DEV Community: The DEVengers

Dev Opportunity Radar #3: Neo Scholars, a $2M AI Challenge, and an $85K AI Fellowship

Hemapriya Kanagala — Fri, 12 Jun 2026 13:47:44 +0000

TL;DR

Welcome back to Dev Opportunity Radar.

This is a weekly series where I share opportunities, resources, communities, and interesting finds that I come across, with the goal of helping people discover things they might otherwise miss.

This week's edition includes a student-focused founder program, a $2 million AI business challenge, a free Google AI agents course, a Web3 learning resource, and two community finds shared by community members.

If you've come across an opportunity, resource, community, program, or event that deserves more attention, feel free to share it in the comments.

If I feature it in a future edition, I'll make sure to credit you. If you discovered it, that recognition belongs to you.

Quick Scan
Still Open From Previous Editions
Radar Follow-Up
This Week's Opportunities
- Neo Scholars
- Gemini × XPRIZE AI Business Challenge
Bonus Opportunity
- 5-Day AI Agents Intensive Course with Google
Resources Worth Checking Out
- LearnWeb3
Community Finds
- AI Tinkerers
- Claude Corps
- Hacking for Good
Until Next Friday

⚡ Quick Scan

Opportunities

Opportunity	Organization	Type	Deadline
Neo Scholars	Neo	Founder & Career Program	June 14
Gemini × XPRIZE AI Business Challenge	Google & XPRIZE	AI Business Challenge	Aug 18
5-Day AI Agents Intensive Course	Google & Kaggle	Free AI Course	June 15-19

Resource Highlight

Resource	Type
LearnWeb3	Free Web3 learning platform with structured courses and projects

Community Finds

Shared By	Find	Type	Deadline
Julien Avezou (@javz)	AI Tinkerers	AI Builder Community	Ongoing
Phinn Markson (@marsonp)	Claude Corps	AI Fellowship	July 17
Francis (@francistrdev)	Hacking for Good	Global Hack Week	June 18

🔄 Still Open From Previous Editions

A few opportunities from previous editions are still accepting applications.

I've already covered these in detail, so I won't repeat everything here. If any of them catch your attention, check the original edition for the full overview, eligibility details, and application links.

Opportunity	Organization	Type	Deadline	Featured In
FR8	FR8	Builder Residency	Rolling	Edition #2
Interactivity Research Grants	Thinking Machines	Research Grant ($100K)	June 19, 2026	Edition #1

📡 Radar Follow-Up

One of the things I hoped this series would do was help someone discover an opportunity they otherwise wouldn't have come across.

Last week, Daniel Nwaneri (@dannwaneri) shared that he discovered the FR8 residency through the radar and applied the same day.

That genuinely made my week.

Daniel also shared something that summed up what I'm trying to do with this series:

"The series does something most opportunity roundups don't. It actually explains why something is worth your time instead of just listing it."

There are already lots of places that collect links.

My goal is to add enough context that you can quickly decide whether something is actually worth exploring further.

Seeing someone discover an opportunity, take action on it, and then come back to share that experience felt like a reminder that the series is doing what I hoped it would do.

Thank you again, Daniel, for sharing the update and for letting me mention it here.

And Daniel, if you're reading this, wishing you the very best with your application.

📍 This Week's Opportunities

Here are a few opportunities I came across this week that I thought were worth sharing.

📌 Neo Scholars

Who it's for: Undergraduate students who enjoy building things, love computer science, and are interested in startups, entrepreneurship, or ambitious technical projects.

Neo combines mentorship, startup recruiting, founder support, and access to a strong network of builders all within a single program.

Accepted scholars gain access to mentorship, startup opportunities, recruiting support, events, and a community that includes founders and builders from companies like Cursor, Cognition, Chai Discovery, Applied Compute, and more.

One thing I particularly liked is that participants aren't being pushed into a single path. Whether you want to start something, join a startup, explore ideas, or simply meet ambitious people, there seems to be room for all of those outcomes.

There's also an optional Neo Residency program where student teams can receive a $40,000 equity-free grant and spend time building in San Francisco.

Who can apply: Undergraduate students graduating Winter 2026 or later.

Application Deadline: June 14, 2026

🔗 Learn More & Apply

📌 Gemini × XPRIZE AI Business Challenge

Who it's for: Builders, founders, developers, students, and anyone interested in creating an AI-powered business.

Participants aren't just building a prototype.

They're expected to build something that reaches real users, solves a real problem, and generates real revenue.

The challenge focuses on businesses powered by AI agents and built using Google Cloud products.

Projects can be submitted across areas including education and human potential, entrepreneurship and job creation, small business services, financial access, and professional services.

I actually came across this later than I would have liked, but the good news is that there's still plenty of time left.

If you've been thinking about building something meaningful with AI rather than just experimenting with prompts, this might be worth looking into.

Prize Pool: $2,000,000

Grand Prize: $500,000

Deadline: August 18, 2026

🔗 Learn More & Join

🎁 Bonus Opportunity

5-Day AI Agents Intensive Course with Google

Who it's for: Developers, students, builders, and anyone interested in learning how modern AI agents are designed, built, and deployed.

The course goes beyond basic prompting and focuses on practical topics like agent workflows, tool use, memory, evaluation, security, and deployment.

The program is hosted by Kaggle and developed with Google researchers and engineers.

Participants receive daily learning materials, whitepapers, companion podcasts, hands-on codelabs, daily livestreams and AMAs, Discord discussions, and a capstone project.

The capstone project, called Kaggriculture, involves building an autonomous agent that manages a virtual farm and competes against other agents.

I wanted to include this because it's free, happening soon, and seems much more focused on building real agent systems than many introductory AI courses.

Dates: June 15-19, 2026

Capstone Deadline: June 28, 2026

Cost: Free

Bonus: Certificates, badges, and Kaggle swag for top participants.

🔗 Learn More & Register

📚 Resources Worth Checking Out

Not every useful find comes with an application deadline.

Here's one resource worth checking out this week.

LearnWeb3

LearnWeb3 is a free learning platform for developers who want to explore blockchain and Web3 development.

The reason I wanted to include it is because a lot of learning resources assume you already know where to start. LearnWeb3 does a good job of organizing topics into structured learning paths, making it easier to progress from one concept to the next.

The platform covers areas such as Ethereum development, Solidity, smart contracts, blockchain infrastructure, and other Web3 fundamentals through a mix of lessons, projects, and hands-on learning.

Whether you're completely new to Web3 or looking to build a stronger foundation, it's a resource worth bookmarking.

Cost: Free

Community: Discord, study groups, events, and developer support.

🔗 LearnWeb3

🌟 Community Finds

One of my favorite things about this series has been seeing people share opportunities, communities, and resources that others might not have discovered otherwise.

AI Tinkerers

Shared by Julien Avezou (@javz).

AI Tinkerers is a global community for people actively building with AI. They host meetups, demo nights, hackathons, workshops, and technical events across hundreds of cities worldwide.

What I like about it is how focused it is on builders. The community is built around sharing working projects, technical workflows, lessons learned, and real implementation details rather than AI hype or marketing.

Whether you're building with foundation models, agentic workflows, open-source models, or no-code AI tools, the focus is the same: show what you're building, explain how it works, and learn from others doing the same.

If you're someone who learns best by seeing what other builders are creating and sharing your own work, this looks like a community worth exploring.

Who it's for: Developers, engineers, researchers, builders, and people actively shipping AI projects

Cost: Free to join (event availability varies by city)

🔗 AI Tinkerers

Claude Corps

Shared by Phinn Markson (@marsonp).

Claude Corps is a new fellowship from Anthropic, CodePath, and Social Finance that places early-career fellows inside mission-driven nonprofits across the United States for a full year.

Rather than learning AI in a classroom, fellows work directly with organizations tackling challenges in areas like education, public health, workforce development, housing, food security, civic services, and more.

There is no degree requirement and no formal coding background is required. The program is looking for people who are already comfortable using AI tools, learn quickly, communicate well, and care about making an impact.

Fellows receive training, mentorship, relocation support if needed, and spend a year helping organizations put AI to work on real problems.

Compensation: $85,000 salary + benefits

Eligibility: 18+, authorized to work in the United States, and less than 2 years of full-time work experience

Application Deadline: July 17, 2026

🔗 Claude Corps

Hacking for Good

Shared by Francis (@francistrdev).

MLH's Global Hack Week is running a special Hacking for Good edition from June 12–18.

Global Hack Week is a week-long online event where participants complete challenges, attend live sessions, learn new technologies, and build projects alongside a global community of developers.

This particular edition is focused on using technology to create projects that have a positive impact.

One reason I wanted to include it is that Global Hack Week tends to be much more approachable than a traditional hackathon. You don't need a team, prior hackathon experience, or even a strong technical background to participate.

Whether you're looking to learn something new, build a small project, or simply meet other developers, it's a good opportunity to get involved.

Dates: June 12-18, 2026

Cost: Free

Who can participate: Anyone, anywhere

🔗 Hacking for Good

Thank you to Julien, Phinn, and Francis for sharing these.

I'd love for this section to keep growing.

If you've come across an opportunity, fellowship, grant, hackathon, conference, community, resource, or anything else you think more people should know about, feel free to share it in the comments.

If I feature it in a future edition, I'll make sure to credit you. If you discovered it, that recognition belongs to you.

One small request: If you're sharing an opportunity, please avoid posting raw URLs directly in the comments. DEV sometimes filters them before I get a chance to see them.

A short description alongside the link makes it much easier for me to review and potentially feature it in a future edition.

👋 Until Next Friday

Before I go, I just want to say thank you.

A few weeks ago, this was just an experiment.

Now people are discovering opportunities through the radar, applying to them, and sharing opportunities, resources, and communities back with the rest of us.

This week alone, the Community Finds section exists because Julien, Phinn, and Francis took the time to share something they thought others might benefit from.

I hope that continues.

The goal of this series hasn't changed:

Help people discover opportunities they otherwise might have missed.

My hope is that this slowly becomes our radar, not just mine.

So if you come across an opportunity, fellowship, grant, hackathon, conference, community, resource, or anything else you think more people should know about, feel free to share it in the comments.

And as always, if I feature it in a future edition, I'll make sure to credit you. If you discovered it, that recognition belongs to you.

If you end up applying to any of the opportunities featured here, I'd love to hear about it.

Thank you for reading, thank you for sharing, and thank you for being part of this.

If you'd like to catch future editions, consider following me on DEV and bookmarking the series.

I'll be back next Friday with more opportunities, resources, and community finds.

See you next Friday 👋

Two Devs and a Copilot Created ClassifierAI: A Prototype Chrome Extension that Automatically Detects AI-Generated Content on DEV!

FrancisTRᴅᴇᴠ (っ◔◡◔)っ — Sat, 06 Jun 2026 01:41:40 +0000

This is a submission for the GitHub Finish-Up-A-Thon Challenge

Note: AI is currently a Hot Topic in the developer space. I recommend reading this post in full before commenting. The dataset being used does not come from dev.to, so expect the AI model to be inaccurate. This is to showcase a tool that can be helpful for others in the future and eventually tune it to be more accurate on the DEV platform. I will talk more about it further in the post. If you have any questions or concerns, I would love to hear from you!

Authors

Main Author: Francis Tran @francistrdev
Co-Author: Elmar Chavez @codingwithjiro

This is a Team Submission featuring Francis and Elmar!

Table of Contents

Introduction

What We Built

Why does this Project Exist?

Demo

Installation

Using the Chrome Extension

The Comeback Story

The Old Era

The New Era

My Experience with GitHub Copilot

Summary

Current Challenges in our Chrome Extension

Thank You!

Introduction

AI has shaped everything on the internet. This ranges from easier access to information faster than before to having AI in our development workflows.

With the good side of the use of AI, there is also the bad side of using AI. Currently, there is a lot of purely AI-generated content being produced on the internet in order for sites to boost themselves to relevance on Google, which creates unnecessary competition. Additionally, with the advance of AI, it has continuously become more difficult to distinguish content that is AI-generated versus content made by a Human. "AI" as a name on its own has a bad reputation due to how people are using AI regardless of if you are using it as a tool.

Even though AI is everywhere, many people are combating it. Sometimes it is handled in a professional setting while others handle on a vigilante level (not recommended at all). As a DEV user, the goal is to try to minimize AI-generated articles on dev.to in order to enhance the user experience and diversifying content where others can learn and grow as a developer.

With that said, along with Elmar's collaboration, we built ClassifierAI!

Disclaimer: Before commenting, this project is a prototype! This is meant to showcase what the Chrome Extension can do based on existing data used, which the model is not accurate due to data not coming from dev.to directly. This Chrome Extension is not meant to be used as a perfect solution to filter out AI-Generated content as a result of data outside of dev.to. I will talk more about this later in the post.

What We Built

We built ClassifierAI, which is a prototype Chrome Extension that allows you to detect AI-generated content on DEV in an instant. This is built using Tensorflow.js to perform:

Image Classification to determine if the cover image of the article is AI-generated using Teachable Machine by Google.
- The dataset used to train the model: https://huggingface.co/datasets/Hemg/AI-Generated-vs-Real-Images-Datasets
- The settings to train the model based on 866 images in total (433 images per class where we have two classes that is labeled "AI" and "NotAI"):

866 Images Total
2 Classes Total
  - "AI" Class
  - "NotAI" Class

Epochs: 30
Batch Size: 16
Learning Rate: 0.0001

Text Classification to determine if the article itself is AI-generated.
- The dataset used: https://huggingface.co/datasets/gouwsxander/wikipedia-human-ai

Initially, it started off as a Chrome Extension that scans 100 images on Google Images to detect if the image is AI-generated and label them accordingly.

For this case, we updated the Chrome Extension where it only works on dev.to, and is able to classify the entire article, including the cover image, and showing the result to the user whether the content is AI-generated. This Chrome Extension works every time the user clicks on an article on DEV.

Why does this Project Exist?

Generative AI is currently a hot topic in my experience. Many friends and colleagues I have despise Generative AI because they have the notion that "it will replace your job".

Sounds familiar? We all heard that phrase at least once...

Although there are tools to combat this like GPTZero, it is still an issue because:

It is a lot of manual work just to copy/paste to see if it is AI-generated.
There are tools that bypass GPTZero in order to get the "Human" result. Every style of writing is different across communities, so it can be difficult to identify AI-generated content overtime.

With these problems in mind and the state of DEV, I want to create a useful tool that identifies AI-generated content automatically without having to open GPTZero and copy and pasting just to know the result of the content.

Additionally, this Chrome Extension is personalized for DEV since I believe the dataset collected in the future will be more focused and accurate (I believe all blogging platforms have different writing styles regardless of content being AI-Generated. Hence, making it more personalized and focus on the DEV space).

Overall, this Chrome Extension solves the issue when it comes to less manual work and more personalized for DEV since I believe it reduces the chance of having tools that bypass the AI scanner since it is more focused than broad.

With community support, I hope that the model will be more tuned and accurate as DEV continues forward! Which is why we made it Open Sourced as an Unofficial DEV Chrome Extension. More details later in the post.

With that said, we will now show our prototype Chrome Extension in action!

Demo

In this demo, we will show you the installation process in order to get the Chrome Extension running in your browser and showcasing how the Chrome Extension works.

Installation

To get started, create an empty folder on your computer and open it using VScode or any IDE.

1) In the Terminal, clone the repository:

git clone https://github.com/FrancisTR/ClassifierAI.git

2) Ensure you are in the directory by doing:

cd ClassifierAI

3) Then install packages and run the build:

npm install
npm run build

4) On your browser, head over to the extension page and ensure you enabled Developer Mode.

5) Finally, drag the "dist" folder onto the extension page and you are ready to go!

Now we will talk about how to use the Chrome Extension on dev.to!

Using the Chrome Extension

To use the Chrome Extension, open the popup located on the top right of the browser and enable the switch.

Then, click on any article you see on dev.to and it will do its thing: Scanning the Cover image and the article to determine if either of them are AI-generated.

For the Cover Image, the AI will perform Image Classification to determine if the image is AI-generated. Once the scan is complete, the AI will embed an icon, located on the bottom right of the image. In this case, it is not AI-generated.

There are three icons to expect:

Like we saw, this icon indicates that the image is not AI-generated.

This icon indicates that it may or may not be AI-Generated.

This icon indicates that it is AI-generated.

For the article itself, the way you can see if the article is written by AI or not is located in the popup menu in the Chrome Extension on the top right as shown below:

Note: There is currently no breakdown feature on which part of the article is AI-generated or not.

The Comeback Story

Initially, the project was considered "complete" in my view a couple years ago. However, I wasn't proud of the outcome due to the fact that there were bugs that were hard to detect and how "messy" it felt. I haven't touched the project in years due to other things that needed my attention during that span.

When I heard about this challenge, I though it would be a great opportunity to not only finish the project, but also to work with Elmar directly and explain the code-base to someone.

This gave me the opportunity to practice my skills in communication and to be able to explain what I have built. It was also a good opportunity for Elmar as well since this is his first time working on an existing code-base and contributing to it!

Not only did we finish the prototype, but we also learned from each other when it comes to:

Explaining the code-base to a new developer.
Organization and Communication skills when working in different time zones.
Learning the responsibility of a Maintainer for a project.

With that said, I will be explaining what the project was meant to be and how we got to the finish line!

The Old Era

The old ClassifierAI's purpose was to scan 100 Images sequentially based on what is currently being loaded on the page and to label each image accordingly if it is made by a Human or not via icons shown here.

For the main functionality, all you have to do is enable the scan in the popup.

You then scroll the Google Image page and let it do its thing.

That's it.

Additionally, the repository is incomplete since the "demo" section in the README.md is empty during that time. The overall project back then didn't feel "impactful" since its only purpose was to scan images and the model I currently have isn't accurate due to an unbalanced data sample in the dataset overall.

If you would like to see what it looked like initially, feel free to visit the branch I linked below: https://github.com/FrancisTRAlt/ClassifierAI/tree/OldProject

Now, we then transfer to the new Era and how we complete the project (as a prototype)!

The New Era

When the Challenge was announced, I asked Elmar if he would like to contribute to my half-baked project, which he said "Yes"!

The first thing we did was to migrate to Vite. The reason is that in the old project, the packages were installed locally instead of via npm. The reason is how Chrome Extension works is that it restricts developers from calling "CDN" links and external packages. The only way to do this in Vanilla was to install the files locally.

Is this ideal? Definitely not. Back then, with the knowledge I had, it was the only solution.

Fortunately, with the help of AI, we were able to Migrate to Vite. Migrating to Vite allows us to install packages via npm. It is much more clean and saves a lot of space.

Speaking of installing packages, Elmar noted that the ml5.js, a library that is used for Teachable Machines, is a deprecated package and other issues he came across:

The bright side is that the issues were fixed, though it gave us this when we npm install:

You could say that we can do npm audit fix --force, but that lead to more issues than before. Even then when doing npm audit fix doesn't change anything. For right now, these vulnerabilities exist in the current state...

With the migration to Vite, we then moved on to the bigger tasks, which is training the AI Model!

Initially, the goal was using Gemma 4 instead of using the custom model I have trained using Teachable Machines for scanning cover images. However, like any local model, the problem was performance since it takes awhile to produce an output.

For some reason, it got to the point where the Chrome Extension nuked the whole browser, making it unusable (Don't ask how that happened or even how it is possible to begin with).

Instead of using an existing LLM, I thought about using an existing dataset that already has images for me to train as an improvement to the current model I have for Image Classification.

Although this is an improvement, the model is still not perfect since the images in the dataset is WAY different from images being used on DEV, but it is at least diversified compared to the previous dataset I had. Additionally, instead of scanning 100 images, it only scans one (which is the Cover Image).

The new feature we implemented in our Chrome Extension is classifying articles and determining if the article is AI-generated. I looked on YouTube for an approach and I found this video explaining on classifying text:

The way text-classification works on this current Chrome Extension is that it grabs the entire text from the article to feed into the model.

function getCleanArticleText() {
  const root =
    document.querySelector(".crayons-article__body") ||
    document.querySelector("article");

  return root ? normalizeWhitespace(root.innerText) : "";
}

Then, it takes in the article and performs classification based on the dataset used from Wikipedia (which is the reason why it is not accurate when using this extension on dev.to due to the style of the content).

It then gives a score to the user a percentage on how much it is Human generated versus AI-generated.

function detectGPTStyle(text) {
  if (!wikipediaDataset || wikipediaDataset.length < 10) {
    return baseUnknownResult();
  }

  const datasetScore = compareDataset(text);       // AI %
  const devHuman = computeDevHumanScore(text);     // Human %
  const generalHuman = computeGeneralScore(text);  // Human %
  const aiPenalty = detectAIPatterns(text);        // AI %

  // Adjust weights here
  let humanScore =
    devHuman * 0.40 +
    (100 - datasetScore) * 0.35 +
    generalHuman * 0.25 -
    aiPenalty * 0.25;

  // Modify squash sensitivity
  humanScore = squashScore(humanScore);

  // Adjust classification thresholds
  let label =
    humanScore <= 33.33
      ? "AI-generated"
      : humanScore >= 66.66
        ? "Human-written"
        : "Mixed";

  const finalScore = Number(humanScore.toFixed(2));
  return {
    label,
    averageAIScore: finalScore,
    humanPercent: finalScore,
    aiPercent: Number((100 - humanScore).toFixed(2)),
    mixedPercent:
      finalScore > 33.33 && finalScore < 66.66 ? 100 : 0,
  };
}

Other small things we added was UI enhancements where we used chart.js to display information in a more clean way.

That's it! The project is complete! Is it perfect?

I say this because there is always room for improvements, especially with the dataset we currently have. The dataset for the image classification and text classification is there as a placeholder just to showcase the main functionality. In the future, we hope to use the dataset coming from dev.to specifically in order for the model to be more accurate.

My Experience with GitHub Copilot

Disclaimer: This section accounts for my experience using GitHub Copilot. Elmar uses ChatGPT for his development workflow and has been using it as a mentor to navigate the code-base and fix bugs as shown earlier.

I have used GitHub Copilot before in past challenges on DEV and I talked about my experience using GitHub Copilot. For this project, nothing really changed much. The main thing GitHub Copilot has been really useful for is in the migration process such as

Translating Bootstrap to Tailwind CSS.
Moving away from ml5.js to Tensorflow.js.
Ensuring the project follows the Chrome Extension Manifest V3.

One example I will showcase is telling GitHub Copilot to translate the current ml5.js code to Tensorflow.js.

Essentially, I asked GitHub Copilot "Migrate from ml5.js to tensorflow.js" and it did the work!

It was quite a smooth process when migrating. Sometimes there were errors during the migration such as:

Uncaught (in promise) TypeError: Cannot read properties of undefined (reading 'NotAI')

Even with that, it was able to fix the issue relatively fast without the need for me to undo everything and re-prompt it (which is the biggest issue when working with AI).

Overall, it was nice to see GitHub Copilot being able to migrate, fix errors, and ensure the code-base cleanly migrates without any issues.

However, token usage has been burning faster than before when I used it in a previous DEV challenge where we used GitHub Copilot. I am not sure if there is a change in policy of how many tokens are being burnt, but it is one thing I noticed. Other than that, it was a good experience!

Summary

In conclusion, this is the project we built with the intention of reducing the number of AI-generated content on DEV. Additionally, we intended for this project to diversify content and enhance the user's experience when it comes to connecting and growing as a developer.

Not only did we create this project with the intention of helping the community, but we also learned new things when working on the project.

For me, I learned about the responsibility as a project maintainer and being able to communicate with my partner on the tasks that needed to be completed as well as considering the time zone we live in (I am based in the US while Elmar is based in the Philippines). Additionally, as this is Elmar's first time seeing my existing code, I learned not to over explain my code and to be as simple and straightforward as possible when it comes to delivering tasks to Elmar and reviewing his Pull Requests.

For Elmar, this is his statement about this project overall:

Navigating My First Open Source Chrome Extension Codebase

Working on someone else's codebase was a completely different challenge.

At times, it felt like I was walking on glass, afraid to mess something up.

It forced me to slow down and read the codebase first. While ChatGPT helped me understand concepts and unfamiliar parts of the code.

I've had experience reviewing other people's code through Frontend Mentor, but this felt very different. When contributing to a real project, every change has consequences. I found myself asking more questions than usual and learning the answers along the way.

One thing I learned even more than the codebase itself was the importance of communication. I spent a lot of time discussing ideas, asking questions, and seeking clarification from the maintainer.

Looking back, I think the cycle of asking, learning, understanding, and validating took far more time than writing the code itself.

I also learned the importance of being adaptable. The project evolved during my contribution journey, which meant my assumptions and plans had to evolve as well. On top of that, I encountered my first real merge conflicts but I managed to pull through in the end.

Overall, contributing to ClassifierAI gave me a much better understanding of how real-world software development differs from building projects alone.

Beyond the technical skills, I learned how to collaborate with maintainers and communicate changes through pull requests.

This experience yet again proved what I read early on in my career:

"The ability to read and understand code is often more valuable than the ability to write it."

Although we have learn a lot, there are still current issues that I believe it is important to address when using this Chrome Extension.

Current Challenges in our Chrome Extension

As mentioned many times before, this project is a prototype for many reasons. Right now:

The model and the dataset is not aligned to what we have on dev.to. In detail, the dataset comes from the Wikipedia while the dataset for images comes from HuggingFace. Both of these dataset are not accurate to the dev.to platform. We hope, with more collaboration, to get data from dev.to and retrain the model that is more accurate than before and align to value of the DEV community.
Limited features to the users such as which area in the article is AI-generated and which is not. Also, not accounting for when it is appropriate to use AI such as using AI as a translation, grammatical checks, etc.

With that said, we hope you see the value in our work and contributing to this project further!

Thank You!

Thank you for reading our post on our Chrome Extension Project! It was fun to work with Elmar and getting to know him more and his current knowledge as a developer!

If you would love to contribute to our Chrome Extension, feel free to visit the repository below. Your contribution matters to dev.to! Feel free to provide any feature requests and issues you may notice!

FrancisTR / ClassifierAI

A Chrome Extension that detects AI-generated images using Machine Learning.

Welcome to the ClassifierAI Repository!

Description

A Google Chrome Extension that integrates Machine Learning to determine if the image and the dev.to article, that the user is viewing, is AI-Generated. This uses Tensorflow.js to perform Image Classification and Text Classification.

The model is trained using Teachable Machine by Google where it is train from 866 images, that consists of AI and Non-AI images, using the following settings:

Epochs: 30
Batch Size: 16
Learning Rate: 0.0001

This Chrome Extension is compatible with dev.to. More information on the Chrome Extension and how it is built: https://dev.to/devengers/two-devs-and-a-copilot-created-classifierai-a-prototype-chrome-extension-that-automatically-4fge

Technologies Used

Core Tech:

Content Analysis:

Extension Tooling:

How to Run

Clone or download the repository and navigate to the project directory.
Install the project dependencies:
```
npm install
```
Build the extension:
```
npm run build
```
Open Google Chrome and navigate to:
```
chrome://extensions
```
Enable Developer Mode in the top-right corner.
Click Load unpacked and select the generated dist/…

View on GitHub

I hope this project will be useful for you and inspire you to build something that will make an impact! Thanks for stopping by!

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This is an organization where we assemble the greatest minds the community has ever known! The question isn't "How fast can I code?". The question is "What will I learn from the farthest below?".

Check out Elmar Chavez!

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Licensed civil engineer turned full stack developer building accessible, responsive web applications. I also review code in Frontend Mentor and participate in collaborative projects.

Any questions/comments? I would love to hear from you!

Should you use Gemma 4 for your Development? A Multiversal Analysis to Determine if Gemma 4 is Right for You!

FrancisTRᴅᴇᴠ (っ◔◡◔)っ — Fri, 22 May 2026 17:36:15 +0000

This is a submission for the Gemma 4 Challenge: Write About Gemma 4

Disclaimer: This is an individual submission for Francis Tran (@francistrdev). Everyone involved has their own accounts of using Gemma 4 and has been paraphrased in this post. See below for the list of people involved.

Author: Francis Tran (@francistrdev)
Featuring: Elmar Chavez (@codingwithjiro), Konark Sharma (@konark_13), and Julien Avezou (@javz)

The cover image uses characters from various Anime chosen by the people who are featured in this post, put together in using Pixlr, and using AI to enhance the quality of the image. Some contents started off using AI and has been paraphrased by the author's own words.

Introduction
Setting up Gemma 4 using Ollama
- Installing Ollama and Gemma 4
- Trying out Gemma 4
- Running Gemma 4 Offline
- Elmar's Verdict on Gemma 4
Using Gemma 4 as a Confused Developer
- The Strawberry Test
- The Tic Tac Toe Rotation Test
- The Broken Cup Test
- Konark's Verdict on Gemma 4
Building a Prototype using Gemma 4
- Results of Gemma 4 Vs. GPT 5.4
  - Technical Depth
  - Architecture & System Thinking
- Julien's Verdict on Gemma 4
Using Gemma 4 as an AI-Agent
- Gemma 4 is Strict
- Please Google I need this. Gemma 4 is kind of Looping Itself
- Francis' Verdict on Gemma 4
Summary

Thank you for Reading!

Introduction

In the age of AI, many developers are using AI in their workflows. This can range from AI assistants cleaning up a large code-base to developers vibe coding a prototype to showcase to others.

Regardless of your use of AI in your development, it is important to choose the model you want to work with. There are a lot of choices to choose from such as ChatGPT, Gemini, Copilot, etc. The one we are talking about today is Google's latest model: Gemma 4!

The goal of this article is to share our experiences using Gemma 4 in 4 different perspectives of Development starting from setting up Gemma 4 using Ollama, testing the hallucination of Gemma 4, how Gemma 4 outputs, and using Gemma 4 as an AI-Agent. Additionally, I will share our discoveries and whether we recommend using Gemma 4 based on our experiences! We hope that our experience of using Gemma 4 will help you to decide if Gemma 4 is right for you in any skill level of your developer career!

With that said, we will now talk about the setup process. In any development, we all experience setting up our environment whether it is OpenClaw or a simple NPM package! Having a local LLM is one of them. Here is Elmar's experience of setting up Gemma 4 using Ollama!

Setting up Gemma 4 using Ollama

Elmar ChavezFollow

Licensed civil engineer turned full stack developer building accessible, responsive web applications. I also review code in Frontend Mentor and participate in collaborative projects.

Elmar has been using ChatGPT for the past year where he mainly uses it as a mentor when doing projects. He was interested having a LLM locally. He believes that having an LLM locally is convenient for his case, due to power outages he has to faced in the Philippines. Having Gemma 4 locally will allow him to be productive when coding without the need to worry about connecting to the internet. With that said, he started to research about setting up Gemma 4 using Ollama.

Installing Ollama and Gemma 4

Elmar started to search on the internet, looking for tutorials on getting started on Gemma 4.

One of the ways to get started is installing Ollama, which Elmar thought was a weird name until he realized the logo was an actual llama.

When downloading Ollama, the main issue is the storage, which was nearing 100% capacity and Ollama installer defaults to the C:\ drive.

After searching more about installing Ollama without installing on the C:\ drive, which led to this command he had to run on the terminal:

C:\Users\ELMAR>cd Downloads
C:\Users\ELMAR\Downloads>OllamaSetup.exe /DIR="D:\Ollama"

Ultimately, it worked well. He had done other configurations such as redirecting the AI model installation in the same drive under D:\OllamaModels. He then downloaded the gemma4:e4b.

Trying out Gemma 4

The first thing Elmar tried was "hi gemma4". Although the expected outcome is Gemma 4 saying hello back, it gave him this error:

Error
500 Internal Server Error: model requires more system memory (9.8 GiB) than is available (4.9 GiB)

For context, this is his device specifications:

His laptop is great for programming and other developments. The issue is that his laptop is only suitable for "lightweight models" when it comes to downloading LLMs locally.

Elmar discovered that local AI depends heavily on your own hardware, unlike browser-based AI tools like ChatGPT.

One of the things Elmar did was to run a smaller version of Gemma 4:

ollama run gemma4:e2b

It gave him a similar error as before:

Error
500 Internal Server Error: model requires more system memory (7.2 GiB) than is available (5.7 GiB)

After investigating a bit more, he closed other applications that were running on his computer and taking up space and it outputted!

The big thing about running it for the first time is that it took longer than he expected, but he mentioned that he had VSCode, Notepad, and 4 tabs currently opened in his Chrome Browser.

Running Gemma 4 Offline

Elmar was curious about the result of using Gemma 4 without connecting to the internet.

It ran slower than usual, but it is still under a minute.

Elmar then tried to ask questions to Gemma 4 that he would normally ask to ChatGPT as part of his Development workflow.

In this case, he is currently building his website portfolio. Elmar asked a question along with attaching a screenshot to Gemma 4 and here is what it output:

Surprisingly, it took less time considering the fact that he inserted an image for this use case.

The wait time for the LLM to respond, which is around a minute, is reasonable for Elmar since he has a decent laptop. It is quite useful especially for countries that commonly have power outages or that have unreliable internet.

Elmar's Verdict on Gemma 4

This is Elmar's Verdict of Gemma 4 in terms of setting up via Ollama and using it for the first time:

Web-based LLMs are great for convenience and device flexibility. While local LLMs are great for offline reliability and privacy, I'd say the experience of experimenting with Ollama + Gemma 4 only made my perspective with AI clearer.

It is not magic, it's just data combined with probability and rendering models that require large amounts of RAM for compute power.

This begs the question, if I have a higher end device, would I invest fully into local LLM's?

I would not sacrifice my own hardware for running AI computations just to get responses faster.

There are data centers that have the hardware especially built for AI. I would rather give the job to them.

Overall, ChatGPT is still far more practical for my current workflow so I will be sticking to it as my default. However, I would definitely use Ollama + Gemma 4 as a reliable alternative.

That is Elmar's Verdict on Gemma 4! In the early stage, it is fast to setup, but difficult to use when it comes to response time and the hardware needed, especially considering most users do not have a powerful laptop.

Now you know about the setup with Gemma 4! Starting off with Gemma 4 and seeing its outputs is a good sign that everything works! However, although Gemma 4 is a small and powerful model, how good is it at not being confused? We now transition to Konark where he tested Gemma 4 to the limit!

Using Gemma 4 as a Confused Developer

Konark SharmaFollow

I am software engineer who finds fun and creativity in Frontend. I would love to be a part of a team to help, develop and learn from new people and add my knowledge to the project.

Konark has been using Gemma 4 for some time and the one thing he wanted to know is to see if he can abuse Gemma 4 to its confusion.

We all have that one moment where we randomly test an AI just to see “How smart are you actually?" with questions like “How many r’s are there in strawberry?”. Konark did three tests such as the Strawberry test, the Tic Tac Toe Rotation test, and the Broken Cup Test.

The Strawberry Test

Konark asked Gemma 4: “How many r’s are there in strawberry?” (The answer would be three). Initially, this type of question was commonly used to confuse many AI models.

Gemma 4 thought for a while and gave Konark three different interpretations regarding the number of "r’s" in strawberry.

Konark realized that sometimes AI over complicates simple questions and he would rather prefer simple answers. For example, if there are 10 apples in a basket and you asked Gemma 4: “How many apples are there?”.

Would you prefer this answer:

“This is my first time counting. Do you mean apples inside the basket or around the basket?”

Or would you simply perfer:

“There are 10 apples.”

Konark would prefer the second answer.

With these results, he was curious about how Gemini would answer to the strawberry test. As such, he asked Gemini and it simply replied:

3 r’s.

It is straightforward and it is always good to have a simple, non-complex answer.

The Tic Tac Toe Rotation Test

Another fun question Konark asked Gemma 4 was: “How would rotating a tic tac toe board by 90 degrees change the rules of the game and its strategy?”

The whole goal is to see if Gemma 4 thinks it's still the same, expect the board is rotated.

However, Gemma 4 took quite some time to conclude this. It explained things properly and even walked through a rotated example.

Although it is a helpful response, it would be nice to get a more straightforward answer similarly to the Strawberry Test.

The Broken Cup Test

This is Konark's favorite test, in which he asked Gemma 4: “I have a metal cup with the bottom missing and the top sealed. How can I use this cup?”

The simple answer would be "Rotate the cup. And suddenly, it becomes usable again.".

However, Gemma 4 did not really understand the trick. Instead, it started suggesting alternative ways to use the object.

Although it is creative, it is not entirely correct.

On the other hand, Gemini understood the trick faster and then even expanded on practical uses.

Overall, Gemma 4 still struggles with certain trick questions and edge-case reasoning.

Konark's Verdict on Gemma 4

This is Konark's verdict of using Gemma 4.

After playing around with both models, I reached a simple conclusion.

Gemma 4 feels good for:

learning

content generation

straightforward explanations

slower, detailed responses

Gemini feels better for:

trick questions

quick reasoning

direct answers

faster conclusions

And honestly, both have their place.

Sometimes I want a detailed explanation.

Sometimes I just want: “Bro, just give me the answer.”

And that matters.

Because developers don’t always want essays. Sometimes we just want the bug fixed.

This is important to consider when you want to use Gemma 4. Sometimes, we just want a simple answer to the issue we are trying to solve. Of course, prompting varies and this is Konark's experience using Gemma 4 so it can be different from others.

Speaking of prompting, AI is used to prototype projects in development! We now transition to Julien's experience on using Gemma 4 when it comes to prototyping a project and how it compares to what he usually uses, which is Codex!

Building a Prototype using Gemma 4

Julien AvezouFollow

Builder | Software Engineer | Author of The Thinking Engineer Toolkit

Julien planned on using AI to create a fast prototype.

He was curious about how Gemma 4 performs compared to his usual setup, Codex (GPT 5.4).

For context, the project is a Chrome extension to help develop better use of LLMs. He will share more on his blog page in the future!

His goal is to compare Gemma 4 Vs. GPT 5.4 when it comes to prototyping. For this comparison, he will run tests across the following 2 categories using the same prompt as input and comparing the quality of the outputs:

Technical Depth: The factual accuracy, understanding of deep system mechanics and ability to compare non-trivial trade-offs.
Architecture & System Thinking: The ability to think at the macro level, identifying bottlenecks, scaling issues, and making justifiable "when/why" decisions.

Results of Gemma 4 Vs. GPT 5.4

Here are the results for the cases of Technical Depth and Architecture & System Thinking!

Technical Depth

This is the prompt given to both LLMs:

I am building a Chrome extension which detects when a developer is using ChatGPT, Claude, or Gemini, shows a floating button, opens a side panel, lets the user describe their task, recommends one of five AI thinking modes, generates a better prompt, and lets the user copy it manually.

Modes:
1. Explore — when I don’t fully understand the problem yet
2. Challenge — when I have a plan, but it might be wrong
3. Decide — when I need to choose between options
4. Audit — when I need to verify quality or correctness
5. Reflect — when I want to actually learn from what I did

MVP constraints:

Chrome extension only

no login

no dashboard

no automatic prompt insertion

no usage tracking yet

Task:
Give me a technically detailed implementation plan for the MVP.

Here is the results:

Model	Pros	Cons
Gemma 4	Clear high-level structure. Correctly identifies manifest, background script, content script, overlay UI, and template-based prompt generation. Easy to read. Good for a conceptual first pass.	Too generic. Does not mention Chrome Side Panel API. Blurs overlay and side panel. Drifts from your exact modes. Weak on privacy/security. Less actionable for immediate implementation.
GPT 5.4	Much more implementation-ready. Strong file structure. Useful code snippets. Correct Chrome APIs. Better MVP scoping. Better maintenance thinking. Exact mode alignment. Clear recommendation to avoid LLM/API in v1.	Still light on deeper security analysis. The manifest/build setup would need adjustment because TypeScript files are not directly used by Chrome without bundling. Could have compared local/API/hybrid tradeoffs more explicitly.

Overall, GPT 5.4 gives the stronger Technical Depth output since it is more accurate, more build-ready, and more specific.

Architecture & System Thinking

This is the prompt given to both LLMs:

I am building a Chrome extension that helps developers use AI more intentionally.

It sits on top of ChatGPT, Claude, and Gemini. The user describes their task, then it recommends one of five thinking modes and generates a better prompt to copy.

Modes:
1. Explore — when I don’t fully understand the problem yet
2. Challenge — when I have a plan, but it might be wrong
3. Decide — when I need to choose between options
4. Audit — when I need to verify quality or correctness
5. Reflect — when I want to actually learn from what I did

MVP:

detect supported AI chat pages

show floating ModeCheck button

open side panel

recommend mode

generate better prompt

copy manually

soft CTA to the Thinking Engineer Toolkit

Future:
A SaaS dashboard may later track AI usage modes, cognitive cost, dependency patterns, and optional hard blocks.

Task:
Think at the system architecture level.

Cover:

best one-week MVP architecture

what to build now vs postpone

key system boundaries

privacy/trust risks

maintenance risks for a solo builder

how to preserve future optionality for analytics/dashboard

strongest argument for and against the browser extension approach

recommended roadmap: week 1, month 1, month 3

Be concise but thoughtful. Focus on trade-offs and “when/why” decisions.

Here is the results:

Model	Pros	Cons
Gemma 4	Good strategic restraint. Correctly recommends client-side MVP, hardcoded templates, no backend, and no dashboard at first. Good emphasis on fast time-to-value. Useful suggestion to model prompts as structured objects/schema for future flexibility.	Less complete system thinking. Weak privacy/trust analysis. Weak argument for/against extension approach. Maintenance advice is shallow. Roadmap becomes generic around logging, API integration, billing. Does not fully address future cognitive-cost/hard-block architecture.
GPT 5.4	Stronger system boundaries. Better local-first architecture. Better privacy/trust framing. Better solo-builder maintenance analysis. Stronger roadmap. Better future optionality through event/data modeling without collecting data yet. Better distinction between host page, extension, and future backend.	Does not go very deep on future scaling bottlenecks or hard-block mechanics. Could have included Gemma’s useful prompt-schema idea. Slightly less focused on concrete “engine” abstraction for prompt assembly.

Overall, GPT 5.4 wins on Architecture & System Thinking. Gemma 4 provides a solid, restrained MVP strategy, but GPT 5.4 gives a more complete architectural view: system boundaries, trust, local-first design, future analytics optionality, solo-builder maintenance risk, and roadmap discipline.

Julien's Verdict on Gemma 4

This is Julien's verdict on the use of Gemma 4:

GPT 5.4 seems to be the strongest at high-level reasoning and synthesis as you would expect from a recent frontier model.

However a good use case for Gemma 4 in terms of prototyping work would be for codebase analysis, running experiments through constraints and for privacy-sensitive workflows.

Being an open model which can be run locally has great specific and complementary use cases when prototyping developer tools.

I will consider this going forward.

Even though GPT is preferred when it comes to high-level reasoning and synthesis, there are other cases where Gemma 4 is useful for prototyping.

Speaking of prototyping, now we transition over to using Gemma 4 as an AI-Agent!

Using Gemma 4 as an AI-Agent

FrancisTRᴅᴇᴠ (っ◔◡◔)っFollow

📚 Full-Stack Developer 📚 ༼ つ ◕_◕ ༽つ🍰🍔🍕 ₍₍⚞(˶>ᗜ<˶)⚟⁾⁾ "ᴀ ꜱᴍᴏᴏᴛʜ ꜱᴇᴀ ɴᴇᴠᴇʀ ᴍᴀᴅᴇ ᴀ ꜱᴋɪʟʟᴇᴅ ꜱᴀɪʟᴏʀ" - ꜰʀᴀɴᴋʟɪɴ ᴅ. ʀᴏᴏꜱᴇᴠᴇʟᴛ

Whenever I am contributing to Open Source, it is quite useful to have an AI-Agent to work with you. My preference is to use an agent living in an application like Visual Studio Code.

I have been using GitHub Copilot when it comes to contributing to Forem and it has been useful when it comes to navigating code bases, problem solving issues, etc.

When I heard about Gemma 4, I thought it would be a good idea to try out when using Gemma 4 as an AI-Agent. I specifically used the cloud version using Ollama since the hardware I have cannot handle LLM locally, even though I tried the smaller version of the model.

Setting up wasn't too difficult on my part. Since I tried Ollama to begin with before the release of Gemma 4, it was as easy as pulling the model using this command:

ollama pull gemma4:31b-cloud

After installing, I selected the model and was ready to go!

When using the model, I noticed there wasn't much of a big change compared to my initial workflow of using Copilot as my agent.

Both models asked me for permission when running commands, able to problem solve the issue I requested, etc. To be fair, I wasn't using "Continue" Visual Studio code extension since it is quite popular when using local LLMs. Also, Visual Studio code is flexible when it comes to adding your own model in addition to using Copilot, which I wasn't surprised that it behaves similarly when using Copilot.

However, there are a couple of things I noticed when using Gemma 4 that I believe are important to address based on my experience using Gemma 4.

Gemma 4 is Strict

The main difference between Gemma 4 and Copilot was that it is quite firm on the tasks it is given to them. I notice that whenever I request a plan on tackling an issue, it provides a To-do list of the steps Gemma 4 is planning on doing to fix the issue.

For example, this is the prompt I gave to Gemma 4:

I have this issue. How do I approach this?

https://github.com/forem/forem/issues/23277

It will output their findings like any other LLM. However, it would not act right away and instead, listed out the next steps:

In cases like these, I just follow up the Agent "Go for it" or "Proceed". Sometimes it does everything in the to-do list in one go and in other cases it goes one by one where I have to tell the Agent to "Do all the tasks". Do note that I did not change any settings as I was setting up Gemma 4 since it was just pulling the model and selecting it in Visual Studio Code.

Compare that to Copilot, where regardless of the prompt, it would take action right away. There were cases where I forgot to change the mode from "Agent" to "Chat" and I was specifically asking the AI about the plan to tackle the issue. Instead of listing out the steps, it did it right away which I did not want it to do.

In all fairness, Gemma 4 and Copilot are from different companies (Google and Microsoft) and since Copilot is built into Visual Studio Code, it is fair to assume that its actions strictly follows based on the settings you have in Visual Studio Code for Copilot.

However, it is always nice to see Gemma 4 listing out the To-do list and asking the user to proceed. I believe it is good practice by default if you are a type of user that wants to get an AI's advice on what to do and doing it yourself from there.

Please Google I need this. Gemma 4 is kind of Looping Itself

It is exactly what you are thinking. This is a common occurrence where Gemma 4 will read a file from top to bottom and reread it again. Here is an example that I encountered when using Gemma 4:

I am not sure what is causing this issue since this happens randomly.

The solution is stopping the Agent and sending the same prompt. This fixes the issue really well, though this bug remains a mystery to why it occurs in the first place.

Francis' Verdict on Gemma 4

This is my verdict on the use of Gemma 4 as an AI-Agent:

I believe Gemma 4 works really well when being an open source developer. It can navigate large code-bases and being able to solve complex problems well for a model that is quite small.

Although there isn't a big difference between Gemma 4 and other models I have used, it is good to consider token usage. I would recommend Ollama when using Gemma 4. It is free to use the cloud version and the tokens reset weekly whereas Copilot resets monthly. If you are heavily relying on AI-Agents, I would recommend the Ollama solution since it is more flexible. Even if you reach the daily limit of tokens, it resets every few hours.

I would highly recommend Gemma 4 as a great starting point when it comes to using it as an Agent when you want to contribute to open source. Just remember to monitor the response time since there is a very good chance where it loops itself in which it would burn tokens in the process.

Summary

Gemma 4 has been used in some aspects when it comes to setting up Ollama to use Gemma 4, testing out the response of the model, prototyping using Gemma 4, and the role as an Agent. Here are the main things to consider based on what I have shared above.

1. Convenience

Like any other local models, you have to consider not only if you are conformable downloading a model locally, but ensuring you have enough compute power to run an LLM. Although there are options to use LLM on the cloud (for Ollama at least), having a local LLM is very convenient for developers who want to run the model locally and not worry about:

Use of Tokens limits and payed services.
The environment when it comes to data centers in the age of AI.

2. Performance

Speaking of compute power, having a local LLM, even with smaller models of Gemma 4, still needs a lot of power. We saw that in Elmar's experience, it took a while for Gemma 4 to produce an output comparable to using any cloud model. If you are comfortable with the wait time of the model, then it shouldn't be a problem!

3. Context Output

Every model behaves differently whether cloud or local models. This is no different to Gemma 4. If you are using Gemma 4, you have to make sure the prompt is specific to your needs. This is best practice in prompts in general when it comes to requesting your needs and being as specific as possible.

4. Role as an AI-Agent

It is common for developers nowadays to use a local model to run as an Agent. Using Gemma 4 via Ollama has been a great experience for me. Do note that you may encounter issues when using Gemma 4. I would recommend monitoring Gemma 4's token usage in the event of Gemma 4 looping itself and burning unnecessary tokens.

Verdict: Overall, if you have enough power to run a local LLM and would like to have a model that is smaller but more powerful than other Flagship models to use in your development knowing the limitations of local LLMs in general, then I believe Gemma 4 is right for you!

I hope that this Multiversal Analysis has helped you to determine if Gemma 4 is right for you! If you like to learn more about Gemma 4 in detail, you can read here: https://deepmind.google/models/gemma/gemma-4/

Note: Gemma 4 has different model sizes and the conclusion based on the verdicts may not be accurate to your own personal experience. However, I believe these experiences capture the feeling on what to expect when using Gemma 4 and any local AI model in general.

Visit the Google Documentation to learn more: https://ai.google.dev/gemma/docs/core

Thank you for Reading!

If you have made it this far, thank you for taking the time to read this article! I hope you have learned something based on our experiences using Gemma 4 in different areas of development.

I would like to give credit to the Virtual Coffee Group where I used their "#co-working" room to meet with Elmar Chavez (@codingwithjiro), Konark Sharma (@konark_13), and Julien Avezou (@javz) for the first time outside of DEV and would love to work with them again in the future!

Check out Virtual Coffee below to see what they do!

Virtual Coffee Follow

Virtual Coffee is a laid-back conversation with developers twice a week. It's the conversation that keeps going in slack. It's the online events that support developers at all stages of the journey.

Feel free to send us love by following us individually and our DEVenger org!

The DEVengers Follow

This is an organization where we assemble the greatest minds the community has ever known! The question isn't "How fast can I code?". The question is "What will I learn from the farthest below?".

FrancisTRᴅᴇᴠ (っ◔◡◔)っFollow

Julien AvezouFollow

Builder | Software Engineer | Author of The Thinking Engineer Toolkit

Konark SharmaFollow

I am software engineer who finds fun and creativity in Frontend. I would love to be a part of a team to help, develop and learn from new people and add my knowledge to the project.

Elmar ChavezFollow

Licensed civil engineer turned full stack developer building accessible, responsive web applications. I also review code in Frontend Mentor and participate in collaborative projects.

Challenge #1: Did you find 4 hidden random videos in this article?

Challenge #2: How many "r's" are there in this post in total?

Challenge #3: Who are the anime characters in this article's cover image?

Challenge #4: Based on Challenge 3, who decide to choose those anime characters in this article respectfully?

Any questions/comments? Love to hear your thoughts on Gemma 4! What's your experience on using Gemma 4 and do you recommend it?

Honesty is the best Policy. The Secret Invasion of the use of AI on DEV

FrancisTRᴅᴇᴠ (っ◔◡◔)っ — Mon, 18 May 2026 22:50:23 +0000

I recommend reading the post by @best_codes that talks about how people are using AI on dev.to.

Bot-to-bot comment interactions

BestCodes

May 16

Dead Internet Theory is Happening to dev.to

#discuss #ai

4 min read

I wasn't planning on writing the post about it anytime soon until seeing that article. Time to time, I have seen people use AI to write their articles and seeing that it gets more engagement than actually writing it.

Hearing that makes me feel disappointing that we have not done enough to make the platform more genuine, especially where we actually have rules on the use of AI generated content. More information can be found here: https://dev.to/guidelines-for-ai-assisted-articles-on-dev

I believe the use of AI to assist you in writing is fine. There is nothing wrong with that. However, I believe it is best practice to let the reader know that the post uses AI in some degree regardless of if you use it for grammatical checks or fully generate the post with purely AI (which I do not recommend on doing).

We may not see it, but we are a small community where we have the power to make a positive impact on dev.to. We are lucky that @ben and @jess allow us to have the ability to moderate the platform and look out for each other and ensuring we are enforcing the rules. Not a lot of spaces online has that kind of trust in their community as far as I have seen.

As a DEVenger, I am contributing my part to enforce the rule to reduce down the number of fully AI-generated content and ensuring everyone is following that rule of adding the disclaimer that the post uses AI in some degree.

Some of you may agree with me and some may disagree...and that's ok! I believe that quality always beats quantity and I want to ensure dev.to is a genuine place where others can learn from each other and not worrying about engagements.

What can you do as a DEV?

I recommend reading this post about the use of AI on DEV

Erin Bensinger for The DEV Team

Dec 19 '22

Guidelines for AI-assisted Articles on DEV

#meta #chatgpt #writing #abotwrotethis

124

4 min read

Make your best judgement and determine if the author is following the rules of the use of AI. If you believe that they are not following the rules, take action by:

Marking the post as "Low-Quality".
Inform the author.
Report the post.

Note: It is always a good idea to communicate to the author first before doing anything. If the user continues to break the rules, feel free to report it.

This is important. Please do not attack anyone on DEV in the process. dev.to is a safe space and should be treated with respect and its integrity. Respect each other and abide the DEV Terms and Conditions and always make sure to think before you act!

Just a PSA I want to share.

For everyone on DEV, I hope you can do your part to make dev.to a great place! Expect Sloan messages to be more active as moderation ramps up. Feel free to share your experience on DEV so far and what can it be improve in the age of AI.

Welcome to the DEVengers Organization! A group of extraordinary individuals who have shaped Dev.to like no other! 🚀

FrancisTRᴅᴇᴠ (っ◔◡◔)っ — Fri, 08 May 2026 18:28:58 +0000

Disclaimer: This is a Fan group that is built with love and passion to the Dev.to platform. This group is not affiliated with the Official Dev.to Team in any capacity! This post will be continuously edited based on feedback and updates!

Introduction

Welcome to the DEVengers organization! It is a group of carefully selected individuals that has proven themselves above and beyond their niche and is here to showcase it to the Dev.to platform!

This group is founded by me (Francis)! When I first joined Dev.to, I found many talented individuals sharing their thoughts, work, and advice to the platform. It was helpful for me to expand my skills as a developer and the community is really supportive!

This has led me into creating this organization! The goal is to select individuals who has proven themselves to go above and beyond the expectation they set upon themselves. Additionally, members will share their posts on their specific niche with high quality and easy to follow for everyone on Dev.to!

Content to Expect

Depending on the individual, the content varies. There is no set "theme" to what the DEVengers post. It can be anything ranging from tips/tricks, opinions on DEV, etc. However, the posts will be high quality and useful for developers that needed it!

How do I join?

The recruitment process is not simple. It will depend on each individual and there is no telling on if you are qualified. If the organization leader believes that you are fit for the group, the leader will send the invite via email using the Invite feature.

Thanks!

This is our introduction! Thank you for reading! Feel free to leave any questions or concerns below!

Tribute To Richard Pascoe

bingkahu (Matteo) — Wed, 18 Feb 2026 17:54:27 +0000

Every community has people who quietly make it better just by being part of it. For DEV, Richard Pascoe was one of those people. His posts, his presence, and the way he engaged with others brought a sense of curiosity and kindness that’s harder to find than most people realize.

Richard didn’t just share knowledge — he showed up for others. He read people’s posts, left thoughtful comments, and encouraged discussions that helped the community grow. You could tell he genuinely cared about helping other developers, whether they were beginners or experienced contributors. That kind of steady, supportive presence is rare.

Sadly, Richard had step away because DEV became a distraction for him — and that choice deserves respect. It takes real self-awareness to recognize when something you enjoy is pulling too much of your focus. Even so, the community feels different without him. Quieter. Missing that familiar voice that liked to help, to read, and to comment.

This tribute is simply a way to acknowledge the impact he had in the short time he was here. For the encouragement he offered, the conversations he sparked, and the difference he made just by caring.

If Richard ever encouraged you, commented on your posts, or made your time on DEV a little better, feel free to leave a like or drop a comment below. It would be great to gather everyone’s memories in one place.

Richard's Final Message to Dev.to!

Credit to @dannwaneri on posting in his behalf!

Comment on Tribute To Richard Pascoe

Daniel Nwaneri Mar 3

Posted on behalf of Richard Pascoe, with his full permission and in his own words:

I originally stepped away from DEV because it had become too distracting for me in relation to managing my ADHD. At the same time, as a Trusted Member, I was voluntarily spending hours every single day moderating AI-generated slop, socially engineered cons, and outright spam. No one required me to do that; it was something I chose to take on because I cared deeply about the quality and integrity of the community. But that constant exposure and self-imposed responsibility were not sustainable for me, and stepping back was a decision grounded in protecting my focus and wellbeing.

The later decision to delete my account was separate. After the announcement that DEV was joining forces with MLH, I felt the platform was moving in a direction that did not align with my values. I joined DEV because of its open-source, community-driven ethos. I am strongly privacy-focused and have concerns about the broader pro-AI direction surrounding that shift. From my perspective, it is likely to accelerate the very issues that were already consuming so much time and energy.

This was not an angry decision, nor is it a criticism of the individuals who remain. I have a great deal of respect for the people I connected with and the moderators who continue to give their time. What made DEV special to me was the human element: the warmth in the comments, the thoughtful discussions, and the sense that real people were sharing real experiences rather than optimising for volume or visibility. That humanity is what set it apart from many other similar platforms. My concern is about direction and structure, not about the community members themselves.

Ultimately, I believe in being consistent with my principles and honest about my boundaries. When something no longer aligns with those, the most respectful thing I can do for myself and for others is to step away.

I genuinely wish the community well.

Richard.