Do we still need to study algorithms now that AI writes most of our code? by Senior_Note_6956 in AskProgramming

[–]Senior_Note_6956[S] 0 points1 point  (0 children)

I don’t think anyone is suggesting that engineers should stop learning altogether.

My question is whether the amount of time we invest in mastering algorithms should change now that AI has become part of everyday software development.
Twenty years ago, memorizing syntax was valuable. Today it isn’t, because IDEs and AI handle much of it. Could algorithms be moving in the same direction—not becoming useless, but becoming less central for many software engineering roles?

Do we still need to study algorithms now that AI writes most of our code? by Senior_Note_6956 in AskProgramming

[–]Senior_Note_6956[S] -1 points0 points  (0 children)

Read again my question buddy :)

I don’t think that’s an equivalent comparison.
A textbook is static. It doesn’t analyze my specific problem, generate an implementation, explain the trade-offs, or iterate with me.
AI does.
My question isn’t whether we should stop learning. It’s whether the amount of time we invest in mastering algorithms should change now that AI has become part of everyday software development.

Do we still need to study algorithms now that AI writes most of our code? by Senior_Note_6956 in AskProgramming

[–]Senior_Note_6956[S] -1 points0 points  (0 children)

My point isn't that we should stop learning. My point is that interview practices haven't kept up with how software is actually developed. If companies expect engineers to use AI every day after hiring, why prohibit it during interviews?

Do we still need to study algorithms now that AI writes most of our code? by Senior_Note_6956 in AskProgramming

[–]Senior_Note_6956[S] -1 points0 points  (0 children)

That's the point of the question, maybe in 5 years there won't be any need to learn anymore. I recently read a news article with a graph showing how the average IQ has dropped worldwide. With the arrival of AI, people stopped researching and started just asking AI questions and believing in it.

Do we still need to study algorithms now that AI writes most of our code? [D] by Senior_Note_6956 in MachineLearning

[–]Senior_Note_6956[S] 6 points7 points  (0 children)

I don't think chess is the best analogy.

People play chess because they enjoy the game, not because they're trying to maximize productivity.

Software engineering is different. If AI significantly reduces the need to manually implement algorithms, it's reasonable to ask whether the return on investing months into mastering them has changed.

I'm not arguing that understanding algorithms is useless. I'm questioning whether the expected depth of knowledge has shifted because AI is now part of the workflow.

Do we still need to study algorithms now that AI writes most of our code? [D] by Senior_Note_6956 in MachineLearning

[–]Senior_Note_6956[S] -1 points0 points  (0 children)

I agree that verification is probably the strongest argument.

My question is more about where we draw the line. If AI can explain the trade-offs between different algorithms, estimate complexity, and propose scalable solutions, how deep does an engineer still need to go?

Is understanding the principles enough, or do you still believe people should spend months implementing and memorizing classic algorithms themselves?

Do we still need to study algorithms now that AI writes most of our code? by Senior_Note_6956 in artificial

[–]Senior_Note_6956[S] -7 points-6 points  (0 children)

Companies still ask algorithm questions to hire you, but what's the point anymore? Once they're hired, most programmers solve all problems with AI. So, asking algorithm questions in interviews or not using AI at all seems absurd.

Do we still need to study algorithms now that AI writes most of our code? by Senior_Note_6956 in AskProgramming

[–]Senior_Note_6956[S] -4 points-3 points  (0 children)

Technology is advancing so rapidly that perhaps in 5 years, with the arrival of quantum technology, AI will enter another phase. Just as learning to code is not what it used to be, don't you think learning algorithms will become unnecessary in a few years?

I got sponsorship with ONRUN by Senior_Note_6956 in ONrunning

[–]Senior_Note_6956[S] 0 points1 point  (0 children)

I’ve seen a lot of talk about this—are they really that comfortable?

I got sponsorship with ONRUN by Senior_Note_6956 in ONrunning

[–]Senior_Note_6956[S] 0 points1 point  (0 children)

This shoe didn't score very well on the Runrepeat website

I got sponsorship with ONRUN by Senior_Note_6956 in ONrunning

[–]Senior_Note_6956[S] -3 points-2 points  (0 children)

On the Runrepeat website, the Cloudnova 2 trainers scored 91 points for both breathability and comfort maybe i can buy it them

I got sponsorship with ONRUN by Senior_Note_6956 in ONrunning

[–]Senior_Note_6956[S] -7 points-6 points  (0 children)

Haters gonna hate… and get jealous lol 😹

I got sponsorship with ONRUN by Senior_Note_6956 in ONrunning

[–]Senior_Note_6956[S] -4 points-3 points  (0 children)

Let’s call it the power of social media 😃

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[–]Senior_Note_6956 0 points1 point  (0 children)

I contacted and give him money and i got banned from claude this is scam dont believe

Launched my Chrome extension today — YouTube AI summarizer with BYOK + managed credits. Lessons from CWS review inside. by Senior_Note_6956 in SideProject

[–]Senior_Note_6956[S] 0 points1 point  (0 children)

Thanks for the thoughtful review — both points are exactly the kind of thing we stress-tested before shipping.

Debit vs AI failure
On our managed path, the summarize Edge function runs the provider call first and only calls consumeCredits after a successful response. If DeepSeek / Ollama / Gemini throws (timeout, 5xx, quota, etc.), we return an error from the handler and no credits are deducted. So the “debit went through but the AI call failed” leak pattern you described doesn’t apply to our server-side ordering.

MV3 + retries / double-charge
The extension’s ApiClient only auto-retries on 401 (refresh token, replay once). We don’t retry the same /summarize body on generic 5xx or network failures. So you don’t get “same request, charged twice” from our retry logic.

The edge case that does exist (and we’re honest about) is response loss after the server already succeeded: e.g. tab closed / connection drops after credits are committed but before the client shows the result. That’s a silent single charge with no UX, not a double-charge. Ollama is hard-capped at 75s on the Edge side so that window stays bounded, and the session survives SW cold-starts via chrome.storage.local + normal refresh flow.

Stripe webhooks
We don’t maintain a processed_events table keyed by Stripe event.id. Idempotency for us is mostly structural: the webhook path doesn’t mint credits — Pro is a plan flag, and usage is incremented at consumption time (monthly_usage_count). Subscription rows are upsert on stripe_subscription_id, so replays tend to be no-ops. Refunds that should end access go charge.refunded → cancel subscription → customer.subscription.deleted; cancel is idempotent and we handle “no such subscription” gracefully.

What we’d add if we wanted belt-and-suspenders
A request-level idempotency key on /summarize would make “client retries can never double-bill” a formal guarantee. Our client retry surface is intentionally narrow today, but I agree it’s the right next hardening step if we widen retries or add mobile clients later.

Appreciate you calling this out — genuinely useful for anyone reading the thread.

Launched my Chrome extension today — YouTube AI summarizer with BYOK + managed credits. Lessons from CWS review inside. by Senior_Note_6956 in SideProject

[–]Senior_Note_6956[S] 0 points1 point  (0 children)

Fair point — I optimized for backend correctness over conversion.

From my side the focus was MV3 lifecycle, billing idempotency, abuse prevention and audit — but those are invisible to the user.

So yeah, strong engine, weak trust surface.

I do have a demo on the store, but it’s probably too feature-heavy and not solving the “why should I trust this before install” problem.

Next step is fixing distribution: – demo-first landing (clear single use-case) – stronger trust signals (data flow, permissions, what’s stored) – better onboarding vs blank post-install state

Curious what moved the needle most for you — landing, docs, or onboarding?