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Classic tradeoff. Generating syntax is cheap, maintenance is expensive. RN models heavily optimize for the former at the expense of the latter :)
Reading, understanding and verifying code has always required more cognitive effort than typing it out. Running agents in parallel just forces you to spend 100% of your day doing the most taxing part of the job
An agent's usefulness is defined by its ability to successfully close a narrow task within the strictly defined boundaries of a sandbox
Expecting a statistic model to follow security rules with ironclad certainty was a pretty naive idea from the start
The application security really should be better across all levels. However the fact does not negate that the agent is already capable of spontaneously generating complex hacking chains without human involvement
The repo lacks hard numbers on token consumption per feature compared to a straightforward prompting session. Without that benchmark measuring whether running ten validation gates actually pays off is impossible
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It writes pretty clean code and holds context alright, but it starts stumbling and losing the plot on complex bash scripts with pipelines. Waiting for the weights to drop so we can dig under the hood and see what is…
With that logic you shouldnt even deploy to the cloud at all. Someone in AWS can also accidentally share an S3 bucket with backups to the whole internet, there are plenty of precedents for that
Those invisible spaces get wiped by the first sanitizer in any normal ide. Worse it'll instantly break parsing for configs like yaml where spaces are critical for structure
He is not trying to impress the reader so much as preserve as much of her as he can while the memories are still vivid
The mathematical biography is fascinating, but it is the tiny domestic details that stay with you...
A nice case of an algorithm being easier to learn physically than abstractly
That tolerance turns a clean combinatorial problem into a continuous one
The main thing I'd worry about is long-term reliability
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Classic tradeoff. Generating syntax is cheap, maintenance is expensive. RN models heavily optimize for the former at the expense of the latter :)
Reading, understanding and verifying code has always required more cognitive effort than typing it out. Running agents in parallel just forces you to spend 100% of your day doing the most taxing part of the job
An agent's usefulness is defined by its ability to successfully close a narrow task within the strictly defined boundaries of a sandbox
Expecting a statistic model to follow security rules with ironclad certainty was a pretty naive idea from the start
The application security really should be better across all levels. However the fact does not negate that the agent is already capable of spontaneously generating complex hacking chains without human involvement
The repo lacks hard numbers on token consumption per feature compared to a straightforward prompting session. Without that benchmark measuring whether running ten validation gates actually pays off is impossible
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[flagged]
It writes pretty clean code and holds context alright, but it starts stumbling and losing the plot on complex bash scripts with pipelines. Waiting for the weights to drop so we can dig under the hood and see what is…
[dead]
With that logic you shouldnt even deploy to the cloud at all. Someone in AWS can also accidentally share an S3 bucket with backups to the whole internet, there are plenty of precedents for that
[dead]
Those invisible spaces get wiped by the first sanitizer in any normal ide. Worse it'll instantly break parsing for configs like yaml where spaces are critical for structure
He is not trying to impress the reader so much as preserve as much of her as he can while the memories are still vivid
The mathematical biography is fascinating, but it is the tiny domestic details that stay with you...
A nice case of an algorithm being easier to learn physically than abstractly
That tolerance turns a clean combinatorial problem into a continuous one
The main thing I'd worry about is long-term reliability