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The theory is that this solves the data shortage problem, they can generate a ton of chain of reasoning data from what we already have. True iterative improveme
by ldjkfkdsjnv 2y ago
The theory is that this solves the data shortage problem, they can generate a ton of chain of reasoning data from what we already have. True iterative improvement, like out of a science fiction novel
These models are going to get embedded deeply into IDE's, like cursor has, and essentially end software development as we know it. A properly written requirements spec, and an engineer, can do the work of 5. Software engineering as done by hand is going to disappear. Saas startups whose moat is a harvard ceo and 5 million in capital will watch their margins disappear. This will be the great equalizer for creative intelligent individuals, true leverage to build what you want
- deleted 2y ago[deleted]
- bamboozled 2y agoWhich will mean...there is going to be a lot more software?
- ldjkfkdsjnv 2y agoProbably yeah
- vrighter 2y agoa lot more broken software. Companies release broken software intentionally just to be quick to market. Now can you imagine the same, but the "engineers" literally cannot make the product better even if they wanted to. They never learned to code properly. So they can't tell whether the code is good.
- OutOfHere 2y ago> A properly written requirements spec, and an engineer, can do the work of 5. I do not think this will scale. GPT o1 is presumably good for bootstrapping a project using tools that the engineer is not familiar with. The model will struggle to update a sizable codebase, however, with dependencies between the files. Secondly, no matter the size of the codebase and no matter the model used, the engineer still has to review every single line before incorporating it into the project. Only a competent engineer can review code effectively.
- ldjkfkdsjnv 2y agoI respectfully, but completely disagree. Right now with sonnet 3.5 + cursor ide, I'm not writing that much of my own code at my FAANG job. I am generating a ton, passing in documentation from internal libraries, iterating on the result. Most of the time, I just accept its changes. This is going to rapidly happen. All we need are a few more model releases, not even a step function improvement
- OutOfHere 2y agoDon't you still have to explain your requirement really well to it, in a lot of detail? In a terse language like Python, I might as well just write the code. In a verbose language like Java, perhaps there is more of a value in detailing the requirement.
- ldjkfkdsjnv 2y agoNot really, most of the changes are straightforward. Also, alot of the time it writes better syntax than i would. Sometimes I write a bunch of psuedo code and hsave it fill in the detials, then write the tests
- OutOfHere 2y ago> Not really How on earth are you conveying your intent to the model? Or is your intent so CRUDdy that it doesn't need to be conveyed?
- w4 2y agoIt depends on what you're doing. If you're writing something specific to your particular problem, or thinking through how to structure your data, or even working on something tough to describe in words like UI design, it probably is easier to just code it yourself in most high-level languages. On the other hand, if you're just trying to get a framework or library to do something and you don't want to spend a bunch of time reading the docs to remember the special incantations needed to just make it do the thing you already know it can do, the AI speeds things up considerably.
- vrighter 2y agoa properly written requirements spec is something that doesn't exist in the vast majority of cases.
- knicholes 2y agoI dunno man. I just spent a couple hours trying to get it to write functioning code to read from my RTSP stream, detect if my kid is playing piano, and send the result to HomeAssistant. It did not succeed.
- cma 2y agoHow many hours without it?
- elicksaur 2y agoNot the OP, but in my experience LLMs fail in ways that indicate they will never solve the problem. Stuck in loops, correct their mistakes with worse mistakes, hallucinating things that don’t exist and being unable to correct. Working on my own, I have the confidence that I know I can make incremental forward progress on a problem. That’s much preferable.
- cma 2y agoBut when working with an LLM you can still contribute.
- knicholes 2y agoThat remains to yet be seen, as I kept insisting that an LLM should be able to write this in its entirety with success with "just one more prompt change".
- 7thpower 2y agoI think SaaS startups with a Harvard founder and 5 million are going to crush it in the world you describe. The marginal cost of building decreases, but brands, trust, and reach do not follow the same scaling laws. Access to capital and pedigree are still going to be a big plus.
- imtringued 2y agoWhat data shortage problem? I'm not convinced that a shortage of data is the problem with current generation LLMs. This isn't like robotics where every robot is unique and you had to historically start from scratch every time you changed to a different robot. It's more likely that we are running into some sort of generalization bottleneck, because the training process is operating without feedback on the information/semantic level. There is no loss function for "does the code compile?". Instead, the loss function checks "does the output conform to the dataset?".
- ai4ever 2y agoSuch statements are made by management folks who dont code, and somehow think coding can be hand-waved away. Sure, this tool will improve the productivity of sw engineers, but so did the compiler which came 50 years back.