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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 📚 "A Smooth Sea never made a Skilled Sailor" - Franklin D. Roosevelt

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

📚 Full-Stack Developer 📚 "A Smooth Sea never made a Skilled Sailor" - Franklin D. Roosevelt

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

Comments 51

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

123

Comments 39

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.

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Introduction

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Trying out Gemma 4

Running Gemma 4 Offline

Elmar's Verdict on Gemma 4

Using Gemma 4 as a Confused Developer

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The Broken Cup Test

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Building a Prototype using Gemma 4

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Using Gemma 4 as an AI-Agent

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Tribute To Richard Pascoe

Richard's Final Message to Dev.to!