DEV Community: Fayaz

You'll not be replaced by AI if ...

Fayaz — Sat, 30 May 2026 03:23:59 +0000

There are many reasons why one may not be replaced by AI, not even by a possible future ASI.

Here's one reason that may just apply to you! ❤️

You'll not be replaced by AI if you can generate creative ideas faster than AI can implement them! 🫡🚀

Note for critics:

Current AI models (as of May, 2026) are not advanced enough to implement complex ideas without human interventions.

But even if a possible future Artificial Super Intelligence (ASI) implementation can do so, laws of physics like massive energy requirements, environmental concerns etc. will prevent the implementation to replace the work of Billions of people world-wide.

Our hardware advancement rate is far far slower compared to our software advancements.

We humans are far more efficient and compatible to planet earth compared to the hardware we've invented.

FayazFollow

A Software Engineer who is not afraid of being replaced by AI, loves coding and writing with and without using AI, and values human life and human dignity far more than technological advancements.

Is it rare to crave for a Super Accessible ASI while also loving your own Human Creativity?

Fayaz — Thu, 28 May 2026 22:30:00 +0000

Am I one of the few "rare" developers who:

1. "Secretly" wants AI to finally succeed ...

... doing all the things AI CEOs continuously promise AI will be able to do, (but fail to deliver) - so that I can implement all those ten thousand ideas I thought I would never have enough time to implement; or will finally be able to implement my unique Open Source Everything App that will do everything from note taking, to time management, to publish blogs on my personal websites and/or social media, to email management, to password management ... everything in a deterministic way (not like the AI agent way) - all while everything being end to end encrypted, secure and private!

2. Yet, still loves the creative self ...

... writing code, poetry and articles by hand, even in a possible future where a super accessible ASI (Artificial Super Intelligence) exists, and even if it's 100% free with unlimited tokens, and even if it can be run locally on cheap hardware?

Or is this seemingly contradictory craving very common among developers?

Just wanted to know!

Note: No AI was ~~harmed~~ used to write this post. All the grammatical mistakes and the lack there of, are mine alone 😁

FayazFollow

A Software Engineer who is not afraid of being replaced by AI, loves coding and writing with and without using AI, and values human life and human dignity far more than technological advancements.

AI fatigue is very real and people are fighting back!

Fayaz — Wed, 27 May 2026 19:45:09 +0000

Don't really like how CEOs are acting like AI is the solution for everything, it certainly is not!

Was watching this video today:

People need to realize: the reality of AI and the promise of AI - are two completely different things!

Treat AI like any other technology:

Act upon the reality, not the promise!

But people are fighting back, that's a good thing.
Hope we'll reach an equilibrium point soon!

My notes:

Don't let companies force you to use AI if it doesn't help your career in the long run.

Is your company simply using you as a Human Tool to train your AI Overlords that'll eventually replace you? - Fight back!

Gain and retain your knowledge and skills by doing (coding, designing, writing, reading etc.) yourself.

Use AI only if it doesn't violate the above two points!

Foundational human knowledge is not replaceable.

Your thoughts?

Disclaimers:

I have no affiliation with the author of the video. Only sharing it because I thought it's interesting for this community.

No AI was ~~harmed~~ used to write this post 😇

FayazFollow

A Software Engineer who is not afraid of being replaced by AI, loves coding and writing with and without using AI, and values human life and human dignity far more than technological advancements.

Join the club! Note to self: - avoid FOMO - focus on one thing at a time - focus on something you can intellectually contribute to That's it! ❤️

Fayaz — Fri, 22 May 2026 08:55:06 +0000

Staying updated as unpaid homework

Harsh

May 21

I Used to Get Excited About New Tools Now I Feel Tired.

#ai #mentalhealth #programming #career

Comments 70

5 min read

The 2026 Agentic Era with Gemini Agent Platform: Surviving Cascading Failures and Runaway Cloud Bills.

Fayaz — Thu, 30 Apr 2026 06:45:48 +0000

This is a submission for the Google Cloud NEXT Writing Challenge

The shift from simple AI pilots to fully autonomous digital task forces is officially here!

With the release of the Gemini Enterprise Agent Platform at Google Cloud Next '26, developers can now build, scale, and govern agents with the same rigor applied to mission-critical systems.

We are moving beyond standalone models and stepping into a brave new world where agents seamlessly discover and collaborate with each other using the Agent Registry and the universal A2A protocol.

But let us talk about the elephant in the room: What exactly happens when a tightly coupled multi-agent system goes off the rails?

Anatomy of a Cascading Failure

In a distributed agent architecture, you might have a Planner Agent generating data, an Evaluator Agent judging it, and a Simulator Agent running the scenarios. These agents rely heavily on one another. Didn't someone say:

introducing LLMs means our systems will start breaking in entirely new and unexpected ways!

Imagine a central agent accumulating massive amounts of uncompressed context during a complex simulation. Suddenly, it crashes because it exceeded the 1-million context token limit.

The immediate result is a severe cascading failure. Dependent agents are left waiting, causing unusually high latency and complete workflow stalls. The domino effect brings the whole interconnected application to a grinding halt.

Or perhaps something far worse can happen: if some of those dependent agents were constructed slightly wrongly, instead of just waiting for the crashed agent, they may even continue the Agentic loop and keep consuming LLM and API calls, causing a totally unexpected platform bill before you can check your Budget exceeded emails!

Failing Gracefully in a Multi-Agent World

Right now, Google Cloud offers features like Event Compaction to force agents to summarize workflows and avoid hitting those massive token limits. We also have Agent Observability to trace underlying reasoning loops and Gemini Cloud Assist to autonomously investigate logs and suggest proactive code fixes.

To survive the agentic era, developers must build in AI-specific resiliency.

When a core agent drops offline, dependent Agents must follow a safe fallback mechanism. This could mean returning a cached response from a previous memory session, skipping a non-critical evaluation step, or safely terminating the workflow while alerting a human operator.

Simply retrying a doomed tool call over and over can be a recipe for disaster!

What Google Cloud may Consider Building "Next"

We currently have fantastic guardrails like Agent Gateway for enforcing zero-trust identity policies, Cloud Assist for AI-powered debugging and Wiz integration for a living Security Graph.

However, to fully support resilient multi-agent environments, Google Cloud should consider introducing a few dedicated stability tools:

Agentic Circuit Breakers: A native feature in the Agent Gateway that detects when an agent fails repeatedly. It would cut off traffic automatically and return immediate fallback errors to prevent system-wide delays.
Dependency Graph Health Dashboards: While we have great open-standards telemetry, developers need live visualizations that instantly highlight deadlocks when agents are stuck waiting for each other in a multi Agent system. An enhanced Graphical Registry with Agent to Agent, Agent to MCP and Agent to API view may improve the overall understanding of the entire system greatly, especially when the users will end up having multiple multi-agent system for running their entire business.
Automated Fallback Routing: Native Agent Registry configurations that route prompts to a simpler, more deterministic backup model if the primary complex reasoning agent fails.

How the Agent development and Orchestration System could be more proactive like an actual Developer

While the agents are proactive in taking action and suggesting fixes, the keynotes do not explicitly demonstrate the agents actively asking the user clarifying questions to improve agent orchestration.

Currently, the system tends to take a user's prompt and immediately generate an architecture, such as instantly spinning up a main agent and a team of sub-agents in the Gemini Enterprise Agent Designer.

To make the Agent Platform act more like a senior developer, it could incorporate a "consultative phase" where it interrogates the user's prompt before orchestrating the agents (if necessary):

Interactive Architecture Reviews: Before deploying an Agent-to-Agent (A2A) network, the system could analyze the dependency graph and ask questions like, "I see the Simulator Agent depends heavily on the Planner Agent. Should we implement an Agentic Circuit Breaker here in case the Planner crashes?"
Prompting for Edge Cases and Limits: Instead of blindly accepting a prompt to build an agent, the system could act as a technical lead and ask, "You are giving this agent access to the live production database. What happens if it loops infinitely? Should we establish a hard Pricing Cap or token limit before I deploy this?"
Refining Orchestration Logic: If a user asks to connect multiple agents, the system might ask, "Do you want these agents to run in parallel to save time, or sequentially so that the Evaluator Agent can grade the Planner Agent's work first?"

By transitioning from simply executing instructions to actively interviewing the developer about constraints, error handling, and cost management, the platform could help prevent the exact types of cascading failures we observed in the keynote.

Controlling Costs: The Need for Pricing Caps

Operating agents safely requires careful attention to both infrastructure scale and token scale. As agents autonomously spawn other sessions and call multiple tools, an unhandled failure can become a massive liability.

When an agent fails, dependent agents might get stuck in an infinite loop of retries and hallucinations. This generates massive token usage very quickly. Implementing a hard pricing cap on individual agents, or a budget limit across the entire multi-agent system, is a critical financial failsafe.

If an agentic loop spirals out of control, a pricing cap would automatically suspend the agent once a specific dollar amount is reached. This ensures companies can innovate with autonomous task forces without the fear of waking up to unforeseeable, astronomically large cloud bills.

As we fully embrace this interconnected agentic era, blending strict technical guardrails with hard financial boundaries is the only way to build truly production-ready AI.

Let us build responsibly!

Computer Science was never about programming, it's about problem solving.

Fayaz — Mon, 11 Aug 2025 18:24:27 +0000

From the very beginning of our CS-101 course, we weren't just taught to write code; we were taught to design solutions. Solutions so sophisticated that, eventually, they are supposed to run without our involvement.

Today, with AI, that vision has almost breached our deepest intellectual work, and made many of us question the future of work itself!

Fayaz

Feb 3 '25

Predicting 2025 - A brave new world!

#devchallenge #newyearchallenge #future #ai

Comments 5

3 min read

So, should we use AI everywhere? 🤔

If it makes sense, yes, absolutely!

The use of AI can no longer be stopped anyway, so the only thing we can do is to regulate it for responsible use.

Should we fear that AI will replace us? 🫣

Not really!

Here is why:

1. AI will never become human:

It can calculate, build, optimize, and predict, but it can never be us. Which means, there will always be a need for us: our judgment, our empathy, our creativity.

Yes, the nature of our work will change, as it always has with technological advancements. But change does not mean irrelevance.

Religion tells us that even God "used" human involvement to shape human history.

Nature shows us that when even a more intelligent species emerges, all others don't simply vanish; they adapt, they evolve, and only those who can't evolve, vanish - but most of them survive in their own way, and some even thrive!

And to be honest, after all the hypes, current AI isn't even that intelligent, and the promise of an all knowing super AI model is just that, a vague promise.

I'll believe it when I see it. And even then, I don't think it's going to replace us for all the reasons mentioned above!

2. Experts will always use AI better:

Experts in a field will always have a far better edge over the layman. Besides, if you don't have expertise in a field like Software Development (especially Software Engineering), how would you know when AI makes mistakes?

Only experts in the field will know that, so companies will always need the experts for reliable and productive development.

3. Transfer of Responsibility:

Modern AI models sometimes work pretty well, and sometimes make mistakes - so, the question is, who is responsible when an AI model makes a terrible mistake?

The model itself?
The organization that made the model?
The company that's using the model?
The employee that's using the model?
Government?
UN?
Aliens?
God?

Of course that's just a rhetoric question.
We simply can't shift responsibility and accountability to an AI model!

If for nothing else, then because of responsibility and accountability that AI will not replace us, it can only enhance and change our nature of work - that's all.

Companies will have to recognize it soon, or will learn it the hard way.

So, as you see, the future isn't about competing with AI.

It's about using AI wisely, efficiently and responsibly to solve problems we encounter at everyday life, all while keeping our expertise intact - only this will probably allow us to tackle problems in a way that was once thought impossible.

Use Algolia MCP Server & VSCode GitHub Copilot to Find and Submit Unique Projects in Dev Challenges! ❤️‍🔥

Fayaz — Mon, 28 Jul 2025 06:03:05 +0000

This is a submission for the Algolia MCP Server Challenge

Introduction

Recently I've found myself participating in quite some Dev Challenges. I'm Loving it!

However, the problem is: Most of my Dev Challenge submissions are done on the weekends. That means by then, there are already many submissions.

That's great! But what's the point in working on something someone had already done! I mean, it's not always a bad thing, but wouldn't you rather work on something unique!

In comes Algolia MCP Server Challenge!

So I thought, would the Algolia MCP Server make it easy for me to check if what I'm going to work on is at least unique so far?

This is what this project is all about.

What I Built and How I Utilized the Algolia MCP Server

Installed Algolia MCP Server on VSCode with GitHub Copilot (Linux).
Developed a CLI Tool to Fetch dev.to Challenge Submissions using the Forem API.
Developed a Web Interface to check the Submissions locally.
Developed an MCP (Model Context Protocol) Server named dcs to get the Submission data in GitHub Copilot Agent Mode.
Then indexed that Challenge submission data in Algolia, using Algolia MCP Server, by simply asking:

#dcs fetch algoliachallenge tag submissions, get all the submissions from this tag, and add them to my Dev Challenges app in #algolia

Finally, asked GitHub Copilot in Agent mode if my project idea is unique!

Once I built everything, all I had to do was ask:

#algolia in Algolia Challenge did anyone submit something related to GitHub Copilot?

And there it was!

I didn't have to dig deep into all the submissions on dev.to
Didn't have to do any complex search

Just one single command to Algolia MCP server and the AI Agent took care of the rest!

In my mind, I've already won something from this challenge! 🚀

I'm sure other developers are going to love this technique, and future Dev Challenges are going to receive more unique submissions using similar solutions.

Who knows, may be dev.to will provide their own MCP server in the near future! 💫

Demo

The following GitHub repo has all the necessary code and instructions for:

Using GitHub copilot with Algolia MCP Server and DCS (Dev Challenge Submission) MCP Server
DCS CLI
DCS Web App

SweaDev / dcs

Dev Challenge Submissions

A modern web application for browsing dev challenge submissions from dev.to. This app consists of a CLI tool to fetch submissions, a React web interface to display them, and an MCP server for AI assistant integration.

Roadmap / Todo list:

More detailed hands on tutorial about using this project effectively.
Daily auto update of Challenge submissions.
Filtering out unrelated posts that uses the Challenge tags.
FullStack Web App with option to use your own LLM API key for unique project brainstorming.

Features

CLI Tool: Fetch, update, and manage submissions from dev.to using the Forem API
Beautiful Web Interface: Browse submissions with sorting and search functionality
MCP Server: Model Context Protocol server for AI assistant integration (GitHub Copilot, Claude, etc.)
Auto-refresh: Web app automatically updates when new data is fetched or removed
Responsive Design: Works great on desktop and mobile devices
Safe Operations…

View on GitHub

Key Takeaways

I've made the initial setup using vite and pnpm.
Then mostly vibe coded the solutions using GitHub Copilot in Agent Mode with Claude Sonnet 4. But I did have to do some work when Claude started to make mistakes. So it's not all vibe coded work.
Algolia MCP Server documentation doesn't provide instructions for Linux or GitHub Copilot, but with a bit of tinkering, I could make it work.

.vscode/mcp.json file:

{
    "servers": {
        "algolia": {
            "command": "${input:algolia_mcp_dir}/algolia-mcp"
        },
        "dcs": {
            "command": "${input:dcs_mcp_dir}/server"
        }
    },
    "inputs": [
        {
            "id": "algolia_mcp_dir",
            "type": "promptString",
            "description": "Path to the Algolia MCP directory",
            "password": false
        },
        {
            "id": "dcs_mcp_dir",
            "type": "promptString",
            "description": "Path to the dev.to Challenges MCP directory",
            "password": false
        }
    ]
}

GitHub Copilot's support for Algolia MCP Server is quite good!
Although, only Claude models support Algolia MCP Server in GitHub Copilot, GPT models don't support it! Gives 400 error.
That said, GitHub's own MCP Server works with GPT 4.1 in GitHub Copilot Agent Mode. Didn't have time to debug why Algolia MCP Server only works with Claude models. Perhaps Algolia team will work on this?

Overall, participating in this challenge was really a happy experience for me.

I hope other developers will also be benefited from this project. ❤️

Automate content and social media post idea generation - using Runner-H.

Fayaz — Mon, 07 Jul 2025 06:50:43 +0000

This is a submission for the Runner H "AI Agent Prompting" Challenge

What I Built

We've all been there:

Completely stuck with writer's block, while trying to come up with social media posts for the day. 🥶

Whether you're building a personal brand or managing marketing content for your company or for your boss, fresh ideas from recent web articles can be a great source of inspiration.

But it gets even better with Runner-H.

In this post, I'll show you how to use Runner-H to search the web for new articles, collect them in a Google Sheet, summarize them in a Google Doc, and even generate sample tweets - all using a single prompt!

Demo

Runner-H Execution Demo

Runner-H Web search Demo

Generated Google Sheet with article data

Generated Google Doc. with story by Runner-H

Generated Google Doc. with sample Tweets by Runner-H

How I Used Runner H

To use Runner-H properly like I've shown in this demo above, first you'll have to connect Runner-H with Gmail, Google Doc and Google Sheet.

This is not necessary to use Runner-H, but helpful if you want Runner-H to create those documents for you.

Once you log-in to your Runner-H account, go the the Connections tab and make necessary connections.

Once your Apps are connected to Runner-H, go to the Run tab and use the Prompt below:

Objective: This task has 5 distinct but connected phases. The main goal is to:
- Collect recent posts from the specified online <source> on a specific <topic>.
- Save all the collected posts in a new Google Sheet.
- Write a report covering all the posts in a new Google Doc.
- Write social media posts in another new Google Doc based on the collected posts.
- Finally, email me the links to the generated Google Sheet and Google Docs.

Inputs: Some inputs will be provided at the end of the prompt within XML tags for clarity. Whenever an XML tag is mentioned, assume it will be provided at the end of the prompt.

Behavior:
- In each phase, you will behave like the world’s best expert on the subject matter.
- Try your best to accomplish the given tasks without user interaction.
- Plan the entire task before the actual execution that'll result in the best possible outcome.
- While executing the tasks as planned, if you find that executing the tasks as planned is not possible, amend the previous plan in a way that will satisfy the user’s main objective or at least come as close as possible within your capability.
- Prefix the name of each new Google Doc or Google Sheet created in this task with today's date. Date format: yyyy-mm-dd
- Format all the created Google Sheets and Google Docs beautifully, in a very professional and presentable manner.

Phase 1: Collect recent posts.
- Collect 10 recent posts on the given <topic> from the specified <source>.
- While gathering information on each post, collect the following data points: Title, Link, Published Date, Post Author Name, and Post Content.
- Don’t just collect information from search results; go to the original link of each post and collect actual post content from the main post.

Phase 2: Save all collected posts in a new Google Sheet.
- Once the necessary data of all the posts are collected, list them in a new Google Sheet with post Title, Link, Author, published Date, and Post Content.

Phase 3: Write a report in a Google Doc.
- Read the Google Sheet created in Phase 2, and based on the content of all the posts in the Google Sheet, write a report in a new Google Doc.
- This report should cover the main topics of all the posts.
- If helpful, link some of the posts within the content of the report.
- Write this report as a story, as if it's an observation and inner thoughts of a reader, while he is going through these posts starting a new day having his morning coffee. 

Phase 4: Write Twitter posts based on the collected posts
- Write 10 Tweets, 5 long form tweets and 5 short form tweets, based on the content of the collected posts.
- Don't write the Tweets like a copy or a report on the collected posts. Rather, write each Tweet uniquely, focusing on different ideas from the content of all those collected posts as a whole.
- Don't use any hashtag or any link within the Tweets.
- Give a separate heading to each Tweet in the Google Doc.

Phase-5: Email
- Once all above 4 phases are completed, email me the links to the created Google Sheets and Google Docs.

<topic>International Politics</topic>
<source>Google News Search</source>

Note about the prompt:

The prompt is designed in a way that you'll only have to change the <topic> and <source> tags (presented at the bottom of the prompt) to fit it for your own needs!
Runner-H is in beta, so if one run doesn't generate the desired result, give it another try a few moment later.
Although I've asked it to use Google news search, it actually used Bing news search. May be it's related to its current preference. You can try with other web source.

Use Case & Impact

Anyone wanting to automate everyday tasks with Runner-H that's related to web search, story writing, social media post generation, writing Google Sheet and Google Doc. can get inspiration from my prompt.
Writing prompt this way and using it with Runner-H can help others create share-able automation techniques. This can really save a lot of time and help people spend more time on other important stuff, or even have more fun! 🥳

Social Love

Social Love 1: Sharing about Runner-H

// Detect dark theme var iframe = document.getElementById('tweet-1940755920297861582-680'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=1940755920297861582&theme=dark" }

Social Love 2: Sharing a sample Run of this prompt

// Detect dark theme var iframe = document.getElementById('tweet-1942110641457934368-154'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=1942110641457934368&theme=dark" }

hotline.email: AI Powered Multi-role single point inbound email categorization with Postmark and Agent.ai

Fayaz — Mon, 09 Jun 2025 06:48:23 +0000

Update: rethinking hotline.email

Working with hotline.email with new and powerful AI based user facing email application. Stay Tuned for future updates!

This is a submission for the Postmark Challenge: Inbox Innovators.

What I Built

This is a MVP concept demonstration project, where I'll demonstrate how Postmark's inbound webhook and domain forwarding features can be directly integrated with an AI agent created with Agent.ai Platform, to act as either a middle-wire layer or as a decision making layer.

Leveraging the power of LLMs and other features of Agent.ai, anyone can supercharge Postmark's inbound email feature and create a full featured AI powered application layer for their business needs.

Demo

Following is the demo MVP AI Agent:

HotLine.email AI Agent

As it is built, any email sent to any @HotLine.email address will be process, categorized and sent to a pre-configured email address based on the content of the email and the configuration JSON.

Note: This agent should not be used directly, rather its webhook should be configured in Postmark's inbound webhook setting like the following:

TESTING

To test if it works as advertised, you'll just have to provide your own configuration JSON within your email's text (say at the end of the original email content).

That way, the processed email will go to an email given in your own provided configuration, instead of the pre-configured email set by HotLine.email Agent.

JSON configuration example:

{
  "support": {
    "description": "All support related email should go here",
    "email": "support@fayaz.dev"
  },
  "offer": {
    "description": "All offer and opportunity related promotional emails should go here. These are external offers.",
    "email": "offers@fayaz.dev"
  },
  "hr": {
    "description": "All human resource related email should go here",
    "email": "hr@fayaz.dev"
  },
  "sales": {
    "description": "All sales related email should go here. These are related to our own product sales query",
    "email": "sales@fayaz.dev"
  },
  "fallback": {
    "description": "This is the fall back email - if nothing matches or you are unsure, choose this email",
    "email": "fallback@fayaz.dev"
  }
}

Later the Agent will be updated so that someone can claim their own @HotLine.email and configure how any inbound email will be processed and forwarded if an email is sent to their own example@HotLine.email address.

Code Repository

This agent's build actions (source) is shared in public on Agent.ai. So anyone will be able to see or clone the AI agent from the following link after registering with Agent.ai.

HotLine.email AI Agent's Source Code

How I Built It

Used Dynadot for registering and DNS configuring the hotline.email domain name - as you'll need a domain name to work with Postmark.

Used Postmark's inbound webhook feature.
Used Postmark's inbound domain forwarding feature.
Used Agent.ai to create an AI agent with webhook.

Conclusion

I was pleasantly surprised by Postmark's inbound feature and that how easy it was to work with Agent.ai to create a powerful AI agent!

It was a great experience for me!

Robonaut's Quest to Black Hole on Alibaba Cloud!

Fayaz — Mon, 28 Apr 2025 06:48:50 +0000

This is a submission for the Alibaba Cloud Challenge: Build a Web Game.*

What I Built

Note: updating domain from robonaut.quest to robonaut.net. So links won't work for now. Please check back later!

Robonaut's Quest to Black Hole is an action-packed space adventure game where you control Robonaut, humanity's last hope against a dangerous exotic blackhole threatening Earth.

Your mission is to collect crucial exotic particles spontaneously created by a strange black hole. This will help scientists understand and defend against this cosmic threat. Navigate carefully at the edge of the singularity while gathering valuable data to save mankind.

Demo

Static Web Game: Robonaut's Quest to Black Hole

1. Game Landing Page

2. Game Starts

3. Game Play

4. Game Over

Alibaba Cloud Services Implementation

I've used Alibaba Cloud's Object Storage Service (OSS) to host static game assets. Later plan is to:

Use Alibaba Cloud CDN for faster access all over the globe.
Use Alibaba Cloud Simple Application Server (SAS) to globally update the Game Leader Board. I chose SAS over Elastic Compute Service (ECS) because in this project my server side requirement is minimal, and to my understanding, SAS will provide a more predictable cost.

Experience

I've faced difficulties in registering some cloud services with Alibaba Cloud. The same Credit Card I've used in AWS, X, Canva, ChatGPT etc. without any issue, Alibaba Cloud required far more personal details than I was comfortable to share for verification purposes. I understand this was done for greater security, so can't complain!

Eventually I've used PayPal, which worked fine, except I was unable to access Free trials (as they require Credit Card for the free trials).

So while the interface was smooth, and the services and pricing is competitive, the onboarding process was not smooth at all!

I recommend Alibaba Cloud to improve upon an easier onboarding process!
If the payment security is a concern, I recommend they add advance top-up option, so that customers can use the cloud services up to their top up amount paid in advance without worrying much about payment security and privacy.
Eventually I could register a Simple Application Server and Object Storage Service with PayPal account using Resource Plan option, which works kind of like advance top-up, but an account wide advance top-up option (along with per service Resource Plan) would be preferable to some users like me.

Game Development Highlights

SweaDev / robonaut-blackhole

Robonaut's Quest to Blackhole

Game Description

An action-packed space adventure game where you control Robonaut, humanity's last hope against a dangerous exotic blackhole threatening Earth. Your mission is to collect crucial exotic particles that will help scientists understand and defend against this cosmic threat. Navigate carefully at the edge of the singularity while gathering valuable data to save mankind.

How to Play

Start the game by clicking the "START MISSION" button on the home page.
Use WASD or Arrow Keys to control Robonaut's spacecraft.
Collect blue exotic particles to increase your data collection score.
Avoid getting too close to the black hole's event horizon.
@todo If trapped in the black hole's pull, you need at least 5 exotic particles to escape.
@todo Press SPACE to use your emergency thrusters and escape (costs 5 exotic particles).
@todo make the gameplay mobile friendly.

Features

Interactive home page with animated background
In-game settings…

View on GitHub

I've heavily used GitHub Copilot (VS Code Insiders Edition) for the development of this game.
Clade 3.7, Gemini 2.5 Pro and GPT 4.1 was my LLM of choice. But mostly it was Claude 3.7
It was 80% vibe coding and 20% developed and designed by me along with debugging.
Using Git, Vite, pnpm, Lazy Git along with VS Code with GitHub Copilot Agent mode was a great experience!
While developing this game, I've realized that LLMs work better with specific Software Engineering (SWE) and Design Pattern instructions. So this means having good SWE understanding will always be a plus for developers, even if you use the so called vibe coding!

Future Updates

For now the game is suitable for Computers, later plan is to implement mobile friendly interface.

Conclusion

I was experimenting the code generating ability of modern AI for some time now. My eventual goal is to 100x my development using AI, so I was actively looking for different Cloud hosting services than can smoothly support my Solopreneur Adventure using advancement in AI!

So even though I didn't use a lot of Cloud resources in this project, developing this game and exploring different Alibaba Cloud's services was a great experience for me in the right direction.

I hope you'll enjoy Playing the Game as much as I've enjoyed developing it! Robonaut.quest

You may reach out to me on X/Twitter: SweaDev

Poll on AI Code generation: do you prefer more accuracy or easier to debug code?

Fayaz — Sat, 01 Mar 2025 09:32:39 +0000

In my post State of generative AI in Software Development: The reality check, I've mentioned a very important observation I had:

Consider the following two hypothetical situations:

1) An LLM generates 80% accurate code, where the remaining 20% inaccurate code is very difficult to debug!

2) Another LLM generates 60% accurate code, but the remaining 40% inaccurate code is very easy to debug.

If this is the case, then the second result would most likely be far more preferable to many developers!

Hence, this scenario must also be considered in judging the code generation performance of an LLM.

Today, I thought I should ask fellow developers about their opinion on this matter!

So I posted a poll on X about it!

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So what do you prefer?
I'd love ♥️ to know your preference on this!

Switching from tmux to Zellij

Fayaz — Fri, 28 Feb 2025 18:50:07 +0000

Terminal Multiplexer

If you've used terminal multiplexer in command line, you know tmux is cool! If you haven't, you really should use something like tmux, especially if you use Terminal or any Command Line Interface (CLI) a lot or SSH into remote servers often!

Why?

Tell me: what do you do, if suddenly your SSH connection is dropped while you're in the middle of an important installation or update process that needs your input?

That terminal session is lost and you cannot easily (if at all) get back to that installation process!

This may make your system a half cooked mess! A terminal multiplexer is what you need to solve this exact issue.

Let's just say: it'll save the day, by letting you log back into the session at the exact point you were dropped off from!

The switch

Tmux is not just a terminal multiplexer though, it's a lot more than that. But this post is not about tmux. It's about me switching from tmux to Zellij. Why? Because Zellij is just better, and with a lot more batteries included.

I was keeping an eye on Zellij for some time now, but didn't get enough time to switch. But today was that day. So here I come Zellij!

P.S. you don't need to learn tmux to start using Zellij. So if you've never used tmux, just start with Zellij right away. Here are some tutorials.

Installation

As it happened, Zellij cannot be directly installed on the OS I'm using: Ubuntu. Also, this is a new laptop I'm setting up, so I needed to take a few steps to install Zellij. So may be this post will be useful to someone!

# install build essentials on Ubuntu
sudo apt update
sudo apt install build-essential

# Install rust / cargo
curl https://sh.rustup.rs -sSf | sh

# install zellij using cargo
cargo install --locked zellij

# That's it - now run Zellij!
zellij

Further info

Here's a useful video walk through with Zellij:

That's it! Happy CLI mastery with Zellij, Oh My zsh and Alacritty!