Rachel Thomas ✓
@math_rachel · Brisbane, Australia · joined 06 May 2013
R&D at answer ai | fast ai co-founder | past: math PhD, immunology MS, early eng at uber, prof at USF Data Institute
92 802Followers
884Following
10 267Posts total
177.8KViews on collected posts
Posts recentes
Procurando novos posts…
"My fear is that AI has become the new PowerPoint and not in a good way. Just as PP usurped the lecture it was supposed to support, many are using AI in ways that are rote & uninteresting. It takes creativity & a sense of ownership & direction to use slides well" @Marc__Watkins
4.2K views · 51 likes
· 6 reposts · 3 replies
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@math_rachel @pol_avec I found this to be a convincing argument against Naur's position: https://t.co/i33fJGG944
253 views · 7 likes
· 1 reposts · 0 replies
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Striving to understand is a process of Theory-simplification. If you let your LLM free rein you can churn out a lot of code quickly. Choosing the simplification route is longer & takes more effort, but in the long run it can pay off & you may find hidden gems along the way. 5/
1K views · 7 likes
· 0 reposts · 1 replies
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OpenAI & Anthropic increasingly hide data server-side (encrypted compaction, reason tokens). Those offering an abstraction layer on top must either expose provider-specific details, reimplement brittle compatibility logic, or accept they can't represent the underlying system. 4/
1.1K views · 6 likes
· 0 reposts · 1 replies
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An external abstraction can greatly simplify your Theory while its contract holds, but its hidden complexity becomes yours whenever you must debug, modify, or reason beyond that contract. 3/
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A program is the Theory held by the people who build & maintain it: which constraints & trade-offs shaped it, why it works, and what changes would fit its design.
The complexity we are trying to reduce is the Theory one, not the code. The needed info doesn't live in the code 2/
1.7K views · 10 likes
· 0 reposts · 1 replies
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LLMs increase the complexity of codebases. They duplicate methods, write overdefensive code against impossible edge cases, & overoptimize too early. Can further training fix this? Naur's “Programming as Theory Building” says no. -- @pol_avec
https://t.co/HGPfSSejFY 1/
20.1K views · 390 likes
· 53 reposts · 13 replies
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@math_rachel https://t.co/8WE8pESFsp (and your work and Jeremy's work in general) has been one of the most inspiring and hopeful aspects of the entire AI boom.
Glad you are continuing to push for AI that benefits more than just investors, founders, or tech company execs.
2.1K views · 20 likes
· 0 reposts · 1 replies
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The biggest companies have made their vision for the technology seem like the only option, but it isn’t. The values & decisions of OpenAI, Anthropic, Google, & xAI aren’t the only version of what AI technology can be. 5/
3.1K views · 36 likes
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Many issues will shape the forms that AI takes. Will open source be protected? Will the major AI labs block the development of a robust ecosystem building atop their work? Will tools be designed to encourage human collaboration, or just to automate as much as possible? 6/
4.2K views · 48 likes
· 2 reposts · 3 replies
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In 2016 @jeremyphoward & I cofounded fast ai because we were alarmed by the direction that major AI comps were headed. We tried to counter concentration of power by getting a more varied group of people with unlikely backgrounds into the field. 3/
https://t.co/LwKVgYtw2T
8.5K views · 52 likes
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The big AI labs behaved as though computing power & money were limitless. Most of the world (and our environment) cannot afford that assumption. Jeremy & I wanted researchers to treat constraints as a source of creativity. 4/
https://t.co/NrkzwJ9UvM
5K views · 33 likes
· 2 reposts · 1 replies
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Execs make claims about AI so overhyped they verge on fraud. People outsource thinking to AI, leading to skill atrophy. I spend weeks or months researching & writing essays that hardly anyone sees in a world awash in slop. Many AI products chase gameable metrics. 2/
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As the backlash against AI was growing stronger (and as my friends were becoming more fervently anti-AI), I decided to return to an increasingly hated field. Why? I agree there is a lot that is terrible about AI. 1/
https://t.co/e1kXkzH2eF
115.5K views · 364 likes
· 32 reposts · 17 replies
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Many view AGI & ASI as near-automatic consequences of recursive self-improvement (RSI). However, RSI, Humanlike AI, Superintelligence, & Economically Transformative AI are 4 different dimensions. None of these implies any of the others.
- @random_walker ICML 2026 Keynote https:/
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Data Ethics course: https://t.co/1vLF2rzuTd
Deep Learning course: https://t.co/KgtHR2B9Vk
Data Science blog: https://t.co/ZWYKPXufDW
Diversity blog: https://t.co/cCuOAEtEAj
NLP: https://t.co/zC31JsKLwz
Talks: https://t.co/msa2Sh3UCI
Medicine, AI, & Bias: https://t.co/w1yK7GP5i0
0 views · 3.1K likes
· 904 reposts · 34 replies
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@chipro Easy access to massive resources can stifle creativity... the world is a resource-constrained place, and ignoring that fact means that you will fail to build things that really help society more widely. -- @jeremyphoward https://t.co/7DQRsxRcWd https://t.co/TT93AdvU8G
0 views · 41 likes
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People sometimes ask if I think it's risky for everyone to have access to AI. I think it's MORE risky for an exclusive & homogeneous group alone to develop tech that impacts us all. https://t.co/0jRfmgXGDJ
0 views · 428 likes
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Em comparação com contas do mesmo porte
15 posts dos últimos 90 dias, ao lado da faixa de 10K–100K seguidores. alcance comum para o seu porte, reação mais fraca que a maioria.
Mediana de visualizações3 203esta conta886mediana para 10K–100K
Alcance, %3.45%esta conta3.22%mediana para 10K–100K
Engajamento, %0.99%esta conta1.99%mediana para 10K–100K
| Métrica | Esta conta | Mediana para 10K–100K | Proporção |
|---|---|---|---|
| Mediana de visualizações por post | 3 203 | 886 | 3.62× |
| Alcance (visualizações ÷ seguidores) | 3.45% | 3.22% | 1.07× |
| Taxa de engajamento | 0.99% | 1.99% | 0.50× |
Outras contas desta faixa → Comparar com outra conta → Como estas referências são construídas →
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
115.5K17 Aug
6.9K
5K
8.5K
4.2K
3.1K
2.1K
20.1K19 Aug
1.7K
906
1.1K
1K
253
4.2K2 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
0.36%17 Aug
0.99%
0.72%
0.66%
1.29%
1.25%
1.01%
2.29%19 Aug
0.64%
0.77%
0.64%
0.76%
3.16%
1.45%2 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.99%.
What the audience does
Likes58.4%1 125 in total
Reposts5.4%105 in total
Replies2.7%52 in total
Quotes0.8%15 in total
Bookmarks32.7%630 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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