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Junior makes random mistakes. LLM makes consistent mistakes.

After a few weeks you know the patterns: where it's reliable, where it hallucinates.


You're right - poor wording. Static vs dynamic is what I meant.


Suddenly feeling better about our 99.9% uptime SLA.

When even Cloudflare goes down, nobody can blame the little guys.


AI made us 10x more productive. Management noticed. Now we are expected to ship 10x more features.

The free time was a lie we told ourselves.


>> AI made us 10x more productive.

I'm wondering what you are doing to feel that much of a productivity boost? There have been three occasions since 2022 where I felt concretely 10X more productive. The rest of the time I'm not feeling a huge / direct impact. While it isn't necessarily drastically more productive in a linear sense (churning out more of the same stuff for instance) things have radically changed nonetheless.


It will be interesting if it leads to a bloat of unnecessary features that customers dont really want


Prediction. The rise of articles discussing AI attributed bloat and technical debt.

As they say.. Garbage in..


From that standpoint, it's definitely happening already. But removal is easier too now. The real question is whether teams will have the discipline to delete. Most won't, but those who do will build better products.


I am beginning to notice more "features" in apps that suspiciously raise questions: "Was that feature really needed and was the AI to sneak it in there!?"


Always the case


I'm building a startup myself and we vibe code everything, except core/complex parts where AI struggles.

My workflow:

1. Give the agent detailed task + relevant file contexts (so it doesn't waste time searching)

2. Guide it with continuous feedback when it veers off

3. Jump in manually if it's going completely wrong direction

I use Claude Code for the agent work, Cursor for manual edits.

Key insight: AI coding works as well as your architecture does. I make all architecture decisions, file structure, organization -- AI just writes the implementation logic.

Clean architecture = better AI output.

I believe This is becoming the norm because it gives startups massive velocity advantage. That's probably why your CTO is pushing it.

Career impact? As a senior who understands what AI writes, you're gaining superpowers, not losing skills. You were previously bottlenecked by implementation speed and needed other engineers. Now you can be a 10x engineer - as long as you can envision the architecture, AI helps you build it faster.

The key is knowing when to guide vs when to take over. That judgment comes from experience.


Thank you for your response.

If it's ok, could you please share if you follow a certain structure when defining the detailed task for the agent.


No specific structure - you'll need to experiment based on your project.

I treat it like briefing a senior engineer. Example:

"Currently we have X, Y, Z but it has ABC limitation. We need to support FOOBAR. What are our options?"

Agent suggests 3 approaches.

"Go with Approach 1 but remove the Z part. Implement it."

Try being specific about constraints and current state. The agent fills in the implementation details, but you drive the decisions.


Once you've gone through the learning resources others shared, LeetCode has a dedicated concurrency section for hands-on practice: https://leetcode.com/problemset/concurrency/

(I actually authored a few of those problems - they cover the classic scenarios like producer-consumer, dining philosophers, etc.)


It had been up for me, from Asia.


My WHY: I need money to pay rent. My company's WHY: Generate shareholder value.

There, saved everyone $97. You're welcome.


What's your take on using AI-generated content for GEO? If it follows E-E-A-T and adds genuine value, does the origin matter for AI visibility?


Nice read though!

Also checkout https://www.youtube.com/watch?v=iT7kq-R3Gjc too. It's absolute gold for GEO/AEO.


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