currently obsessing over developers + apis @perplexity_ai
sf
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quants are cooked
just one-shotted arb
prediction markets (Polymarket, Kalshi) and sportsbooks (DraftKings, FanDuel) often price the same event differently. buy both sides across platforms and you lock in guaranteed profit regardless of outcome
this scans all of them in real-time and surfaces the gaps
free internet alpha. yw
perplexity.ai/computer/a/arb…
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nat jin retweeted
Ranked:
#9 by Monthly Revenue
#12 by Unique Monthly Visits (Web)
#25 by Monthly Active Users (Mobile App)
Only 7 products (of ALL AI products) rank in the Top Group on Web, Mobile, and Revenue. @perplexity_ai is one of those 7 companies
So again, when will they "get there?" 🤣🤣
By the looks of it, they've been "there" done that!
By getting there, I meant in terms of positioning and not in terms of product. The idea of this tweet was: the adoption for agents in companies. Positioning will play an important role for that adoption, and they are first ones to move towards that positioning.
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if u know my chinese name we are basically bffs or u knew me in elementary school when teachers did roll call and verbally desecrated my family name
you’ll know a chinese person for years before finding out they have a legal name. who tf is jiawei and why is he paypalling me, i thought ur name was lebron
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nat jin retweeted
we added web grounding to our decisions api, so now you can ask questions against online data, either with a search query or a url. it's the most accurate and fastest api out there. give it a try:
docs.perplexity.ai/docs/cook…
The Decisions API can now search the web before it answers.
A decision model returns a probability, but only knows what it saw in training, and your state. Add "mode": "web_grounding" and Fast Search runs one query per question, so pplx-decider-v1.2 answers from today's web.
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nat jin retweeted
contextual embedding improves relevance by encoding the context of a full document into each chunk's embedding
we worked with @perplexity_ai to iteratively evaluate their model checkpoints against our private benchmark, resulting in a state-of-the-art contextual embedding model
Introducing pplx-embed-v2-context: open-weight vision-language embedding models in 9B and 0.6B.
Both return one int8 vector per document, or one per chunk that sees the whole document.
Read the paper, written with @turbopuffer: arxiv.org/abs/2610.38854
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nat jin retweeted
the fact that this was written … last year … breaks my brain
There's a new kind of coding I call "vibe coding", where you fully give in to the vibes, embrace exponentials, and forget that the code even exists. It's possible because the LLMs (e.g. Cursor Composer w Sonnet) are getting too good. Also I just talk to Composer with SuperWhisper so I barely even touch the keyboard. I ask for the dumbest things like "decrease the padding on the sidebar by half" because I'm too lazy to find it. I "Accept All" always, I don't read the diffs anymore. When I get error messages I just copy paste them in with no comment, usually that fixes it. The code grows beyond my usual comprehension, I'd have to really read through it for a while. Sometimes the LLMs can't fix a bug so I just work around it or ask for random changes until it goes away. It's not too bad for throwaway weekend projects, but still quite amusing. I'm building a project or webapp, but it's not really coding - I just see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works.
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nat jin retweeted
In a weird way, everybody is rooting for you. When you go to a job interview, the interviewer wants you to be amazing so they don’t have to look anymore. when you go on a date, the person wants to have an amazing date with you. If you make a piece of content, people want to see amazing pieces of content and they have no problem if it’s yours.
the instant you get into the mindset that people want you to do well, your entire world gets better. And you’ll be shocked to know this pattern keeps repeating in different areas of life.
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