Source is a data management layer built for the decentralized web, a fast-growing market expected to reach $40B by 2025

Source

source

, 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

I recently launched Postiz, an open-source social media scheduling tool. After four months, it's making $2.1k per month! Thanks to open-source software in one of the most competitive markets, you can do the same. Here is how. Find an Idea 💡 You don't have to have an original idea; you can take something already existing and open-source it (of course, make it good). Why? Because once you have open-sourced it, you turn to a new audience that was never served before - Developers and companies that want to self-host your product (primarily because of privacy.) I prefer to find big markets. For example, Postiz is a social media scheduling tool. It's one of the most saturated markets, meaning many people and underserved audiences are looking for it. You also get tremendous credibility by showing all your code and get more credibility by getting stars. Market It 🏪 Every product needs marketing; you can imagine from this post that I have some sense of it (Squid game picture + title); attracting more people is essential to get the initial visibility. Open source gets some superpowers because it allows you to promote it in channels usually unavailable in typical startups. You can find a lot of content about it on my website Gitroom. In short: Post your repository every time you have a new version on Reddit /r/selfhosted
If you have an AI startup, post it on /r/LocalLLaMA
Post it in Lemmy's self-hosted community
Post it on DEV.to/HackerNoon/Medium/Hashnode every week
Post it on Hackernews: "Show HN: product name"
Post it on LinkedIn/X as much as you can. Use Postiz for that 😉 If you do it often, you might be trending on GitHub, increasing your visibility by 10x. Then, use your audience to post it on Product Hunt and get upvotes from the community members. Postiz finished 1st of the day/week/month. Collect Feedback 💭 Using Discord and GitHub issues, I am collecting tons of feedback on what to implement and what not. With over 800k downloads from Docker, tons of people are always playing with it and giving feedback. When you implement feedback, people trust you more, and you learn faster about your product. Just Do It ✔ Stop looking for excuses. Just write some code - the nice thing about open-source is that you don't even have to start by making the main website - build the open-source! And of course! Help me out if you can ❤️ I'm happy to get a star to produce more features for the open-source! https://github.com/gitroomhq/postiz-app/ I recently launched Postiz , an open-source social media scheduling tool. After four months, it's making $2.1k per month! Thanks to open-source software in one of the most competitive markets, you can do the same. Postiz Postiz Here is how. Find an Idea 💡 You don't have to have an original idea; you can take something already existing and open-source it (of course, make it good). Why? Because once you have open-sourced it, you turn to a new audience that was never served before - Developers and companies that want to self-host your product (primarily because of privacy.) I prefer to find big markets. For example, Postiz is a social media scheduling tool. It's one of the most saturated markets, meaning many people and underserved audiences are looking for it. You also get tremendous credibility by showing all your code and get more credibility by getting stars. Market It 🏪 Every product needs marketing; you can imagine from this post that I have some sense of it (Squid game picture + title); attracting more people is essential to get the initial visibility. Open source gets some superpowers because it allows you to promote it in channels usually unavailable in typical startups. You can find a lot of content about it on my website Gitroom . Gitroom Gitroom In short: Post your repository every time you have a new version on Reddit /r/selfhosted If you have an AI startup, post it on /r/LocalLLaMA Post it in Lemmy's self-hosted community Post it on DEV.to/HackerNoon/Medium/Hashnode every week Post it on Hackernews: "Show HN: product name" Post it on LinkedIn/X as much as you can. Use Postiz for that 😉 Post your repository every time you have a new version on Reddit /r/selfhosted /r/selfhosted /r/selfhosted If you have an AI startup, post it on /r/LocalLLaMA /r/LocalLLaMA /r/LocalLLaMA Post it in Lemmy's self-hosted community self-hosted community self-hosted community Post it on DEV.to/HackerNoon/Medium/Hashnode every week Post it on Hackernews : "Show HN: product name" Hackernews Hackernews Post it on LinkedIn/X as much as you can. Use Postiz for that 😉 Postiz Postiz If you do it often, you might be trending on GitHub , increasing your visibility by 10x. trending on GitHub trending on GitHub Then, use your audience to post it on Product Hunt and get upvotes from the community members. Postiz finished 1st of the day/week/month. Product Hunt Product Hunt Collect Feedback 💭 Using Discord and GitHub issues, I am collecting tons of feedback on what to implement and what not. With over 800k downloads from Docker, tons of people are always playing with it and giving feedback. When you implement feedback, people trust you more, and you learn faster about your product. Just Do It ✔ Stop looking for excuses. Just write some code - the nice thing about open-source is that you don't even have to start by making the main website - build the open-source! And of course! Help me out if you can ❤️ I'm happy to get a star to produce more features for the open-source! https://github.com/gitroomhq/postiz-app/ https://github.com/gitroomhq/postiz-app/ https://github.com/gitroomhq/postiz-app/

4 Months, $2.1k Per Month - All Open-Source Here's How

**PALO ALTO, United States, January 9th, 2025/Chainwire/--**Story, the world’s intellectual property blockchain has announced its use of Stability AI’s cutting-edge models to usher in a new era of open-source AI development, that allows contributors – creators, developers, artists – to capture the value they create by contributing to the AI ecosystem. With the use of Stability AI’s technology, Story aims to address the critical challenge of properly attributing, tracking, and monetizing creative work generated via AI. Story is focused on addressing the lack of a clear path for creators to monetize their derivative works in the open-source ecosystem. Despite the incredible progress in AI, proper attribution and monetization for creators’ IP has not kept up with the rate of innovation. “We’re thrilled to leverage Stability AI’s models, to tackle the most pressing challenges we face with the rapid rise of AI,” said Jason Zhao, Co-Founder and Chief Protocol Officer at PIP Labs, Story’s initial core contributor. “The combination of AI and blockchain is not only incredibly powerful, but necessary. Blockchains secure digital property rights in the era of AI driven creative abundance. By leveraging Stability AI’s technology and Story’s technology, we’re showcasing how the proper incentive structures can ensure attribution and empower creators, driving AI development forward.” Story and its ecosystem applications will use Stability AI’s leading foundational image models to build AI applications that embed tracking of contributions across the AI development life cycle to enable fair compensation to all creators involved with a monetized output. Mahojin and ABLO are two AI applications building on Story that leverage Stability AI’s foundation models and Story’s blockchain technology. Mahojin, a search-to-generate AI remixing platform and ABLO, a collaborative AI platform that allows creators to design physical goods with leading brand IPs use Stability AI’s models to allow users to easily bring their creative vision to life and Story’s technology to enable better provenance and attribution across the AI stack. These two projects showcase real-world use cases and illustrate how to unlock new ways for creators to safeguard their IP and earn from their contributions in a dynamic, shared creative economy. “Empowering creators is at the core of everything we do at Stability AI. We are thrilled to see our models used in Story’s blockchain technology to ensure proper attribution and rewards contributors," said Scott Trowbridge, Vice President of Stability AI. Story is committed to exploring different use cases for how AI and blockchain can come together to meet the evolving needs of creators and developers in the age of generative AI. For example, one area of exploration is registering training data like an artist's unique style or voice as IP with transparent usage terms on Story. Anyone can then train and fine-tune their own model using this IP. If a creator uses this model to generate an output that is monetized, everyone in this chain of creation wins and benefits together. By leveraging Stability AI’s cutting-edge models, Story is taking a key step toward creating a sustainable and fair internet in the age of AI. About Story ​Story is the world’s IP blockchain, transforming IPs into networks that transcend mediums and platforms, unleashing global creativity and liquidity. Visit Story's website to learn more. About PIP (Programmable IP) Labs: PIP Labs, an initial core contributor to the Story Network, is backed by investors, including a16z crypto, Endeavor, and Polychain. PIP Labs was co-founded by a serial entrepreneur with a $440M exit and Deepmind’s youngest PM with the veteran founding executive team with a diverse background in consumer tech, generative AI, and Web3 infrastructure. Contact Head of Communications HV PIP Labs henri.vies@storyprotocol.xyz This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here **PALO ALTO, United States, January 9th, 2025/Chainwire/--**Story, the world’s intellectual property blockchain has announced its use of Stability AI ’s cutting-edge models to usher in a new era of open-source AI development, that allows contributors – creators, developers, artists – to capture the value they create by contributing to the AI ecosystem. With the use of Stability AI’s technology, Story aims to address the critical challenge of properly attributing, tracking, and monetizing creative work generated via AI. Stability AI Stability AI Story is focused on addressing the lack of a clear path for creators to monetize their derivative works in the open-source ecosystem. Despite the incredible progress in AI, proper attribution and monetization for creators’ IP has not kept up with the rate of innovation. “We’re thrilled to leverage Stability AI’s models, to tackle the most pressing challenges we face with the rapid rise of AI,” said Jason Zhao, Co-Founder and Chief Protocol Officer at PIP Labs, Story’s initial core contributor. “The combination of AI and blockchain is not only incredibly powerful, but necessary. Blockchains secure digital property rights in the era of AI driven creative abundance. By leveraging Stability AI’s technology and Story’s technology, we’re showcasing how the proper incentive structures can ensure attribution and empower creators, driving AI development forward.” “We’re thrilled to leverage Stability AI’s models, to tackle the most pressing challenges we face with the rapid rise of AI,” said Jason Zhao, Co-Founder and Chief Protocol Officer at PIP Labs, Story’s initial core contributor. “The combination of AI and blockchain is not only incredibly powerful, but necessary. Blockchains secure digital property rights in the era of AI driven creative abundance. By leveraging Stability AI’s technology and Story’s technology, we’re showcasing how the proper incentive structures can ensure attribution and empower creators, driving AI development forward.” Story and its ecosystem applications will use Stability AI’s leading foundational image models to build AI applications that embed tracking of contributions across the AI development life cycle to enable fair compensation to all creators involved with a monetized output. Mahojin and ABLO are two AI applications building on Story that leverage Stability AI’s foundation models and Story’s blockchain technology. Mahojin , a search-to-generate AI remixing platform and ABLO , a collaborative AI platform that allows creators to design physical goods with leading brand IPs use Stability AI’s models to allow users to easily bring their creative vision to life and Story’s technology to enable better provenance and attribution across the AI stack. These two projects showcase real-world use cases and illustrate how to unlock new ways for creators to safeguard their IP and earn from their contributions in a dynamic, shared creative economy. Mahojin Mahojin ABLO ABLO “Empowering creators is at the core of everything we do at Stability AI. We are thrilled to see our models used in Story’s blockchain technology to ensure proper attribution and rewards contributors," said Scott Trowbridge, Vice President of Stability AI. “Empowering creators is at the core of everything we do at Stability AI. We are thrilled to see our models used in Story’s blockchain technology to ensure proper attribution and rewards contributors," said Scott Trowbridge, Vice President of Stability AI. Story is committed to exploring different use cases for how AI and blockchain can come together to meet the evolving needs of creators and developers in the age of generative AI. For example, one area of exploration is registering training data like an artist's unique style or voice as IP with transparent usage terms on Story. Anyone can then train and fine-tune their own model using this IP. If a creator uses this model to generate an output that is monetized, everyone in this chain of creation wins and benefits together. By leveraging Stability AI’s cutting-edge models, Story is taking a key step toward creating a sustainable and fair internet in the age of AI. About Story ​Story is the world’s IP blockchain, transforming IPs into networks that transcend mediums and platforms, unleashing global creativity and liquidity. Visit Story's website to learn more. Story's website Story's website About PIP (Programmable IP) Labs: PIP Labs, an initial core contributor to the Story Network, is backed by investors, including a16z crypto, Endeavor, and Polychain. PIP Labs was co-founded by a serial entrepreneur with a $440M exit and Deepmind’s youngest PM with the veteran founding executive team with a diverse background in consumer tech, generative AI, and Web3 infrastructure. Contact Head of Communications HV PIP Labs henri.vies@storyprotocol.xyz 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

Story Revolutionizes Open Source AI for Creator Monetization

Story revoluciona la inteligencia artificial de código abierto para la monetización de los creadores

स्टोरी ने क्रिएटर के मुद्रीकरण के लिए ओपन सोर्स AI में क्रांति ला दी

Story revolucionira AI otvorenog koda za unovčavanje kreatora

Story Merevolusi AI Open Source untuk Monetisasi Kreator

La storia rivoluziona l'intelligenza artificiale open source per la monetizzazione dei creatori

Story はクリエイターの収益化のためにオープンソース AI を革新します

Nanova ny AI Open Source ho an'ny Mpamorona vola ny Tantara

Story revolutioneert open source AI voor monetaire verdiensten van makers

கிரியேட்டர் பணமாக்குதலுக்கான ஓப்பன் சோர்ஸ் AIஐ ஸ்டோரி புரட்சிகரமாக்குகிறது

Story ปฏิวัติ AI โอเพ่นซอร์สสำหรับการสร้างรายได้จากผู้สร้าง

Ibali liguqula uMthombo oVulekileyo we-AI kuMdali wokwenza imali

Story 为创作者货币化带来革命性开源人工智能

5 Open-source and Free Operating Systems to Donate via Kivach

5 oopbron- en gratis bedryfstelsels om via Kivach te skenk

በኪቫች በኩል ለመለገስ 5 ክፍት ምንጭ እና ነፃ ኦፕሬቲንግ ሲስተሞች

5 sistemas operativos gratuitos y de código abierto que puedes donar a través de Kivach

5 բաց կոդով և անվճար օպերացիոն համակարգեր՝ նվիրաբերելու համար Kivach-ի միջոցով

5 Sistem Operasi Open-source dan Gratis yang Dapat Disumbangkan melalui Kivach

5 מערכות הפעלה בקוד פתוח ובחינם לתרומה דרך Kivach

Kivach 経由で寄付できる 5 つのオープンソースおよび無料オペレーティング システム

5 atvirojo kodo ir nemokamos operacinės sistemos, kurias galite paaukoti per „Kivach“.

5 Kivach හරහා පරිත්‍යාග කිරීමට විවෘත මූලාශ්‍ර සහ නොමිලේ මෙහෙයුම් පද්ධති

ระบบปฏิบัติการโอเพ่นซอร์สและฟรี 5 ระบบที่สามารถบริจาคผ่าน Kivach

Kivach Üzerinden Bağışlayabileceğiniz 5 Açık Kaynaklı ve Ücretsiz İşletim Sistemi

5 відкритих і безкоштовних операційних систем, які можна пожертвувати через Kivach

Hey dev community! With the holidays right around the corner, it feels like the perfect time to talk about one of the best gifts we can give our users: an accessible web. Here at TestZeus, we’ve been hard at work adding Accessibility Testing to our open-source Hercules Agent—and I’m super excited to share the details with you! Why Accessibility Matters Accessibility is about making sure everyone can use your website or app, regardless of ability. That includes users who rely on screen readers, have motor difficulties, or experience color blindness, among many other scenarios. It’s not just a moral responsibility or a legal checkmark—it’s about crafting better user experiences for everyone. Meet Hercules If you haven’t heard of it yet, Hercules is our open-source testing agent that automates various aspects of software testing. By adding Accessibility Testing, we aim to reduce the friction developers often face when trying to meet WCAG (Web Content Accessibility Guidelines). Some highlights: Covers WCAG 2.0, 2.1, and 2.2
Costs less than $1 per scan (so, super accessible for smaller teams too!)
Open-source and easy to integrate into your CI/CD pipeline In other words, we’re focusing on practicality and affordability, so you don’t need a huge budget or specialized skillset to make your site more inclusive. Quick Setup Clone the Repo

Head over to our GitHub repo and clone it.


Install Dependencies

A quick 'pip install testzeus-hercules'


Write Your Test in Gherkin

Hercules uses Gherkin-like .feature files. It’s straightforward and keeps the logic clear.


Run Hercules

A single command to scan for accessibility issues. Reports are automatically generated, so you can jump right into fixing any violations. Sample Test Scenario We’ve included a snippet below to show how a typical .featurefile might look, using the Zerodha brokerage calculator page as an example: Feature: Check accessibility for brokerage calculator on Zerodha website
  # This feature tests the brokerage calculator, and contract notes on Zerodha website

  Scenario Outline: Check calculations on brokerage and SEBI charges
    Given the user is on "https://zerodha.com/brokerage-calculator#tab-equities"
    Then validate the page for accessibility.
    Then there should not be any issues. Here’s the breakdown: Feature: Provides context—testing Zerodha’s brokerage calculator.
Scenario Outline: Explains the specific scenario.
Steps:

Given: Navigate to a specific URL.
Then: Validate accessibility.
Then: Ensure zero issues remain (fingers crossed!). Hercules interprets this scenario, visits the page, scans it, and flags any WCAG non-compliances. Viewing Your Results Once your tests complete, Hercules shows you a report detailing any accessibility issues found: Issue Description: A quick summary of the violation.
WCAG Reference: Which guideline is being broken.
Severity: Ranges from minor to critical.
Location: Points you to the exact DOM element or area in the UI. This immediate feedback loop makes it easy for devs to fix issues before they reach production. Integrating with CI/CD Because Hercules is open-source and script-friendly, you can plug it into your CI pipeline. For example, in GitHub Actions, you can create a workflow that runs Hercules every time you push new code. That way, you’ll catch accessibility regressions early in the development process. Going Beyond Automated Checks While Hercules does a fantastic job with automated scans, keep in mind that accessibility is a multifaceted area. You might still want to: Test with screen readers like NVDA or VoiceOver.
Check color contrasts in real-world scenarios.
Solicit feedback from users with disabilities for real-life insights. Automated testing is a huge help, but manual testing still plays a key role in thorough accessibility coverage. Wrapping Up This holiday season, consider giving the gift of an accessible experience to your users. With Hercules, you can weave accessibility checks right into your development cycle—reducing barriers and making the web a more inclusive place. Here are some helpful links: Repo: TestZeus Hercules GitHub
Latest Release: Version 0.0.16
Demo Video: Check it out on YouTube If you have any questions, feedback, or ideas for improvement, we’d love to hear from you! Let’s work together to make the internet more welcoming for all. Happy Holidays and Happy Testing! Hey dev community! With the holidays right around the corner, it feels like the perfect time to talk about one of the best gifts we can give our users: an accessible web . Here at TestZeus, we’ve been hard at work adding Accessibility Testing to our open-source Hercules Agent —and I’m super excited to share the details with you! an accessible web Accessibility Testing Hercules Agent Why Accessibility Matters Accessibility is about making sure everyone can use your website or app, regardless of ability. That includes users who rely on screen readers, have motor difficulties, or experience color blindness, among many other scenarios. It’s not just a moral responsibility or a legal checkmark—it’s about crafting better user experiences for everyone. Meet Hercules If you haven’t heard of it yet, Hercules is our open-source testing agent that automates various aspects of software testing. By adding Accessibility Testing , we aim to reduce the friction developers often face when trying to meet WCAG (Web Content Accessibility Guidelines) . Some highlights: Hercules Accessibility Testing WCAG (Web Content Accessibility Guidelines) Covers WCAG 2.0, 2.1, and 2.2 Costs less than $1 per scan (so, super accessible for smaller teams too!) Open-source and easy to integrate into your CI/CD pipeline Covers WCAG 2.0, 2.1, and 2.2 Covers WCAG 2.0, 2.1, and 2.2 Costs less than $1 per scan (so, super accessible for smaller teams too!) Costs less than $1 per scan Open-source and easy to integrate into your CI/CD pipeline Open-source In other words, we’re focusing on practicality and affordability, so you don’t need a huge budget or specialized skillset to make your site more inclusive. Quick Setup Clone the Repo

Head over to our GitHub repo and clone it. Install Dependencies

A quick 'pip install testzeus-hercules' Write Your Test in Gherkin

Hercules uses Gherkin-like .feature files. It’s straightforward and keeps the logic clear. Run Hercules

A single command to scan for accessibility issues. Reports are automatically generated, so you can jump right into fixing any violations. Clone the Repo Head over to our GitHub repo and clone it. Clone the Repo Head over to our GitHub repo and clone it. Head over to our GitHub repo and clone it. GitHub repo GitHub repo Install Dependencies A quick 'pip install testzeus-hercules' Install Dependencies A quick 'pip install testzeus-hercules' A quick 'pip install testzeus-hercules' Write Your Test in Gherkin Hercules uses Gherkin-like .feature files. It’s straightforward and keeps the logic clear. Write Your Test in Gherkin Hercules uses Gherkin-like .feature files. It’s straightforward and keeps the logic clear. Hercules uses Gherkin-like .feature files. It’s straightforward and keeps the logic clear. .feature Run Hercules A single command to scan for accessibility issues. Reports are automatically generated, so you can jump right into fixing any violations. Run Hercules A single command to scan for accessibility issues. Reports are automatically generated, so you can jump right into fixing any violations. A single command to scan for accessibility issues. Reports are automatically generated, so you can jump right into fixing any violations. Sample Test Scenario We’ve included a snippet below to show how a typical .feature file might look, using the Zerodha brokerage calculator page as an example: .feature Feature: Check accessibility for brokerage calculator on Zerodha website
  # This feature tests the brokerage calculator, and contract notes on Zerodha website

  Scenario Outline: Check calculations on brokerage and SEBI charges
    Given the user is on "https://zerodha.com/brokerage-calculator#tab-equities"
    Then validate the page for accessibility.
    Then there should not be any issues. Feature: Check accessibility for brokerage calculator on Zerodha website
  # This feature tests the brokerage calculator, and contract notes on Zerodha website

  Scenario Outline: Check calculations on brokerage and SEBI charges
    Given the user is on "https://zerodha.com/brokerage-calculator#tab-equities"
    Then validate the page for accessibility.
    Then there should not be any issues. Here’s the breakdown: Feature: Provides context—testing Zerodha’s brokerage calculator. Scenario Outline: Explains the specific scenario. Steps:

Given: Navigate to a specific URL.
Then: Validate accessibility.
Then: Ensure zero issues remain (fingers crossed!). Feature : Provides context—testing Zerodha’s brokerage calculator. Feature Scenario Outline : Explains the specific scenario. Scenario Outline Steps : Given: Navigate to a specific URL.
Then: Validate accessibility.
Then: Ensure zero issues remain (fingers crossed!). Steps Given: Navigate to a specific URL. Then: Validate accessibility. Then: Ensure zero issues remain (fingers crossed!). Given : Navigate to a specific URL. Given Then : Validate accessibility. Then Then : Ensure zero issues remain (fingers crossed!). Then Hercules interprets this scenario, visits the page, scans it, and flags any WCAG non-compliances. Viewing Your Results Once your tests complete, Hercules shows you a report detailing any accessibility issues found: Issue Description: A quick summary of the violation. WCAG Reference: Which guideline is being broken. Severity: Ranges from minor to critical. Location: Points you to the exact DOM element or area in the UI. Issue Description : A quick summary of the violation. Issue Description WCAG Reference : Which guideline is being broken. WCAG Reference Severity : Ranges from minor to critical. Severity Location : Points you to the exact DOM element or area in the UI. Location This immediate feedback loop makes it easy for devs to fix issues before they reach production. Integrating with CI/CD Because Hercules is open-source and script-friendly, you can plug it into your CI pipeline. For example, in GitHub Actions, you can create a workflow that runs Hercules every time you push new code. That way, you’ll catch accessibility regressions early in the development process. Going Beyond Automated Checks While Hercules does a fantastic job with automated scans, keep in mind that accessibility is a multifaceted area. You might still want to: Test with screen readers like NVDA or VoiceOver. Check color contrasts in real-world scenarios. Solicit feedback from users with disabilities for real-life insights. Test with screen readers like NVDA or VoiceOver. Test with screen readers Check color contrasts in real-world scenarios. Check color contrasts Solicit feedback from users with disabilities for real-life insights. Solicit feedback from users with disabilities Automated testing is a huge help, but manual testing still plays a key role in thorough accessibility coverage. Wrapping Up This holiday season, consider giving the gift of an accessible experience to your users. With Hercules, you can weave accessibility checks right into your development cycle—reducing barriers and making the web a more inclusive place. Here are some helpful links: Repo: TestZeus Hercules GitHub Latest Release: Version 0.0.16 Demo Video: Check it out on YouTube Repo : TestZeus Hercules GitHub Repo TestZeus Hercules GitHub TestZeus Hercules GitHub Latest Release : Version 0.0.16 Latest Release Version 0.0.16 Version 0.0.16 Demo Video : Check it out on YouTube Demo Video Check it out on YouTube Check it out on YouTube If you have any questions, feedback, or ideas for improvement, we’d love to hear from you! Let’s work together to make the internet more welcoming for all. Happy Holidays and Happy Testing! Happy Holidays and Happy Testing!

Making the Web More Inclusive: Accessibility Testing with open source testing agents

New Open-Source Platform Is Letting AI Researchers Crack Tough Languages

The TechBeat: How I Made $700 a Month With My Open-source Scheduling Tool (12/28/2024)

Stand Out From Other Developers By Contributing to Open Source

التميز عن المطورين الآخرين من خلال المساهمة في المصدر المفتوح

Destaque entre otros desarrolladores contribuyendo al código abierto

Istaknite se od drugih programera doprinoseći otvorenom kodu

Tűnjön ki a többi fejlesztő közül azáltal, hogy hozzájárul a nyílt forráskódhoz

オープンソースに貢献して他の開発者より目立つ

ឈរចេញពីអ្នកអភិវឌ្ឍន៍ផ្សេងទៀតដោយការរួមចំណែកដល់ប្រភពបើកចំហ

Издвојте се од другите програмери со тоа што придонесувате за софтвер со отворен код

Wyróżnij się spośród innych programistów, przyczyniając się do rozwoju oprogramowania typu Open Source

د خلاصې سرچینې سره د مرستې له لارې د نورو پراختیا کونکو څخه ودریږئ

Ieșiți în evidență față de alți dezvoltatori, contribuind la Open Source

Hagarara Mubandi Bateza Imbere Mugutanga Inkomoko

Stick ut från andra utvecklare genom att bidra till öppen källkod

The TechBeat: How I Made $700 a Month With My Open-source Scheduling Tool (12/26/2024)

Contributing to open source has always seemed like an intimidating leap. For years, I admired projects like Anastruct, a Python library for preliminary structural analysis, for their power and elegance. Recently, I had the chance to step into this world myself — and to my surprise, my very first pull request was accepted! Here’s the story of how I added a method to retrieve and return the stiffness matrix of a structure, and what I learned along the way. The Idea: Bridging a Gap This journey started while I was helping a student develop Python code for a structural analysis project. The task was to analyze a truss roof resting on retaining walls. The project required determining the displacements of the truss, shear and bending moments in the walls, and lateral loads acting on the structure. The examiner wanted the student to display the stiffness matrix for each element in the structure. While Anastruct already offers powerful features like defining geometry, setting material properties (e.g., EA and EI), and generating displacement, shear, and bending moment plots, it lacked a way to directly view the stiffness matrix of individual elements. This missing functionality inspired me to act. I decided to implement a method that could return the stiffness matrix when provided with an element ID. Taking the Leap: Writing My First Contribution Although I’ve used Anastruct for over three years and admired its utility in preliminary analysis and validation tasks, contributing to such an established library felt daunting. I wasn’t sure if the maintainers would accept my idea or if my implementation would meet their standards. Despite my hesitation, I toot the next step, that is implementation. The Development Process Research and Implementation: I started by diving deep into Anastruct’s codebase to understand its structure. The method I wrote leveraged existing geometry and material property definitions in the library to access and return the stiffness matrix for a given element ID.
Testing: I tested my code on a variety of truss structures to ensure accuracy. The matrix output aligned with theoretical calculations, confirming that my method worked as intended.
Submitting the Pull Request: Crafting the pull request was nerve-wracking. I described the problem, the feature, and its implementation, and pushed it to GitHub, hoping for the best. Feedback and Iteration To my delight, one of the maintainers responded within a few days. They appreciated the value of my contribution but flagged some issues, including linting errors and type mismatches. I took their feedback seriously, quickly making the necessary corrections. The Merge Moment Two weeks later, I received the news: my pull request had been merged! The joy of seeing my code become part of a library used by engineers worldwide was indescribable. What began as a way to help a student turned into a meaningful open-source contribution. Impact and Lessons Learned Impact on Anastruct: The stiffness matrix functionality I added makes Anastruct even more versatile, allowing users to gain deeper insights into their structural models.
Personal Growth: This experience taught me the importance of perseverance, attention to detail, and collaboration. It also boosted my confidence to contribute to other open-source projects. Advice for Aspiring Contributors Start Small: Begin with a feature or fix that aligns with your skills.
Read the Docs: Familiarize yourself with the project’s contribution guidelines.
Seek Feedback: Maintain an open mind and learn from reviewers. Conclusion Contributing to Anastruct not only helped me grow as a developer but also allowed me to give back to a project I’ve relied on for years. If you’ve ever considered contributing to open source, don’t let doubt hold you back. Your ideas and skills might be exactly what a project needs. Contributing to open source has always seemed like an intimidating leap. For years, I admired projects like Anastruct , a Python library for preliminary structural analysis, for their power and elegance. Recently, I had the chance to step into this world myself — and to my surprise, my very first pull request was accepted! Here’s the story of how I added a method to retrieve and return the stiffness matrix of a structure, and what I learned along the way. Anastruct The Idea: Bridging a Gap This journey started while I was helping a student develop Python code for a structural analysis project. The task was to analyze a truss roof resting on retaining walls. The project required determining the displacements of the truss, shear and bending moments in the walls, and lateral loads acting on the structure. The examiner wanted the student to display the stiffness matrix for each element in the structure. While Anastruct already offers powerful features like defining geometry, setting material properties (e.g., EA and EI), and generating displacement, shear, and bending moment plots, it lacked a way to directly view the stiffness matrix of individual elements. This missing functionality inspired me to act. I decided to implement a method that could return the stiffness matrix when provided with an element ID. Anastruct Taking the Leap: Writing My First Contribution Although I’ve used Anastruct for over three years and admired its utility in preliminary analysis and validation tasks, contributing to such an established library felt daunting. I wasn’t sure if the maintainers would accept my idea or if my implementation would meet their standards. Despite my hesitation, I toot the next step, that is implementation. Anastruct The Development Process Research and Implementation: I started by diving deep into Anastruct’s codebase to understand its structure. The method I wrote leveraged existing geometry and material property definitions in the library to access and return the stiffness matrix for a given element ID. Testing: I tested my code on a variety of truss structures to ensure accuracy. The matrix output aligned with theoretical calculations, confirming that my method worked as intended. Submitting the Pull Request: Crafting the pull request was nerve-wracking. I described the problem, the feature, and its implementation, and pushed it to GitHub, hoping for the best. Research and Implementation: I started by diving deep into Anastruct’s codebase to understand its structure. The method I wrote leveraged existing geometry and material property definitions in the library to access and return the stiffness matrix for a given element ID. Anastruct’s Testing: I tested my code on a variety of truss structures to ensure accuracy. The matrix output aligned with theoretical calculations, confirming that my method worked as intended. Submitting the Pull Request: Crafting the pull request was nerve-wracking. I described the problem, the feature, and its implementation, and pushed it to GitHub, hoping for the best. Feedback and Iteration To my delight, one of the maintainers responded within a few days. They appreciated the value of my contribution but flagged some issues, including linting errors and type mismatches. I took their feedback seriously, quickly making the necessary corrections. The Merge Moment Two weeks later, I received the news: my pull request had been merged! The joy of seeing my code become part of a library used by engineers worldwide was indescribable. What began as a way to help a student turned into a meaningful open-source contribution. Impact and Lessons Learned Impact on Anastruct: The stiffness matrix functionality I added makes Anastruct even more versatile, allowing users to gain deeper insights into their structural models. Personal Growth: This experience taught me the importance of perseverance, attention to detail, and collaboration. It also boosted my confidence to contribute to other open-source projects. Impact on Anastruct: The stiffness matrix functionality I added makes Anastruct even more versatile, allowing users to gain deeper insights into their structural models. Anastruct Personal Growth: This experience taught me the importance of perseverance, attention to detail, and collaboration. It also boosted my confidence to contribute to other open-source projects. Advice for Aspiring Contributors Start Small: Begin with a feature or fix that aligns with your skills. Read the Docs: Familiarize yourself with the project’s contribution guidelines. Seek Feedback: Maintain an open mind and learn from reviewers. Start Small: Begin with a feature or fix that aligns with your skills. Start Small: Read the Docs: Familiarize yourself with the project’s contribution guidelines. Read the Docs: Seek Feedback: Maintain an open mind and learn from reviewers. Seek Feedback: Conclusion Contributing to Anastruct not only helped me grow as a developer but also allowed me to give back to a project I’ve relied on for years. If you’ve ever considered contributing to open source, don’t let doubt hold you back. Your ideas and skills might be exactly what a project needs. Anastruct

There's No Feeling Like Making Your First Contribution to a Github Open Source Project

The TechBeat: How I Made $700 a Month With My Open-source Scheduling Tool (12/24/2024)

Table of Links Abstract and 1. Introduction 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 4 The Future of GPT4All Limitations and References Abstract Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. The accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure; are only accessible via rate-limited, geo-locked, and censored web interfaces; and lack publicly available code and technical reports. In this paper, we tell the story of GPT4All, a popular open source repository that aims to democratize access to LLMs. We outline the technical details of the original GPT4All model family, as well as the evolution of the GPT4All project from a single model into a fully fledged open source ecosystem. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem. 1 Introduction On March 14 2023, OpenAI released GPT-4, a large language model capable of achieving human level performance on a variety of professional and academic benchmarks. Despite the popularity of the release, the GPT-4 technical report (OpenAI, 2023) contained virtually no details regarding the architecture, hardware, training compute, dataset construction, or training method used to create the model. Moreover, users could only access the model through the internet interface at chat.openai.com, which was severely rate limited and unavailable in several locales (e.g. Italy) (BBC News, 2023). Additionally, GPT-4 refused to answer a wide ∗ Shared Senior Authorship variety of queries, responding only with the now infamous "As an AI Language Model, I cannot..." prefix (Vincent, 2023). These transparency and accessibility concerns spurred several developers to begin creating open source large language model (LLM) alternatives. Several grassroots efforts focused on fine tuning Meta’s open code LLaMA model (Touvron et al., 2023; McMillan, 2023), whose weights were leaked on BitTorrent less than a week prior to the release of GPT-4 (Verge, 2023). GPT4All started as one of these variants. In this paper, we tell the story of GPT4All. We comment on the technical details of the original GPT4All model (Anand et al., 2023), as well as the evolution of GPT4All from a single model to an ecosystem of several models. We remark on the impact that the project has had on the open source community, and discuss future directions. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem. This paper is available on arxiv under CC BY 4.0 DEED license. Authors:
(1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai;
(2) Zach Nussbaum, Nomic AI, zach@nomic.ai;
(3) Adam Treat, Nomic AI, adam@nomic.ai;
(4) Aaron Miller, Nomic AI, aaron@nomic.ai;
(5) Richard Guo, Nomic AI, richard@nomic.ai;
(6) Ben Schmidt, Nomic AI, ben@nomic.ai;
(7) GPT4All Community, Planet Earth;
(8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship;
(9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 The Original GPT4All Model 2 The Original GPT4All Model 2.1 Data Collection and Curation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 2.2 Model Training, 2.3 Model Access and 2.4 Model Evaluation 3 From a Model to an Ecosystem 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.1 GPT4All-J: Repository Growth and the implications of the LLaMA License 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.2 GPT4All-Snoozy: the Emergence of the GPT4All Ecosystem 3.3 The Current State of GPT4All 3.3 The Current State of GPT4All 4 The Future of GPT4All 4 The Future of GPT4All Limitations and References Limitations and References Abstract Large language models (LLMs) have recently achieved human-level performance on a range of professional and academic benchmarks. The accessibility of these models has lagged behind their performance. State-of-the-art LLMs require costly infrastructure; are only accessible via rate-limited, geo-locked, and censored web interfaces; and lack publicly available code and technical reports. In this paper, we tell the story of GPT4All, a popular open source repository that aims to democratize access to LLMs. We outline the technical details of the original GPT4All model family, as well as the evolution of the GPT4All project from a single model into a fully fledged open source ecosystem. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem. 1 Introduction On March 14 2023, OpenAI released GPT-4, a large language model capable of achieving human level performance on a variety of professional and academic benchmarks. Despite the popularity of the release, the GPT-4 technical report (OpenAI, 2023) contained virtually no details regarding the architecture, hardware, training compute, dataset construction, or training method used to create the model. Moreover, users could only access the model through the internet interface at chat.openai.com, which was severely rate limited and unavailable in several locales (e.g. Italy) (BBC News, 2023). Additionally, GPT-4 refused to answer a wide ∗ Shared Senior Authorship variety of queries, responding only with the now infamous "As an AI Language Model, I cannot..." prefix (Vincent, 2023). These transparency and accessibility concerns spurred several developers to begin creating open source large language model (LLM) alternatives. Several grassroots efforts focused on fine tuning Meta’s open code LLaMA model (Touvron et al., 2023; McMillan, 2023), whose weights were leaked on BitTorrent less than a week prior to the release of GPT-4 (Verge, 2023). GPT4All started as one of these variants. In this paper, we tell the story of GPT4All. We comment on the technical details of the original GPT4All model (Anand et al., 2023), as well as the evolution of GPT4All from a single model to an ecosystem of several models. We remark on the impact that the project has had on the open source community, and discuss future directions. It is our hope that this paper acts as both a technical overview of the original GPT4All models as well as a case study on the subsequent growth of the GPT4All open source ecosystem. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship. Authors: Authors: (1) Yuvanesh Anand, Nomic AI, yuvanesh@nomic.ai; (2) Zach Nussbaum, Nomic AI, zach@nomic.ai; (3) Adam Treat, Nomic AI, adam@nomic.ai; (4) Aaron Miller, Nomic AI, aaron@nomic.ai; (5) Richard Guo, Nomic AI, richard@nomic.ai; (6) Ben Schmidt, Nomic AI, ben@nomic.ai; (7) GPT4All Community, Planet Earth; (8) Brandon Duderstadt, Nomic AI, brandon@nomic.ai with Shared Senior Authorship; (9) Andriy Mulyar, Nomic AI, andriy@nomic.ai with Shared Senior Authorship.

GPT4All: An Ecosystem of Open-Source Compressed Language Models

I am a big advocate of open-source startups. Over the next year, you will see many more of them. You take an existing product and open-source it. I built a social media scheduling tool (many exist in the market) and created an open-source version. This is Postiz, an open-source social media scheduling tool. And of course, if you could help me with a star, it would be amazing. The thing about the source It's open-source, and everybody can come and take your code, so what's the catch? Open source is a community; when you start to push your product, thousands of developers will fork and clone it and help you on your journey. It will bring massive exposure to your product. So far, the Postiz docker has been downloaded 26k times. On the other hand, everybody can be a competitor and use your open-source solution instead of paying you, and you have to live with this. Some licenses can save you, such as apache-2 or Agpl-3. It means that people can't compete with you without open-sourcing your code and giving you credit, but it doesn't prevent commercial use. Support is harder Having an open-source repository (with docker and all) will attract many self-hosters and require much support.So far, 3-5 tickets from Coolify, Portainer, and Unraid are received daily. This is only the start; I am sure there will be more deployment platforms soon. Make sure you give other contributors the respect they deserve. They will help you tremendously with support. Revenue is uncertain If there is one thing developers are known for, it is not to pay for stuff if it's not needed. We were born with this gene, I guess 😂 So don't expect developers to pay you. They'll host you on Raspberry Pi or a $5 Coolify server. It's important to know this. The goal of the contributors is: Help you to build the product
Help you with exposure
Build a fun and active community where everybody can grow I can't tell you how often I have seen a contributor tagging me on some X post about Postiz. Or some top trending open-source article. Enterprise It depends on your product, but some enterprises can use only self-hosted solutions and will pay you for your support and custom implementation. This is super important because that's something only open-source solutions can offer. Play with the suitable license There are no secrets. Monetized open-source (COSS) is sometimes misused in the wrong ways, for example: Adding dual licensing to the open-source, so when you use the code, you use the enterprise version and need to figure out how to remove it from the code base.
Adding non open-source license. You can put something like BSL, but it is not counted as "open-source," and fewer people see your solution as attractive. You would need to refer to your solution as self-hosted instead.
Holding out on SSO - having SSO for enterprise is only considered a destructive pattern. I have discovered lately that you can find many websites like SSO.tax because SSO is a security thing. SSO can still be commercialized, but it's better to take a stand like Tailscale, which limits seats or enterprise providers.
Be a part of the community; don't talk like "We. " Say "I" and connect with your audience; nobody likes communicating with a corporation. Go open-source. For me, it's the only way to build Please help me out with a star. It would be awesome ❤️https://github.com/gitroomhq/postiz-app I am a big advocate of open-source startups. Over the next year, you will see many more of them. You take an existing product and open-source it. I built a social media scheduling tool (many exist in the market) and created an open-source version. This is Postiz , an open-source social media scheduling tool. Postiz Postiz And of course, if you could help me with a star, it would be amazing. The thing about the source It's open-source, and everybody can come and take your code, so what's the catch? Open source is a community; when you start to push your product, thousands of developers will fork and clone it and help you on your journey. It will bring massive exposure to your product. So far, the Postiz docker has been downloaded 26k times. On the other hand, everybody can be a competitor and use your open-source solution instead of paying you, and you have to live with this. Some licenses can save you, such as apache-2 or Agpl-3. It means that people can't compete with you without open-sourcing your code and giving you credit, but it doesn't prevent commercial use. Support is harder Having an open-source repository (with docker and all) will attract many self-hosters and require much support. So far, 3-5 tickets from Coolify, Portainer, and Unraid are received daily. This is only the start; I am sure there will be more deployment platforms soon. Make sure you give other contributors the respect they deserve. They will help you tremendously with support. Revenue is uncertain If there is one thing developers are known for, it is not to pay for stuff if it's not needed. We were born with this gene, I guess 😂 So don't expect developers to pay you. They'll host you on Raspberry Pi or a $5 Coolify server. It's important to know this. The goal of the contributors is: Help you to build the product Help you with exposure Build a fun and active community where everybody can grow Help you to build the product Help you with exposure Build a fun and active community where everybody can grow I can't tell you how often I have seen a contributor tagging me on some X post about Postiz. Or some top trending open-source article. Enterprise It depends on your product, but some enterprises can use only self-hosted solutions and will pay you for your support and custom implementation. This is super important because that's something only open-source solutions can offer. Play with the suitable license There are no secrets. Monetized open-source (COSS) is sometimes misused in the wrong ways, for example: Adding dual licensing to the open-source, so when you use the code, you use the enterprise version and need to figure out how to remove it from the code base. Adding non open-source license. You can put something like BSL, but it is not counted as "open-source," and fewer people see your solution as attractive. You would need to refer to your solution as self-hosted instead. Holding out on SSO - having SSO for enterprise is only considered a destructive pattern. I have discovered lately that you can find many websites like SSO.tax because SSO is a security thing. SSO can still be commercialized, but it's better to take a stand like Tailscale, which limits seats or enterprise providers. Be a part of the community; don't talk like "We. " Say "I" and connect with your audience; nobody likes communicating with a corporation. Adding dual licensing to the open-source, so when you use the code, you use the enterprise version and need to figure out how to remove it from the code base. Adding non open-source license. You can put something like BSL, but it is not counted as "open-source," and fewer people see your solution as attractive. You would need to refer to your solution as self-hosted instead. Holding out on SSO - having SSO for enterprise is only considered a destructive pattern. I have discovered lately that you can find many websites like SSO.tax because SSO is a security thing. SSO can still be commercialized, but it's better to take a stand like Tailscale , which limits seats or enterprise providers. SSO.tax SSO.tax Tailscale Tailscale Be a part of the community; don't talk like "We. " Say "I" and connect with your audience; nobody likes communicating with a corporation. Go open-source. For me, it's the only way to build Please help me out with a star. It would be awesome ❤️ https://github.com/gitroomhq/postiz-app https://github.com/gitroomhq/postiz-app https://github.com/gitroomhq/postiz-app

How I Made $700 a Month With My Open-source Scheduling Tool

كيف تمكنت من ربح 700 دولار شهريًا باستخدام أداة الجدولة مفتوحة المصدر الخاصة بي

Как направих $700 на месец с моя инструмент за планиране с отворен код

Cómo gané $700 al mes con mi herramienta de programación de código abierto

Kuinka tienasin 700 dollaria kuukaudessa avoimen lähdekoodin aikataulutyökalullani

Como gañei 700 dólares ao mes coa miña ferramenta de programación de código aberto

איך הרווחתי 700 $ לחודש עם כלי התזמון שלי בקוד פתוח

Kijan mwen te fè $700 pa mwa ak zouti ouvè pou pwogramasyon mwen an

Bagaimana Saya Menghasilkan $700 Sebulan Dengan Alat Penjadwalan Open-source Saya

オープンソースのスケジュール管理ツールで月に700ドル稼いだ方法

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我如何利用开源调度工具每月赚取 700 美元

This Open-Source Image Generator From Russian Scientists Uses A Novel Approach to Diffusion

Authors:
(1) Anton Razzhigaev, AIRI and Skoltech;
(2) Arseniy Shakhmatov, Sber AI;
(3) Anastasia Maltseva, Sber AI;
(4) Vladimir Arkhipkin, Sber AI;
(5) Igor Pavlov, Sber AI;
(6) Ilya Ryabov, Sber AI;
(7) Angelina Kuts, Sber AI;
(8) Alexander Panchenko, AIRI and Skoltech;
(9) Andrey Kuznetsov, AIRI and Sber AI;
(10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 7 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction
Related Work
Demo System
Kandinsky Architecture
Experiments
Results
Conclusion & Limitations
Ethical Considerations, Acknowledgements and References 7 Conclusion We presented Kandinsky, a system for various image generation and processing tasks based on a novel latent diffusion model. Our model yielded the SotA results among open-sourced systems. Additionally, we provided an extensive ablation study of an image prior to design choices. Our system is equipped with free-to-use interfaces in the form of Web application and Telegram messenger bot. The pre-trained models are available on Hugging Face, and the source code is released under a permissive license enabling various, including commercial, applications of the developed technology. In future research, our goal is to investigate the potential of the latest image encoders. We plan to explore the development of more efficient UNet architectures for text-to-image tasks and focus on improving the understanding of textual prompts. Additionally, we aim to experiment with generating images at higher resolutions and to investigate new features extending the model: local image editing by a text prompt, attention reweighting, physics-based generation control, etc. The robustness against generating abusive content remains a crucial concern, warranting the exploration of real-time moderation layers or robust classifiers to mitigate undesirable, e.g. toxic or abusive, outputs. 8 Limitations The current system produces images that appear natural, however, additional research can be conducted to (1) enhance the semantic coherence between the input text and the generated image, and (2) to improve the absolute values of FID and image quality based on human evaluations. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Authors: Authors: (1) Anton Razzhigaev, AIRI and Skoltech; (2) Arseniy Shakhmatov, Sber AI; (3) Anastasia Maltseva, Sber AI; (4) Vladimir Arkhipkin, Sber AI; (5) Igor Pavlov, Sber AI; (6) Ilya Ryabov, Sber AI; (7) Angelina Kuts, Sber AI; (8) Alexander Panchenko, AIRI and Skoltech; (9) Andrey Kuznetsov, AIRI and Sber AI; (10) Denis Dimitrov, AIRI and Sber AI. Editor's Note: This is Part 7 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 7 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Editor's Note: This is Part 7 of 8 of a study detailing the development of Kandinsky, the first text-to-image architecture designed using a combination of image prior and latent diffusion. Read the rest below. Table of Links Abstract and Introduction Related Work Demo System Kandinsky Architecture Experiments Results Conclusion & Limitations Ethical Considerations, Acknowledgements and References Abstract and Introduction Abstract and Introduction Related Work Related Work Demo System Demo System Kandinsky Architecture Kandinsky Architecture Experiments Experiments Results Results Conclusion & Limitations Conclusion & Limitations Ethical Considerations, Acknowledgements and References Ethical Considerations, Acknowledgements and References 7 Conclusion We presented Kandinsky, a system for various image generation and processing tasks based on a novel latent diffusion model. Our model yielded the SotA results among open-sourced systems. Additionally, we provided an extensive ablation study of an image prior to design choices. Our system is equipped with free-to-use interfaces in the form of Web application and Telegram messenger bot. The pre-trained models are available on Hugging Face, and the source code is released under a permissive license enabling various, including commercial, applications of the developed technology. In future research, our goal is to investigate the potential of the latest image encoders. We plan to explore the development of more efficient UNet architectures for text-to-image tasks and focus on improving the understanding of textual prompts. Additionally, we aim to experiment with generating images at higher resolutions and to investigate new features extending the model: local image editing by a text prompt, attention reweighting, physics-based generation control, etc. The robustness against generating abusive content remains a crucial concern, warranting the exploration of real-time moderation layers or robust classifiers to mitigate undesirable, e.g. toxic or abusive, outputs. 8 Limitations The current system produces images that appear natural, however, additional research can be conducted to (1) enhance the semantic coherence between the input text and the generated image, and (2) to improve the absolute values of FID and image quality based on human evaluations. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Russian Scientists Unveil Open-Source Image Generator With a Groundbreaking Diffusion Method

SAN FRANCISCO, California, December 16th, 2024/Chainwire/--Forte has officially unveiled and launched the Forte Rules Engine, an open-source solution for developers to build safe, on-chain environments and manage digital asset economies for web3 apps. With the Rules Engine, developers can define and enforce rules, establish transaction guardrails, manage compliance obligations, and mitigate the risks of volatility and bad actors – all while supporting long-term digital asset utility and economic health. Developers can now utilize the Forte Rules Engine by visiting: forte.io/developers “The future of blockchain development is at a pivotal moment where the need to build strong foundations that foster safe, sustainable environments is paramount for blockchain projects and communities to thrive,” said Bela Pandya, CEO of Forte, “The Rules Engine was built to deliver these foundational technologies to developers that enable on-chain safeguards across a wide array of critical functions. From anti-dumping controls on airdrops to guardrails ensuring digital assets never interact with sanctioned wallets, and custom controls designed to mitigate volatility and market manipulation, the Rules Engine empowers developers to launch their projects confidently. This marks a new chapter for blockchain development, driven by compliance, economic stability, and a renewed sense of trust in blockchain development with much more on the horizon for the Forte Rules Engine.” Fully compatible with all EVM chains and web3 wallets, the Rules Engine provides developers the on-chain technology they need to build a safe, sustainable economy that their communities trust. This innovative suite of solutions aims to support: Safe Environments for Digital Assets The Forte Rules Engine employs on-chain guardrails to implement protective layers and safeguards that help mitigate risk and manage digital asset markets. The technology streamlines compliance navigation by leveraging Forte’s ecosystem of regulated partners to facilitate Know Your Customer (KYC) and Wallet protocols as well as sanctions enforcement, fostering responsible practices and building trust among users and communities. Through enhanced features such as Zero Knowledge (ZK) capabilities, developers can ensure privacy, verify identities, and assure transaction integrity. Economic Stability Developers will have access to a growing set of features designed to help launch, grow and scale a sustainable economy that their community can trust. This includes both templated and bespoke rulesets which can be designed to mitigate market volatility and manipulation, enforce token utility requirements, and effectively manage trading volume. The on-chain rulesets are designed for seamless integration and equipped with third-party integration options, ready to meet developer needs from day one. They offer the flexibility to adapt and evolve alongside the project, ensuring scalability and stability. Developers interested in leveraging the Forte Rules Engine for their next project can start building here. About Forte Forte provides open-source, on-chain solutions that foster safe environments and support healthy and stable digital asset economies. Our trust and privacy-preservation solutions empower developers to manage compliance risk, promote economic stability, and leverage instant liquidity. Developers can deploy flexible and adaptable blockchain solutions that evolve with their dynamic needs - fully compatible with all EVM chains and web3 wallets. Forte and its ecosystem partners are currently working with acclaimed developers to redefine the future of blockchain innovation. Contact Sibel Sunar 47 communications on behalf of Forte forte@fortyseven.com This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program here. SAN FRANCISCO, California, December 16th, 2024/Chainwire/--Forte has officially unveiled and launched the Forte Rules Engine, an open-source solution for developers to build safe, on-chain environments and manage digital asset economies for web3 apps. With the Rules Engine, developers can define and enforce rules, establish transaction guardrails, manage compliance obligations, and mitigate the risks of volatility and bad actors – all while supporting long-term digital asset utility and economic health. Developers can now utilize the Forte Rules Engine by visiting: forte.io/developers forte.io/developers forte.io/developers “The future of blockchain development is at a pivotal moment where the need to build strong foundations that foster safe, sustainable environments is paramount for blockchain projects and communities to thrive,” said Bela Pandya, CEO of Forte, “The future of blockchain development is at a pivotal moment where the need to build strong foundations that foster safe, sustainable environments is paramount for blockchain projects and communities to thrive,” said Bela Pandya, CEO of Forte, “The Rules Engine was built to deliver these foundational technologies to developers that enable on-chain safeguards across a wide array of critical functions. From anti-dumping controls on airdrops to guardrails ensuring digital assets never interact with sanctioned wallets, and custom controls designed to mitigate volatility and market manipulation, the Rules Engine empowers developers to launch their projects confidently. This marks a new chapter for blockchain development, driven by compliance, economic stability, and a renewed sense of trust in blockchain development with much more on the horizon for the Forte Rules Engine.” “The Rules Engine was built to deliver these foundational technologies to developers that enable on-chain safeguards across a wide array of critical functions. From anti-dumping controls on airdrops to guardrails ensuring digital assets never interact with sanctioned wallets, and custom controls designed to mitigate volatility and market manipulation, the Rules Engine empowers developers to launch their projects confidently. This marks a new chapter for blockchain development, driven by compliance, economic stability, and a renewed sense of trust in blockchain development with much more on the horizon for the Forte Rules Engine.” Fully compatible with all EVM chains and web3 wallets, the Rules Engine provides developers the on-chain technology they need to build a safe, sustainable economy that their communities trust. This innovative suite of solutions aims to support: Safe Environments for Digital Assets The Forte Rules Engine employs on-chain guardrails to implement protective layers and safeguards that help mitigate risk and manage digital asset markets. The technology streamlines compliance navigation by leveraging Forte’s ecosystem of regulated partners to facilitate Know Your Customer (KYC) and Wallet protocols as well as sanctions enforcement, fostering responsible practices and building trust among users and communities. Through enhanced features such as Zero Knowledge (ZK) capabilities, developers can ensure privacy, verify identities, and assure transaction integrity. Economic Stability Developers will have access to a growing set of features designed to help launch, grow and scale a sustainable economy that their community can trust. This includes both templated and bespoke rulesets which can be designed to mitigate market volatility and manipulation, enforce token utility requirements, and effectively manage trading volume. The on-chain rulesets are designed for seamless integration and equipped with third-party integration options, ready to meet developer needs from day one. They offer the flexibility to adapt and evolve alongside the project, ensuring scalability and stability. Developers interested in leveraging the Forte Rules Engine for their next project can start building here . here here About Forte Forte provides open-source, on-chain solutions that foster safe environments and support healthy and stable digital asset economies. Our trust and privacy-preservation solutions empower developers to manage compliance risk, promote economic stability, and leverage instant liquidity. Forte Forte Developers can deploy flexible and adaptable blockchain solutions that evolve with their dynamic needs - fully compatible with all EVM chains and web3 wallets. Forte and its ecosystem partners are currently working with acclaimed developers to redefine the future of blockchain innovation. Contact Sibel Sunar 47 communications on behalf of Forte forte@fortyseven.com 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

Forte Unveils Open-Source Rules Engine For Safety And Economic Stability In Blockchain Development

Forte разкрива двигател с правила с отворен код за безопасност и икономическа стабилност при разработването на блокчейн

Forte presenta un motor de reglas de código abierto para la seguridad y la estabilidad económica en el desarrollo de blockchain

Forte-k iturburu irekiko arauen motorra aurkeztu du Segurtasunerako eta egonkortasun ekonomikorako Blockchain garapenean

Forte、ブロックチェーン開発の安全性と経済的安定性を実現するオープンソースのルールエンジンを発表

Forte Abimisi Moteur Ya Mibeko Ya source Ouverte Pona Sécurité Na Stabilité Economique Na Développement Blockchain

Forte dia namoaka maotera fitsipika misokatra ho an'ny fiarovana sy ny fahamarinan-toerana ara-toekarena amin'ny fampandrosoana Blockchain

Forte Memperkenalkan Enjin Peraturan Sumber Terbuka Untuk Keselamatan Dan Kestabilan Ekonomi Dalam Pembangunan Blockchain

Forte Yerekanye Gufungura-Inkomoko y'amategeko Moteri yumutekano no guhungabana mubukungu mugutezimbere

Forte ayaa daaha ka qaaday Matoorka Xeerarka Isha Furan ee Badbaadada iyo Deganaanshaha Dhaqaalaha ee Horumarinta Blockchain

Forte Inafichua Injini ya Sheria za Chanzo Huria kwa Usalama na Uthabiti wa Kiuchumi Katika Ukuzaji wa Blockchain

“Forte” “Blockchain” -iň ösüşinde howpsuzlyk we ykdysady durnuklylyk üçin açyk çeşme düzgünleriniň hereketlendirijisini açdy

Forte ra mắt công cụ mã nguồn mở để đảm bảo an toàn và ổn định kinh tế trong phát triển Blockchain

You know security is an overwhelming term and while I had the luxury to work for security firms, I find it infuriating at times. There are just too many layers to add and protect. It’s a real thing to say that only a properly disconnected system can be made secure. The rest depends on the handling. But, when I see firms advocating for more and more layers of security, it just makes me nauseated. One way to do it is to stop installation of the programs at its core. Meaning that the platform itself can’t be modified by anything that the platform doesn’t comply with. It’s sort of good to have things in theory but the practicality of implementing it makes the life of an average technical user a living hell. https://youtu.be/-CYPlMCWDR4?embedable=true Google introduced Android App Bundle (AAB) in May 2018 and made a subtle announcement to replace Android Package Kit (APK) with AAB for its value on smaller size. It’s no secret that AAB does require applications to be signed and then pushed to Google Play Store. But, till date, you can still sideload an APK application in your Android device without the need to sign your application. Google never enforced anything on the users at least yet. It sort of gives you an upper hand as compared to iOS devices where you are only allowed to install a signed application through the App Store. While Google gives you more freedom, it still collects your data more as well and that too without notifying you. The difference is in the privacy policy of Apple and Google. Both collect data and warn at the time of application installation. Apple takes a step forward and lets you know each time an application tries to collect your data with prompts and messages. I’m not sure how much that is useful but you can’t really argue on that with Apple. At least, this has been the story so far with iOS. But, now Apple wants to take total control over all of its platforms. macOS was one of my favorite variants of the BSD kernel (Berkeley Software Distribution on which it is built.) And, I have preferred macOS over iOS for one of the most important reasons for allowing sideloading. It meant for me a little freedom to install applications. But, the recent announcement by Apple on Monday, October 28th, rendered my hopes down. With macOS 15.1 update, they have made it clear that the applications need to be signed (or say notarized) first before you install them. It means that any developer who creates an application for macOS has to pay $99 to Apple Developer Connection to get their application notarized and then release it to the public. So, if you’re like me, who tends to use open source software on macOS, your days of freedom are over. It’s just not practical for most open source developers to pay an extra $99 to Apple. It’s not only a sign of software monopoly on developers but also on users to give up the choice they had before. Basically, when you buy an Apple product now, you’re actually surrendering your entire freedom with no choice left. But, don’t get too excited on Google as they can too exercise this anytime in the future! You know security is an overwhelming term and while I had the luxury to work for security firms, I find it infuriating at times. There are just too many layers to add and protect. It’s a real thing to say that only a properly disconnected system can be made secure. The rest depends on the handling. But, when I see firms advocating for more and more layers of security, it just makes me nauseated. One way to do it is to stop installation of the programs at its core. Meaning that the platform itself can’t be modified by anything that the platform doesn’t comply with. It’s sort of good to have things in theory but the practicality of implementing it makes the life of an average technical user a living hell. https://youtu.be/-CYPlMCWDR4?embedable=true https://youtu.be/-CYPlMCWDR4?embedable=true Google introduced Android App Bundle (AAB) in May 2018 and made a subtle announcement to replace Android Package Kit ( APK ) with AAB for its value on smaller size. It’s no secret that AAB does require applications to be signed and then pushed to Google Play Store . But, till date, you can still sideload an APK application in your Android device without the need to sign your application. Google never enforced anything on the users at least yet. It sort of gives you an upper hand as compared to iOS devices where you are only allowed to install a signed application through the App Store . Google Android App Bundle ( AAB ) AAB Android Package Kit APK AAB AAB AAB AAB Google Play Store APK Android Google iOS App Store While Google gives you more freedom, it still collects your data more as well and that too without notifying you. The difference is in the privacy policy of Apple and Google. Both collect data and warn at the time of application installation. Apple takes a step forward and lets you know each time an application tries to collect your data with prompts and messages. I’m not sure how much that is useful but you can’t really argue on that with Apple . At least, this has been the story so far with iOS . But, now Apple wants to take total control over all of its platforms. Google Apple Google. Apple Apple iOS Apple macOS was one of my favorite variants of the BSD kernel (Berkeley Software Distribution on which it is built.) And, I have preferred macOS over iOS for one of the most important reasons for allowing sideloading. It meant for me a little freedom to install applications. But, the recent announcement by Apple on Monday, October 28th, rendered my hopes down. With macOS 15.1 update, they have made it clear that the applications need to be signed (or say notarized) first before you install them. It means that any developer who creates an application for macOS has to pay $99 to Apple Developer Connection to get their application notarized and then release it to the public. macOS macOS iOS Apple macOS 15.1 applications need to be signed (or say notarized) first macOS Apple Developer Connection So, if you’re like me, who tends to use open source software on macOS , your days of freedom are over. It’s just not practical for most open source developers to pay an extra $99 to Apple . It’s not only a sign of software monopoly on developers but also on users to give up the choice they had before. Basically, when you buy an Apple product now, you’re actually surrendering your entire freedom with no choice left. But, don’t get too excited on Google as they can too exercise this anytime in the future! macOS $99 Apple Apple Google

Installing Open-source Software on Your MacOS May Be Coming to an End

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