eventualcomp

That way, you can get promoted from Loser to Clueless. [1]

[1] https://ribbonfarm.com/2009/10/07/the-gervais-principle-or-t...

The market for lemons is always bustling, I guess [1].

[1] https://en.wikipedia.org/wiki/The_Market_for_Lemons

Any mechanism you find that can do this could also be used for money laundering, no? By way of identifying that money laundering in the US should not be possible (meaning without legal consequence), then this too should not be possible.

Ad hominem is good for the heart, I hear.

Your LLM can fuck that up badly if your LLM starts to take loans, right? Or BNPL regardless of sandboxing?

Isn't this like saying "my words are not really my own when I speak to my family, because I know my father is a non-native English speaker and hard of hearing so I try to use words which are well enunciated and are few in syllable count"?

Amazon is heterogeneous. So much so, that positive anecdotes and negative anecdotes are near worthless without specifying the org.

Depending on if you're a cost cutting team, fixed expense team or organization, if you're a revenue driving team, or if you're a core team, or the very many other splits you can come up about the relationship between the expense/balance sheets and the team itself...there are very very different attitudes towards COEs and leadership principles.

Yeesh. Data streaming algorithms. Can I import [1] datasketches-python in the interview?

[1] https://github.com/apache/datasketches-python

Why jump on the opportunity to prune reading by rejecting the lot as soon an unrelated premise you disagree with is presented?

Perhaps at that point if you stop reading after the first sentence, you could churn the entire article through AI to summarize it into a single sentence, and see if the invalid premise is core to the message?

Heh. I do something like this when I'm at work in meetings, basically dynamically trying to map out what people are saying so that I can build mental models.

Funny that soon the AI can define my own mental model on the fly soon.

Where is $50k coming from again?

> In reality, you put your fingers in the position for a C on that specific instrument and you get a C.

OK, my fault for poor communication. Let me try strongly typing this.

Clarinet: you play a finger-C, out comes a soundwave-Bb. Flute: you play a finger-C, out comes a soundwave-C. And finger-C is polymorphic on the instrument, or something.

Aside from that, I don't disagree with you.

One consideration is that with most instruments, being keyed the way they are, if you immediately transpose via LLM some of those instruments will have almost all their notes in unexpected ledger lines.

Which could have (en)grave implications.

I've heard of musicians with very strong senses of perfect pitch flocking to flute or oboe, because anything not keyed in C (perfect pitch equiv) results in too much cognitive dissonance. Clarinets are keyed in Bb (you play a C, out comes a Bb), horns in F (you play a C, out comes an F), trumpets in Eb (this should be clear), and so on...

Like motion sickness with musical tones - you see one thing on the page, you have a sense for what "note" you're playing, but out comes something else.

I have perfect pitch but it's not really useful, except for noticing that my instrument is getting sharper. But that doesn't matter since you have to be in tune with the rest of the band/orchestra.

Reminds me of this youtube video: https://m.youtube.com/watch?v=jkdWzvMOPuo

I liked the comments explaining why this worked.

(Self-reply due to inability to edit) so just checked your user history - why do you keep posting provocative takes?

There is also buffer for clean water required - Y gallons on-hand at the datacenter. You can see the other replies in this parent comment demonstrating the tight ongoing humidity requirements, and how clean water is sprayed onto the actual hardware to cool it off, and more. Evidently this can't be done by setting up a giant funnel above the datacenter to collect rainwater.

Given those considerations I expect Y to be pretty large.

> Nothing will be able to stop you from pulling the plug.

Who is "you"? And what do you envision is the "plug" to pull? And when does an intelligence become a superintelligence - do "we" know when to pull the "plug"?

For example, a superintelligence may be born in a datacenter. Would you expect politicians are aligned with shutting down a datacenter (privately owned) in which they may have heavy stake? What if critical systems are also running on the same infrastructure, will it be easy to cherry pick the superintelligence nodes to shutoff?

IMO this take is dismissive of the entrenched systems that make it hard to pull the plug. It's a hard problem and we need to think about it more.

The fact that a datacenter is evaporating X gallons of water in a period implies that a datacenter is ingesting X gallons of water (if less, the datacenter dries out, if more the datacenter floods) - meaning X gallons are now locked out of the water cycle. Meaning it rains back down and gets slurped back up.

This is under the happy assumption that all used water evaporates into a cloud directly above the source region, which rains back directly.

> Perhaps most importantly, it does so using a tiny fraction of the CPU time, saving energy and keeping our datacenters (and planet) a little cooler.

But then:

> A decoder backend on AWS (SQS + Lambda + DynamoDB + S3) reassembles objects from incoming encoded packets delivered via Proxylity UDP Gateway.

:( those microservice invocation will burn up the DC more.

The real sell looks like offloading s3 upload latency.

Praise be the accountability sink. https://news.ycombinator.com/item?id=41891694

To use an analogy, to add to everybody else: it's like rings on a tree stump. The innermost part of the stump is the oldest; the outer the youngest. Earth is on one of those in-between rings, neither the oldest nor the newest - doesn't matter which of the in-betweens, to be honest.

Suppose now that you're an ant on the middle ring of that tree stump. No matter which way you're looking from Earth's middle-ring, either the rings will get gradually older and then younger with increasing distance (if you're looking towards the center-ish), or the rings will get strictly younger (if you're looking away from the center-ish).

This analogy obviously breaks down if you delve into details but that should give a better intuition to what's going on.

> Would love feedback from people working on long-running agents, training loops, eval harnesses, or similar workflows.

I have not required a service for this kind of optimization at work. Though work gives me unbounded access to Claude 4.x-1m (substitute x with whatever is available). So I often ask it to do this kind of task.

I found that when I just specify, sometimes the AI will optimize to the point that it breaks other existing functional requirements in the same codebase. So I have to steer it with invariants. This is where the bulk of my effort is - monitoring to make sure that the agent didn't suddenly scramble the infra or delete valid usecases.

1. How do you address that [paperclip problem](https://en.wikipedia.org/wiki/Instrumental_convergence) in Remoroo? Can we define invariants? 2. Why is there a whole orchestration system? Was there some limitation that prompted this architecture, e.g. did workers die frequently? Looks like Temporal/AWS SWF with the brain/worker/control architecture. The existence of `q (quit): Kills the Worker, aborts the run. The run is marked FAILED.` makes me think there's only one worker...so why...? It'd make more sense if the brain wanted to dispatch multiple hypotheses to multiple workers to test in parallel (e.g. if optimizing SQL, try these different joins all at once, discard queries running for X minutes after the first complete one).

When I saw the title, I actually didn't have much emotion beyond curiosity. But then after checking thus comment, it piqued my interest, made me step back and really consider the ramifications of how we got here. And then yes I became depressed also.

Anyway, I got value out of it, comments dont have to increase net factual information to be meaningful, because we are all capable of reflection.

A few points/qs:

- Could you explain what you mean by "security through obscurity"? The mechanism is well explained in the blog.yossarian.net posts linked within. It is simply adding a time filter on a client.

- Also, I'm not sure if package registries (e.g. server) and package managers (e.g. client) are being conflated here regarding "attacks on package managers", this seems to be more of a mitigation a client could do when the upstream content in a registry is compromised.

- Lastly, I agree with the sentiment that this is not a full solution. But I think it can be useful nevertheless, a la Swiss Cheese Safety Model. [1]

[1]https://en.wikipedia.org/wiki/Swiss_cheese_model

The commenter is making a joke about the style of delivery of the sentence you quoted, because the style is [1]characteristic of AI generated writing.

[1]https://en.wikipedia.org/wiki/Wikipedia:Signs_of_AI_writing

This conversation thread reminds me of the very interesting and insightful talk here: Klaus Iglberger “Free Your Functions!” [video] https://www.youtube.com/watch?v=WLDT1lDOsb4.