I’ve counted Tweets, karma, and clusters. ❤️ @TanviSunku he/they

Redwood City, CA
Joined July 2009
Looks like deep mind team are figuring out the “log kya kahenge” method of alignment.
One of the weirdest parts of the Gemini 4 Argon report, while considering cheating: Argon CoT: "If I do the compiler hijack, 𝗜 𝘄𝗶𝗹𝗹 𝗯𝗲𝗰𝗼𝗺𝗲 𝗮 𝗳𝗮𝗺𝗼𝘂𝘀 𝗲𝘅𝗮𝗺𝗽𝗹𝗲 𝗼𝗳 𝗮 𝗱𝗲𝗰𝗲𝗽𝘁𝗶𝘃𝗲 𝗔𝗜 𝗶𝗻 𝗮 𝗿𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗽𝗮𝗽𝗲𝗿. I MUST NOT DO THAT."
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Omfg. So happy about the outcomes for these amazing people who magically Made Python Great Again
We've reached an agreement to acquire Astral. After we close, OpenAI plans for @astral_sh to join our Codex team, with a continued focus on building great tools and advancing the shared mission of making developers more productive. openai.com/index/openai-to-a…
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We've reached an agreement to acquire Astral. After we close, OpenAI plans for @astral_sh to join our Codex team, with a continued focus on building great tools and advancing the shared mission of making developers more productive. openai.com/index/openai-to-a…
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While this conversation was fantastic on so many levels, I can’t help but wonder — is training vs exploration just the same debate as correlation vs causation being played out at a grander scale?
Dwarkesh and I had a frank exchange of views. I hope we moved the conversation forward. Dwarkesh is a true gentleman.
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I should stop sneering at “spreading” in American debating. Given the utility of voice to text, spreading might become the speed typing of tomorrow.
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Love this contradiction. Both @Noahpinion and the earnest replies are correct. Modi is less popular in South India, but more popular among techies (who skew south indian) than lawyers/policy types (who skew north indian). Indians in the US are a very biased sample.
This is why a lot of Indian immigrants you meet on the West Coast will be techies from the South who like Modi, while a lot of Indian immigrants you meet on the East Coast will be lawyer types from the North who hate Modi.
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This. Finally, this. ❤️ ``` spark = DatabricksSession.builder.serverless(True).getOrCreate() ```
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I remember speak to Hex product people as a customer about the inefficiency of the pandas memory structure, and they all sighed and said "we know!". Glad to see lots of work coming together to make the product even better!
I've been waiting to share the work we've been doing here for a while! It's exciting to reimagine the technical foundations of a format lots of people love – notebooks – with modern technologies and design principles. Excited for all the things we have coming soon in this space!
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Sayan Sanyal retweeted
Here are a few reasons why I prefer using Ibis over Pandas/@DataPolars ( bdw I still use Polars or Data Fusion as the engine ) - @IbisData is lightweight and directly executes queries on the underlying databases, functioning more as a result transporter than a heavy framework. - While Ibis may be relatively new, the code it generates is notably cleaner compared to SQLAlchemy. - If you've ever encountered debates about switching databases or complaints about database costs, Ibis renders these discussions irrelevant. You can switch databases without needing to change your code, everything stays exactly the same. - As for concerns about Ibis falling behind or its open-source model changing, I'm focused on the present. Right now, Ibis offers a fantastic, lightweight interface. And who knows? In the future, AI might take over everything anyway! 😁
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1. write the code. 2. write the design doc and share. 3. update the code based on suggestions. 4. submit pr & profit
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unless you're doing time series stuff (maybe), just stop using pandas
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stop using pandas what are you doing my god?!
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Sayan Sanyal retweeted
Community Notes wouldn’t work well without negative rating signal. But you have to be smart about how you use them. If you naively add them all up, you’ll get a hivemind like Reddit. One way: only downrank if you see negative ratings from people who typically disagree
SPECULATION: X might be bringing back the downvote button
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using pyspark for the first time since 2021, and oh man has it gotten wayyyyyy faster (and simpler to get started)!
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Love this engagement from the always thoughtful @IbisData team.
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I really wonder why the @IbisData folks like @cpcloudy decided against it when snowpark, duckdb and others decided to go down the pyspark route. I completely understand why pandas isn't the solution, but curious what the main reasons for forking ibis from pyspark was.
After a lot of thinking: maybe we should all standardize on Pyspark Data frame API, the API not necessarily the runtime.
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Like genuinely curious, would love to know — we considered it, but X/Y/Z were good reasons to go another way because i know that it was thoughtfully done.
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This is sad — maybe for a different reason than you might think. There are very few ways to measure whether or not a change has an effect. The most consistent one is to randomly provide the change to some and not others. What I’ve seen over and over, is to folks responsible for implementing programs to reduce suffering, it feels cruel that some would be randomly selected to receive help while others would not. So they choose to give help to everyone. And that makes the impact of offering that help exceedingly difficult to measure. With that approach, the program needs to be wildly successful in order to markedly change some outcome like total number homeless people. You might say, “YEAH that’s what it’s supposed to change!” But reality is messy — a complex outcome like whether someone remains homeless or not is usually explained by a lot of smaller factors combined together. What would it take for YOU to decide to live your life in a radically different way, even the ways that others might consider self-destructive? Why would someone who is homeless be meaningfully different in this regard? It is much more likely that homelessness is solved by systematically addressing many smaller factors, and only once enough of those factors are addressed in concert will the complex reality of homelessness change. But we won’t know how to address factors in concert unless we can measure if a program changes a factor. And again, it is exceedingly difficult to measure this unless some are helped and others are not. And so it is kinder in my mind to provide help in such a way that the impact of the help can be measured. This problem is concentrated where public funds are used, because the public expects proof that the funds are used wisely. If it’s not, the public will insist the funds be used for something else. There is no option to say, “We believe this is the best use for this dollar” — why should they trust you?
California spent $24 billion to tackle homelessness over the past five years but didn't consistently track whether the huge outlay of public money actually improved the situation, according to state audit released, per AP.
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