Prithvir

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Age 2.5 Year
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open source models feel like lewis carroll's red queen.
no matter how fast they run, they’re still running in place. moonshot's kimi 2.6 is a fantastic model. it would be the best in the world...
if only it had shipped in january.
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Anyone have ideas on how to expedite iOS apps getting approved?
It says 90% of apps are reviewed within 48 hours and the average review times are much lower on Runway
But we're still stuck in "waiting for review"
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i've given codex full access to all my documents and folders
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the labor market is not ready for what codex is becoming
today i gave codex an api key and it:
> pulled 100k rows
> figured out the endpoints
> cleaned the data
> answered questions that would’ve taken a $250k data scientist 2-3 business days
> sanity checked it
it did this in 2-3 minutes
btw these were not simple sql queries
messy questions where it was unclear what formula to use, what data was missing, and how to iterate based on prior experience working directly with me
could see the unhobbling, CoT, and mechanistic interpretability all working in tandem
absolutely fantastic
2026 is incre
CODEX1.42%
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everyone is salivating over spacex's s1
while 99% would benefit more from reading bending spoon's f-1
their path is more reproducible and less circumstantial
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Anyone paying attention can tell that, within a year or two, Kalshi and Poly will generate more revenue from perps than from prediction markets
KALSHI-8.17%
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LLMs are changing who starts companies, but not in the way you think.
The obvious effect is people with less technical backgrounds can build software more easily.
The more interesting effect is they’re pulling in people who were kept out by opportunity cost.
Previously, my smart friends in banking, PE, hedge funds, and VC would have startup ideas, talk about them, maybe sketch them out, but rarely test them.
Now a surprising number have side projects with real users and revenue because the threshold to try something has fallen so much.
That seems like a much bigger deal than people realize
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Coase's Theorem and Jevon's Paradox in an AGI world
Two laws are colliding in the labor market right now: Jevons' Paradox and Coase's Theorem.
Everyone keeps debating the surface-level question, "will AI replace engineers?", and missing the actual structural shift underneath.
Jevon: when you make a resource cheaper, total consumption goes up, not down. Cheaper coal means more coal burned. Cheaper light means more light consumed. Cheaper code means more code written.
Coase: firms exist because transaction costs make it cheaper to coordinate inside a company than across a market. Lower those c
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the spacex s-1 is the perfect rorschach test for bifurcating marxist eschatologists from techno-optimist accelerationists.
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ai founders pivoting to crypto will raise the median iq of both cohorts.
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Rome looked hopeless against Carthage in 260BC.
Carthage had the wealth, fleet, trade networks, and technology.
Today, China is leading on electricity generation, trade networks, and purchasing power parity.
Sicily was the flashpoint then.
Taiwan is today.
How do we avoid the Thucydides trap?
IN-0.05%
ON-0.94%
POWER-0.71%
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How far are we from the first Butlerian Jihad?
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if you follow the trend lines:
1. pure software co token spend surpasses employee spend by 2027
2. agency / services firms by 2028
3. SMB back offices by 2029
4. regulated enterprises by 2030
AGI labs generate $1T in annualized revenue by 2031
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turing test on hard-mode
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mallaby delivers again
the infinity machine is hard to put down
sounds banal but the single most important factor in any moonshot is talent density of the founding team
and building it is entropic, non-linear, artistic, scientific, and fortuitous
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just talked to a friend who's a quant at one of the largest hedge funds in the world and he hadn't heard of perps
we are still very early
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2026 is the first year we can measure high agency engineers compounding faster than high skill engineers
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which of these claude tools do you actually use?
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frontier models are becoming veblen goods
the marginal gain from “thinking-max” over “thinking-high” is tiny
the marginal cost difference is not
this is getting insane
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Has anyone built an Etherscan or Arkham style block explorer for LLM CoT reasoning traces?
Would do wonders for mechanistic interpretability
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