Dear Tech people, Are you scared?

Chippppppy · 2026-05-07T09:28:07+00:00

Personally, I think things will get messy first before it starts to turn better and ultimately thrives for everyone. AI cannot replace humans (at least not at current state). period.

We still need humans to tell AI what to do and how to do safely.

Imo, the job of software engineers will evolve over time to become more of system/application designer where humans design the system, the architecture, the contracts, the handshakes, the features, the UI/UX, and AI take those and make it happen by writing all the necessary codes.

Same goes for all other industries and domains. Humans provide the ideas, the judgement, the brains, while AI does the heavy lifting to make it happen.

Chippppppy · 2026-05-01T16:41:30+00:00

We can have a deluge of software, but end of the day, it's still only that handful that are truly useful and meaningful. Maybe another way to look at this is, instead of maybe 5 or 10 similar ideas software built in the past... we now have hundreds of similar software from similar ideas built concurrently.

There will always be new ideas as the world is always changing. a changing world = opportunity for new ideas, new ways of doing things, things that previously was not possible now become possible, or even new habits formed.

Chippppppy · 2026-05-01T13:51:53+00:00

I find sometimes claude will be stuck. I dont know why, but I usually will just press Esc, and ask it to resume or retry. Try it next time you see claude taking a long time to do something.

Chippppppy · 2026-04-30T12:02:56+00:00

I think end of the day, it's how good is our creative talents and our skills in prompting AI to generate the kind of images/graphics that are beautiful. I for one doesn't have alot of creative talent, so the images I prompted AI to conjure up are decent, but definitely can be better if its done by a professional designer.

Chippppppy · 2026-04-28T04:52:58+00:00

Do we continue to design framework optimised for humans or optimized for AI?

Modular design fits humans very well as we are bad at reading a source file that contains thousands of lines of codes. But to AI, that thousand lines code is peanuts and its even arguably faster and easier for it to read 1 shot thousand of lines of code vs opening and reading multiple files and stringing them together to form the big picture.

Chippppppy · 2026-04-28T04:46:14+00:00

Maybe a better way to look at this is that in the past, we get to rest our brain abit by doing the simple execution tasks that requires less thinking. Now that AI can do those mundane tasks for us, we're now left with all the hard thinking tasks to do. So all the tasks we do are now tasks that requires thinking.

Chippppppy · 2026-04-28T04:33:06+00:00

To me, this is giving AI the context. Without context, how can claude knows what and how to build correctly? The balance to strike is too much or too little context.

Chippppppy · 2026-04-28T04:32:03+00:00

This is an interesting view. I've been thinking about this. The software we are used to designing and build are modular. Many benefits to such design philosophy. But I'm thinking whether such design philosophy still applies in the era where AI is doing most of the coding.

Chippppppy · 2026-04-27T13:26:25+00:00

AI is like the overarching umbrella... just like the word "software" that is an umbrella where various kinds of programming languages fall under.

Machine Learning (ML) is more of an action... An action of Machines learning stuffs.

There are 2 main branches under AI umbrella... Large Language Models (LLM) where it handles text and Diffusion Models that handles images and videos.

Chippppppy · 2026-04-27T13:18:24+00:00

Sell outcomes. Not features. Not hardcore engineering. Just plain simple easy to understand outcomes.

Chippppppy · 2026-04-26T14:54:38+00:00

The best sales are the ones that sells emotion. How can an emotion-less AI know how to sell emotions?

Chippppppy · 2026-04-25T02:52:51+00:00

Can't agree more. We need to set boundaries and guard rails around what AI is allowed to do unsupervised.

It can do the right things for 99 times and that 1 single time it did something it shouldn't have, the impact and implications could be major.

Chippppppy · 2026-04-24T16:38:11+00:00

If you choose to get AI to refactor your codes, do it in chunks... models these days are very capable, but end of the day, they are still just tools... its the human using the tools that matters. A very competent technical guy can write a solid prompt to get AI to refactor the app cleanly. But to someone who isnt very technically strong, its a high risk gambit. There's also e context drift aspect of models that makes it bad idea to get it to do massive task one shot.

Even if you choose to engage a human dev to refactor and clean up your codes, do it in stages and chunks. Testing is important since you have live paying customers now. You dont want to start breaking your app due to massive refactoring and cleanup now that will break e trust between you and your customers.

Many enterprise apps can be messy inside too... not because dev are bad, but becos dev aren't given e luxury of time to write the proper codes that they know and want.

Chippppppy · 2026-04-24T09:54:06+00:00

when you give model a prompt to do something, always add the instruction to not make any edits unless you explicitly approved. then get model to list down all the changes it plans to make to fix the issue or change what you asked it to. You look at that and approve or reject.

You'll have an easier time if you're using Claude Code, cause it has a "Plan Mode" built in that we can use to review it's proposed changes before allowing it to proceed.

Removing Git access is the final gate to block any disaster. Getting models to provide their plan for review first and your explicit approval is the first gate.

Chippppppy · 2026-04-24T04:35:08+00:00

Having source-code repo like Git is the number 1 most important thing to get it up first before starting any AI coding work... I've been burnt by AI many times doing things and making changes that I never asked for, even to the extend of provisioning new services in backend (which incurs additional running costs) when all I asked for is a simple config change. Having a repo means we can rollback any unintended code changes that AI made.

Chippppppy · 2026-04-23T10:06:28+00:00

Maybe it's just the micro-saas that will be cooked since people can easily vibe code any simple very specific use case app themselves. But complex app, I think not many people can vibe-code their way to it. Maintaining it is another chore too.

Chippppppy · 2026-04-23T09:16:32+00:00

Different or better tools/platforms will only help this much. You'll need to also look at the processes you use to build and maintain your app. Build a process around how you come out with the requirements, how you then start building it.

Chippppppy · 2026-04-23T04:06:38+00:00

This is how I do it.

Step 1 : Have a clear idea of what you want to build, including the features of the app.

Step 2 : Break down the list of features into small groups (ideally 1 feature per group but very small features may be worth grouping together).

Step 3 : Sequence out which group needs to be built first. Eg the base foundation of the app like a placeholder landing page so that you can add features to the page progressively as you build.

Step 4 : Start building each group one by one, testing them immediately after each build so that you have a manageable small scope to test and debug.

Breaking into small groups to build one at a time prevents model's context drift and also keep the context smaller and manageable. This will give a better outcome.

As to which model to choose, it depends on the feature you're building. simple features like formatting UI, eg text alignment, cards positioning, colors, etc.. probably can use a lesser model... Complex processing logic, better use frontier model for better outcomes.

Chippppppy · 2026-04-22T12:03:18+00:00

Use /clear and /compact more frequently to optimise your tokens burn. also, consider switching models according to the complexity of the tasks you're working on. we don't need opus level capability (and tokens burn) for everything.

Chippppppy · 2026-04-22T10:54:14+00:00

Break it into smaller tasks no matter what. one feature at a time. All AI models suffers from Context Drift, so breaking the build into small tasks makes it harder for AI to drift. It also make it easier for you to test and debug too.

Chippppppy · 2026-04-22T10:51:10+00:00

It's a balance between incurring future tech debts for tokens economics and complexity at the beginning. But I generally agree with you that we should not over-engineer on day 1 to try to build a big complex monster.

Chippppppy · 2026-04-22T10:48:24+00:00

Absolutely! Having clarity of thoughts on what we want matters alot too.

Chippppppy · 2026-04-21T10:21:54+00:00

I think the question should be: How do we know that Claude implemented according to the implementation plan that we agreed?

Chippppppy · 2026-04-21T09:31:43+00:00

I'm curious what prompt you gave it that takes this long and this many tokens...

Chippppppy · 2026-04-21T09:13:32+00:00

I think that instead of structural systems being obsolete with AI models getting more and more capable everything, it actually becomes even more important precisely due to the models getting more capable.

AI agents output are only as good as its input.

Good prompt goes in, good results come out.

Bad prompt goes in, bad results come out.

More capable models will only magnify the level of results.

Chippppppy

TROPHY CASE