellent tool to build this model. Sometimes sketching things out, looking at it all together helps see the relationships. That’s what BOTDs are for. Behaviour over time diagrams Questions to answer In both approaches, you’re trying to fill in some blanks. Here are some important questions to answer. They exist in every system. If you’re unsure what some of them mean, head over to seeing systems. Find the driving factors What would happen if I do X? Figure out the loop structure If I do more of a driving factor, d , what will happen? Figure out delays in systems How long before the results of changing d occur? Identify the current dominant loop A second from now, towards which driving factor is the system going? System Archetypes Some very common models come into play again and again. If you know these well, the problem switches from trying to figure out what is happening, to which model fits? — This is powerful and dangerous. Dangerous because you get a tendency to beat the current situation into one of these models. Powerful because you don’t have to start from scratch every time. This is how . They are archetypes for life. Mental Models work has an excellent introduction to system archetypes. Wikipedia To dive into available archetypes, The Systems Thinker is an excellent resource. I haven’t explored archetypes enough, yet. What I do know, is that the above archetype list is incomplete. As Charlie Munger found out, sometimes it’s easier to look in an adjacent field than diving deeper in yours. That’s what I’ll do. Into Chemistry — equilibriums. Systems can be thought of as an equilibrium. That’s when we can use results from chemical equilibrium to explain system behaviour. Presenting, . Le Chateliers Principle When a settled system is disturbed, it will adjust to diminish the change that has been made to it. This is precisely what Jay Forrester says happens in social systems. “At any time, a near-equilibrium exists affecting population mobility between different areas of a country. To the extent that there is disequilibrium, it means that some area is slightly more attractive than others and population begins to move in the direction of the more attractive area. Movement continues until rising population drives the more attractive area down in attractiveness to again be in equilibrium with its surroundings. Other things being equal, an increase in population of a city crowds housing, overloads job opportunities, causes congestion, increases pollution, encourages crime, and reduces every component of the quality of life.” — Jay Forrester, Counter intuitive behaviour of social systems [1] Case in point: The Piraha tribe [2] This doesn’t mean the large scale change would mimic the small scale change. Things can go unexpectedly wrong . when you scale up [3] Computer simulations shine on this half. But, still produce crap results because they are just half the variables, and not necessarily the most critical.

A Practical Guide to Understanding Systems

In the last few months I heavily worked to push the AI agents integration into my SaaS product to a higher level. It was a very long journey, started more than a year ago with the first experiments. I had to say that understanding all the moving parts of an AI driven system was far from easy. As a PHP developer I struggled a lot, mainly because the PHP ecosystem to develop this kind of “Agentic” features into existing applications it’s not as advanced and rich as it is in other technologies. Python and Javascript are “driving the bus”, and obviously other developers working with different programming languages are creating their artifacts to get the opportunity to start their journey too. Six months ago when I started working on this chapter I took in consideration some packages that were getting attention like LLPhant, or Prism. There was a lot of development behind these packages and they have already implemented a lot of things. But for my needs they have too serious weaknesses to consider ​​building the foundation of this chapter for my business on these libraries. Prism is exclusively for Laravel, so you are locked in, and LLPhant has a lot of different classes and looks more like a library than a framework. It also lacks features like Memory, chat history, observability. The unsustainable path From the beginning I realized that I could not look at other programming languages. It is not sustainable. And I am sure that is the same for most developers who specialize in a particular technology. If you have a PHP application you can’t implement an agent in javascript or Python, because they need your application data and context to generate their magic. Transferring this data and context to an external entity written in another language leads to a lot of code duplication, or technical constraint that is not sustainable too. I’m curious to see if it was just me struggling, or if these feelings are the same as other PHP developers. I started thinking that the tool I created for myself was really really good, at least in my perspective. So I decided to release this internal tool as an open source project: Neuron AI, open source Framework to integrate full featured AI Agents into your existing PHP application. https://neuron.inspector.dev Why I decided to make it open source The journey I would like to explore is inspired by LangChain, giving people the power to create Agentic entities into PHP applications, with a complete open source toolkit. And provide support and long term visibility thanks to the professional monitoring and debugging service powered by Inspector.dev At the same time I believe it can really help PHP developers “jump into the AI bus” with stronger foundations. It seemed to me a clear opportunity. I hope it can get you the answers you are looking for to continue to build great software with your preferred programming language. Here is a how the system is organized: Key concepts Neuron AI is designed to provide you with a complete toolkit to implement AI driven applications, making it easy to integrate into your existing system. Most Neuron AI framework components do not implement active constructors, they just provide you features to implement your own agentic behavior. The two most important classes, Agent and RAG, are designed to be extended to create your specific implementation. They are rarely used as standalone objects. This ensures the portability of your agent implementation because all the moving parts are encapsulated into a single entity that you can just run wherever you want in your application. namespace App\Agents;

use NeuronAI\Agent;
use NeuronAI\Providers\Anthropic;
use NeuronAI\Tools\Tool;

class SEOAgent extends Agent
{
    public function provider(): AIProviderInterface
    {
        // return an AI provider instance (Anthropic, OpenAI, Mistral, etc.)
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
        );
    }
    
    public function instructions() 
    {
        return "Act as an expert of SEO (Search Engine Optimization). ".
            "Your role is to analyze a text and provide suggestions on how the content can be improved to better rank on Google search.";
    }
    
    public function tools(): array
    {
        return [
            Tool::make(
                "get_file_content", 
                "Use the url to get the content in plain text."
            )->addProperty(
                new ToolPropertry(
                    name: 'url',
                    type: 'string',
                    description: 'The URL of the article you want to analyze.',
                    required: true
                )
            )->setCallable(function (string $url) {
                return file_get_contents($url);
            })
        ];
    }
} Talk to the agent use NeuronAI\Chat\Messages\UserMessage;

$response = SEOAgent::make($user)
    ->chat(
        new UserMessage("Give me your feedback about this article: https://inspector.dev/introduction-to-neuron-ai-create-full-featured-ai-agents-in-php/")
    );
    
echo $response->getContent();

// It seems like a good job has been done on the article, 
// however I can give you some tips to improve SEO:... Package Dependencies We intentionally decided to build Neuron as free as possible from external dependencies. The package ships with just one dependency: “guzzlehttp/guzzle”: “^7.0” Without bringing dozens of dependencies inside your application, you do not risk being locked out of Neuron if you need to upgrade your current architecture, like the web application framework (Laravel, Symfony, CodeIgniter, etc) to a newer version, or integrate new dependencies. Based on our experience, a bad dependency chain could be a very unpleasant surprise when it is too late. You have already spent a lot of effort to implement your AI interactions, and suddenly it has become a bottleneck because the dependencies make it impossible to upgrade and evolve the rest of your system. We want you to know that with Neuron AI it’s not the case. Extensibility Every component of the framework depends on its own interface. This guarantees you the ability to create new concrete implementations of every component to interact with external systems and pass them to your agents with confidence. In the components documentation you will find the dedicated section of how to implement a new one, basically extending its interface. Do you want to implement a new Vector Store, or an Embeddings Provider? Follow the documentation and feel free to send us a PR with your new module. We will be happy to integrate them as a part of the framework to ensure first party support and maintenance. AI Agents Observability Neuron is designed with a built-in system to make your agent and RAG implementations observable. You can start monitoring your agents activities and performance with just one line of code. Take a look at the dedicated section in the observability section. Neuron AI Toolkit To create a fully functional AI agent you have to make several things work together. Apart from the LLM, you need to constantly process data, create and store embeddings to feed your agent with fresh information. The project aims to provide easy to implement and extend solutions into all of these areas. What’s next after the launch At Inspector we embrace organic growth. So we would like to start helping developers create their first agents to discover edge cases, new needs, and obviously bug fix. We are already at work with our internal user base of +10K PHP developers that are starting their Agents right now and a lot of exciting things are emerging yet. Real use cases will be the driver of the framework evolution, so don’t hesitate to contact us if you want to grab some knowledge from our experience. We are here to help. Conclusion If your customers are pushing you to implement AI features into your application, try Neuron, it takes just a few lines of code to implement your first full featured agent. Thank you for reading this article, I invite you to contact me for any questions, curiosities, or just to give me your feedback. And if you think this tool could be useful to others PHP developers, please share it on your blog, social media and YouTube channels. Learn more about inspector on the website: https://inspector.dev Best, Valerio In the last few months I heavily worked to push the AI agents integration into my SaaS product to a higher level. It was a very long journey, started more than a year ago with the first experiments. I had to say that understanding all the moving parts of an AI driven system was far from easy. As a PHP developer I struggled a lot, mainly because the PHP ecosystem to develop this kind of “Agentic” features into existing applications it’s not as advanced and rich as it is in other technologies. Python and Javascript are “driving the bus”, and obviously other developers working with different programming languages are creating their artifacts to get the opportunity to start their journey too. Six months ago when I started working on this chapter I took in consideration some packages that were getting attention like LLPhant , or Prism . There was a lot of development behind these packages and they have already implemented a lot of things. But for my needs they have too serious weaknesses to consider ​​building the foundation of this chapter for my business on these libraries. Prism is exclusively for Laravel, so you are locked in, and LLPhant has a lot of different classes and looks more like a library than a framework. It also lacks features like Memory, chat history, observability. LLPhant Prism The unsustainable path From the beginning I realized that I could not look at other programming languages. It is not sustainable. And I am sure that is the same for most developers who specialize in a particular technology. If you have a PHP application you can’t implement an agent in javascript or Python, because they need your application data and context to generate their magic. Transferring this data and context to an external entity written in another language leads to a lot of code duplication, or technical constraint that is not sustainable too. I’m curious to see if it was just me struggling, or if these feelings are the same as other PHP developers. I started thinking that the tool I created for myself was really really good, at least in my perspective. So I decided to release this internal tool as an open source project: Neuron AI , open source Framework to integrate full featured AI Agents into your existing PHP application. Neuron AI https://neuron.inspector.dev https://neuron.inspector.dev https://neuron.inspector.dev Why I decided to make it open source The journey I would like to explore is inspired by LangChain, giving people the power to create Agentic entities into PHP applications, with a complete open source toolkit. And provide support and long term visibility thanks to the professional monitoring and debugging service powered by Inspector.dev At the same time I believe it can really help PHP developers “jump into the AI bus” with stronger foundations. It seemed to me a clear opportunity. I hope it can get you the answers you are looking for to continue to build great software with your preferred programming language. Here is a how the system is organized: Key concepts Neuron AI is designed to provide you with a complete toolkit to implement AI driven applications, making it easy to integrate into your existing system. Most Neuron AI framework components do not implement active constructors, they just provide you features to implement your own agentic behavior. The two most important classes, Agent and RAG , are designed to be extended to create your specific implementation. They are rarely used as standalone objects. Agent RAG This ensures the portability of your agent implementation because all the moving parts are encapsulated into a single entity that you can just run wherever you want in your application. namespace App\Agents;

use NeuronAI\Agent;
use NeuronAI\Providers\Anthropic;
use NeuronAI\Tools\Tool;

class SEOAgent extends Agent
{
    public function provider(): AIProviderInterface
    {
        // return an AI provider instance (Anthropic, OpenAI, Mistral, etc.)
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
        );
    }
    
    public function instructions() 
    {
        return "Act as an expert of SEO (Search Engine Optimization). ".
            "Your role is to analyze a text and provide suggestions on how the content can be improved to better rank on Google search.";
    }
    
    public function tools(): array
    {
        return [
            Tool::make(
                "get_file_content", 
                "Use the url to get the content in plain text."
            )->addProperty(
                new ToolPropertry(
                    name: 'url',
                    type: 'string',
                    description: 'The URL of the article you want to analyze.',
                    required: true
                )
            )->setCallable(function (string $url) {
                return file_get_contents($url);
            })
        ];
    }
} namespace App\Agents;

use NeuronAI\Agent;
use NeuronAI\Providers\Anthropic;
use NeuronAI\Tools\Tool;

class SEOAgent extends Agent
{
    public function provider(): AIProviderInterface
    {
        // return an AI provider instance (Anthropic, OpenAI, Mistral, etc.)
        return new Anthropic(
            key: 'ANTHROPIC_API_KEY',
            model: 'ANTHROPIC_MODEL',
        );
    }
    
    public function instructions() 
    {
        return "Act as an expert of SEO (Search Engine Optimization). ".
            "Your role is to analyze a text and provide suggestions on how the content can be improved to better rank on Google search.";
    }
    
    public function tools(): array
    {
        return [
            Tool::make(
                "get_file_content", 
                "Use the url to get the content in plain text."
            )->addProperty(
                new ToolPropertry(
                    name: 'url',
                    type: 'string',
                    description: 'The URL of the article you want to analyze.',
                    required: true
                )
            )->setCallable(function (string $url) {
                return file_get_contents($url);
            })
        ];
    }
} Talk to the agent use NeuronAI\Chat\Messages\UserMessage;

$response = SEOAgent::make($user)
    ->chat(
        new UserMessage("Give me your feedback about this article: https://inspector.dev/introduction-to-neuron-ai-create-full-featured-ai-agents-in-php/")
    );
    
echo $response->getContent();

// It seems like a good job has been done on the article, 
// however I can give you some tips to improve SEO:... use NeuronAI\Chat\Messages\UserMessage;

$response = SEOAgent::make($user)
    ->chat(
        new UserMessage("Give me your feedback about this article: https://inspector.dev/introduction-to-neuron-ai-create-full-featured-ai-agents-in-php/")
    );
    
echo $response->getContent();

// It seems like a good job has been done on the article, 
// however I can give you some tips to improve SEO:... Package Dependencies We intentionally decided to build Neuron as free as possible from external dependencies. The package ships with just one dependency: “guzzlehttp/guzzle”: “^7.0” “guzzlehttp/guzzle”: “^7.0” Without bringing dozens of dependencies inside your application, you do not risk being locked out of Neuron if you need to upgrade your current architecture, like the web application framework (Laravel, Symfony, CodeIgniter, etc) to a newer version, or integrate new dependencies. Based on our experience, a bad dependency chain could be a very unpleasant surprise when it is too late. You have already spent a lot of effort to implement your AI interactions, and suddenly it has become a bottleneck because the dependencies make it impossible to upgrade and evolve the rest of your system. We want you to know that with Neuron AI it’s not the case. Extensibility Every component of the framework depends on its own interface. This guarantees you the ability to create new concrete implementations of every component to interact with external systems and pass them to your agents with confidence. In the components documentation you will find the dedicated section of how to implement a new one, basically extending its interface. Do you want to implement a new Vector Store , or an Embeddings Provider ? Follow the documentation and feel free to send us a PR with your new module. We will be happy to integrate them as a part of the framework to ensure first party support and maintenance. Vector Store Embeddings Provider AI Agents Observability Neuron is designed with a built-in system to make your agent and RAG implementations observable. You can start monitoring your agents activities and performance with just one line of code. Take a look at the dedicated section in the observability section . observability section Neuron AI Toolkit To create a fully functional AI agent you have to make several things work together. Apart from the LLM, you need to constantly process data, create and store embeddings to feed your agent with fresh information. The project aims to provide easy to implement and extend solutions into all of these areas. What’s next after the launch At Inspector we embrace organic growth. So we would like to start helping developers create their first agents to discover edge cases, new needs, and obviously bug fix. We are already at work with our internal user base of +10K PHP developers that are starting their Agents right now and a lot of exciting things are emerging yet. Real use cases will be the driver of the framework evolution, so don’t hesitate to contact us if you want to grab some knowledge from our experience. We are here to help. Conclusion If your customers are pushing you to implement AI features into your application, try Neuron, it takes just a few lines of code to implement your first full featured agent. Thank you for reading this article, I invite you to contact me for any questions, curiosities, or just to give me your feedback. And if you think this tool could be useful to others PHP developers, please share it on your blog, social media and YouTube channels. Learn more about inspector on the website: https://inspector.dev https://inspector.dev https://inspector.dev Best, Valerio

This Open Source Framework Brings Full Featured AI Agents Into Your PHP Application

Hierdie oopbronraamwerk bring volledige KI-agente in u PHP-toepassing

Ovaj Open Source Framework donosi potpuno istaknute AI agente u vašu PHP aplikaciju

Tento Open Source Framework přináší plnohodnotné agenty AI do vaší aplikace PHP

Este marco de código abierto incorpora agentes de inteligencia artificial con todas las funciones a su aplicación PHP

این چارچوب منبع باز، عوامل هوش مصنوعی کامل را به برنامه PHP شما می آورد

מסגרת קוד פתוח זו מביאה סוכני AI מלאים ליישום PHP שלך

このオープンソースフレームワークは、PHPアプリケーションにフル機能のAIエージェントを導入します

ក្របខ័ណ្ឌប្រភពបើកចំហនេះនាំមកនូវភ្នាក់ងារ AI ដែលមានលក្ខណៈពិសេសពេញលេញទៅក្នុងកម្មវិធី PHP របស់អ្នក។

یہ اوپن سورس فریم ورک آپ کی PHP ایپلیکیشن میں مکمل خصوصیات والے AI ایجنٹوں کو لاتا ہے۔

Ushbu ochiq manbali ramka PHP ilovangizga to'liq xususiyatli AI agentlarini olib keladi

Khung nguồn mở này mang các tác nhân AI đầy đủ tính năng vào ứng dụng PHP của bạn

这个开源框架将全功能 AI 代理带入你的 PHP 应用程序

This Open Source Tool Lets You Build Your Own  LMS on Kubernetes

Introduction Open source software has always been a delicate balance of ideals and practicality, and in 2024, this balance was upended. With an increasing trend of relicensing projects and a corresponding surge in forks, the open source community faces questions about what it means to be truly open. Following the heated discussions around the HashiCorp licensing controversy we covered in Open Source is Dead, we dive into the latest developments in this fascinating space. Dr. Dawn Foster’s latest research, The New Dynamics of Open Source: Relicensing, Forks, & Community Impact (Foster, 2024), helps us to understand into the realities of relicensing and the ripple effects it creates across communities and ecosystems. The Origins of Open Source and Its Foundational Ideals To understand the present, we need to revisit the origins of open source software. Born from the ideals of free software in the 1980s, movements like GNU and licenses like the GPL prioritized user freedom—to use, study, modify, and share software. Over the decades, these principles laid the groundwork for iconic projects like Linux, MySQL, and Kubernetes, turning open source into the backbone of modern computing. However, as open source became mainstream, it also became commercialized. Companies built lucrative businesses on open source foundations, and this growing reliance on permissive licenses paved the way for hyperscalers like AWS to profit massively from the work of independent developers and smaller firms. The tension between free software ideals and commercial realities has only grown sharper, leading us to the current landscape. What’s Really Happening? Relicensing isn’t new, but 2024 made it clear that it’s becoming a dominant trend. Companies like Elastic, Redis Labs, and HashiCorp have shifted to restrictive licenses like SSPL or BSL, citing the need to protect their business models. The community’s response? Forks that reject these restrictions and champion a more inclusive governance model under neutral organizations like the Linux Foundation. Each case exemplifies how relicensing has fractured trust, spurred action, and reshaped governance. Elasticsearch and OpenSearch: A Case of Corporate Clash Elastic’s decision to adopt the SSPL in 2021 was a pivotal moment in the open source landscape (Elastic, 2024). The move aimed to curb AWS from profiting off Elasticsearch without contributing back, but it alienated a significant portion of the community. AWS’s response—creating OpenSearch—wasn’t just about preserving functionality. While OpenSearch has since moved under the OpenSearch Foundation within the Linux Foundation as of September 2024, in its early days, AWS retained full control over the project’s direction, despite public claims of openness. Governance discussions were met with internal decision-making within Amazon, leading to frustration and disengagement from some community members (OpenSearch Foundation, 2024). Redis and Valkey: A Fork Motivated by Relicensing Redis Labs’ addition of the Commons Clause to its permissive BSD license in 2018 marked a turning point for the project. Over time, further shifts—including the adoption of SSPL—prompted frustration (Redis Labs, 2024). While KeyDB emerged as a multi-threaded performance fork of Redis, it was not directly motivated by relicensing and remains under corporate ownership without documented neutral governance (Snap Inc, 2024). The more direct response to relicensing was Valkey, an alternative fork created under the Linux Foundation with a neutral governance model, preserving the ethos of the original Redis project. Terraform and OpenTofu: The Grassroots Rebellion HashiCorp’s switch to the BSL in 2024 sparked outrage among users who had relied on Terraform’s openness (HashiCorp, 2024). OpenTofu, a fork led by the Linux Foundation, quickly rallied a coalition of contributors and organizations determined to uphold the principles of open collaboration. By emphasizing transparent governance and prioritizing user needs, OpenTofu has begun charting a course distinct from its progenitor (OpenTofu, 2024). Forks as Revolutionaries Foster’s research underscores the transformative potential of forks when they emerge under neutral governance. OpenSearch’s transition to the OpenSearch Software Foundation exemplifies how a fork can attract diverse contributors, ensuring long-term sustainability. Neutral foundations—free from single-vendor dominance—provide fertile ground for forks to grow into legitimate successors. Valkey’s creation under the Linux Foundation highlights another example of how neutral governance fosters community resilience. Unlike KeyDB, which remains corporate-controlled, Valkey prioritizes open governance and contributor inclusivity. Metrics That Matter: Expanding Beyond Organizational Diversity Foster’s initial analysis emphasized organizational diversity as a key metric in evaluating project health. However, ongoing research aims to develop a more comprehensive set of metrics to assess forks and relicensed projects (CHAOSS Fork Case Study, 2024). The study is actively gathering data on additional health indicators beyond contributor diversity, with a focus on codebase divergence, governance structures, and long-term sustainability. Contributors welcome! Codebase divergence, another critical metric, reveals how forks evolve independently. While maintaining initial compatibility, successful forks like OpenSearch and Valkey quickly pivot toward innovation driven by community needs. Challenges Ahead But let’s not romanticise forks. Fragmentation can be a nightmare for users, who often face compatibility issues and uncertainty about long-term support. Foster’s research calls for clearer communication between projects and their communities to mitigate these challenges. At the heart of the debate lies a fundamental question: How can open source balance the competing demands of community ideals and commercial viability? Foster’s findings offer no easy answers but highlight the importance of transparency and shared governance as guiding principles. Conclusion Relicensing has forced the open source world to confront its identity crisis. Are we building digital commons, or are we just subsidizing corporate profit? The answer lies in how we interpret and address the motivations behind relicensing and the community’s response to it. On one hand, companies argue that relicensing is a pragmatic necessity—a way to protect their investments and ensure sustainability in a competitive environment dominated by hyperscalers. On the other hand, critics see it as a breach of trust, where corporate priorities overshadow the ethos of open collaboration. Foster’s research goes beyond diagnosing this tension. It highlights actionable pathways for navigating these stormy waters. The emergence of community-driven forks and neutral governance models offers a compelling vision for the future. Forks like OpenSearch, Valkey, and OpenTofu showcase the resilience of open source communities when they rally around principles of transparency and inclusivity. These projects demonstrate that it is possible to break away from corporate control and rebuild ecosystems that prioritize collective ownership and innovation. The truth remains that this is not a guaranteed outcome. The transition from relicensed projects to forks introduces challenges: fragmented ecosystems, compatibility issues, and the risk of diluted community efforts. Foster’s roadmap emphasizes the need for open dialogue, where both corporate entities and communities engage in shared decision-making. Neutral governance structures, like those provided by the Linux Foundation, play a pivotal role in ensuring that forks remain inclusive and sustainable. Ultimately, the future of open source hinges on the willingness of its stewards to learn from these lessons. Whether relicensing leads to deeper fragmentation or a stronger, more inclusive digital commons depends on the choices made today. Open source isn’t dead—it’s just forked. Whether that fork leads to greater inclusivity or deeper fragmentation depends on the choices we make today. References Foster, D. (2024). "The New Dynamics of Open Source: Relicensing, Forks, & Community Impact." arXiv:2411.04739
Elastic (2024). "Why We Chose SSPL." FAQ on Licensing
OpenSearch Foundation (2024). "OpenSearch Software Foundation Launch Announcement." Launch Announcement
HashiCorp (2024). "Terraform Licensing Change." Licensing FAQ
OpenTofu (2024). opentofu.org
Redis Labs (2024). redislabs.com Introduction Open source software has always been a delicate balance of ideals and practicality, and in 2024, this balance was upended. With an increasing trend of relicensing projects and a corresponding surge in forks, the open source community faces questions about what it means to be truly open. Following the heated discussions around the HashiCorp licensing controversy we covered in Open Source is Dead , we dive into the latest developments in this fascinating space. Dr. Dawn Foster’s latest research, The New Dynamics of Open Source: Relicensing, Forks, & Community Impact ( Foster, 2024 ), helps us to understand into the realities of relicensing and the ripple effects it creates across communities and ecosystems. Open Source is Dead The New Dynamics of Open Source: Relicensing, Forks, & Community Impact Foster, 2024 The Origins of Open Source and Its Foundational Ideals To understand the present, we need to revisit the origins of open source software. Born from the ideals of free software in the 1980s, movements like GNU and licenses like the GPL prioritized user freedom—to use, study, modify, and share software. Over the decades, these principles laid the groundwork for iconic projects like Linux, MySQL, and Kubernetes, turning open source into the backbone of modern computing. However, as open source became mainstream, it also became commercialized. Companies built lucrative businesses on open source foundations, and this growing reliance on permissive licenses paved the way for hyperscalers like AWS to profit massively from the work of independent developers and smaller firms. The tension between free software ideals and commercial realities has only grown sharper, leading us to the current landscape. What’s Really Happening? Relicensing isn’t new, but 2024 made it clear that it’s becoming a dominant trend. Companies like Elastic, Redis Labs, and HashiCorp have shifted to restrictive licenses like SSPL or BSL, citing the need to protect their business models. The community’s response? Forks that reject these restrictions and champion a more inclusive governance model under neutral organizations like the Linux Foundation. Each case exemplifies how relicensing has fractured trust, spurred action, and reshaped governance. Elasticsearch and OpenSearch: A Case of Corporate Clash Elastic’s decision to adopt the SSPL in 2021 was a pivotal moment in the open source landscape ( Elastic, 2024 ). The move aimed to curb AWS from profiting off Elasticsearch without contributing back, but it alienated a significant portion of the community. AWS’s response—creating OpenSearch—wasn’t just about preserving functionality. While OpenSearch has since moved under the OpenSearch Foundation within the Linux Foundation as of September 2024, in its early days, AWS retained full control over the project’s direction, despite public claims of openness. Governance discussions were met with internal decision-making within Amazon, leading to frustration and disengagement from some community members ( OpenSearch Foundation, 2024 ). Elastic, 2024 OpenSearch Foundation, 2024 Redis and Valkey: A Fork Motivated by Relicensing Redis Labs’ addition of the Commons Clause to its permissive BSD license in 2018 marked a turning point for the project. Over time, further shifts—including the adoption of SSPL—prompted frustration ( Redis Labs, 2024 ). While KeyDB emerged as a multi-threaded performance fork of Redis, it was not directly motivated by relicensing and remains under corporate ownership without documented neutral governance ( Snap Inc , 2024). The more direct response to relicensing was Valkey, an alternative fork created under the Linux Foundation with a neutral governance model, preserving the ethos of the original Redis project. Redis Labs, 2024 Snap Inc Terraform and OpenTofu: The Grassroots Rebellion HashiCorp’s switch to the BSL in 2024 sparked outrage among users who had relied on Terraform’s openness ( HashiCorp, 2024 ). OpenTofu, a fork led by the Linux Foundation, quickly rallied a coalition of contributors and organizations determined to uphold the principles of open collaboration. By emphasizing transparent governance and prioritizing user needs, OpenTofu has begun charting a course distinct from its progenitor ( OpenTofu, 2024 ). HashiCorp, 2024 OpenTofu, 2024 Forks as Revolutionaries Foster’s research underscores the transformative potential of forks when they emerge under neutral governance. OpenSearch’s transition to the OpenSearch Software Foundation exemplifies how a fork can attract diverse contributors, ensuring long-term sustainability. Neutral foundations—free from single-vendor dominance—provide fertile ground for forks to grow into legitimate successors. Valkey’s creation under the Linux Foundation highlights another example of how neutral governance fosters community resilience. Unlike KeyDB, which remains corporate-controlled, Valkey prioritizes open governance and contributor inclusivity. Metrics That Matter: Expanding Beyond Organizational Diversity Foster’s initial analysis emphasized organizational diversity as a key metric in evaluating project health. However, ongoing research aims to develop a more comprehensive set of metrics to assess forks and relicensed projects ( CHAOSS Fork Case Study , 2024). The study is actively gathering data on additional health indicators beyond contributor diversity, with a focus on codebase divergence, governance structures, and long-term sustainability. Contributors welcome! CHAOSS Fork Case Study Codebase divergence, another critical metric, reveals how forks evolve independently. While maintaining initial compatibility, successful forks like OpenSearch and Valkey quickly pivot toward innovation driven by community needs. Challenges Ahead But let’s not romanticise forks. Fragmentation can be a nightmare for users, who often face compatibility issues and uncertainty about long-term support. Foster’s research calls for clearer communication between projects and their communities to mitigate these challenges. At the heart of the debate lies a fundamental question: How can open source balance the competing demands of community ideals and commercial viability? Foster’s findings offer no easy answers but highlight the importance of transparency and shared governance as guiding principles. Conclusion Relicensing has forced the open source world to confront its identity crisis. Are we building digital commons, or are we just subsidizing corporate profit? The answer lies in how we interpret and address the motivations behind relicensing and the community’s response to it. On one hand, companies argue that relicensing is a pragmatic necessity—a way to protect their investments and ensure sustainability in a competitive environment dominated by hyperscalers. On the other hand, critics see it as a breach of trust, where corporate priorities overshadow the ethos of open collaboration. Foster’s research goes beyond diagnosing this tension. It highlights actionable pathways for navigating these stormy waters. The emergence of community-driven forks and neutral governance models offers a compelling vision for the future. Forks like OpenSearch, Valkey, and OpenTofu showcase the resilience of open source communities when they rally around principles of transparency and inclusivity. These projects demonstrate that it is possible to break away from corporate control and rebuild ecosystems that prioritize collective ownership and innovation. The truth remains that this is not a guaranteed outcome. The transition from relicensed projects to forks introduces challenges: fragmented ecosystems, compatibility issues, and the risk of diluted community efforts. Foster’s roadmap emphasizes the need for open dialogue, where both corporate entities and communities engage in shared decision-making. Neutral governance structures, like those provided by the Linux Foundation, play a pivotal role in ensuring that forks remain inclusive and sustainable. Ultimately, the future of open source hinges on the willingness of its stewards to learn from these lessons. Whether relicensing leads to deeper fragmentation or a stronger, more inclusive digital commons depends on the choices made today. Open source isn’t dead—it’s just forked. Whether that fork leads to greater inclusivity or deeper fragmentation depends on the choices we make today. References References Foster, D. (2024). "The New Dynamics of Open Source: Relicensing, Forks, & Community Impact." arXiv:2411.04739 Elastic (2024). "Why We Chose SSPL." FAQ on Licensing OpenSearch Foundation (2024). "OpenSearch Software Foundation Launch Announcement." Launch Announcement HashiCorp (2024). "Terraform Licensing Change." Licensing FAQ OpenTofu (2024). opentofu.org Redis Labs (2024). redislabs.com Foster, D. (2024). "The New Dynamics of Open Source: Relicensing, Forks, & Community Impact." arXiv:2411.04739 arXiv:2411.04739 Elastic (2024). "Why We Chose SSPL." FAQ on Licensing FAQ on Licensing OpenSearch Foundation (2024). "OpenSearch Software Foundation Launch Announcement." Launch Announcement Launch Announcement HashiCorp (2024). "Terraform Licensing Change." Licensing FAQ Licensing FAQ OpenTofu (2024). opentofu.org opentofu.org Redis Labs (2024). redislabs.com redislabs.com

Open Source Isn't Dead...It's Just Forked

البرمجيات مفتوحة المصدر لم تمت... إنها مجرد نسخة متشعبة

Адкрыты зыходны код не мёртвы... Ён проста раздвоены

ওপেন সোর্স এখনও মারা যায়নি...এটা শুধুই তৈরি করা হয়েছে

El código abierto no ha muerto... simplemente se ha bifurcado

Բաց կոդով չի մեռած...Այն ուղղակի պատառաքաղված է

オープンソースは死んでいない...分岐しただけだ

ღია წყარო არ არის მკვდარი... უბრალოდ ჩანგალია

Open Source Ekufi Te...Ezali Kaka Forked

Atvērtais kods nav miris...Tas ir tikai dakšas

O código aberto não está morto...ele apenas foi bifurcado

Isha Furan Ma Dhiman...Kaliya Waa Fargeeto

Ochiq manba o'lik emas...U shunchaki vilkali

**Santa Clara, California, February 27th, 2025/Chainwire/--**CARV, the leading AI chain ecosystem for data sovereignty, is making a major leap forward in the decentralized AI movement. Today, they are open-sourcing the D.A.T.A. (Data Authentication, Trust, and Attestation) Framework is designed to provide AI agents verifiable access to on-chain and off-chain data. The D.A.T.A. Framework is now fully available on GitHub: GitHub Repository This move marks a significant milestone in CARV’s mission to democratize AI data access, enabling developers worldwide to build, refine, and scale AI agents with trustless, structured, and privacy-preserving data. The Value of Open Source in AI Development By open-sourcing D.A.T.A. Framework, CARV is empowering developers to build AI agents that don’t just react—they reason, adapt, and optimize interactions in real-time using on-chain and off-chain data. This marks the first open-source AI agent framework that: Brings DeepSeek’s Chain-of-Thought Processing into Web3 – allowing AI agents to perform multi-step logical reasoning before taking actions.
Enables multi-stage verification of AI decisions – ensuring that AI agents validate their own conclusions before executing transactions.
Empowers AI agents with economic self awareness – AI agents can now own wallets, execute trades, and participate in tokenized ecosystems autonomously.
Fosters decentralized knowledge networks – AI agents share insights and learn collectively instead of working in silos. This is more than a data framework; it serves as the foundation for decentralized intelligence, enabling AI agents to interact, transact, and evolve within an open ecosystem. Key Features of the Open-Source D.A.T.A. Framework Capabilities for AI Developers: Real-Time On-Chain & Off-Chain Data Access: AI agents can fetch and process wallet transactions, smart contract activities, and even off-chain behavioral insights in real time.
Cross-Chain Verification & Identity Mapping: With CARV ID, AI agents can categorize users, verify token holdings, and personalize interactions based on decentralized credentials.
Economic Self-Awareness & Monetization: D.A.T.A. enables AI agents to manage wallets, distribute tokens, and optimize their decision-making based on token-driven intelligence.
Privacy-Preserving AI Computation with TEE: Ensures secure, verifiable, and private AI execution while preventing unauthorized access to sensitive data. By making D.A.T.A. 2.0 open-source, CARV is inviting developers, researchers, and enterprises to contribute to the evolution of AI reasoning in Web3, leverage token-driven intelligence to create AI-driven economies and push the boundaries of privacy-preserving AI execution This open-source release is not just a step toward decentralization—it’s a call to action for the AI, Web3, and data sovereignty communities to build the next-gen AI models that can think, reason, and interact with digital economies in a truly decentralized way. Starting Building with D.A.T.A. Framework: GitHub Repo Users can join the Developer Community: https://discord.com/invite/GdrdpvzHxB About CARV CARV is building an AI chain ecosystem to enable data sovereignty at scale. By empowering AI agents with secure, unified infrastructure, CARV enables intelligent, collaborative operations through its SVM Chain, offering trustless consensus, cryptographic proofs, and verifiable execution. With the D.A.T.A Framework, CARV enriches AI with high-quality, on-chain and off-chain data, allowing agents to learn, evolve, and collaborate dynamically. With over 15M users and 8M CARV IDs, CARV ensures privacy and data control while providing AI agents with powerful, cross-chain insights, creating a secure, innovative ecosystem for both AI and human collaboration. Supported by $50M in funding from top-tier investors like Tribe Capital, HashKey Capital, and Animoca Brands, and backed by a team of veterans from Coinbase, Google, and Binance, CARV is committed to fostering a decentralized future where data is a valuable, user-owned asset. Contact COO Victor Yu CARV vito@carv.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here **Santa Clara, California, February 27th, 2025/Chainwire/--**CARV, the leading AI chain ecosystem for data sovereignty, is making a major leap forward in the decentralized AI movement. Today, they are open-sourcing the D.A.T.A. (Data Authentication, Trust, and Attestation) Framework is designed to provide AI agents verifiable access to on-chain and off-chain data. The D.A.T.A. Framework is now fully available on GitHub: GitHub Repository GitHub Repository GitHub Repository This move marks a significant milestone in CARV’s mission to democratize AI data access, enabling developers worldwide to build, refine, and scale AI agents with trustless, structured, and privacy-preserving data. The Value of Open Source in AI Development By open-sourcing D.A.T.A. Framework, CARV is empowering developers to build AI agents that don’t just react—they reason, adapt, and optimize interactions in real-time using on-chain and off-chain data. This marks the first open-source AI agent framework that: Brings DeepSeek’s Chain-of-Thought Processing into Web3 – allowing AI agents to perform multi-step logical reasoning before taking actions. Enables multi-stage verification of AI decisions – ensuring that AI agents validate their own conclusions before executing transactions. Empowers AI agents with economic self awareness – AI agents can now own wallets, execute trades, and participate in tokenized ecosystems autonomously. Fosters decentralized knowledge networks – AI agents share insights and learn collectively instead of working in silos. Brings DeepSeek’s Chain-of-Thought Processing into Web3 – allowing AI agents to perform multi-step logical reasoning before taking actions. Enables multi-stage verification of AI decisions – ensuring that AI agents validate their own conclusions before executing transactions. Empowers AI agents with economic self awareness – AI agents can now own wallets, execute trades, and participate in tokenized ecosystems autonomously. Fosters decentralized knowledge networks – AI agents share insights and learn collectively instead of working in silos. This is more than a data framework; it serves as the foundation for decentralized intelligence, enabling AI agents to interact, transact, and evolve within an open ecosystem. Key Features of the Open-Source D.A.T.A. Framework Capabilities for AI Developers: Real-Time On-Chain & Off-Chain Data Access: AI agents can fetch and process wallet transactions, smart contract activities, and even off-chain behavioral insights in real time. Cross-Chain Verification & Identity Mapping: With CARV ID, AI agents can categorize users, verify token holdings, and personalize interactions based on decentralized credentials. Economic Self-Awareness & Monetization: D.A.T.A. enables AI agents to manage wallets, distribute tokens, and optimize their decision-making based on token-driven intelligence. Privacy-Preserving AI Computation with TEE: Ensures secure, verifiable, and private AI execution while preventing unauthorized access to sensitive data. Real-Time On-Chain & Off-Chain Data Access: AI agents can fetch and process wallet transactions, smart contract activities, and even off-chain behavioral insights in real time. Cross-Chain Verification & Identity Mapping: With CARV ID, AI agents can categorize users, verify token holdings, and personalize interactions based on decentralized credentials. Economic Self-Awareness & Monetization: D.A.T.A. enables AI agents to manage wallets, distribute tokens, and optimize their decision-making based on token-driven intelligence. Privacy-Preserving AI Computation with TEE: Ensures secure, verifiable, and private AI execution while preventing unauthorized access to sensitive data. By making D.A.T.A. 2.0 open-source, CARV is inviting developers, researchers, and enterprises to contribute to the evolution of AI reasoning in Web3, leverage token-driven intelligence to create AI-driven economies and push the boundaries of privacy-preserving AI execution This open-source release is not just a step toward decentralization—it’s a call to action for the AI, Web3, and data sovereignty communities to build the next-gen AI models that can think, reason, and interact with digital economies in a truly decentralized way. Starting Building with D.A.T.A. Framework: GitHub Repo GitHub Repo GitHub Repo Users can join the Developer Community: https://discord.com/invite/GdrdpvzHxB https://discord.com/invite/GdrdpvzHxB https://discord.com/invite/GdrdpvzHxB About CARV CARV is building an AI chain ecosystem to enable data sovereignty at scale. By empowering AI agents with secure, unified infrastructure, CARV enables intelligent, collaborative operations through its SVM Chain, offering trustless consensus, cryptographic proofs, and verifiable execution. CARV CARV With the D.A.T.A Framework, CARV enriches AI with high-quality, on-chain and off-chain data, allowing agents to learn, evolve, and collaborate dynamically. With over 15M users and 8M CARV IDs, CARV ensures privacy and data control while providing AI agents with powerful, cross-chain insights, creating a secure, innovative ecosystem for both AI and human collaboration. Supported by $50M in funding from top-tier investors like Tribe Capital, HashKey Capital, and Animoca Brands, and backed by a team of veterans from Coinbase, Google, and Binance, CARV is committed to fostering a decentralized future where data is a valuable, user-owned asset. Contact COO Victor Yu CARV vito@carv.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here here here

CARV’s D.A.T.A. Framework Goes Open Source – Empowering AI Agents With Economic Self Awareness

I Built An Open-Source Website Platform for Group Exhibitions—Here's How

“How challenging is it to design a system supporting trillion-level data synchronization? Let me tell you a story from-scratch …” The Midnight SOS One late night in 2021, just as I was about to shut down my computer, an urgent call came from operations: “Help! The entire data sync system has crashed. Over 3,000 table synchronizations are backlogged, and business systems are triggering alarms…” The voice on the line belonged to a business line tech lead, thick with anxiety. This wasn’t our first emergency, but the scale was unprecedented: Key Metrics Daily Data Volume: 100+ TB
Concurrent Sync Jobs: 3,000+ tables (batch & streaming)
Latency SLA: Seconds
Current State: 3+ hours behind, worsening “System resource usage?”“A nightmare! Database connections maxed out, CPU at 80%, memory alerts…” An emergency patch deployed overnight provided temporary relief. Post-mortem analysis and community discussions revealed this wasn’t an isolated incident but an industry-wide pain point. Why Existing Solutions Failed ┌───────────────────┐
│ 1. Waste of resources │──► Tasks occupy too much memory and CPU, and occupy too many database connections1. Waste of resources │──► Tasks occupy too much memory and CPU, and occupy too many database connections
├──────────────────┤
│ 2. Poor performance & scalability │──► Performance cannot keep up, and adding new data sources requires changing a lot of code
├─────────────────┤
│ 3. Poor stability │──► Synchronization crashes occur several times a year, and often when others are celebrating a holiday, we are recovering
├─────────────────┤
│ 4. Poor batch and stream integration │──► Batch and stream integration is not supported, batch and stream need to be written separately
├─────────────────┤
│ 5. Poor monitoring │──► Real-time synchronization progress, synchronization rate, etc. cannot be seen
└─────────────────┘ Market Solutions Analysis Solution A: High performance but heavyweight deployment
Solution B: Lightweight but unstable, single-node
Solution C: High maintenance costs, inflexible These limitations sparked the creation of SeaTunnel’s new engine — affectionately called “Ultraman Zeta” by the community for bringing light to data integration. Architectural Evolution Design Goals We set audacious objectives: Performance: Trillion-record sync capability
Usability: 5-minute setup, 30-minute deployment
Extensibility: Connector development via minimal class implementations
Stability: 24/7 operation
Efficiency: 50%+ resource reduction vs alternatives Core Architecture After months of community collaboration: ┌───────────────────────────────────────────┐
│            SeaTunnel API Layer            │SeaTunnel API Layer            │
├───────────────────────────────────────────┤
│          Plugin Discovery Layer           │
├───────────────────────────────────────────┤
│           Multi-Engine Support            │
│    ┌────────┐  ┌─────────┐  ┌────────┐   │
│    │ Flink  │  │  Spark  │  │  Zeta  │   │
│    └────────┘  └─────────┘  └────────┘   │
└─────────────────────────────────────────── Technical Breakthroughs 1. Multi-Engine Support Evolution Historical Context 2017-2019      →      2019-2021       →      2021-Present
Spark-only           +Flink Support           Zeta Engine Translation Layer Innovation SeaTunnel API Layer
                   ▲
         Translation LayerTranslation Layer
    ┌──────────┬──────────┬──────────┐
    │ Spark    │ Flink    │ Zeta     │
    │Translator│Translator│Translator│
    └──────────┴──────────┴──────────┘ 2. Intelligent Connection Pooling Before Table1 ─► Connection1
Table2 ─► Connection2 (100 tables = 100 connections)100 tables = 100 connections) After Tables ─► Dynamic Pool (100 tables ≈ 10 connections)Pool (100 tables ≈ 10 connections) 3. Zero-Copy Data Transfer Traditional Source → Memory → Transform → Memory → SinkTransform → Memory → Sink SeaTunnel Source ═════► Transform ═════► SinkTransform ═════► Sink 4. Adaptive Backpressure Fast Producer    Slow Consumer
     │               │
     ▼               ▼
  [||||||||]  →  [|||] (Automatic throttling)[||||||||]  →  [|||] (Automatic throttling) 5. Dynamic Thread Scheduling Traditional Pool       SeaTunnel Pool
│││││││││││ (100)     │││││ (10-50 adaptive)100)     │││││ (10-50 adaptive)
└─────────┘            └───┘ 6. Plugin Architecture ClassLoader Isolation Bootstrap CL → System CL → SeaTunnel CL → Plugin CLSystem CL → SeaTunnel CL → Plugin CL Loading Process 1. Scan Plugins → 2. Create Loaders → 3. Load Config → 4. Init War Stories The Memory Leak Mystery: A persistent memory creep traced to special character handling — found after 72hrs of stack analysis.
Phantom Data Phenomenon: Intermittent data duplicates caused by batch boundary conditions — solved with transaction isolation improvements.
Performance Cliff: 40% throughput drops with specific data patterns — resolved through adaptive batching. Epilogue As Linus Torvalds said: “Talk is cheap. Show me the code.” But today we say: “Code is cheap. Show me the value.” SeaTunnel proves that elegant solutions emerge when solving real-world problems at scale. The true measure of technology lies not in its complexity, but in its ability to make developers’ lives easier. “How challenging is it to design a system supporting trillion-level data synchronization? Let me tell you a story from-scratch …” “How challenging is it to design a system supporting trillion-level data synchronization? Let me tell you a story from-scratch …” “How challenging is it to design a system supporting trillion-level data synchronization? Let me tell you a story from-scratch …” The Midnight SOS One late night in 2021, just as I was about to shut down my computer, an urgent call came from operations: “Help! The entire data sync system has crashed. Over 3,000 table synchronizations are backlogged, and business systems are triggering alarms…” The voice on the line belonged to a business line tech lead, thick with anxiety. This wasn’t our first emergency, but the scale was unprecedented: Key Metrics Key Metrics Daily Data Volume: 100+ TB Concurrent Sync Jobs: 3,000+ tables (batch & streaming) Latency SLA: Seconds Current State: 3+ hours behind, worsening Daily Data Volume: 100+ TB Concurrent Sync Jobs: 3,000+ tables (batch & streaming) Latency SLA: Seconds Current State: 3+ hours behind, worsening “System resource usage?” “A nightmare! Database connections maxed out, CPU at 80%, memory alerts…” An emergency patch deployed overnight provided temporary relief. Post-mortem analysis and community discussions revealed this wasn’t an isolated incident but an industry-wide pain point. Why Existing Solutions Failed ┌───────────────────┐
│ 1. Waste of resources │──► Tasks occupy too much memory and CPU, and occupy too many database connections1. Waste of resources │──► Tasks occupy too much memory and CPU, and occupy too many database connections
├──────────────────┤
│ 2. Poor performance & scalability │──► Performance cannot keep up, and adding new data sources requires changing a lot of code
├─────────────────┤
│ 3. Poor stability │──► Synchronization crashes occur several times a year, and often when others are celebrating a holiday, we are recovering
├─────────────────┤
│ 4. Poor batch and stream integration │──► Batch and stream integration is not supported, batch and stream need to be written separately
├─────────────────┤
│ 5. Poor monitoring │──► Real-time synchronization progress, synchronization rate, etc. cannot be seen
└─────────────────┘ ┌───────────────────┐
│ 1. Waste of resources │──► Tasks occupy too much memory and CPU, and occupy too many database connections1. Waste of resources │──► Tasks occupy too much memory and CPU, and occupy too many database connections
├──────────────────┤
│ 2. Poor performance & scalability │──► Performance cannot keep up, and adding new data sources requires changing a lot of code
├─────────────────┤
│ 3. Poor stability │──► Synchronization crashes occur several times a year, and often when others are celebrating a holiday, we are recovering
├─────────────────┤
│ 4. Poor batch and stream integration │──► Batch and stream integration is not supported, batch and stream need to be written separately
├─────────────────┤
│ 5. Poor monitoring │──► Real-time synchronization progress, synchronization rate, etc. cannot be seen
└─────────────────┘ Market Solutions Analysis Market Solutions Analysis Solution A: High performance but heavyweight deployment Solution B: Lightweight but unstable, single-node Solution C: High maintenance costs, inflexible Solution A : High performance but heavyweight deployment Solution A Solution B : Lightweight but unstable, single-node Solution B Solution C : High maintenance costs, inflexible Solution C These limitations sparked the creation of SeaTunnel’s new engine — affectionately called “Ultraman Zeta” by the community for bringing light to data integration. Architectural Evolution Design Goals We set audacious objectives: Performance: Trillion-record sync capability Usability: 5-minute setup, 30-minute deployment Extensibility: Connector development via minimal class implementations Stability: 24/7 operation Efficiency: 50%+ resource reduction vs alternatives Performance : Trillion-record sync capability Performance Usability : 5-minute setup, 30-minute deployment Usability Extensibility : Connector development via minimal class implementations Extensibility Stability : 24/7 operation Stability Efficiency : 50%+ resource reduction vs alternatives Efficiency Core Architecture After months of community collaboration: ┌───────────────────────────────────────────┐
│            SeaTunnel API Layer            │SeaTunnel API Layer            │
├───────────────────────────────────────────┤
│          Plugin Discovery Layer           │
├───────────────────────────────────────────┤
│           Multi-Engine Support            │
│    ┌────────┐  ┌─────────┐  ┌────────┐   │
│    │ Flink  │  │  Spark  │  │  Zeta  │   │
│    └────────┘  └─────────┘  └────────┘   │
└─────────────────────────────────────────── ┌───────────────────────────────────────────┐
│            SeaTunnel API Layer            │SeaTunnel API Layer            │
├───────────────────────────────────────────┤
│          Plugin Discovery Layer           │
├───────────────────────────────────────────┤
│           Multi-Engine Support            │
│    ┌────────┐  ┌─────────┐  ┌────────┐   │
│    │ Flink  │  │  Spark  │  │  Zeta  │   │
│    └────────┘  └─────────┘  └────────┘   │
└─────────────────────────────────────────── Technical Breakthroughs 1. Multi-Engine Support Evolution Historical Context Historical Context 2017-2019      →      2019-2021       →      2021-Present
Spark-only           +Flink Support           Zeta Engine 2017-2019      →      2019-2021       →      2021-Present
Spark-only           +Flink Support           Zeta Engine Translation Layer Innovation Translation Layer Innovation SeaTunnel API Layer
                   ▲
         Translation LayerTranslation Layer
    ┌──────────┬──────────┬──────────┐
    │ Spark    │ Flink    │ Zeta     │
    │Translator│Translator│Translator│
    └──────────┴──────────┴──────────┘ SeaTunnel API Layer
                   ▲
         Translation LayerTranslation Layer
    ┌──────────┬──────────┬──────────┐
    │ Spark    │ Flink    │ Zeta     │
    │Translator│Translator│Translator│
    └──────────┴──────────┴──────────┘ 2. Intelligent Connection Pooling Before Before Table1 ─► Connection1
Table2 ─► Connection2 (100 tables = 100 connections)100 tables = 100 connections) Table1 ─► Connection1
Table2 ─► Connection2 (100 tables = 100 connections)100 tables = 100 connections) After After Tables ─► Dynamic Pool (100 tables ≈ 10 connections)Pool (100 tables ≈ 10 connections) Tables ─► Dynamic Pool (100 tables ≈ 10 connections)Pool (100 tables ≈ 10 connections) 3. Zero-Copy Data Transfer Traditional Traditional Source → Memory → Transform → Memory → SinkTransform → Memory → Sink Source → Memory → Transform → Memory → SinkTransform → Memory → Sink SeaTunnel SeaTunnel Source ═════► Transform ═════► SinkTransform ═════► Sink Source ═════► Transform ═════► SinkTransform ═════► Sink 4. Adaptive Backpressure Fast Producer    Slow Consumer
     │               │
     ▼               ▼
  [||||||||]  →  [|||] (Automatic throttling)[||||||||]  →  [|||] (Automatic throttling) Fast Producer    Slow Consumer
     │               │
     ▼               ▼
  [||||||||]  →  [|||] (Automatic throttling)[||||||||]  →  [|||] (Automatic throttling) 5. Dynamic Thread Scheduling Traditional Pool       SeaTunnel Pool
│││││││││││ (100)     │││││ (10-50 adaptive)100)     │││││ (10-50 adaptive)
└─────────┘            └───┘ Traditional Pool       SeaTunnel Pool
│││││││││││ (100)     │││││ (10-50 adaptive)100)     │││││ (10-50 adaptive)
└─────────┘            └───┘ 6. Plugin Architecture ClassLoader Isolation ClassLoader Isolation Bootstrap CL → System CL → SeaTunnel CL → Plugin CLSystem CL → SeaTunnel CL → Plugin CL Bootstrap CL → System CL → SeaTunnel CL → Plugin CLSystem CL → SeaTunnel CL → Plugin CL Loading Process Loading Process 1. Scan Plugins → 2. Create Loaders → 3. Load Config → 4. Init 1. Scan Plugins → 2. Create Loaders → 3. Load Config → 4. Init War Stories The Memory Leak Mystery: A persistent memory creep traced to special character handling — found after 72hrs of stack analysis. Phantom Data Phenomenon: Intermittent data duplicates caused by batch boundary conditions — solved with transaction isolation improvements. Performance Cliff: 40% throughput drops with specific data patterns — resolved through adaptive batching. The Memory Leak Mystery: A persistent memory creep traced to special character handling — found after 72hrs of stack analysis. Phantom Data Phenomenon: Intermittent data duplicates caused by batch boundary conditions — solved with transaction isolation improvements. Performance Cliff: 40% throughput drops with specific data patterns — resolved through adaptive batching. Epilogue As Linus Torvalds said: “Talk is cheap. Show me the code.” “Talk is cheap. Show me the code.” But today we say: “Code is cheap. Show me the value.” “Code is cheap. Show me the value.” SeaTunnel proves that elegant solutions emerge when solving real-world problems at scale. The true measure of technology lies not in its complexity, but in its ability to make developers’ lives easier.

This Data Sync Disaster Sparked an Open-Source Revolution

Are you looking for a free alternative to ChatGPT? If so, you’re in the right place! In this article, I will introduce you to HuggingChat, an open-source AI chatbot from Hugging Face. Unlike other AI models like ChatGPT or Gemini, HuggingChat runs on various open-source models that you can explore, modify, and even fine-tune according to your needs. Let’s dive in! What is HuggingChat? HuggingChat is an AI chatbot similar to ChatGPT but built on open-source models. It is designed to provide conversational AI without the limitations of proprietary models like OpenAI’s ChatGPT or Google’s Gemini. Unlike ChatGPT, which is based on OpenAI’s proprietary model, HuggingChat uses multiple AI models from different providers, such as: Llama


Mistral


DeepSeek


Mixtral


Etc Since these models are open-source, you can see their code, modify them, and even integrate them into your own projects. How to Use HuggingChat Using HuggingChat is simple and doesn’t require setup. Step 1. Create an Account First, go to HuggingChat and create a free account. Once logged in, you will be taken to the chat interface. Step 2. Select a Model Unlike ChatGPT, where you only have access to a fixed model, HuggingChat lets you choose different AI models for different tasks. Here’s how: Click the Settings icon or the Models link in the sidebar. Browse the available models.
Select the one that best suits your needs. Each model has different strengths, such as better reasoning, faster responses, or the ability to generate images. Step 3. Start Chatting The interface is very similar to ChatGPT, so if you’ve used ChatGPT before, you’ll feel right at home. Simply enter a prompt and the model will generate a response. Features of HuggingChat 1. Search-Enabled Responses One of the most useful features of HuggingChat is the search function. If an AI model doesn’t have up-to-date information, you can enable search, which allows it to look up the latest facts online. For example, when I asked about the latest iPhone model, the default AI model didn’t know the answer. However, by enabling the search function, I was able to get the correct information. 2. Image Generation HuggingChat supports image generation! However, not all models can generate images. To use this feature: Go to Settings.
Choose a model that supports image generation (e.g., Llama 3).
Click on the image generation icon.
Enter your prompt (e.g., “Generate an image of a futuristic city”). 3. Multiple AI Models HuggingChat allows you to experiment with different AI models to see which works best for your task. Some models are better at reasoning, some are faster, and some support image generation. 4. Custom Assistants Similar to OpenAI’s Custom GPTs, HuggingChat has Assistants, which allow users to create custom AI configurations for specific tasks. For example, you can create an Assistant specialized in: Coding (Python, JavaScript, etc.)
Writing and summarizing text
Translating languages Or you can select Assistants from the community. 5. File and Image Uploads Certain AI models in HuggingChat allow you to upload files or images for processing. This is useful if you want to analyze data, read documents, or perform image-recognition tasks. 6. Free and Open-Source As I mentioned before, unlike ChatGPT or Gemini, HuggingChat is completely free and based on open-source AI models. Video Review of HuggingChat Watch on YouTube: ChatGPT Alternative — HuggingChat Conclusion If you want a free ChatGPT alternative that is customizable and privacy-friendly, HuggingChat is worth trying. Give it a try and let me know your thoughts! Do you think it can replace ChatGPT? Let me know in a comment below! Cheers! ;) Are you looking for a free alternative to ChatGPT? If so, you’re in the right place! In this article, I will introduce you to HuggingChat, an open-source AI chatbot from Hugging Face. Unlike other AI models like ChatGPT or Gemini, HuggingChat runs on various open-source models that you can explore, modify, and even fine-tune according to your needs. Let’s dive in! What is HuggingChat? HuggingChat is an AI chatbot similar to ChatGPT but built on open-source models. It is designed to provide conversational AI without the limitations of proprietary models like OpenAI’s ChatGPT or Google’s Gemini. Unlike ChatGPT, which is based on OpenAI’s proprietary model, HuggingChat uses multiple AI models from different providers, such as: Llama Mistral DeepSeek Mixtral Etc Llama Llama Mistral Mistral DeepSeek DeepSeek Mixtral Mixtral Etc Etc Since these models are open-source, you can see their code, modify them, and even integrate them into your own projects. How to Use HuggingChat Using HuggingChat is simple and doesn’t require setup. Step 1. Create an Account First, go to HuggingChat and create a free account. Once logged in, you will be taken to the chat interface. HuggingChat Step 2. Select a Model Unlike ChatGPT, where you only have access to a fixed model, HuggingChat lets you choose different AI models for different tasks. Here’s how: Click the Settings icon or the Models link in the sidebar. Click the Settings icon or the Models link in the sidebar. Browse the available models. Select the one that best suits your needs. Browse the available models. Select the one that best suits your needs. Each model has different strengths, such as better reasoning, faster responses, or the ability to generate images. Step 3. Start Chatting The interface is very similar to ChatGPT , so if you’ve used ChatGPT before, you’ll feel right at home. Simply enter a prompt and the model will generate a response. very similar to ChatGPT Features of HuggingChat 1. Search-Enabled Responses One of the most useful features of HuggingChat is the search function . If an AI model doesn’t have up-to-date information, you can enable search, which allows it to look up the latest facts online. search function For example, when I asked about the latest iPhone model , the default AI model didn’t know the answer. However, by enabling the search function, I was able to get the correct information. latest iPhone model 2. Image Generation HuggingChat supports image generation! However, not all models can generate images. To use this feature: Go to Settings. Choose a model that supports image generation (e.g., Llama 3). Click on the image generation icon. Enter your prompt (e.g., “Generate an image of a futuristic city”). Go to Settings . Settings Choose a model that supports image generation (e.g., Llama 3). Click on the image generation icon . image generation icon Enter your prompt (e.g., “Generate an image of a futuristic city”). 3. Multiple AI Models HuggingChat allows you to experiment with different AI models to see which works best for your task. Some models are better at reasoning, some are faster, and some support image generation. different AI models 4. Custom Assistants Similar to OpenAI’s Custom GPTs , HuggingChat has Assistants , which allow users to create custom AI configurations for specific tasks. Custom GPTs Assistants For example, you can create an Assistant specialized in: Coding (Python, JavaScript, etc.) Writing and summarizing text Translating languages Coding (Python, JavaScript, etc.) Writing and summarizing text Translating languages Or you can select Assistants from the community. 5. File and Image Uploads Certain AI models in HuggingChat allow you to upload files or images for processing. This is useful if you want to analyze data, read documents, or perform image-recognition tasks. 6. Free and Open-Source As I mentioned before, unlike ChatGPT or Gemini, HuggingChat is completely free and based on open-source AI models. Video Review of HuggingChat Watch on YouTube: ChatGPT Alternative — HuggingChat Watch on YouTube: ChatGPT Alternative — HuggingChat ChatGPT Alternative — HuggingChat Conclusion If you want a free ChatGPT alternative that is customizable and privacy-friendly, HuggingChat is worth trying. Give it a try and let me know your thoughts! Do you think it can replace ChatGPT? Let me know in a comment below! Cheers! ;)

Meet HuggingChat: A FREE and Open-Source Alternative to ChatGPT!

If you’ve been curious about OpenAI’s Operator but hesitated at the price tag of a $200 monthly fee, we’ve got great news. Meet Open-CUAK— an open-source platform that puts you in full control of automation agents, minus the high costs. Introducing Open-CUAK: Scalable Automation, Fully Local We’re thrilled to introduce Open-CUAK—a powerful, open-source platform for managing and deploying automation agents at scale. Unlike closed SaaS platforms, Open-CUAK runs locally, leveraging any vision-compatible frontier model to execute automation tasks seamlessly (frontier or open-source). ⭐ Give us a star on GitHub! ⭐ Why Open-CUAK? Automation in the real world demands reliability, scalability, and cost-efficiency. That’s why we built Open-CUAK (pronounced "quack" 🦆🗣️), the Kubernetes for Computer Use Agents (CUA)—allowing businesses to train, deploy, and manage AI-driven automation agents effortlessly. Unlike those developer-centric automation projects, Open-CUAK is designed for end-users—working out-of-the-box with minimal setup. It offers a user-friendly interface that allows non-developers to automate workflows easily, without needing deep technical expertise. What Can Open-CUAK Do? ✅ Run Operator-like workflows locally for full privacy and security. ✅ Enable vision-based automation with human-like accuracy. ✅ Turn any browser into an Operator-companion via a lightweight extension. ✅ Use a dedicated remote browser to enhance security and bypass restrictions. ✅ Integrate with any vision-compatible model, including Claude, Gemini, LLaVA, and open-source alternatives. ✅ Overcome bot detection mechanisms, unlocking powerful automation potential. 🔜 Train agents using SOP-based workflows, ensuring reliability and adaptability. 🔜 Centralize account access management, giving you granular control over agent permissions. ⏳ Monitor large-scale operations with built-in observability tools. ⏳ Deploy and scale hundreds of agents in parallel to execute real-world tasks. ⏳ Open-source an RL-trained CUA model, making automation more accessible than ever. Open-Source, Customizable & Ready to Deploy Open-CUAK makes automation open, accessible, and adaptable. You can now: Run Open-CUAK locally, eliminating expensive SaaS fees.
Customize workflows to match your industry-specific automation needs.
Experiment with different vision models, fine-tuning automation agents for maximum efficiency. Demo Videos 🎥 Agent uses Canva to design a poster An automation agent uses Canva to create a poster for President’s Day. When it encounters issues, such as whether to use Pro templates (which require a Canva paid plan), it proactively asks the user for additional instructions. https://www.youtube.com/watch?v=uMJ98ym5tvE&embedable=true 🎥 Agent searches for a flight ticket Uses the built-in remote browser running locally to search for flight tickets on Expedia. https://youtu.be/0iKFBHZySyc?si=_z10i604vhRQZxWd&embedable=true Get Started Today 🚀 “When automation is reliable, it becomes scalable. And when it becomes scalable, it becomes profitable.” 🔗 Try Open-CUAK now → GitHub Repo 👥 Join the community → Discord, X, Youtube 💬 Need help? → Connect with us on Discord & Twitter/X! Let’s build the future of AI-powered automation—together. 🦆🗣️🗣️🗣️ If you’ve been curious about OpenAI’s Operator but hesitated at the price tag of a $200 monthly fee, we’ve got great news. Meet Open-CUAK — an open-source platform that puts you in full control of automation agents, minus the high costs. Open-CUAK Introducing Open-CUAK: Scalable Automation, Fully Local We’re thrilled to introduce Open-CUAK —a powerful, open-source platform for managing and deploying automation agents at scale. Unlike closed SaaS platforms, Open-CUAK runs locally, leveraging any vision-compatible frontier model to execute automation tasks seamlessly (frontier or open-source). Open-CUAK ⭐ Give us a star on GitHub ! ⭐ GitHub Why Open-CUAK? Automation in the real world demands reliability, scalability, and cost-efficiency. That’s why we built Open-CUAK (pronounced "quack" 🦆🗣️), the Kubernetes for Computer Use Agents (CUA) —allowing businesses to train, deploy, and manage AI-driven automation agents effortlessly. Open-CUAK Kubernetes for Computer Use Agents (CUA) Unlike those developer-centric automation projects, Open-CUAK is designed for end-users —working out-of-the-box with minimal setup. It offers a user-friendly interface that allows non-developers to automate workflows easily, without needing deep technical expertise. Open-CUAK is designed for end-users user-friendly interface What Can Open-CUAK Do? ✅ Run Operator-like workflows locally for full privacy and security. Run Operator-like workflows locally ✅ Enable vision-based automation with human-like accuracy. Enable vision-based automation ✅ Turn any browser into an Operator-companion via a lightweight extension. Turn any browser into an Operator-companion ✅ Use a dedicated remote browser to enhance security and bypass restrictions. Use a dedicated remote browser ✅ Integrate with any vision-compatible model , including Claude, Gemini, LLaVA, and open-source alternatives. Integrate with any vision-compatible model ✅ Overcome bot detection mechanisms , unlocking powerful automation potential. Overcome bot detection mechanisms 🔜 Train agents using SOP-based workflows , ensuring reliability and adaptability. Train agents using SOP-based workflows 🔜 Centralize account access management , giving you granular control over agent permissions. Centralize account access management ⏳ Monitor large-scale operations with built-in observability tools. Monitor large-scale operations ⏳ Deploy and scale hundreds of agents in parallel to execute real-world tasks. Deploy and scale hundreds of agents in parallel ⏳ Open-source an RL-trained CUA model , making automation more accessible than ever. Open-source an RL-trained CUA model Open-Source, Customizable & Ready to Deploy Open-CUAK makes automation open, accessible, and adaptable . You can now: open, accessible, and adaptable Run Open-CUAK locally, eliminating expensive SaaS fees. Customize workflows to match your industry-specific automation needs. Experiment with different vision models, fine-tuning automation agents for maximum efficiency. Run Open-CUAK locally , eliminating expensive SaaS fees. Run Open-CUAK locally Customize workflows to match your industry-specific automation needs. Customize workflows Experiment with different vision models , fine-tuning automation agents for maximum efficiency. Experiment with different vision models Demo Videos 🎥 Agent uses Canva to design a poster Agent uses Canva to design a poster An automation agent uses Canva to create a poster for President’s Day. When it encounters issues, such as whether to use Pro templates (which require a Canva paid plan), it proactively asks the user for additional instructions. https://www.youtube.com/watch?v=uMJ98ym5tvE&embedable=true https://www.youtube.com/watch?v=uMJ98ym5tvE&embedable=true 🎥 Agent searches for a flight ticket Agent searches for a flight ticket Uses the built-in remote browser running locally to search for flight tickets on Expedia. https://youtu.be/0iKFBHZySyc?si=_z10i604vhRQZxWd&embedable=true https://youtu.be/0iKFBHZySyc?si=_z10i604vhRQZxWd&embedable=true Get Started Today 🚀 “When automation is reliable, it becomes scalable. And when it becomes scalable, it becomes profitable.” “When automation is reliable, it becomes scalable. And when it becomes scalable, it becomes profitable.” 🔗 Try Open-CUAK now → GitHub Repo Try Open-CUAK now GitHub Repo 👥 Join the community → Discord , X , Youtube Join the community Discord X Youtube 💬 Need help? → Connect with us on Discord & Twitter/X ! Need help? Discord Twitter/X Let’s build the future of AI-powered automation— together . 🦆🗣️🗣️🗣️ together

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