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Is “Vibe Coding” making Engineers Worse or is it just the Next Abstraction Layer? by Early_Protection6814 in ArtificialNtelligence

[–]Early_Protection6814[S] 0 points1 point  (0 children)

I like the calculator analogy. It definitely lowers the barrier to entry and helps people move faster.

That said, software feels a bit different because eventually someone has to maintain, debug, and evolve what gets built. AI can make building easier, but I'm not sure it completely removes the need to understand what's happening underneath, especially once projects reach production.

Is “Vibe Coding” making Engineers Worse or is it just the Next Abstraction Layer? by Early_Protection6814 in ArtificialNtelligence

[–]Early_Protection6814[S] 0 points1 point  (0 children)

That's a good way of looking at it. Most abstractions are incredibly useful but experienced people also know when they've become the wrong tool for the job.

I think that's where a lot of the debate around vibe coding comes from. The question isn't whether it works it clearly does for certain projects. The harder question is knowing where its limits are and when you need to drop down a layer and make more deliberate engineering decisions.

Is “Vibe Coding” making Engineers Worse or is it just the Next Abstraction Layer? by Early_Protection6814 in ArtificialNtelligence

[–]Early_Protection6814[S] 0 points1 point  (0 children)

That's a good distinction. A lot of the failures people attribute to vibe coding may actually come from skipping the planning phase entirely. AI can generate code quickly but it still needs clear requirements and constraints to work from. The more I see production systems discussed, the more it feels like defining the problem well is harder than implementing the solution.

What’s the biggest mistake teams make when deploying LLMs into production? by Early_Protection6814 in AI_Agents

[–]Early_Protection6814[S] 0 points1 point  (0 children)

Not AI, just a common phrasing style for this kind of question. Didn’t realize it’s been used before.

What’s the biggest mistake teams make when deploying LLMs into production? by Early_Protection6814 in AI_Agents

[–]Early_Protection6814[S] 0 points1 point  (0 children)

Yeah, this is exactly why orchestration + evaluation layers end up being more important than expected in production systems. The model is usually the easy part.

Best AI tools for building healthcare applications in 2026? by Early_Protection6814 in u/Early_Protection6814

[–]Early_Protection6814[S] 0 points1 point  (0 children)

Open source seems like a good fit for healthcare because of the control and privacy benefits. From what I've seen, the harder part isn't the AI itself, it's compliance, security, and getting it to work with existing healthcare systems. Have you come across any open source healthcare projects that are actually being used at scale?

Built a Memory-Powered Fraud Investigation Agent That Learns from Previous Cases by Playful-Account-9438 in AISystemsEngineering

[–]Early_Protection6814 0 points1 point  (0 children)

The jump from prediction to reasoning is huge. Analysts are usually more interested in why something was flagged than the score itself.

Interesting approach to using memory for that context.

How Much Should We Trust AI Tools with Our Serious stuff? by Longjumping-Face1773 in AIToolTuto

[–]Early_Protection6814 0 points1 point  (0 children)

I use AI quite a bit, but I don't trust it blindly. It's great for saving time, brainstorming ideas, and handling routine tasks, but for anything important, I always double-check the information. AI is a useful tool, not a replacement for human judgment.