HN user

gfysfm

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www.seangoedecke.com 4d ago

Screwing Up

gfysfm
1pts0
www.seangoedecke.com 6d ago

Blood in the Datacenter

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8pts0
www.seangoedecke.com 7d ago

I don't like the "staff engineer archetypes"

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4pts0
www.seangoedecke.com 1mo ago

Screwing Up

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4pts0
skifreedle.com 1mo ago

SkiFreedle, a daily game version of SkiFree (1991)

gfysfm
3pts1
www.seangoedecke.com 4mo ago

What's so hard about continuous learning?

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3pts0
www.seangoedecke.com 5mo ago

We are in the "gentleman scientist" era of AI research

gfysfm
3pts0
www.seangoedecke.com 5mo ago

How does AI impact skill formation?

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www.seangoedecke.com 6mo ago

Grok is enabling mass sexual harassment on Twitter

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20pts1
www.seangoedecke.com 7mo ago

How good engineers write bad code at big companies

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407pts320
www.seangoedecke.com 8mo ago

It's not surprising that 95% of AI enterprise projects fail

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2pts0
github.blog 12mo ago

Solving the inference problem for open source AI projects with GitHub Models

gfysfm
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github.com 1y ago

Scan and resolve fixed GitHub issues and PRs with AI

gfysfm
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gist.github.com 1y ago

Show HN: OpenAI Codex for Free with GitHub Models

gfysfm
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www.seangoedecke.com 1y ago

The Illusion of "The Illusion of Thinking"

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6pts1
www.seangoedecke.com 1y ago

Sycophancy is the first LLM "dark pattern"

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9pts1
www.seangoedecke.com 1y ago

The OpenAI house style is exhausting

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2pts0
www.seangoedecke.com 1y ago

Designing software that could possibly work

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2pts0
www.seangoedecke.com 1y ago

Dangerous Advice for Software Engineers

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www.seangoedecke.com 1y ago

How to work in tech when your job isn't safe

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2pts1
stevenlaidlaw.com 1y ago

Reduce, Reuse, Refactor

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www.seangoedecke.com 1y ago

Model Context Protocol explained as simply as possible

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www.seangoedecke.com 1y ago

What's next after the AI bubble bursts?

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21pts26
www.seangoedecke.com 1y ago

Building your sense of what's important at a tech company

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15pts3
www.seangoedecke.com 1y ago

Refactoring won't save you from a layoff

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www.seangoedecke.com 1y ago

How I know I'm working with a strong engineer

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www.seangoedecke.com 1y ago

Beyond Prompting

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www.seangoedecke.com 1y ago

Engineers Who Won't Commit

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www.seangoedecke.com 1y ago

Learning how to think better from LLMs

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3pts0
www.seangoedecke.com 1y ago

To avoid being replaced by LLMs, do what they can't

gfysfm
57pts79

This is really cool! It's interesting that you can "cheat" by knowing the historical situation in advance. But I guess there's no way around that - if the situations involved were hypothetical, it would be unsatisfying when you guessed "wrong". It'd be neat to see really obscure examples drawn from history.

Hi, I wrote the post! Also not a ML researcher, just an interested engineer, so I'm sure I got some things wrong.

MoE should be the better tradeoff for the local/single-user scenario since the downside of batching being harder / less efficient doesn't matter.

What I meant was that the single-user scenario is going to get dramatically worse throughput-per-GPU, because they're not able to reap the benefits of multi-user batching (unless they're somehow doing massively parallel inference requests, I suppose).

Is it really that the matrixes being multiplied are larger? My mental model is that the purpose of batching isn't to get larger input matrices. It's to move the bottleneck from memory bandwidth to compute.

As I understand it, you want larger input matrices in order to move the bottleneck from memory to compute: if you do no batching at all, your multiplications will be smaller (the weights will be the same, of course, but the next-token data you're multiplying with the weights will be 1xdim instead of batch-size x dim), so your GPUs will be under-utilized and your inference will spend more time doing memory operations and less time multiplying.

The post has no numbers on the time to first token for any of the three providers.

I probably should have hunted down specific numbers, but I think people who've played with DeepSeek and other models will notice that DeepSeek is noticeably more sluggish.

Hi, I wrote the post. Thank you!

That’s how it does work, but unfortunately denoising the last paragraph requires computing attention scores for every token in that paragraph, which requires checking those tokens against every token in the sequence. So it’s still much less cacheable than the equivalent autoregressive model.

It’s partially because my articles often sit in draft for a while. Then while editing I have another thought that spins off into a separate article, which then gets posted in quick succession.

Yeah, I agree this post would have been better with a concrete example. It's hard to talk about a specific project though, since it comes down to describing in detail facts about a company's internal workings (often embarrassing facts). I couldn't figure out how to anonymize it sufficiently.

Nope! I threw this up in the time I had just to see what it'd look like. I think it'd be a lot of fun to try different formats or different prompts (e.g. eliciting CoT before making a decision).

This repo is sort of a companion piece to https://github.com/sgoedecke/fish-tank, which is a more physics-based 2d simulation. Interestingly, 4o-mini absolutely cleans up in the 2d space but doesn't win all the time in poker.

Wrt mining/ads, I'm considering to default to mining if I detect ad-blocker, or you're on Chrome + Desktop PC (not laptops).

Do what you've gotta do to get paid for your work, but if I found out this was happening it would strongly discourage me from remaining on the website.

Thanks everyone for giving this a go! I'll admit I indulged my "I want it to look like a terminal" urges at the expense of proper usability or mobile users. It's far from a finished product, as many of you noticed, but it's great that people were able to have some fun with it anyway.

It's worth noting that the only real criticism you level against Shakespeare here - that "shall I compare thee to a summer's day..." is incoherent - rests on an embarrassing misreading of the sonnet. Far from being a strike against Shakespeare, his point is that summer days are not temperate but hot! Likewise, Autumn is too short, May is too windy, and so on. The subject of the poem, Shakespeare claims, does not suffer any of these flaws. I think this serves as a useful yardstick for the quality of your other claims about Shakespeare.