[randfish]

Deeply curious to know if this is an outlier opinion, a mainstream but pessimistic one, or the general consensus. My LinkedIn feed and personal network certainly suggests that it's an outlier, but I wonder if the people around me are overly optimistic or out of synch with what the HN community is experiencing more broadly.

[MobiusHorizons]

My impression has been that in corporate settings (and I would include LinkedIn in that) AI optimism is basically used as virtue signaling, making it very hard to distinguish people who are actually excited about the tech from people wanting to be accepted.

My personal experience has been that AI has trouble keeping the scope of the change small and targeted. I have only been using Gemini 2.5 pro though, as we don’t have access to other models at my work. My friend tells me he uses Claud for coding and Gemini for documentation.

[bGl2YW5j]

I reckon this opinion is more prevalent than the hyped blog posts and news stories suggest; I've been asking this exact question of colleagues and most share the sentiment, myself included, albeit not as pessimistic.

Most people I've seen espousing LLMs and agentic workflows as a silver bullet have limited experience with the frameworks and languages they use with these workflows.

My view currently is one of cautious optimism; that LLM workflows will get to a more stable point whereby they ARE close to what the hype suggests. For now, that quote that "LLMs raise the floor, not the ceiling" I think is very apt.

LinkedIn is full of BS posturing, ignore it.

[WD-42]

I think it’s pretty common among people whose job it is to provide working, production software.

If you go by MBA types on LinkedIn that aren’t really developers or haven’t been in a long time, now they can vibe out some react components or a python script so it’s a revolution.

[danielbln]

Hi, my job is building working production software (these days heavily LLM assisted). The author of the article doesn't know what they're talking about.

[Terretta]

Which part of the opinion?

I tend to strongly agree with the "unpopular opinion" about the IDEs mentioned versus CLI (specifically, aider.chat and Claude Code).

Assuming (this is key) you have mastery of the language and framework you're using, working with the CLI tool in 25 year old XP practices is an incredible accelerant.

Caveats:

- You absolutely must bring taste and critical thinking, as the LLM has neither.

- You absolutely must bring systems thinking, as it cannot keep deep weirdness "in mind". By this I mean the second and third order things that "gotcha" about how things ought to work but don't.

- Finally, you should package up everything new about your language or frameworks since a few months or year before the knowledge cutoff date, and include a condensed synthesis in your context (e.g., Swift 6 and 6.1 versus the 5.10 and 2024's WWDC announcements that are all GPT-5 knows).

For this last one I find it useful to (a) use OpenAI's "Deep Research" to first whitepaper the gaps, then another pass to turn that into a Markdown context prompt, and finally bring that over to your LLM tooling to include as needed when doing a spec or in architect mode. Similarly, (b) use repomap tools on dependencies if creating new code that leverages those dependencies, and have that in context for that work.

I'm confused why these two obvious steps aren't built into leading agentic tools, but maybe handling the LLM as a naive and outdated "Rain Man" type doesn't figure into mental models at most KoolAid-drinking "AI" startups, or maybe vibecoders don't care, so it's just not a priority.

Either way, context based development beats Leroy Jenkins.

[throwdbaaway]

> use repomap tools on dependencies if creating new code that leverages those dependencies, and have that in context for that work.

It seems to me that currently there are 2 schools of thought:

1. Use repomap and/or LSP to help the models navigate the code base

2. Let the models figure things out with grep

Personally, I am 100% a grep guy, and my editor doesn't even have LSP enabled. So, it is very interesting to see how many of these agentic tools do exactly the same thing.

And Claude Code /init is a great feature that basically writes down the current mental model after the initial round of grep.

[Terretta]

I agree with the 2 schools, but different conclusion:

The strategy of one or the other brings differing big gaps and require context or prompt work to compensation.

They should be using 1 to keep overall lay of the land, and 2 before writing any code.

[procaryote]

Linkedin posts seems like an awful source. The people I see posting for themselves there are either pre-successful or just very fond of personal branding

[sensanaty]

Speaking to actual humans IRL (as in, non-management colleagues and friends in the field), people are pretty lukewarm on AI, with a decent chunk of them who find AI tooling makes them less productive. I know a handful of people who are generally very bullish on AI, but even they are nowhere near the breathless praise and hype you read about here and on LinkedIn, they're much more measured about it and approach it with what I would classify as common sense. Of course this is entirely anecdotal, and probably depends where you are and what kind of business you're in, though I will say I'm in a field where AI even makes some amount of sense (customer support software), and even then I'm definitely noticing a trend of disillusionment.

On the management side, however, we have all sorts of AI mandates, workshops, social media posts hyping our AI stuff, our whole "product vision" is some AI-hallucinated nightmare that nobody understands, you'd genuinely think we've been doing nothing but AI for the last decade the way we're contorting ourselves to shove "AI" into every single corner of the product. Every day I see our CxOs posting on LinkedIn about the random topic-of-the-hour regarding AI. When GPT-5 launched, it was like clockwork, "How We're Using GPT-5 At $COMPANY To Solve Problems We've Never Solved Before!" mere minutes after it was released (we did not have early access to it lol). Hilarious in retrospect, considering what a joke the launch was like with the hallucinated graphs and hilarious errors like in the Bernoulli's Principle slide.

Despite all the mandates and mandatory shoves coming from management, I've noticed the teams I'm close with (my team included) are starting to push back themselves a bit. They're getting rid of the spam generating PR bots that have never, not once, provided a useful PR comment. People are asking for the various subscriptions they were granted be revoked because they're not using them and it's a waste of money. Our own customers #1 piece of feedback is to focus less on stupid AI shit nobody ever asked for, and to instead improve the core product (duh). I'm even seeing our CTO who was fanboy number 1 start dialing it back a bit and relenting.

It's good to keep in mind that HN is primarily an advertisement platform for YC and their startups. If you check YC's recent batches, you would think that the 1 and only technology that exists in the world is AI, every single one of them mentions AI in one way or another. The majority of them are the lowest effort shit imaginable that just wraps some AI APIs and is calling it a product. There is a LOT of money riding on this hype wave, so there's also a lot of people with vested interests in making it seem like these systems work flawlessly. The less said about LinkedIn the better, that site is the epitome of the dead internet theory.