[pragmomm]

Canvas is closer to Cursor (https://www.cursor.com) than Claude.

I wonder how Paul Graham thinks of Sam Altman basically copying Cursor and potentially every upstream AI company out of YC, maybe as soon as they launch on demo day.

Is it a retribution arc?

[JumpCrisscross]

> wonder how Paul Graham thinks of Sam Altman basically copying Cursor

If OpenAI can copy Cursor, so can everyone else.

[jsheard]

And everyone has, YC alone has funded at least four Cursor clones, Double, Void, Continue and Pear, with Pear being a literal fork of Continue's OSS code. AFAICT Cursor isn't even the original, I think Copilot X was the first of its kind and Cursor cloned that.

[FridgeSeal]

Turns out they’re all just elaborate feature branches, in a giant branch-stacking-PR, and they’re all going to merge code and funding, like some kind of VC-money-fuelled-power-ranger.

[amarcheschi]

I wonder whether so many clones companies funded can eventually bring in a positive return when (if) a single company manages to rise above the others and become successful. Does anybody know if yc funding is publicly available? And how to know what return they get if a company gets ipo'd?

[dartos]

Yup. Prompts have no moat.

[danielmarkbruce]

It depends on who the moat is supposed to keep out. A reasonable case from an antitrust regulator would be that if a provider of models/apis gleans the prompts from the users of the apis to build competing products... they are in trouble.

Good prompts may actually have a moat - a complex agent system is basically just a lot of prompts and infra to co-ordinate the outputs/inputs.

[dartos]

> Good prompts may actually have a moat - a complex agent system is basically just a lot of prompts.

The second part of that statement (is wrong and) negates the first.

Prompts aren’t a science. There’s no rationale behind them.

They’re tricks and quirks that people find in current models to increase some success metric those people came up with.

They may not work from one model to the next. They don’t vary that much from one another. They, in all honesty, are not at all difficult or require any real skill to make. (I’ve worked at 2 AI startups and have seen the Apple prompts, aider prompts, and continue prompts) Just trial and error and an understanding of the English language.

Moreover, a complex agent system is much more than prompts (the last AI startup and the current one I work at are both complex agent systems). Machinery needs to be built, deployed, and maintained for agents to work. That may be a set of services for handling all the different messaging channels or it may be a single simple server that daisy chains prompts.

Those systems are a moat as much as any software is.

Prompts are not.

[danielmarkbruce]

That prompts aren't science means little. If anything it makes them more important because you can't systematically arrive at good ones.

If one spends a lot of time building an application to achieve an actual goal they'll realize the prompts make a gigantic difference and it takes an enormous amount of fiddly, annoying work to improve. I do this (and I built an agent system, which was more straightforward to do...) in financial markets. It so much so that people build systems just to be able to iterate on prompts (https://www.promptlayer.com/).

I may be wrong - but I'll speculate you work on infra and have never had to build a (real) application that is trying to achieve a business outcome. I expect if you did, you'd know how much (non sexy) work is involved on prompting that is hard to replicate.

Hell, papers get published that are just about prompting!

https://arxiv.org/abs/2201.11903

This line of thought effectively led to Gpt-4-o1. Good prompts -> good output -> good training data -> good model.

[dartos]

> If anything it makes them more important because you can't systematically arrive at good ones

Important and easy to make are not the same

I never said prompts didn’t matter, just that they’re so easy to make and so similar to others that they aren’t a moat.

> I may be wrong - but I'll speculate you work on infra and have never had to build a (real) application that is trying to achieve a business outcome.

You’re very wrong. Don’t make assumptions like this. I’ve been a full stack (mostly backend) dev for about 15 years and started working with natural language processing back in 2017 around when word2vec was first published.

Prompts are not difficult, they are time consuming. It’s all trial and error. Data entry is also time consuming, but isn’t difficult and doesn’t provide any moat.

> that is hard to replicate.

Because there are so many factors at play _besides prompting. Prompting is the easiest thing to do in any agent or RAG pipeline. it’s all the other settings and infra that are difficult to tune to replicate a given result. (Good chunking of documents, ensuring only high quality data gets into the system in the first place, etc)

Not to mention needing to know the exact model and seed used.

Nothing on chatgpt is reproducible, for example, simply because they include the timestamp in their system prompt.

> Good prompts -> good output -> good training data -> good model.

This is not correct at all. I’m going to assume you made a mistake since this makes it look like you think that models are trained on their own output, but we know that synthetic datasets make for poor training data. I feel like you should know that.

A good model will give good output. Good output can be directed and refined with good prompting.

It’s not hard to make good prompts, just time consuming.

They provide no moat.

[sanex]

Amazon Basics is kind of the same thing, they haven't been sued. Yet.

[jamiek88]

Suing Amazon unless you are also a mega corp is basically impossible so until they rip off Apple or MS they’ll be fine.

[sanex]

I guess I should have said sued by the FTC.

[danielmarkbruce]

They have indeed.

[adamrezich]

It's just a company that promised AGI would somehow come from developing LLM-based products, rapidly scrambling to keep up with other LLM-based products, to distract from the fact that it's becoming increasingly apparent that AGI is not coming anytime soon.

[valval]

The idea of AGI is silly. It’s ludicrous. Who’s been counting on it to happen?

OpenAI are in the money making business. They don’t care about no AGI. They’re experts who know where the limits are at the moment.

We don’t have the tools for AGI any more than we do for time travel.

[ben_w]

There's good reasons to expect time travel is physically impossible.

Your brain is an existential proof that general intelligence isn't impossible.

Figuring out the special sauce that makes a human brain able to learn so much so easily? Sure that's hard, but evolution did it blindly, and we can simulate evolution, so we've definitely got the tools to make AGI, we just don't have the tools to engineer it.

[dennisy]

Yeah I completely agree with this, it makes me sad that OpenAI are spending time on this when they should be pushing the foundation models ahead.

[freediver]

Cursor was one of the first AI editors I used, but recently Aider has completely replaced the AI assisted coding for me. I still use cursor but just as an editor, all LLM work is done with aider in the shell.

[bachittle]

I replaced Cursor with continue.dev. It allows me to run AI models locally and connect it with a vscode plugin instead of replacing vscode with a whole new IDE, and it's open source.

[campers]

Check out https://sophia.dev Its AI tooling I've built on top of Aider for the code editing. I initially built it before Aider added support for running compile and lint commands, as it would often generate changes which wouldn't compile.

I'd added seperate design/implementation agents before that was added to Aider https://aider.chat/2024/09/26/architect.html

The other different is I have a file selection agent and a code review agent, which often has some good fixes/improvements.

I use both, I'll use Aider if its something I feel it will right the first time or I want control over the files in the context, otherwise I'll use the agent in Sophia.

[randDev210901]

Do you mind elaborating on your setup and workflow?

I tried using aider but either my local LLM is too slow or my software projects requires context sizes so large they make aider move at a crawl.

[tomduncalf]

I was going to ask what size and complexity of projects OP uses it on. I can’t imagine doing my work just with a tool like that. Cursor is pretty impressive and a definite sooner boost though.

[freediver]

Fair point, most projects I do are prototypes and concepts for ideas I have. Up to 2000 lines of code, built from scratch. The mode of work is me commanding 95% of the time and coding about 5%.

[ada1981]

Like Amazon cloning the best selling products, bringing them in house, and then closing the accounts of competitors.

Met a guy who got brought in by Amazon after they hit 8 figures in sales, wined and dined, then months later Amazon launched competing product and locked them out of their accounts, cost them 9 figures.

[truetraveller]

> potentially every upstream AI company out of YC

You mean downstream.