Dave White ✓
@_Dave__White_ · joined 15 Oct 2019
human
42 387Followers
1 600Following
7 920Posts total
291.8KViews on collected posts
Latest posts
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i wonder if there's now room for a new modality of maintainer-free open source
rather than issuing a pull request, all code contributions have equal footing, and then as a user you can have an ai agent be the "maintainer" of the repo you actually use which is cobbled together
2K views · 17 likes
· 1 reposts · 8 replies
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@_Dave__White_ Glad to have you back, sounds awesome
76 views · 1 likes
· 0 reposts · 0 replies
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@_Dave__White_ custom chopping interface is the part that would eat my whole morning
field recorder to sampler pads is a clean pipeline though
34 views · 0 likes
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back in the season of wanting to share what i'm up to
this morning i'm working on a script to pull recordings off my field recorder, put them all in a custom sample chopping interface, and then load them onto my sampler pads
2.8K views · 37 likes
· 0 reposts · 5 replies
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it may be true that the singularity is fast approaching but it is *also* true that i have completed a ridiculous cover of “untrust us” in ableton, which in the final accounting must count for something
2K views · 22 likes
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fable is the first model to pass my holdout pun benchmark
“Please explain the joke: Isaac Marco Pagliacci, proctologist”
1.7K views · 13 likes
· 2 reposts · 1 replies
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@_Dave__White_ gm ser wen collab?
cc @paradigm @FrankieIsLost @sshmatrix_
https://t.co/Eu0Z4ggpue
4.3K views · 35 likes
· 4 reposts · 6 replies
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The mechanism is an AMM over functions, with the invariant being a constant l2 norm.
When restricted to specific distributions like the Normal distribution, the math is surprisingly simple, and the resulting AMMs can be implemented efficiently onchain.
4/ https://t.co/gMZrYvNr
10.4K views · 87 likes
· 3 reposts · 6 replies
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We hope Distribution Markets help to spark ideas for builders and researchers on the cutting edge of information finance.
If that's you, we'd love to hear from you.
https://t.co/ZkaDzscKfn
5/5
13.3K views · 211 likes
· 10 reposts · 14 replies
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Take the question "When will GPT-5 be released?"
In a traditional prediction market, traders might have to choose between preset options like "Q2 2025" or "2026."
Sites like Metaculus instead let users submit curves showing how likely they think each possible date is.
2/ https
9.7K views · 72 likes
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But Metaculus is a competition, not a market, and has no way for participants to put skin in the game and trade on their beliefs.
Distribution Markets let traders specify their whole estimated distribution and profit by moving the consensus view in the right direction.
3/ https
8.9K views · 62 likes
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I've been working on prediction markets for events whose outcomes aren't just "yes" or "no."
Instead of betting on just one outcome, traders in Distribution Markets can express how likely they think each different possibility is across the whole infinite range of outcomes.
1/ h
236.1K views · 1.6K likes
· 171 reposts · 162 replies
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Against accounts of the same size
5 posts from the last 90 days, next to the 10K–100K follower range. reaches fewer people than peers of the same size.
Median views266this account924median for 10K–100K
Reach, %0.63%this account3.62%median for 10K–100K
Engagement, %1.31%this account1.52%median for 10K–100K
| Metric | This account | Median for 10K–100K | Ratio |
|---|---|---|---|
| Median views per post | 266 | 924 | 0.29× |
| Reach (views ÷ followers) | 0.63% | 3.62% | 0.17× |
| Engagement rate | 1.31% | 1.52% | 0.86× |
Others in this range → Compare with another account → How these benchmarks are built →
Growth & engagement
How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.
Views per post
236.1K10 Dec
8.9K
9.7K
13.3K
10.4K
4.3K11 Dec
1.7K9 Jun
2K10 Jun
2.8K18 Aug
266
34
76
2K
Last 13 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.84%10 Dec
0.72%
0.78%
1.78%
0.95%
1.04%11 Dec
0.92%9 Jun
1.33%10 Jun
1.49%18 Aug
0.00%
0.00%
1.32%
1.31%
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes60.3%2 138 in total
Reposts5.4%193 in total
Replies5.9%210 in total
Quotes2.0%70 in total
Bookmarks26.3%932 in total
Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.
The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.
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