Shreya Shankar ✓

@sh_reya · SF & Pittsburgh · joined 11 Jan 2014

Incoming assistant professor @CSDatCMU @CMUDB. Putting LLMs in databases and BI tools. Created https://t.co/PmuOqAXVgS and https://t.co/8MQt4na2cj.

56 268Followers

807Following

6 052Posts total

372.1KViews on collected posts

Posts recentes

Shreya Shankar ✓@sh_reya18 h

“I used my agent to formally verify my software and it fixed all these bugs” is the new “I asked an LLM judge to fix my LLM output”. The devil is in the details and it is insanely difficult to get the formalisms “right” (eg interpretable, expressive, extensible) for humans and 13.2K views · 125 likes · 6 reposts · 19 replies Open on X →

Shreya Shankar ✓@sh_reya18 h

Very exciting to see how much this post resonated with the community. The uncomfortable dialectic is that (1) evals are necessary to build good agents, but also (2) agents are extremely helpful (and necessary) for automating tedious parts of the evals lifecycle. Human attention 3.7K views · 38 likes · 2 reposts · 6 replies Open on X →

Shreya Shankar ✓@sh_reya21 Sep

AI-powered operators in SQL are having their day in the sun 11.1K views · 53 likes · 7 reposts · 8 replies Open on X →

Shreya Shankar ✓@sh_reya21 Sep

Today we're launching prompt_jev(), which brings Jev, a new kind of AI model from @typesafeai, to MotherDuck SQL. Jev makes text classification super fast, dirt cheap, and as easy as prompting an LLM. In our tests, it ran at 50x the speed and 1% the cost of comparable frontier h

0:15

19.8K views · 172 likes · 18 reposts · 6 replies Open on X →

Shreya Shankar ✓@sh_reya20 Sep

https://t.co/jIzbx6B4L5 10.3K views · 28 likes · 0 reposts · 5 replies Open on X →

Shreya Shankar ✓@sh_reya20 Sep

We re-worked my Jev/DuckDB example, and boy, was my original vibecoded extension excruciatingly inefficient. At LEAST 20x-40x faster now, often way more. Jev is going to revolutionize analytics. Something very cool to share tomorrow :) 64.9K views · 623 likes · 38 reposts · 23 replies Open on X →

Shreya Shankar ✓@sh_reya12 May

@sh_reya @serinachang5 Welcome!! We should hangout! 1.4K views · 3 likes · 0 reposts · 0 replies Open on X →

Shreya Shankar ✓@sh_reya12 May

@sh_reya HUGE congrats, Shreya!! Thank you for being such an incredible mentor to me. Excited to follow along with everything your new lab does! 🥳 4.8K views · 7 likes · 0 reposts · 1 replies Open on X →

Shreya Shankar ✓@sh_reya12 May

@sh_reya @oshaikh13 Congratulations Shreya!!!! 1.8K views · 5 likes · 0 reposts · 0 replies Open on X →

Shreya Shankar ✓@sh_reya12 May

I'm joining Carnegie Mellon's CS Department (and HCII by courtesy) as an assistant professor in Fall 2027! I'll be recruiting PhD students next cycle. If you're interested in AI systems or human-AI collaboration, list me in your application. Stay tuned for more about my new lab! 241.3K views · 2.1K likes · 111 reposts · 121 replies Open on X →

Em comparação com contas do mesmo porte

6 posts dos últimos 90 dias, ao lado da faixa de 10K–100K seguidores. aparece para muita gente, mas poucos desses espectadores reagem.

Mediana de visualizações12 182esta conta995mediana para 10K–100K

Alcance, %21.65%esta conta3.85%mediana para 10K–100K

Engajamento, %1.04%esta conta1.52%mediana para 10K–100K

Métrica	Esta conta	Mediana para 10K–100K	Proporção
Mediana de visualizações por post	12 182	995	12.2×
Alcance (visualizações ÷ seguidores)	21.65%	3.85%	5.62×
Taxa de engajamento	1.04%	1.52%	0.68×

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

241.3K12 May

1.8K

4.8K

1.4K

64.9K20 Sep

10.3K

19.8K21 Sep

11.1K

3.7K23 Sep

13.2K

Last 10 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.96%12 May

0.28%

0.17%

0.22%

1.07%20 Sep

0.32%

1.01%21 Sep

0.61%

1.26%23 Sep

1.13%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes67.6%3 121 in total

Reposts3.9%182 in total

Replies4.1%189 in total

Quotes0.5%25 in total

Bookmarks23.8%1 099 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.

Posts recentes

Em comparação com contas do mesmo porte

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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