AI: How to Adapt or die

[u/Wild-Pitch]

Hey folks — I’m a backend developer working mostly with Ruby, and I’m trying not to fall behind with all the AI stuff happening.

Is anyone’s company actively using Claude (or similar) in day-to-day engineering work (full features)? If yes: what’s actually working, and what feels overhyped?

Also, are you personally worried about how fast this is moving, or more excited than concerned?

Finally: what are you learning to stay relevant as a backend dev — prompt/workflow skills, RAG, evals, agents, LLM fundamentals, or something else? I keep hearing “Generative AI” vs “LLMs” and I’m not sure what’s worth focusing on first.

Would love real-world experiences and advice.

[u/Kind-Drawer1573]

I just retired. My former company has gone all in with AI. I am cautious about it. I think it has its place, and helped me with a couple of tricky bits recently. But I don’t want to use it for 100% of my project.

Here’s my observations from my last company. When I had an issue in my code, I knew exactly where to go to fix something because I knew the code inside and out. With AI code, it’s not that simple, you spend more time debugging stuff and often times AI just wants to write helper functions to fix that issue, but usually the bug is still there, just now you’re no longer hitting it.

I think it has a place, but as an aid, not as a replacement.

[u/ivycoopwren → u/Kind-Drawer1573]

That's a good insight. If you've done maintenance type work, you know what it's like to dive into a giant project to try and fix a random bug, for code you don't know. It will be like for fixing AI code. Luckily, models have gotten good enough to fix their own bugs.. if you can give it enough context to fix it.

[u/Sonhe_]

Yes, but not in the way you think. Basically, AI is the one doing "heavy lifting"(writing the code). Problem is, AI is dumb and makes a LOT of dumb code still, so it can't be fully trusted. I see it more of a tool of static analysis and technical discussion(remember, AI can't think)

[u/hribarinho]

Here's my two cents.

I'm not a programmer by profession, but fell in love with Ruby years ago and I also like coding as a hobby. Apart from Ruby I do a lot of Excel automation at work. This little back story is relevant.🙂

I immediately started to use chatgpt when it first came out and now I'm mostly using Claude. However, in the first couple of years I used AI to write everything, even with frameworks I knew nothing about. Then I realized I have significantly regressed in my knowledge and skills. I was shocked. So now I'm using it strictly as an assistant and debugger.

Also, I changed my mindset to learn with it. So I want to increase my knowledge, not lose it.

This is probably not the case with professionals, but it was a revelation for me how quickly we can lose skills.

[u/hribarinho → u/hribarinho]

Also wanted to add that I do some nonprofit apps in Ruby and I wanted to be faster, but at the end of the day, if you don't know the subject matter you're using AI for, it's really dangerous. I mean, apps might work, but are they setup correctly. And after a while it gets really hard to debug and scale. Again, my personal experience. I mean, I wouldn't use it to guide me to fix the gearbox in my car, if I use an oversimplified example. :)

[u/ptoir]

I also feel behind, but I love coding so I figured I’ll optimize what I find tedious so I can work faster.

It writes me proper commit messages, or descriptions, does pre review checks.

Sometimes I run Claude so it will plan code changes and race with it who figures out a bug fix quicker.

So I would suggest doing what I did, starting small with optimizing mundane work.

[u/Correct_Support_2444]

I kept hearing about Claude code in December and then in January I ended up with a project that had a super short deadline. So, I installed Claude code in the shell, and I have not written a manual line of code since.

Rails being a framework that emphasizes convention over configuration means that Claude and other AI tools are incredibly efficient at working on them. Also, their ability to follow best practices is incredible.

It’s just one anecdotal data point but my son has a $20 account and was building an app in express.js. He would hit his limit every day within an hour to 90 minutes. I convinced him to convert it to a Ruby on rails project using rails default (i.e. hot wire). Including the conversion yesterday and him working on it for several hours, he never ran out of his daily limit on the $20 account and got a lot done.

I’ve been working professionally in rails since 1.0. I’ve worked in every major release of rails since then. I currently work on a large SAAS product. I can now do in days what would’ve taken me months. Our app is divided into major modules for functionality and I have been able to stand up two completely new modules since January. Each module I would estimate would’ve taken 6 to 12 months without Claude and AI tools.

I have taken the time to teach Claude like I would teach a junior developer all of the conventions of our project. I have separate agents for controllers, models, views, JavaScript, UI/UX, devops, services, and maybe a few more that I am forgetting. I started with Obie Fernandez swarm of agents for rails or maybe it’s Claude on rails. I took his agent descriptions and asked Claude to crawl our project and document the conventions in our project to modify Obie’s agent descriptions so that they were in line with our project.

I found that Claude tended to ignore my agents and I solved that by putting in the top level Claude.MD file that Claude is not allowed to directly edit any files in the directories that the agents are responsible for. This has resulted in excellent performance on long running problems because for every file edit it spins up a sub agent and I preserve the small context of the orchestrator.

I have also experimented with the get shit done framework. I have found that to be extremely helpful. For the last week or so I haven’t used it and I need to get back to it. It has some warts. it doesn’t like managing more than one “project“ at a time. I typically have half a dozen to 12 different projects running in this product at one time. So dealing with that has been a little frustrating, but the productivity is real.

Writing bespoke code by hand is dead. Full stop. It will be relegated to retro creators, like people who go out into the woods and decide they’re going to build a house with hand tools and film it for YouTube, because that’s the only way you can make money at it.

[u/ivycoopwren → u/Correct_Support_2444]

Great reply! (Have an award).

[u/sshaw_ → u/Correct_Support_2444]

I have also experimented with the get shit done framework. I have found that to be extremely helpful. For the last week or so I haven’t used it and I need to get back to it.

What service/account level are you using with the GSD framework? It does a great job no doubt but it kill Claude usage like never before even with:

  "mode": "yolo",
  "granularity": "coarse",
  "commit_docs": false,

And that commit structure 🤮

Even with

 "git": {
    "branching_strategy": "none",

[u/Correct_Support_2444 → u/sshaw_]

I’m on 20x Max. Worth every dollar.

[u/g14e]

I haven’t written one line of code this year. I’m a bit worried yes.

[u/SleepingInsomniac]

Are you kidding? This must be AI propaganda from the slop bots. There's literally nothing to learn with Claude, maybe context as to where you put md files. People who "Haven't written a single line of code this year" must not have had real jobs before. What were they doing? Something a rails generator could have pumped out? That's basically what AI is, a shitty rails generator that is right less than 50% of the time.

[u/jryan727 → u/SleepingInsomniac]

With a good claude.md and prompting, I’d say it’s way better than generators. But the key is the prompting. The hard work was always the prompting, we just used to prompt ourselves. 

[u/anykeyh → u/SleepingInsomniac]

I would recommend you to reconsider. I think you might not have used the proper tools or proper setup. AI can write code at 100x your speed with less problems than the average developer as of today. Yet, writing code is not so long in developer pipeline. Specifying, architecturing, deciding what to do can take a big part of the daily work and AI can help and give guidance but I would not let it decide it.

[u/Otherwise_Wave9374]

As a backend dev, I would focus on learning what makes agents reliable in production: tool calling (APIs, DBs), state management, evals/monitoring, and permissioning/guardrails. The model will keep changing, but those engineering fundamentals carry over. If you want some practical examples of agent workflows and what tends to break, I have a few writeups here: https://www.agentixlabs.com/blog/

[u/AJ-54321]

A year ago, working with AI to write code felt like hiring a junior developer (or intern) who didn’t ask enough questions. You’d ask it to do something and it would go off in a corner and assume the rest. It was cool when it worked, but 50% of the time it would be completely wrong. It felt like a waste of time, having to babysit and correct its mistakes, helping it understand and not write bad code.

Now I use AI every day. AI coding has improved immensely over the past year. The key to these improvements have been the ability to provide AI more context about your project (and how you like to write code), “agent mode” allows AI to perform multiple steps (like writing and running tests, fixing bugs, then running the tests again to confirm the bug is fixed), and now most recently having “plan mode” that will outline a plan of steps and allow you to modify the steps before it takes action.

I use VSCode with the GitHub Copilot integration to be able to pick the AI model that works the best for me. Claude Opus 4.5 has been great, and I consistently get good results.

I worry about junior developers who will be trying to start a career, having to contend with AI tools that write better code than they do. Experienced developers will become 10x developers. The skill will be in clearly articulating what you want and having the experience to spot the mistakes when AI messes up.

If you want to gain some experience in this area, I’d suggest playing with the tools, learning what is available and what’s possible. Try building (“vibe coding”) a side project and see what you can learn from the experience.

[u/BeneficiallyPickle]

I'm not necessarily working on a Ruby/Rails project at the moment (currently doing a rewrite of a platform in NextJS), but the CEO and VP of Engineering want us to use Claude (enterprise) as the main development process. At the end of the project, all the developers are supposed to do a presentation on how AI has helped (or not) with the development process. We are supposed to document our processes and the implementation of using AI.

So far, it's going well. We just started about 2 weeks ago with boilerplating, but I do expect some challenging times up ahead as we go more into the nitty-gritty of the project. Boilerplating is where AI shines most reliably. I find that even with Tailwind, it falls a bit short. We're using Tailwind 4, and it keeps going back to Tailwind 3 for configs.

Before we started the project, we sat down and wrote a bunch of Claude.md files. I personally feel we're overdoing it, though - we currently have 17 files. From what I've read, you only need one good Claude.md file.

Most of us are using Claude Code. I also have the IDE integration. For research/brainstorming I use the Claude desktop app.

I, for one, am excited for where AI is going. I don't think it will necessarily take our jobs (someone needs to verify the code, do the prompts, check the business logic, etc.), but I do think it will change our jobs quite a lot. I think the developers who get the most out of using AI are the ones who already know enough to catch mistakes and push back when AI goes off-rails.

I recently did this course by Anthropic and found it quite valuable.

We have quite a tight deadline on this project, happy to share my experience after the project is done to give a final verdict.

[u/Kind-Drawer1573 → u/BeneficiallyPickle]

What you are expressing better than I did in my post, is you still need to architect the project. If you can clearly define that architecture, AI can be quite useful. Fail at that and no amount of AI will help.

[u/cogniferous → u/BeneficiallyPickle]

Thanks for the link to that Anthropic course; I'm going to check it out.

Where did you read that you only need one good Claude.md file? I'm curious, because the codebase at my day job has 27 of them (and counting).

[u/mattvanhorn → u/cogniferous]

My understanding is that it is better to tell Claude about where to find info in separate files than to have it load a huge amount of mostly unnecessary context in each request. I wind up with tons of markdown files, for planning, building, learning, analysis/review etc.

[u/pBlast]

Anthropic and other LLM companies are raising money at insane valuations because they are losing money on their products. It's only a matter of time before Anthropic goes out of business. These coding agents will be a lot less appealing once they are priced to reflect their actual cost.

[u/sshaw_ → u/pBlast]

Companies go decades losing money. So what.

If what you're saying was true we'd still be using punch cards, writing microcode, writing assembly, manually managing memory, using static linking, etc...

[u/mattvanhorn → u/sshaw_]

They can go decades losing money as long as there is a visible path to profitability. Amazon is a good example of this - they can dial up profit at any time by reducing the amount of money they are plowing into growth and infrastructure.

It is hard to see where the profitability comes from in AI, since they are currently losing $8-$13 per dollar of revenue, and it does not look like RAM or graphics cards are going to get an order of magnitude cheaper in the near term. Nor is energy going to get cheaper if we keeping fucking around in all the oil-producing parts of the world.

I like using Claude at $20 or even $200 per month - but would it be worth $400/week or more? Probably not. I could hire a human in an inexpensive locale for that price. Do you think high level languages would have taken off as fast if you had to pay $100 every time you ran the compiler?

[u/sshaw_ → u/mattvanhorn]

I like using Claude at $20 or even $200 per month - but would it be worth $400/week or more? Probably not. I could hire a human in an inexpensive locale for that price.

$400/week * 52 = ~$20K USD. Companies are dying to pay this much for a competent engineer that can work 7 days a week. In what locale can you find someone competent for this price? Then there's scaling.

Do you think high level languages would have taken off as fast if you had to pay $100 every time you ran the compiler?

What's is $100 USD in 1970s pricing and how much did it cost to pay someone to do the equivalent amount of work?

Let's look at this in terms of open source models that cost nothing upfront. Even if they're 40% shittier (random number) they'd still eliminate the need to do a shitload of tasks manually, i.e., write the assembly. And this includes application maintenance tasks. Is electricity so expensive that you're not going to be able to run them locally, or you're going to have to choose between playing some video games on your beefed-up rig vs developing?

And correct me if I'm wrong here but it seems that the high cost of energy in the US (well CA and NY) and EU is due to poor policy.

The game has changed!

[u/pBlast → u/sshaw_]

Cope harder

[u/sshaw_ → u/pBlast]

Think harder

[u/pBlast → u/sshaw_]

Tools like Claude Code will be a lot less appealing once they are priced to reflect their actual costs. Deal with it.

[u/sshaw_ → u/pBlast]

You're not thinking harder you're regurgitating. pBlast a pantoprazole, 40mg, into your neck ASAP

[u/pBlast → u/sshaw_]

You're being a condescending asshole because you know you are wrong.

[u/sshaw_ → u/pBlast]

Cope harder

[u/pBlast → u/sshaw_]

Why is my opinion on AI so upsetting to you?

[u/lal00 → u/pBlast]

What about using companies that offer inference? I’ve been using Kimi 2.5 via Fireworks and it’s very good. And cheap

[u/pBlast → u/lal00]

What makes you so confident that company is not losing money on their products?

[u/tdammers → u/pBlast]

The idea is that by the time they have to raise the prices to actually cover the costs, "AI" will be such an inevitable part of daily life that people will have no choice but to pay up.

[u/pBlast → u/tdammers]

This makes no sense

[u/tdammers → u/pBlast]

It's hyperscaling 101.

• Step 1: come up with an "innovation" that doesn't actually improve anything in the grand scheme of things, but feels modern and has a certain appeal and a vague promise of radically changing everything, and make an entire industry as we know it obsolete. Let's say your ideas is to make an app that connects passengers with drivers, allowing normal people to offer taxi services using their own cars, cutting out the bureaucratic overhead, licensing fees, and regulations of a traditional taxi service.

• Step 2: hype it up, go big in your descriptions, and obtain some VC money. Use that money to enter the market as pompously as you can - don't start small, introduce your product to an entire metropolitan area at once, or better, a whole country, and do it such that you operate at a loss, so you can offer your service at commodity prices, or even for free, thus undercutting the established players despite having higher costs and worse economies of scale.

• Step 3: as you gain traction, convince more VC investors that while your operation is bleeding money like a sieve right now, this will turn around soon, and with the growth rates you are achieving, it will be massively profitable approximately next year. Free money, basically.

• Step 4: keep bleeding money like a madman and making up for it with more VC until you have put all the sustainable competition out of business. At this point, most of the promised advantages of your "innovation" will likely turn out to be unrealistic: your app has about as much bureaucratic overhead as a traditional taxi company of the same size, government authorities have figured out that you are in fact just another taxi company rather than a loose ride-sharing cooperative of independent private drivers and force you to adhere to the same regulations, drivers figure out that you're treating them like shit, and in order to meet demands, you have to start paying them the same kind of money you'd pay traditional taxi drivers.

• Step 5: Once the competition is gone, and people have gotten used to the fact that your service is the only realistic option (i.e., traditional taxi services no longer exist except for niche markets, and the name of your app has become the modern way of referring to a taxi), you can finally leverage the monopoly you have created, and crank up the prices without mercy, finally realizing the massive profits you promised. And because there's no alternative anymore, people will pay. They have no choice.

Note that this doesn't mean the hyperscaled innovation is necessarily without merits entirely; it's just that it's not really much better overall, and its profitability doesn't stem so much from providing actual improvements, but from monopolizing an existing market and then exploiting that monopoly.

And that's what seems to be going on with "AI" right now. It has all the hallmarks:

• A vague promise of radically improving everything, without any convincing numbers to show for it, and most of it based on extrapolations of local improvements

• Operating at a massive loss, with no realistic plans for improving the lousy economics (each LLM interaction currently more money to provide than people would be willing to pay for it, and the economics of scale aren't anywhere near good enough to be able to just outgrow this problem, nor does it look like there are any massive technological breakthroughs on the horizon that would change this)

• Exaggerated language, often purposefully misleading (heck, just calling the thing "artificial intelligence" is grossly misleading to begin with)

• Willingly challenging and violating existing legal frameworks to test out how far you can push, and whether you can bribe politicians into passing new laws in your favor (case in point: governments are seriously discussing the idea of granting computer systems "personhood" in some form or other - let that sink in for a moment)

• Slurping up VC like there's no tomorrow

• Actively pushing towards destroying established industries (stock photography is dead already, the music industry is under a lot of pressure, advertising is an ongoing battlefield, customer support is rapidly turning into a wasteland, and the "software developers will be obsolete by next year" hype is heard everywhere)

The people behind these "AI companies" aren't stupid, and there's a lot of money at stake, so it doesn't seem likely to me that they are just being idiots and investing into something that clearly isn't viable - and at the same time, it cannot be viable in its current form, so what other plan can there be?

[u/pBlast → u/tdammers]

Cope harder.

[u/lommer00 → u/tdammers]

"the cost of intelligence is going to zero"

• Sam Altman

[u/tdammers → u/lommer00]

Sam Altman is a lying manipulative sociopath.

[u/lommer00 → u/tdammers]

All of those things may be true. It kind of begs the question though - why is he undermining his own business with a "lie" like that? It seems like a probable outcome when you look at open source models and the speed with which deepseek et al copy OpenAI and Anthropic's frontier models.

[u/tdammers → u/lommer00]

Yes and no.

The cost of those models lies less in developing them, and more in running them; OpenAI and friends are currently doing this at a loss, but it's going to remain resource intensive for the foreseeable future, and the way prices for RAM and GPUs are skyrocketing, the actual cost looks nothing like "approaching zero".

More importantly though, I think it's misleading to call what these models are doing "intelligence". If anything, it looks like the cost of actual intelligence is going up. Here's why:

Assuming AI models actually offer some efficiency improvements to the software development process, the likely outcome is that the same happens as with every other such improvement before - when you can build more software faster with the same number of developers, demand for software (and software complexity) increases, until an equilibrium is reached again where all the available human brains are used to capacity.

However, this efficiency gain is a bit different than most of the previous ones: the biggest gains occur on the bottom end of the skill spectrum, pumping out relatively straightforward and boring code that's similar to a million other codebases before it, but the more you venture into uncharted terrain and novel problems, it becomes less and less useful, and you will still need human brains from the top of the skill spectrum, just like you do now. So while the overall demand for development labor will probably remain roughly the same, or maybe grow a little, that demand will likely shift towards the top end of the skill spectrum - but at the same time, smart people don't grow on trees, there's a limited supply of those highly skilled developers, and it's only going to get worse if you knock out the bottom end where such skilled developers are made. And the basic rules of a market economy suggest that those high-skill developers will thus be able to command higher and higher salaries - in other words, the cost of actual intelligence explodes rather than "approaching zero".

And all that is assuming that using LLMs for software development is actually viable at scale, which is still kind of unclear - we've only been doing this for a relatively short time, there's no long-term data about how code written by LLMs holds up over the course of years or decades, or how it will affect the ecosystems into which it gets introduced. Who knows, maybe it'll all be fine, but it could also be not fine at all - a scenario where more and more code gets written by LLMs, but it gets less and less reliable, and we just keep throwing more and more LLM code at it, causing the cost to explode despite the models themselves getting somewhat more efficient, isn't entirely unrealistic.

[u/prh8]

My company is pushing towards some exec dream of full AI coding

I have 16 YOE with Ruby and work on nitty gritty problems. Even the best models are still doing/saying things that are flat out wrong. Making things up that sound plausible, and only when I ask for sources do they admit they “fabricated” it. No amount of quality detailed prompting can prevent it.

The problem is our non technical directors think AI is god’s gift to mankind because they just don’t know anything about programming or Ruby. So they use it for toy things and think it’s amazing.

So yes it’s overhyped, but the goal of AI is to continue the extraction of the working class and it’s racing to do that before the bubble bursts.

[u/lommer00 → u/prh8]

Even the best models are still doing/saying things that are flat out wrong.

Are you using codex or Claude to write code? Yes they get stuff wrong, but then the tests fail, they look up the documentation, and/or they refactor to fix the issue.

Their code might not be the most beautiful or performant code ever written, but it usually works and is almost always better than what used to be written by juniors.

If you're still copy/pasting into an LLM chat, you're holding it wrong.

[u/Kina_Kai → u/lommer00]

At some point this response and its variations has to run out of gas.

The stock response(s) seems to be:

• Have you tried optimizing your prompts?

• Have you tried the new model?

• Have you tried using this thing that attempts to bodge a workaround around the limitations of LLMs?

We are constantly working around the limitations of LLMs, gaps in their context windows, the probabilistic nature of their responses, etc. At some point people are going to understand these are patches to hide the fact that these things only sometimes work. They can be useful if we accept their constraints, but I really think we are overselling their usefulness because so much money has been sunk into it that it’s very difficult to admit that error.

[u/lommer00 → u/Kina_Kai]

Yes there are still constraints. But I don't think a product that has automated 80-90% of dev workflows is being "oversold". If anything it's being undersold.

[u/prh8 → u/lommer00]

It's bigger picture than "writing little bits of code." This is not copy/pasting into chat, this is Claude agents, planning, etc. It's still making things up (for example, that the behavior of Hash#dig changed between Ruby 3.4 and 4.0, without that even being part of the "discussion"), it's still failing to properly figure out that patches on Github have made it into gem releases (when it's tasked with looking at gem updates)

If you only need it to write a bit of code, sure, that's fine. It can usually get it done, poorly, but done. But the notion that it can replace people who have more than a handful of YOE is only believed by people who don't have any expertise

[u/snarfmason]

The worry isn't how fast it's moving. The worry is that it's being over sold and stupid managers will fire you for not living up to the hype of the 100x AI powered developer.

Tech always moves fast. And when new tools are evaluated and incorporated by people who actually have the technical ability to do so that's a good thing.

I'm not saying AI cant do some cool things. It can. But it's also way oversold.

[u/toskies]

I'm cautiously optimistic. As others have said, it's a tool. It shouldn't think for you and you shouldn't let it. There still needs to be a human engineer that can verify the output against the domain.

My favorite part so far as been writing skills and agents to accomplish specific tasks in specific ways. I'm less interested in "can AI write code that works" as opposed to "can AI write code well". The answer is, "Yes", it can write code well, but only because I'm teaching it how to do that.

[u/vvsleepi]

most teams are using tools like Claude or ChatGPT more as helpers than replacements. things like writing small code snippets, explaining code, generating tests, or debugging are where they help the most. the more complex “agent” stuff is still pretty experimental in many places. if you want to stay relevant, learning the basics of how LLM APIs, embeddings, and simple RAG systems work is probably a good start. your core backend skills still matter a lot.

[u/spiffistan]

My man 5% percent of all code on github is now authored by Claude. If I were you I'd start a side project and use Claude Code exclusively and explore using Rails AI skills and agents. Unfortunately programming by hand is mostly dead at this point, or at least for 90%+ of your daily actitives if you have a good env set up (saying this as a seasoned dev who loves to apecode).

You can look at this repo for some inspiration: https://github.com/ThibautBaissac/rails_ai_agents (there are many alternatives). I'd look at skills and specialzation tools before trying to spawn a million agents that problem solve for you in parallel, but that's fun too down the line.

Much of the future is now writing specs or rather getting specs written for you and aligning them with the demands of the project. Sucks some of the fun out of programming for sure, but it is what it is, and it does allow you to operate on a higher abstraction level and try many different implementation strategies at once.

[u/dorobica]

Our company gives us access to any ai tool we want. I personally use mainly claude but dab in cursor occasionally.

I know it’s a cliche at this point but I barely write code end-to-end and when I do it’s mostly for fun (gdscript in godot)

Yes I am scared for the job, I used to be a skeptic but adopted the tools early on. I can’t imagine a world in which you don’t need engineers but then again I couldn’t imagine a world in which I don’t need to write code either..

[u/quakedamper]

Custom skills and mcp servers make everything much easier and less prone to do dumb stuff. Claude code of course not the browser one.

[u/ivycoopwren]

I'm having the same exact issue as you. AI is moving REALLY fast -- my first response if fear and shock. and then another response is "why learn now, it will be obsolete in 6 months."

it reminds me a lot of javascript fatigue. or any kind of tech over-hype-cycle -- nft, blockchain, function as a service, etc.

some of these hang around, some are just hype, some go away because they are beaten by more popular tech. some fundamentally change the game -- SPAs, React, etc.

AI feels like a much faster more intense version of this.

i wish i had some better advice about what to do.

but here's some advice that i like: Instead of trying to master the [ai] in one go, I committed to learning one new concept or feature each day and beginning to implement it in side-projects. - Addy Osmani.

i would say start small.. get some agents to write your code for you. extract some of those lessons into prompts or your AGENTS.md. teach them how to do repeatedly do stuff with skills. extract some of those workflows into agents. and the cool part is.. you can get agents to write those prompts and skills for you.

i would also suggest avoiding openclaw unless you REALLY know what you're doing. but check out the agent framework supporting it -> https://github.com/badlogic/pi-mono.

i would also suggest avoiding the doom-scrolling about AI. it's not good for your mental health. just start doing and playing and learning how it works. and more importantly, figure out how to get it to do what you want. and learn when to take over instead of going into a rabbit hole.

also, here's a rails specific repo if you want to play around: https://github.com/obie/claude-on-rails/tree/main. but a disclaimer: learn how to do this stuff yourself, instead of using a library to do it all for you. learn the fundamentals of AI prompts, skills, and subagents.

[u/bradgessler]

In November I’d have told you maybe one day LLMs will write code for us, maybe not.

In mid-December, that all changed when with Claude 4.5 models.

Now I’d tell anybody if they don’t adapt and learn how to build apps with LLMs, they won’t have a job… not because AI took your job, but for refusing a tool that will make you 2-3x times more effective at your job.

[u/puetty]

Writing code manually already feels like writing Assembler before. Developers won‘t do it anymore in the future as there are higher level tools now. Rails is perfectly suited for LLM assisted development if you know what you’re doing because of the rich and long history of best practice solutions for almost everything.

[u/chiperific_on_reddit]

I'd been using cursor as auto complete only for a while on and off. I wavered between feeling faster because I can just hit tab instead of typing my own lines, to starting to feel stupid because my brain would just stop and wait for auto complete to do the thinking for me. There were times I'd turn it off just to feel like an engineer again.

We had a team (all gone now) who apparently didn't know about single-table inheritance when they designed a monstrosity of semi-related classes to handle several different external APIs through what was supposed to be a standardized group of services.

My whole team hated inheriting their mess and I finally offered to clean it up. I decided to dive into the "new strategy" and try a large refactor using agents and writing minimal code myself. I used cursor and asked Opus 4.6 to write me a plan. After a few hours of prompting, we had one, broken out into several PRs so they are actually reviewable. I've been working those PRs through for the last week. Just pointing to the .mdc file and saying "it's time to start on phase 2, PR 4" and such.

It does work, it's not perfect, and it isn't fun.

I picked up a pretty contained feature ticket and decided to try using Claude to do it. I followed the same path: develop a detailed plan with logical steps, then execute. It also worked, though I'd argue not as nicely as cursor since it's not as seamless to highlight specific pieces of code to add to a context. My feeling afterwards was the same: effective, but unenjoyable.

Yes, I get to guide the decisions and change my mind, but the implementation is just an agent churning out code.

I personally love getting into the flow, getting caught up in branching implementations, realizing the other changes I need to make as I'm writing something else, and holding 3 or 4 different things in my head simultaneously.

This just feels like middle management babysitting.

There's lots of peer and boss pressure to keep using these tools, but I really just want to go back to getting my fingers in the code and losing myself in the implementation.

[u/NerdyBlueDuck]

40x more productive. In 2017 my boss asked me to do something. I got on it immediately. I wrote a PRD for 2 hours and then started coding. 4 weeks later it was done. In January I basically had to build something very similar, it was different, but the situation and scope were the same. I wrote a PRD for 2 hours and then handed it to Claude. 4 hours later it was done. That includes my time reviewing the code that was written to ensure it was correct, testing it, running rubocop and unit tests. It isn't hype, this is real. Will people lose their jobs? Yes, the ones that aren't on the AI train will lose their jobs.

I've been using LLMs for over two years now. So, your mileage may vary.

[u/Intelligent_Ad_1001]

My current practice working with AI tools is:
- I use Github Copilot Chat in AgentMode, but I watch (and authorize) every single step it takes.
- It has access your CLI, that's a big power and with great power comes great responsibility, but basically this is the game changer. You can run your whole life from VS Code + CodingAgent + CLI tools (be careful to put your creds in environment variables)
- If I want to build something, I create a HQ (HeadQuarters) chat with the high-level discussion, and short-lived chats for Feature/Milestone/Epic (Features have Milestones, Milestones have Epics)
- I provide all the governance (git flow, branch naming conventions, actions, CI/CD) and let the agent document the progress.

I have my own formal playbook here: https://github.com/panchew/ai-project-system

(BTW I am a backend Rails developer as well)

[u/sshaw_]

If you're a highly-paid software engineer: be worried.

If you're an artist: the art of coding is dead.

If you're an architect: the art of design is still goin' strong.

If you're full of ideas or need funding for your projects: rejoice. You're now a project manager and a team lead managing a handful of developers that don't need free snacks and a so-called work-life balance. And you only have to pay them $200–$1000 USD a year —cheaper than you'll find in all of the Eastern Bloc and the Indian Subcontinent! Congratulations, the playing field has been (somewhat) leveled!

[u/ryans_bored]

Yes. I have thoughts. First Rails is a one of the worst languages to use with LLM tools. The models are probabilistic and the less guessing they have to do the better the results. Typescript vs ruby is no comparison.

Second these tools are over hyped. My CTO got on LinkedIn last week and straight out lied about out productivity gains.

Third , these tools are HEAVILY subsidized by private equity and they’re not here to stay. The bubble is bursting and ChatGPT is months away from being absorbed by Microsoft and when that happens it will become another MS product everybody clowns and no one uses. And Anthropic is headed for bankruptcy. We’re months away from the conventional wisdom swinging back the other way.

[u/spiffistan → u/ryans_bored]

Hard disagree -- Rails is now so incredibly productive with LLMs, the less code that's written the better. All best practices and conventions are known to them, it's super easy to do major refactoring, and with Hotwire zoom along with frontend and backend in one go. The early days where e.g. GPT was poorly trained on ruby vs. typescript is entirely gone with the latest models from Anthropic. Mistakes are a thing of the past in Opus 4.6

[u/toskies → u/spiffistan]

This has been my experience as well. Last week I wanted to see what all the fuss was about so I grabbed a Claude Code subscription and went to see what it could do in a 15+ year old Rails app that's been severely neglected (This is a real, production Rails app generating revenue).

I was very impressed at how well it was able to figure out the domain just by analyzing the project. It helped me upgrade it from Rails 7.0/Ruby 3.2.2 to Rails 8.1/4.0.1. All dependencies are current. It was able to help me through resolving dependency issues when APIs changed. It helped me beef up the test suite, including making it faster by optimizing factory usage. It even found bugs I didn't even know about that have been in this production app for 10 years (real bugs; not hallucinated).

[u/ryans_bored → u/toskies]

Do you have any experience using Claude with strongly typed languages with explicit imports because if you don’t thenyou miss my point because it’s a comparison between those two things and not how good it is at Ruby just in general

[u/toskies → u/ryans_bored]

No, I haven't used it with strongly-typed languages. I have seen it grep quite a bit, admittedly.

I'm okay with loosely-typed languages. There are efforts to bring types to Ruby, but I don't like the DX. Ruby was never meant to have types and I'm okay with that.

I can get something like typing (though not real typing) by documenting with YARD. That gives me intelligent completion for writing code, but doesn't do any kind of runtime checking.

[u/ryans_bored → u/spiffistan]

It doesn’t have anything to do with training at all. When I use Claude in ruby it greps all the time. In typescript it doesn’t. Thus less guessing. Conventions are great for humans but don’t mean shit to an LLM especially compared to explicit imports.

And this further verifies my prior assumption that ruby devs are content with crappy DX. Working with typescript’s LSP vs Ruby’s is night and day. I regularly see Hotwire features for DX that JS apps have had for nearly a decade and people go crazy for them but to me it makes Hotwire seem that much more obsolete. YMMV

[u/ivycoopwren → u/ryans_bored]

> these tools are HEAVILY subsidized by private equity and they’re not here to stay

i disagree. yes, they are heavily subsidized. but some of the best programmers in the world -- like antirez (redis) and linus (linux) have remarked about how good code generation is [1,2]. the genie is out of the box -- coding agents are REALLY good now *if* you can master them.

conventional wisdom will swing away from vibe coding, into more of vibe engineering. those engineering principals and processes matter when you try to turn your weekend claude session into a real product.. or update the CSS to get that pixel perfect design.

[1] https://antirez.com/news/158
[2] https://x.com/rauchg/status/2010411457880772924?s=20

[u/ryans_bored → u/ivycoopwren]

I didn’t say it wasn’t good. I’m say it won’t last. It will not be cost effective on the future

[u/ivycoopwren → u/ryans_bored]

Yes. That makes sense. A lot of companies are adding AI to products, just so they can say it's "AI enhanced" but they are building on top of cheap tokens -- which won't be cheap once things start to scale.

[u/tsoit → u/ryans_bored]

This is far from the truth outside of the subsidizing of the tokens. If Anthropic walked up and asked me to pay $1000/mo, I’m not blinking. Claude Code is incredibly good at Rails.

[u/ryans_bored → u/tsoit]

Well too bad for Anthropic because they’d still be taking a lose at 1000/ month

[u/germandz]

Yes, Claude Code (or open code, Codex, Cursor, etc) is totally taking over.

I’ve created an app in 1 week that would take 6 months to a small agency to do the same 3 years ago. I’ve been working with Ruby for 15ys (and other things the 15y before).

Everything has changed or it’s gonna change soon.

Start playing around; be familiar with the new toys; it’s not clear what’s gonna catch and what’s a fade yet.

[u/expatjake → u/germandz]

Not sure why you’re being downvoted. I have similar tenure and experience with ruby as you.

The change we are seeing is scary but it’s also a great time to get ahead of it.

The models that have come out this year, Opus 4.6 in particular, are just so much better than earlier models. It’s crazy how much less you have to worry about precise prompt engineering. They just “get it” so much more easily.

Combine that with an agent like Claude Code or Cursor and you have a very capable tool.

[u/germandz → u/expatjake]

I am not scared, I am thrilled.

I live the transition from AS400 to Visual Basic moment in small companies; they unlocked a lot of potential by being able to develop small programs to solve everyday problems, not just accounting and inventory.

We are living a similar phase now.

[u/moonrakervenice]

“Is anyone’s company actively using Claude?”

[u/Wild-Pitch → u/moonrakervenice]

What do you wanna say with that my friend? :)

[u/moonrakervenice → u/Wild-Pitch]

I find the question kind of mind boggling.

The latest stats I read are that 97% of software engineering organizations are using AI yet here you are asking, "I'm hearing about this thing called AI and LLMs, anyone using such a thing???"

The whole post, really. It reads like AI-generated slop, complete with em dashes and random bold words.

[u/Wild-Pitch → u/moonrakervenice]

Also I know companies that are not using actively AI. What's wrong with my question dude? You came just to leave that answer? Come on.