thornewolf

Joined 4 years ago

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162

Member since

Jul 7

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Comment · 1 month ago

We have a bad universal classifier now (via Jev). 0->1, one might say.

A bad universal classifier does suggest a good one later. And that is exactly what I would call "theoretically doable"

That said, I don't think that Jev is a magic breakthrough or anything. I think it is just a particularly good narrative with an easy way to try it out.

Comment · 1 month ago

Write a prompt, evaluate the prompt, understand that is succeeds 95% of the time.

Write a new prompt, evaluate, it now succeeds 99% of the time. Measure what changes between prompt #1 and prompt #2, understand what contributed to the performance jump.

Write a third prompt, this one succeeds 100% of the time. Increase the size of your evaluation set, find a 1/5000 error-class and a 1/10000 error-class, add some explicit code to correct for this cases.

Roll out to production, collecting usage metrics. You make some tweaks to your harness, your prompts. Eventually you have confidence that your system has fewer mistakes than 1 in 100k.

Now, multiply this iteration across all your different prompts and different ways that they might interact with one another.

Comment · 1 month ago

without getting too far into it i would just like to note that i am in both these circles

Comment · 1 month ago

some initial function like

  v = setup()
  if v == 1:
    side_effect_1()
  elif v > 1:
    side_effect_1()
    side_effect_2(v)
  else:
    raise Exception()

then we can "refactor"

  v = setup()
  if v < 1:
    raise Exception()
  
  side_effect_1()
  if v > 1:
    side_effect_2(v)

i know that this might seem "dumb" that the code was ever setup the first way but code can grow into that shape pretty easily. this refactor "removes" the v==1 branch. this new code also follows the "early return" pattern, which improves readability.

Comment · 1 month ago

to add some context from "the other side": last year, i listened to 2930 distinct albums of which 50% were albums I had not listened to before. Even on a 30 year timeframe and a good discount factor for album repetition I don't think I can fit into the budget you propose without making significant concessions in my discovery process.

Comment · 3 months ago

My response is going to be about Gemini generally and less about NotebookLM.

Google's last frontier model release was Gemini 3.1 Pro, which was in February of this year[1]. At the time, it was ahead of the (at the time) flagship models of Opus 4.6 and GPT 5.2/5.3. From my recollection of the time, it was the best model in the world.

Anthropic released Opus 4.5 Nov '25, 4.6 in Feb '26, 4.7 in April, 4.8 in (late) May. Then Fable in June. 4.7 beat 3.1 Pro on multiple metrics. Fable eats it for breakfast. However, I want to note the 3 month gap between those first two Opus versions.

OpenAI released 5.2 Dec '25, 5.3 Codex Feb '26, 5.3 Instant Mar, 5.4 Mar, 5.5 (late) May, 5.6 July. 5.4 beats 3.1 Pro on agentic benchmarks[2], seems to be similar/losing on non-agentic. 5.5 seems stronger than 3.1 Pro[3].

Gemini 3.5 Pro is alleged to be launching within the week. Why do I type this all out? Because I think Google is getting a bad rap. They are delayed on a frontier release by a month or two and are being regarded as if they cannot release frontier models. I think their last release demonstrates strength and we need to see a weak release before we call them "behind" (in any reasonable sense). These companies swap back and forth constantly. I recall a multi-month span where 2.5 Pro was just the best thing out there by a large margin (in my opinion).

[1]: https://blog.google/innovation-and-ai/models-and-research/ge...

[2]: https://www.anthropic.com/news/claude-opus-4-7

[3]: https://www.anthropic.com/news/claude-opus-4-8

Comment · 3 months ago

just read this when reviewing OpenAI's "spinning up" documentation as it was linked there!

Comment · 5 months ago

The author explain this in more detail in the article; I also was initially confused. The key details come from their broken down pandoc command. Specifically, these two flags:

  --pdf-engine=typst
  -V template=article.typ

Despite the input content being a .md file, we see that the .md contents populate the .typ file. Pandoc then understands how to convert the populated .typ file into a .pdf. The author also notes in their hyperlinked .typ document where the body content is placed:

  // THIS IS THE ACTUAL BODY:

What is not clear to me is the actual mechanism that tells pandoc where to place the body contents in the template. I presume it's some "magic" from the pandoc docs. The pandoc templates documentation[1] does reveal to us that `-V` is setting a variable called `template`. I don't have pandoc locally, but from another post[2] from the same author and a chatgpt query, it seems that the `body` symbol in the template is the chosen substitution symbol.

[1]: https://pandoc.org/demo/example33/6-templates.html

[2]: https://imaginarytext.ca/posts/2024/pandoc-typst-tutorial/

Comment · 5 months ago

not dead, they are on "luma". quality is higher in some ways, lower in some ways when compared to "old school" meetups

Comment · 5 months ago

I can't smell much at all. one time when i was 17 my friend told me i was kinda smelly (i had just exercised). I've been stressed about it since. the human brain can latch on to the oddest things

Comment · 5 months ago

there is something "wrong" with the ux that is hard to pin down. these things generate even text summaries more rapidly than i can read them. i need a better method for dumping info into my brain + dynamic control (if necessary)

Comment · 5 months ago

They have named themselves sqliteai and are releasing a library called adam. this is just advanced seo. seems neat though

Comment · 6 months ago

it's a security feature. otherwise my malicious site could check for cdn.sensitivephotoswebsite.com and blackmail you if it was cached already

Comment · 6 months ago