Objectively. I’m now tackling tasks I wouldn’t have even considered two or three years ago, but the biggest breakthrough has been overcoming procrastination. When AI handles over 50% of the work, there’s a 90% chance I’ll finish the entire task faster than it would normally take me just to get started on something new.
This. I had this long standing dispute that I just never had the energy to look up what needed to be done to resolve it. I just told it to ChatGPT and it generated everything -- including the emails I needed to send and who to send them to. Two weeks later and it was taken care of. I had sat on it for literally 3 months until then.
If I could have something that said, "Here are some things that it looks like you're procrastinating on -- do you want me to get started on them for you?" -- that would probably be crazy useful.
GPT-4 got me seriously considering making a product for school-age kids w/ ADHD. It’d be a physical device (like a StarTrek communicator). That listens during your day and keeps track of a) things you say that you’ll do or b) tasks that other people ask you to do. Then it compiles those tasks and attempts to be basically a secretary. It can also plug into your email, texts & school assignments.
The privacy implications are horrifying. But if done right, you’re taking about a kind of digital ‘executive function’ that could help a lot of kids that struggle with things like prioritization and time blindness.
Marshall MacLuhan said something to the effect that every new communication technology results in a sort of self-amputation of that same faculty in the individual person.
I was diagnosed with ADHD and my interpretation of that diagnoses was not "I need something to take over this functionality for me," but "I need to develop this functionality so that I can function as a better version of myself or to fight against a system which is not oriented towards human dignity but some other end."
I guess I am reluctant to replace the unique faculties of individual children with a generic faculty approved by and concordant with the requirements of the larger society. How dismal to replace the unique aspects of children's minds with a cookie cutter prosthetic meant to integrate nicely into our bullshit hell world. Very dismal.
Sure, the implications are horrifying, but tech companies have proven themselves quite trustworthy over the past few decades, so I'm sure it'd be fine.
As someone with ADHD, I say: Please don't build this.
It’s not just for people with ADHD. Someone will build this very soon and people will use it a lot. Hopefully Apple builds it because I guess I trust them a little more.
We're not too far away from a smaller LLM that could be run locally that could do this, which would make it more privacy friendly. The plugging into my email seems like a great way to begin or complete a lethal trifecta and I don't have a good solution there, though.
Every iPhone, iPad, and Mac that either ships with or is upgraded to iOS 26, iPadOS 26 and macOS 26 has a 3-billion parameter LLM that’s available to developers and operates on-device. Mail, Notes, Reminders are already integrated.[1]
I might be out of the loop, but if anyone else is confused about the version number:
> If you were expecting iOS 19 after iOS 18, you might be a little surprised to see Apple jump to iOS 26, but the new number reflects the 2025-2026 release season for the software update.
This is what I wanted to build the day chat gpt came out. Except being unable to guarantee the output due to hallucinations drove me into figuring out evals, and then the dream died due to complexity.
Same, I created a todo list with a simple MCP and it's been game changing, just being able to talk/discuss with my todo list somehow seems to keep me coming back to it rather than after 3 weeks it just becoming a sterile and abandoned list of random things
Yes, it's also useful against writer's block. (Which might be a subset of ADHD, I don't know?)
For many people, it's easier to improve a bad first version of a piece of writing than to start from scratch. Even current mediocre LLM are great at writing bad first drafts.
> Even current mediocre LLM are great at writing bad first drafts.
Anyone is great at creating a bad first draft. You don’t need help to create something bad, that’s why that’s a common tip. Dan Harmon is constantly hammering on that advice for writer’s block: “prove you’re a bad writer”.
Little plot twist, you can pitch an LLM an idea for a scene, then tell it to interrogate you thoroughly for the details, then tell it to generate a clean, tight draft optimized for efficient use of language and readability, and you basically get your own ideas back but with a lot of the boring parts of writing already done.
But it’s not helping them “be productive” (which is a horrible metric anyway, life is not about productivity above everything else), it’s sucking their lives and severing their relationships and connection to the real world.
I don't think it's helped me do anything I couldn't do, in fact I've learned it's far easier to do hard things myself than trying to prompt an AI out of the ditches it will dig trying to do it. But I also find it's great for getting painful and annoying tasks out of the way that I really can't motivate to do myself.
> I don't think it's helped me do anything I couldn't do
I am seeing a pattern here. It appears that AI isn't for everyone. Not everyone's personality may be a good fit for using AI. Just like not everybody is a good candidate for being a software dev, or police officer etc.
I used to think that it is a tool. Like a car is. Everybody would want one. But that appears not be the case.
For me, I used AI every day as a tool, for work and and home tasks. It is a massive help for me.
Is it? It'll take a while for fertilizer and sun placement to take visually effect, and there's risk that short term effects aren't indicative of long term effects.
How can you verify the recommendations are sound, valid, safe, complete, etc., without trying them out? And trying out unsound, invalid, unsafe, incomplete, etc., recommendations might result in dead plants in a couple of weeks.
I personally use chatgpt for initial discovery on these sorts of problems, maybe ask a probing question or two and then go back to traditional search engines to get a very rough second opinion(which might also lead to another round of questions). By the end of that process I'll either have seen that the llm is not helpful for that particular problem, or have an answer that I'm "reasonably confident" is "good enough" to use for something medium to low risk like potentially killing a plant. And I got there within 10-20 minutes, half of that being me just reading the 'bots summary.
> How can you verify the recommendations are sound, valid, safe, complete, etc., without trying them out?
Such an odd complaint about LLMs. Did people just blindly trust Google searches before hand?
If it's something important, you verify it the same way you did anything else. Check the sources and use more than a single query. I have found the various LLMs to very useful in these cases, especially when I'm coming at something brand new and have no idea what to even search for.
Eh, for something like this the cost of it being wrong might be pretty small, but I'd bet odds are good that its recommendations will be better than whatever I might randomly come up with without doing any research. And I don't have the time to do the research on normal old google where it's really hard to find exactly what I want.
I've found it immensely helpful for giving real world recommendations about things like this, that I know how to find on my own but don't have the time to do all the reading and synthesizing.
That's an interesting perspective, I don't think it's an innate thing though, I think it's a mindset issue. Humans are adaptable, but we're even more stubborn.
I think there might be cases, for some people or some tasks, where the difficulty of filling in a blank page is greater than the difficulty of fixing an entire page of errors. Even if you have to do all the same mental work, it feels like a different category of work.
Same. It takes the drudgery out of creating, so I can at least start the projects. Then I can go down into the detail just enough that the AI doesn't produce crap, but without needing to write the actual writes of code myself.
Same here. I’ve single-shot created a few Raycast plugins for TLV decoding that save me several seconds to a few minutes per task which I use almost daily at work.
there's some minority that's in the venn diagram of being good at programming, being good at AI, then also being good at using AI for programming (which is mostly project management), and if everything aligns then there are superhuman productivity gains.
I'm tackling projects solo I never would have even attempted before but I could see people getting bad results and giving up.
> if everything aligns then there are superhuman productivity gains.
This is a truism and, I believe, is at the core of the disagreement on how useful AI tools are. Some people keep talking about outlier success. Other people are unimpressed with the performance in ordinary tasks, which seem to take longer because of back-and-forth prompting.
Same here! Started learning self hosted k3s, with terraform and IaC and all the bells and whistles. I would never have had the energy to look up how to even get started. In three hours I have a cluster.
Is that really a fair comparison? I think the amount of people who can memorize each and every configuration item is vanishingly small... even when I was bootstrapping k8s clusters before the dawn of LLMs I had to lookup current documentation and maybe some up to date tutorials.
Knowing the abstract steps and tripwires yes, but details will always have to be looked up. If just not to miss any new developments.
> It doesn't matter - GP is now able to do things they were unable to do before. A distinction without a (real-world) difference.
I get that point, but the original post I replied to didn't say "Hey, I know have $THING set up when I never had it before", he said "I learned to do $THING", which is a whole different assertion.
I'm not contending the assertion that he now has a thing he did not have before, I'm contending the assertion that he has learned something.
My programming productivity has improved a lot with Claude Code.
One thing I've noticed is that I don't have a circle of people where I can discus programming with, and having an LLM to answer questions and wireframe up code has been amazing.
My job doesn't require programming, but programming makes my job much easier, and the benefits have been great.
Incredible how many people here just don’t believe you because it doesn’t reflect their personal experience.
I want to second your experience as I’ve had the same as well. Tackling SO many more tasks than before and at such a crazy pace. I’ve started entire businesses I wouldn’t have just because of AI.
But at the same time, some people have weird blockers and just can’t use AI. I don’t know what it is about it - maybe it’s a mental block? Wrong frame of mind? It’s those same people who end up saying “I spend more time fighting the ai and refining prompts than I would on the end task”.
I’m very curious what it is that actually causes this divide.
Now is a good time to use it to make money, before it gets to the point where everyone is using it.
I've been using it for almost a year now, and it's definitely improved my productivity. I've reduced work that normally takes a few hours to 20 minutes. Where I work, my manager was going to hire a junior developer and ended up getting a pro subscription to Claude instead.
I also think it will be a concern for that 50-something developer that gets laid off in the coming years, has no experience with AI, and then can't find a job because it's a requirement.
My cousin was a 53 year old developer and got laid off two years ago. He looked for a job for 6 months and then ended up becoming an auto mechanic at half the salary, when his unemployment ran out.
The problem is that he was the subject matter expert on old technology and virtually nobody uses it anymore.
You obviously have never worked a company that spends time arguing about the "definition of done". It's one of the most subjective topics I know about.
Sounds like a company is not adequately defining what the deliverables are.
Task: Walk to the shops & buy some milk.
Deliverables:
1. Video of walking to the shops (including capturing the newspaper for that day at the local shop)
2. Reciept from local store for milk.
3. Physical bottle of Milk.
1. A whitish liquid containing proteins, fats, lactose, and various vitamins and minerals that is produced by the mammary glands of all mature female mammals after they have given birth and serves as nourishment for their young.
2. The milk of cows, goats, or other animals, used as food by humans.
3. Any of various potable liquids resembling milk, such as coconut milk or soymilk.
And when on the receiving end of the deliverables list, it's always a good idea to make sure they are actually deliverable.
There's nothing worse than a task where you can deliver one item and then have to rely on someone else to be able to deliver a second. Was once in a role where performance was judged on closing tasks; getting the burn-down chart to 0, and also having it nicely stepped. Was given a good tip to make sure each task had one deliverable and where possible—be completed independent of any other task.
Well, I think in this example someone else wrote down “buy milk”. Of course I would generally know what that’s likely to mean, and not buy the ridiculous thing. But someone from a culture that’s not used to using milk could easily get confused and buy the wrong thing, to further the example. I guess my point was that it’s never possible to completely unambiguously define when a task is done without assuming some amount of shared knowledge with the person completing the task that lets them figure out what you meant and fill in any gaps
I have been coding on and off (more off than on) for 47 years. I kinda stopped paying attention when we got past jquery and was never a fan of prototypical inheritance. Never built anything with tailwind, Next.js, etc. After spending some time writing copy, user stories and a design brief (all iterative with ChatGPT) cursor one shot my (simple) web app and I was live (once I'd spent a couple hours documenting my requirements and writing my copy) in 20 minutes of vibe coding.
I've been adding small features in a language I don't program in using libraries I'm not familiar with thhat meet my modest functional requirements in a couple minutes each. I work with an LLM to refine my prompt, put it into cursor, run the app locally, look at the diffs, commit, push and I'm live on vercel within a minute or two.
I don't have any good metrics for productivity, so I'm 100% subjective but I can say that even if I'd been building in Rails (it's been ~4 years but I coded in it for a decade) it would have taken me at least 8 hours to have an app where I was happy with both the functionality and the look and feel so a 10x improvement in productivity for that task feels about right.
And having a "buddy" I can discuss a project with makes activation energy lower allowing me to complete more.
Also, YC videos I don't have the time to watch, I get a transcript, feed into chatGTP, ask for the key take aways I could apply to my business (it's in a project where it has context on stage, industry, maturity, business goals, key challenges, etc) so I get the benefits of 90 minutes of listening plus maybe 15 minutes of summarizing, reviewing and synthesis in typically 5-6 minutes - and it'd be quicker if I built a pipeline (something I'm vibe coding next month)
How do you deal with security for web stuff? I wouldn't host anything vibe-coded publicly because I'm not enough of an expert in web/frontend to even double-check that it's not generating some giant holes.
The same way you do security for manually written code. Rigorously. But in this case, you can also have AI also do your code reviews and suggest/write unit tests. Or write out a spec and refine it. Or point it to OWASP and say, look at this codebase and make a plan to check for these OWASP top 10.
And have another AI review your unit tests and code. It's pretty amazing how much nuance they pick up. And just rinse and repeat until the AI can't find anything anymore (or you notice it going in circles with suggestions)
Yeah, some of these comments make it sound we had zero security issues pre-AI. I think the challenge is what you touched on, you have to tell the AI to handle it just like anything else you want as a requirement. I've use AI to 'vibe' code things and they have turned out pretty well. But, I absolutely leaned on my 20+ years of experience to 'work' with the AI to get what I wanted.
If you never put your personal side-project on the public web you had very few security issues resulting from your personal projects. We weren't talking about companies in this thread.
Are the frontend folks having such great results from LLMs that they're OK with "just let the LLM check for security too" for non-frontend-engineer created projects that get hosted publicly?
Not who you asked, but I upgraded NextJS in a couple of repos by just telling Claude Code to do it. I've had it swap out and upgrade libraries successfully in one shot too. It will usually create good enough Page Objects for E2Es and scaffold out the test file, which speeds up the development process a bit. Same for upgrading Node versions in some Lambda projects, just tell it to go and come back later. Instruct it to run the test and build steps and it's also like having a mini CI system running too.
Personally, I think it really shines at doing the boring maintenance and tech debt work. None of these are hard or complex tasks but they all take up time and for a buck or two in tokens I can have it doing simple but tedious things while I'm working on something else.
> Personally, I think it really shines at doing the boring maintenance and tech debt work.
It shines at doing the boring maintenance and tech debt work for web. My experiences with it, as a firmware dev, have been the diametric opposite of yours. The only model I've had any luck with as an agent is Sonnet 4 in reasoning mode. At an absolutely glacial pace, it will sometimes write some almost-correct unit tests. This is only valuable because I can have it to do that while I'm in a meeting or reading emails. The only reason I use it at all is because it's coming out of my company's pocket, not mine.
For sure. There's tons of training data in the models for the JS and TS language and the specific tasks I outlined, but not specifically just the web, I have several Node or Bun + Typescript + SQLite CLI utilities that it also helps with. I definitely pick my battles and lean in to what it works best for though. Anything it appears to struggle at I'll just do manually and develop it like we always did. It's rarely not a net positive to me but it's very frequently a negligible improvement. Anything that doesn't pay off in spades I typically don't try again until new models release or new tools or approaches are available.
If you're doing JS/Python/Ruby/Java, it's probably the best at that. But even with our stack (elixir), it's not as good as, say, React/NextJS, but it's definitely good enough to implement tons of stuff for us.
And with a handful of good CLAUDE.md or rules files that guide it in the right direction, it's almost as good as React/NextJS for us.
I can see how these things are convenient, if it succeeds. I struggle because my personal workflow is to always keep two copies of a repo up at once. One is deep thought vs drone work. I have always just done these kinds of background tasks whenever I am in meetings, compiling etc. I haver not seen much productivity boost due to this. oddly, you would think being able to further offload during that time would help, but reviewing the agent output ends up being far more costly (and makes the context switch significantly harder, for some reason). It's just not proving to be useful consistently, for me.
Just off the top of my head (and I exclusively use Claude Code now):
Random Postgres stuff:
- Showed a couple of Geo/PostGIS queries that were taking up more CPU according to our metrics, asked it to make it faster, it rewrote it in away that it actually used the index. (using the <-> operator for example for proximity). One-shotted. Whole effort was about 5 mins.
- Regularly asking for maintenance scripts (like give me a script that shows me the most fragmented tables, or highest storage, etc).
CSS:
Built a whole horizontal logo marquee with CSS animations, I didn't write a single line, then I asked for little things like "have the people's avatars gently pulsate" – all this was done in about 15 mins. I would've normally spent 8-16 hours on all that pixel pushing.
Elixir App:
- I asked it to look at my GitHub actions file and make it go faster. In about 2-3 iterations, it cut my build time from 6 minutes to 2 minutes. The effort was about an hour (most of it spent waiting for builds, or fiddling with some weird syntax errors or just combining a couple extra steps, but I didn't have to spend a second doing all the research, its suggestions were spot on)
- In our repo (900 files) we had created an umbrella app (a certain kind of elixir app). I wanted to make it a non-umbrella. This one did require more work and me pushing it, but I've been putting off this task for 3 YEARS since it just didn't feel like a priority to spend 2-3 days on. I got it done in about 2 hours.
- Built a whole discussion board in about 6 hours.
- There are probably 3-6 tickets per week where I just say "implement FND-234", and it one-shots a bugfix, or implementation, especially if it's a well defined smaller ticket. For example, make this list sortable. (it knows to reuse my sortablejs hook and look at how we implemented it elsewhere).
- With the Appsignal MCP, I've had it summarize the top 5 errors in production, and write a bug fix for one I picked (I only did this once, the MCP is new). That one was one-shotted.
- Rust library (It's just an elixir binding to a rust library, the actual rust is like 20 lines, so not at all complex)... I've never coded a day of rust in my life, but all my cargo updates and occasional syntax/API deprecations, I have claude do my upgrades and fixes. I still don't know how to write any Rust.
NextJS App:
- I haven't fixed a single typescript error in probably 5 months now, I can't be bothered, CC gets it right about 99% of the time.
- Pasted in a Figma file and asked it to implement. This rarely is one-shotted. But it's still about 10x faster than me developing it manually.
The best combination is if you have a robust component library and well documented patterns. Then stuff goes even faster.
All on the $100 plan in which I've hit the limit only twice in two months. I think if they raised the price to $500, it would still feel like a no-brainer.
I think Anthropic knows this. My guess is that they're going to get us hooked on the productivity gains, and we will happily pay 5x more if they raised the prices, since the gains are that big.
