we literally creating solution for our own problem
Maintaining a codebase isn't going to be a thing in the future, at least not in the traditional/current sense.
It depends, because you now have to pay in order to be able to compete against other programmers who're also using AI tools, it wasn't like that in what I'd call the true "golden age", basically the '90s - early part of the 2000s, when the internet was already a thing and one could put together something very cool with just a "basic" text editor.
We used to serve others, but now people are so excited about serving themselves first that there's almost no talk of service to others at all anymore
"..the user suggests using XYZ to move forward, but that would be rather inefficient, perhaps the user is not totally aware of the characteristics of XYZ. We should suggest moving forward with ABC and explain why it is the better choice..."
I get popcorn and wait for more work coming my way 5 years down the road. Someone will have tidy this mess up and gen-covid will have lost all ability to think on their own by then.
Pay-as-you-go with Gemini does not snort your data for their own purposes (allegedly...).
Not happening. Investors would riot.
Looking forward to stage 2 - start serving the advertisers while placating the users, and finally stage 3 - offering it all up to the investors while playing the advertisers off each other and continuing to placate the users.
The Omega Directive: https://snth.prose.sh/the_omega_directive
I tend to form the story arc in my head, and outline the major events in a timeline, and create very short summaries of important scenes, then use AI to turn those summaries into rough narrative outlines by asking me questions and then using my answers to fill in the details.
Next I'll feed that abbreviated manuscript into AI and brainstorm as to what's missing/where the flow could use improvement/etc with no consideration for prose quality, and start filling in gaps with new scenes until I feel like I have a compelling rough outline.
Then I just plow from beginning to end rewriting each chapter, first with AI to do a "beta" draft, then I rewrite significant chunks by hand to make things really sharp.
After this is done I'll feed the manuscript back into AI and get it to beta read given my target audience profile and ambitions for the book, and ask it to provide me feedback on how I can improve the book. Then I start editing based on this, occasionally adding/deleting scenes or overhauling ones that don't quite work based on a combination of my and AI's estimation. When Gemini starts telling me it can't think of much to improve the manuscript that's when it's time for human beta readers.
I've been wondering about what the legalities of the generated content are though since we know that a lot of the artistic source content was used without consent?C an I put the stories on my blog? Or, not that I wanted to, publish them? I guess people use AI generated code everywhere so I guess for practical purposes the cat is out the bag and won't be put back in again.
It's pretty useful as long as you hold it back from writing code too early, or too generally, or sometimes at all. It's a chronic over-writer of code, too. Ignoring most of what it attempts to write and using it to explore the design space without ever getting bogged down in code and other implementation details is great though.
I've been doing something that's new to me but is going to be all over the training data (subscription service using stripe) and have often been able to pivot the planned design of different aspects before writing a single line of code because I can get all the data it already has regurgitated in the context of my particular tech stack and use case.
Would you expect that to be Google employing cost-saving measures?
// Moved to foo.ts
Ok, great. That’s what git is for.
// Loop over the users array
Ya. I can read code at a CS101 level, thanks.
https://developers.google.com/gemini-code-assist/docs/overvi...
What about Zed or something else?
I have not used any IDEs like Cursor or Zed, so I am not sure what I should be using (on Linux). I typically just get on Claude (claude.ai) or ChatGPT and do everything manually. It has worked fine for me so far, but if there is a way to reduce friction, I am willing to give it a try. I do not really need anything advanced, however. I just want to feed it the whole codebase (at times), some documentation, and then provide prompts. I mostly care about support for Claude and perhaps Gemini (would like to try it out).
That doesn't mean it's worse than the others just not much better. I haven't found anything that worked better than o1-preview so far. How are you using it?
Essentially we were hoping to tie that to data inputs and have a system to regularly output the visualisation but with dynamic values. I bet my colleague it would one shot it: it did.
What I’ve also found is that even a sloppy prompt still somehow is reading my mind on what to do, even though I’ve expressed myself poorly.
Inversely, I’ve really found myself rejecting suggestions from ChatGPT, even o4-mini-high. It’s just doing so much random crap I didn’t ask and the code is… let’s say not as “Gemini” as I’d prefer.
But more seriously, they need to uncap temperature and allow more samplers if they want to really flex on their competition.
Is there any concrete example that makes it really obvious? I had no such success with it so far and i would really like to see the clear cut between the gemini and the others.
But seriously, yeah, Gemini is pretty great.
I couldn’t find a way to use Gemini like a prepaid plan. I ain’t giving my credit card to Google for an LLM that can easily charge me hundreds or thousands of EUR.
Anyone else concerned about this kind of statements? Make no mistake, everyone. We are living in a LLM bubble (not an AI bubble as none of these companies are actually interested in AI as such as moving towards AGI). They are all trying to commercialise LLMs with some minor tweaks. I don't expect LLMs to make the kind of progress made by the first 3 iterations of GPT. And when the insanely hyped overvaluations crashed, the bubble WILL crash. You BETTER hope there is any money left to run this kind of tools at a profit or you will be back at Stackoverflow trying to relearn all the skills you lost using generative coding tools.
Not really my idea of good.
I wonder if "robot" was being used here in its original sense too of a "forced worker" rather than the more modern sense of "mechanical person". If not, I propose it.
They all seem to work remarkably well writing typescript or python but in my experience, they fall short when it comes to shell and more broadly dev ops
Every time in the last three or four weeks, there is a post here about Gemini, the top comment, or one of the top comments is something along these lines. And every time I spend a few minutes making empirical tests to check if I made a mistake in cancelling my paid Gemini account after giving up on it...
So I just did a couple of tests sending the same prompt on some AWS related questions to Gemini Pro 2.5 (free) and Claude paid, and no, Claude still better.
Each new release is “game changing”.
The implication being the last release y’all said was “game changing” is now “from a different century”.
Do you see it?
For this to be an accurate and true assessment means you were wrong both before and wrong now.
I got particularly burned by that press release a little before Christmas, where it was claimed that 4o was doing difficult maths and programming stuff. A friend told me about it very excitedly, I imagined they were talking about something that had really happened.
A few days later when I'd time to look into it, it turned out that essentially we'd internal testing and press releases to go off, I couldn't believe it. I said - so, marketing. A few months later it was revealed that a lot of the claimed results in those world-changing benchmarks were due to answers that had been leaked, etc etc. The usual hype theatre, in the end
That was just the cover story for being greedy with the data.
If we were actually getting revolutionary, game changing, improvements to productivity every couple of months. The whole industry would be in such chaos that people would be pausing investments until the chaos stopped.
Are you suggesting that a rush to hyperbole which you don't like means advances in a technology aren't groundbreaking?
Or is it that if there is more than one impressive advance in a technology, any advance before the latest wasn't worthy of admiration at the time?
Anyway, that period ended and I went until a few months ago without touching anything like this and I was hearing all these amazing things about using Cursor with Claude Sonnet 3.5, so I decided to try it out with a few use cases:
1. Have it write a tokenizer and parser from scratch for a made up Clojure-like language
2. Have it write the parser for the language given the tokenizer I had already written previously
3. Have it write only single parsing functions for very specific things with both the tokenizer and parsing code to look at to see how it works
#1 was a complete and utter failure, it couldn't even put together a simple tokenizer even if shown all of the relevant parts of the host language that would enable a reasonable tokenizer end result.
#2 was only slightly better, but the end results were nowhere near usable, and even after iteration it couldn't produce a runnable result.
#3 is the first one my previous experience with Copilot suggested to me should be doable. We started out pretty badly, it misunderstood one of the tokenizer functions it had examples for and used it in a way that doesn't really make sense given the example. After that it also wanted to add functions it had already added for some reason. I ran into myriad issues with just getting it to either correct, move on or do something productive until I just called it quits.
My personal conclusion from all of this is that yes, it's all incredibly incremental, any kind of "coding companion" or agent has basically the same failure modes/vectors they had years ago and much of that hasn't improved all that much.
The odds that I could do my regular work on 3D engines with the coding companions out there are slim to none when it can't even put together something as simple as a tokenizer together, or use an already existing one to write some simple tokenizer functions. For reference I know that it took my colleague who has never written either of those things 30 minutes until he productively and correctly used exactly the same libraries the LLM was given.
I want something running in a VM I can safely let all tools execute without human confirmation and I want to write my own tools and plug them in.
Right now a pro max subscription with Claude code plus my MCP servers seems to be the sweet spot, and a cursory look at the Google ecosystem didn’t identify anything like it. Am I overlooking something?
It's my daily driver so far. I switch between the Claude and Gemini models depending on the type of work I'm doing. When I know exactly what I want, I use Claude. When I'm experimenting and discovering, I use Gemini.
> For fun, I tried asking a bunch of AI models to figure out what shapes those arrays have. Here were the results:
Based on the results from the top 8 state-of-the- art AI models, Gemini is the best and consistently got the right results:
[1] I don't like NumPy (204 comments):
https://news.ycombinator.com/item?id=43996431
[2] I don't like NumPy: I don’t like NumPy indexing:
It leaves Claude and ChatGPT's coding looking like they are from a different century. It's hard to believe these changes are coming in factors of weeks and months. Last month i could not believe how good Claude is. Today I'm not sure how I could continue programming without Google Gemini in my toolkit.
Gemini AI Studio is such a giant leap ahead in programming I have to pinch myself when I'm using it.