arkmm

5 ms·

1.
by arkmm 27d ago
Very cool! Can you say a little bit about the size of the DPO training examples and how long training took?
2.
by arkmm 1mo ago
"Claude keeps responses focused, brief, and concise to avoid overwhelming the person." Claude and I must have a different idea of what brief and concise mean.
3.
by arkmm 1mo ago
Since reasoning tokens are just text, I think the models have learned to squeeze in some computation in their output writing as well. So they're incentivized to be correct but long-winded, as it gives them more time to think. It's
4.
by arkmm 1mo ago
I'd also be really curious about the cost to run something like this, and what things you think it's particularly helpful for?
5.
by arkmm 1mo ago
Building a simple sandboxed way to run open weight models over a copy of all my personal data (email, docs, messaging, photos, etc). Think it'd be cool to have a self hosted AI "chief of staff" that has full read access to my
6.
by arkmm 2mo ago
And the human has to explain it at 2 tokens/sec (average speaking speed ~130-150 wpm). That's another constraint against slop - humans need to be able to explain their code succinctly.
7.
by arkmm 2mo ago
I think part of the problem is the context windows for humans are actually much smaller than what an LLM can keep track of today. The small context window of humans is a feature that forces modularity and abstraction in software engineering
8.
by arkmm 3mo ago
Seems like LLMs embrace that last point as well.
9.
by arkmm 4mo ago
everything is computer
10.
by arkmm 6mo ago
Didn't know this technique had a name, but I would think a modern compiler could make this optimization on its own, no?
11.
by arkmm 7mo ago
You can fine tune a small LLM with a few thousand examples in just a few hours for a few dollars. It can be a bit tricky to host, but if you share a rough idea of the volume and whether this needs to be real-time or batched, I could list so
12.
by arkmm 7mo ago
Can you share more details about your use case? The good applications of fine tuning are usually pretty niche, which tends to make people feel like others might not be interested in hearing the details. As a result it's really hard to
13.
by arkmm 7mo ago
Payment fees are crazy when you think about them from the perspective of a merchant in a low margin business. E.g. in retail or restaurants, margins aren't much better than ~10%. If they didn't have to pay ~3% credit card fees, th
14.
by arkmm 7mo ago
I used to also have this optimistic take, but over time I think the reality is that most people will instead just distrust unknown online sources and fall into the mental shortcuts of confirmation bias and social proof. Net effect will be e
15.
by arkmm 7mo ago
Get ready for the acquisition offers.
16.
by arkmm 7mo ago
Sorry Ploum, just getting a chance to read this now and comment. Great insights!
17.
by arkmm 8mo ago
this is a really cool insight, going to use this on my team from now on!
18.
Ask HN: For those of you building AI agents, how have you made them faster?
2 points by arkmm 8mo ago|1 comments
19.
by arkmm 8mo ago
They're still very good for finetuned classification, often 10-100x cheaper to run at similar or higher accuracy as a large model - but I think most people just prompt the large model unless they have high volume needs or need to self
20.
by arkmm 8mo ago
Maybe a bit off-topic, but how'd you meet your partner while on your adventures?
21.
by arkmm 11mo ago
As a follow-up to this, even though water makes up 70% of the Earth's surface, it's only 0.02% of the Earth's mass.
22.
by arkmm 11mo ago
Wow, this deserves its own submission.
23.
by arkmm 11mo ago
Neat approach, but seems like the eventual goal of caching DOM maps for all users would be a privacy nightmare?
24.
by arkmm 11mo ago
What's misleading about that? You rent $100 of time on an H100 to train the model.
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by arkmm 11mo ago
What sorts of automations were you able to get working with the Chrome dev tools MCP?
26.
by arkmm 1y ago
The irony of this is so much of Reddit comments these days are AI generated.
27.
by arkmm 1y ago
Unfortunately I think they have stopped doing this since COVID.
28.
by arkmm 1y ago
This misses the forest from the trees IMO: - The datacenter GPU market is 10x larger than the consumer GPU market for Nvidia (and it's still growing). Winning an extra few percentage points in consumer is not a priority anymore. - Nvid
29.
by arkmm 1y ago
Looking forward to reading corroborating essays from other non-human species.
30.
by arkmm 1y ago
"There was one surprise when I revisited costs: OpenAI charges an unusually low $0.0001 / 1M tokens for batch inference on their latest embedding model. Even conservatively assuming I had 1 billion crawled pages, each with 1K toke

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