Yuchen Jin ✓
@Yuchenj_UW · another Galaxy · joined 22 Nov 2016
Cooking fun AI systems & products @databricks. Prev: co-founder & CTO @ Hyperbolic, OctoAI (acquired by @nvidia) Apache TVM, PhD @ University of Washington.
290 724Followers
840Following
14 273Posts total
4.5MViews on collected posts
Ultimi post
Cerchiamo nuovi post…
@Yuchenj_UW We're in luck we got Zuck https://t.co/x0m5dYPeHQ
262 views · 6 likes
· 0 reposts · 0 replies
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GIF
@Yuchenj_UW Having some experience with software engineering probably helps.
856 views · 13 likes
· 0 reposts · 1 replies
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Zuck’s AI safety take is basically the opposite of most frontier labs:
Most labs:
The more powerful the model, the more dangerous it is, so access should be restricted.
Zuck:
The more powerful the model, the more dangerous it is to let a few labs control it.
Zuck is Based. htt
54.4K views · 900 likes
· 57 reposts · 33 replies
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OpenAI’s “significantly more capable than GPT-6 Astra” model solving a Millennium Prize Problem is huge, an AlphaGo moment for math.
AI is now smarter than a math PhD, but dumber than an intern in many tasks.
“Jagged intelligence” needs to be solved.
9.2K views · 296 likes
· 18 reposts · 22 replies
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OpenAI’s comeback is the biggest comeback after Meta’s comeback, after xAI’s comeback, after Google’s comeback.
AI is an endless cycle of “it’s over” and “we’re so back.”
46.7K views · 1.3K likes
· 52 reposts · 79 replies
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Sam and Jensen say “AGI has arrived.”
But “AGI” means different things depending on who you ask.
- Sam’s bar: outperform humans at most economically valuable work.
- Dario and Demis’s bar: smarter than a Nobel Prize winner across most fields.
These are wildly different bars.
54.5K views · 900 likes
· 39 reposts · 112 replies
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If you want to understand how fast AI is improving, look at intelligence per dollar.
1.5 years ago:
o1 Pro: $150 / $600 per M tokens
Today:
GLM-5.3 Flash: $0.15 / $0.50
That’s a ~1000x collapse in price in under 1.5 years, while GLM-5.3 Flash is more intelligent than o1 Pro.
411K views · 5.4K likes
· 314 reposts · 133 replies
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@Yuchenj_UW The title has a long history, but here’s how we decided to bring it to OpenAI:
143K views · 761 likes
· 24 reposts · 16 replies
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@Yuchenj_UW it is a beautiful thing! it cuts through all the egoistic titles which are empty and meaningless!
23.8K views · 74 likes
· 1 reposts · 1 replies
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Whoever invented “Member of Technical Staff” was a genius.
It filters out Staff/Principal title-maxxers, protects engineering and research from corporate ladder brain, and leaves recruiters staring at LinkedIn like: “Is this person L4 or L7?”
MTS is the best title. Happy to be
649.1K views · 4.8K likes
· 127 reposts · 127 replies
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@Yuchenj_UW It’s interesting that you can 3X the LR. You’d expect the original paper to be well tuned near what is tolerable.
44.3K views · 138 likes
· 1 reposts · 3 replies
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@karpathy I'm unsure why the evaluation score dropped near the end. It might be due to some bad training data batch.
@karpathy, would love to hear your insights. It reminds me of the lossfunctions Tumblr though lol: https://t.co/9MQivsrCPI.
10.3K views · 27 likes
· 2 reposts · 0 replies
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Outperform GPT-3 with @karpathy's llm.c using just 1/3 training tokens ✨
Another day has passed, and I trained GPT-2 (124M) with llm.c for 150B tokens, achieving 35.5% accuracy on HellaSwag. This surpasses the GPT-3 paper’s 33.7% accuracy trained for 300B tokens. It matched the
2.4M views · 1.6K likes
· 137 reposts · 126 replies
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When starting OpenAI, we thought hard about what job titles to use—didn't want to bucket people into researchers & engineers. Alan Kay advised that they used "Member of Technical Staff" at Xerox Parc, we loved & adopted it. Recently have seen many companies doing same—very cool!
580.3K views · 2.3K likes
· 115 reposts · 66 replies
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Rispetto ad account della stessa dimensione
8 post degli ultimi 90 giorni, accanto alla fascia di 100K–1M follower. arriva ben oltre il proprio pubblico, e quel pubblico reagisce.
Visualizzazioni mediane27 982questo account5 983mediana per 100K–1M
Copertura, %9.62%questo account1.49%mediana per 100K–1M
Interazione, %2.13%questo account0.93%mediana per 100K–1M
| Metrica | Questo account | Mediana per 100K–1M | Rapporto |
|---|---|---|---|
| Visualizzazioni mediane per post | 27 982 | 5 983 | 4.68× |
| Copertura (visualizzazioni ÷ follower) | 9.62% | 1.49% | 6.46× |
| Tasso di interazione | 2.13% | 0.93% | 2.29× |
Altri account di questa fascia → Confronta con un altro account → Come sono costruiti questi parametri →
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
10.3K1 Jun
44.3K2 Jun
649.1K24 May
23.8K
8.2K
143K
411K6 Sep
54.5K7 Sep
46.7K
9.2K8 Sep
54.4K15 Sep
157
856
26216 Sep
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.28%1 Jun
0.32%2 Jun
0.78%24 May
0.32%
2.02%
0.56%
1.43%6 Sep
1.96%7 Sep
3.17%
3.65%8 Sep
1.84%15 Sep
3.82%
1.64%
2.29%16 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.
What the audience does
Likes78.3%18 671 in total
Reposts3.8%896 in total
Replies3.0%724 in total
Quotes0.8%182 in total
Bookmarks14.1%3 362 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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