491–500 of 1001 posts
Earlier quoted context omitted.
> that realistically could easily replace any entry-level or junior developer. This is a massive, unsubstantiated leap.
I’d take pair programming with a junior over a GPT bot any day.
Earlier quoted context omitted.
> the kind you might as well copy&paste from stackoverflow This bothers me. I completely understand the conversational aspect - "what approach might work for this?", "how could we reduce the crud in this function?" - it worked a lot for me last year when I tried learning C. But the vast majority of AI use that I see is...not that. It's just glorified, very expensive search. We are willing to burn far, far more fuel t…
What I can't wrap my head around is that making good, efficient software doesn't (by and large) take significantly longer than making bloated, inefficient enterprise spaghetti. The problem is finding people to do it with who care enough to think rigorously about what they're going to do before they start doing it. There's this bizarre misconception popular among bigtech managers that there's some tunable tradeoff bet…
Without redoing their work or finding a way to have deep trust (which is possible, but uncommon at a bigcorp) it's hard enough to tell who is earnest and who is faking it (or buying their own baloney) when it comes to propositions like "investing in this piece of tech debt will pay off big time"
As a result, if managers tend to believe such plans, bad ideas drive out good and you end up investing in a tech debt proposal that just wastes time. Burned managers therefore cope by undervaluing any such proposals and preferring the crappy car that at least you know is crappy over the car that allegedly has a brand new 0 mile motor on it but you have no way of distinguishing from a car with a rolled back odometer. They take the locally optimal path because it's the best they can do.
It's taken me 15 years of working in the field and thinking about this to figure it out.
The only way out is an organization where everyone is trusted and competent and is worthy of trust, which again, hard to do at most random bigcorps.
This is my current theory anyway. It's sad, but I think it kind of makes sense.
Claude 3.5-sonnet (latest) is barely able to stay coherent on 500 LOC files, and easily gets tripped up when there are several files in the same directory.
I have tried similarly with o1-preview and 4o, and gemini pro...
If google is using a 5M token context window LLM with 100k+ token-output trained on all the code that is not public... then I can believe this claim.
This just goes to show how critical of an issue this is that these models are behind closed doors.
I feel like, given my experience lately with all the API models currently available, that this is only a fact if the models google is using internally are SIGNIFICANTLY better than what is available publicly even on closed models. Claude 3.5-sonnet (latest) is barely able to stay coherent on 500 LOC files, and easily gets tripped up when there are several files in the same directory. I have tried similarly with o1-pr…
How is competitive advantage, using in-house developed/funded tools, a critical issue? Every company has tools that only they have, that they pay significantly for to develop, and use extensively. It's can often be the primary thing that really differentiates companies who are all doing similar things.
To my experience, AIs can generate perfectly good code relatively easy things, the kind you might as well copy&paste from stackoverflow, and they'll very confidently generate subtly wrong code for anything that's non-trivial for an experienced programmer to write. How do people deal with this? I simply don't understand the value proposition. Does Google now have 25% subtly wrong code? Or do they have 25% trivial code…
Well, Rob Pike said same thing about experience and that seemed to pissed lot of people endlessly.
However I don't think it as indictment It just seems very reasonable to me. In fact 25% seem to be on lower end. Amazon seems to have thousands of software engineers who are doing API calling API calling API.. kind of crap. Now their annual income might be more than my lifetime earnings. But to think that all these highly paid engineers are doing highly complex work that need high skills seems just a myth that is useful to boost ego of engineers and their employers alike.
To my experience, AIs can generate perfectly good code relatively easy things, the kind you might as well copy&paste from stackoverflow, and they'll very confidently generate subtly wrong code for anything that's non-trivial for an experienced programmer to write. How do people deal with this? I simply don't understand the value proposition. Does Google now have 25% subtly wrong code? Or do they have 25% trivial code…
I feel like, given my experience lately with all the API models currently available, that this is only a fact if the models google is using internally are SIGNIFICANTLY better than what is available publicly even on closed models. Claude 3.5-sonnet (latest) is barely able to stay coherent on 500 LOC files, and easily gets tripped up when there are several files in the same directory. I have tried similarly with o1-pr…
Earlier quoted context omitted.
I remember in like 5th grade rural PA schools learning about Boolean operators in search engines and falling in love with them. For context, they were presenting alta vista and yahoo kids search as the most popular with Google being a "simple but effective new search platform" we might want to check out. By the time I graduated highschool you already couldn't trust that Boolean operators would be treated literally. B…
Even though I miss using boolean operators in search, I doubt that it was ever sustainable outside of specialized search engines. Very few people seem to think in those terms. Many of those who do would still have difficulty forming complex queries. I suspect the real problem is that search engines ceased being search engines when they stopped taking things literally and started trying to interpret what people mean.…
Then Google decided to start enforcing that, because they had this idea that they would be able to divine your "intent" from a "natural question" rather than just matching documents including your search terms.
Hi, I lead the teams responsible for our internal developer tools, including AI features. We work very closely with Google DeepMind to adapt Gemini models for Google-scale coding and other Software Engineering usecases. Google has a unique, massive monorepo which poses a lot of fun challenges when it comes to deploying AI capabilities at scale. 1. We take a lot of care to make sure the AI recommendations are safe and…
> We work very closely with Google DeepMind to adapt Gemini models for Google-scale coding and other Software Engineering usecases. Considering how terrible and frequently broken the code that the public facing Gemini produces, I'll have to be honest that that kind of scares me. Gemini frequently fails at some fairly basic stuff, even in popular languages where it would have had a lot of source material to work from;…
Financial incentives at large companies are not aligned with low volumes of code. There are no rewards for less code. People get rewarded for another bullshit framework to slap on their resume. Box me in, no, cube me in to a morass of a thick ingress layer, that uses 1/8th of my CPU.