111–120 of 236 posts
Just completely hilarious how 6 months ago about 50% of hackernews comments were AI denialists telling everybody they were full of shit and that LLM was not useful. That group is awfully quiet nowadays. The bar has clearly moved to "eventually we won't even need to do code reviews". LLM denialists were always wrong and they should be embarrassed to share their bad model of reality and how the world works.
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Is this satire? I can't tell any more.
I don't understand why people try so hard to anthropormophize these tools and map them to human sociology...
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First of all natural selection doesn't happen per se, nor is controlled by some inherent mechanism, it's the by product of many factors external and internal. So the comparison is just wrong. Human engineering is an interative process not a selection. And if we want to call it selection, even though it is a stretch, we're controlling it, we the master of puppets, natural selection is anything but a controlled process…
We do select for a more resistant wing. How did we determine that this wing is more resistant? We modeled its bending tolerance and selected this particular design against other designs that had worse evaluated results for bending tolerance.
Natural selectiom:
- is not an intentional process
- does not find "the strongest the fittest the fastest etc."
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> Behavior does not need to be fully specified at the outset. It could be evaluated after the run. This doesn't work when the software in question is written by competent humans, let alone the sort of random process you describe. A run of the software only tells you the behavior of the software for a given input, it doesn't tell you all possible behaviors of the software. "I ran the code and the output looked good" i…
Genetic algorithms created our species, which are far more complex than anything we have written in computer science. I think they have stood up to the tests of creating a viable product for a given behavior. And with future compute, you will be able to evaluate behavior across an entire range of inputs for countless putative functions. There will be a time when none of this is compute bound. It is today, but in thre…
Yes, and our species is a fragile barely functioning machinery with an insane number of failing points, and hillariously bad and inefficiently placed components.
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I can see a lot of negatives in relation to removing the human readable aspect of software development. Thorough testing would be virtually impossible because we’d be relying on fuzzing to iron out potential edge cases or bugs. In this situation, AI companies are incentivised to host the services their tooling generates. If we don't get source code, it is much easier for them to justify not sharing it. Plus, who is t…
All these questions are true for agriculture, yet you say "yes thank you, and please continue" for that industry I am sure, which seeks to improve product through random walk and unknown mechanisms. Maybe take a step back and examine your own biases.
Tell me you know nothing about modern agriculture without telling me that
Meanwhile here’s me still just using the ChatGPT web chat asking it for code snippets.
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It's doesn't work...yet. I agree my stomach churns a little at this sentence. However, paying customers care about reliability and performance. Code review helps that today, but it's only a matter of time before it is more performative than useful in serving those goals at the cost of velocity.
the (multi) billon dollar question is when that will happen, I think, case in point: the OP is a kid in his 20s describing the history of the last 3 years or so of small scale AI Development ( https://www.linkedin.com/in/silen-naihin/details/experience/ ) How does that compare to those of us with 15-50 years of software engineering experience working on giant codebases that have years of domain rules, customers and u…
No mention of the results when targeting bigger, more complex projects, that require maintainability, sound architectural decisions, etc… which is actually the bread and butter of SW engineering and where the big bucks get made.
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the (multi) billon dollar question is when that will happen, I think, case in point: the OP is a kid in his 20s describing the history of the last 3 years or so of small scale AI Development ( https://www.linkedin.com/in/silen-naihin/details/experience/ ) How does that compare to those of us with 15-50 years of software engineering experience working on giant codebases that have years of domain rules, customers and u…
Thats the problem, the most “noise” regarding AI is made by juniors who are wowed by the ability to vibe code some fun “sideproject” React CRUD apps, like compound interest calculators or PDF converters. No mention of the results when targeting bigger, more complex projects, that require maintainability, sound architectural decisions, etc… which is actually the bread and butter of SW engineering and where the big buc…
Caught you! You have been on HN very actively the last days, because these were exactly the projects in "Show HN: .." category and you would not be able to tell them if you wouldnt have spent your whole time here :-D
Ha! :-D
> You no longer need to review the code. Or instruct the model at the level of files or functions. You can test behaviors instead. Maybe for a personal project but this doesn't work in a multi-dev environment with paying customers. In my experience, paying attention to architecture and the code itself results in a much more pliable application that can be evolved.
Counter argument... High velocity teams also observe production system telemetry and use error rates, tracing and more to maintain high SLAs for customers. They set a "budget" and use feature flagging to release risky code and roll back or roll forward based on metrics. So agentic coding can feed back on observed behaviors in production too.
But we have to use this "innovation budget" in a careful way.
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If this were viable, we'd all be running Haiku ten times in the time it took to run Opus once, but nobody does.
We don't have the compute for this today. We will in several centuries, if compute growth continues.
But I hope we have more efficient ways to do this in a century.