conducted a survey across my non sv/pa network (~80 above avg persons thus far) to discover:
1. most have not heard of Instinct and if they have they don’t care bc they’re “not lazy”
2. if they did care enough to try it, immediate churn bc it “sucks and keeps messing up”
palo alto people are building agents for a world where everyone has 47 tasks at once while the average person is so fucking bored they’re doomscrolling during work hours
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the early-stage vc barbell has shifted to investing in founders experiencing their own power law phenomena (only 1-2 out of the 30 things they've built are hits) and missionary founders working on problems so big and so hard that focused commitment on one thing is still a req
.@sama says the advice to pick one thing to build and pour all your focus into it no longer applies.
"The canonical advice that was correct two years ago was, 'Pick one, and put all your effort into it, because you can't build all 30 things.'"
"You can now build all 30 things. And you can get people to use them all. And you can work more on the ones that work."
"So I think this mindset of lots of ideas, quick feedback, and being open to a lot of things not working is great for this new era for how people build."
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who is ACTUALLY trading compute (ie, ocpi-h100-perp on architect or gpu rental prices on kalshi/pm) today?
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here's the development and progress of my digital brain (1,616 nodes and 2,413 edges) across all my agents and LLMs courtesy of @NowledgeMem
for the topology view, height is shaped by influence where high points reveal knowledge that is important and well connected
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who's building software-native market structures or novel fintech apps on top of Jev? I'm deploying and my DMs are open
existing LLMs price on a reply where a user reads the output (i.e., a paragraph). that unit was never the optimal unit for software-native work. since Jev now prices a typed judgment on a state, there can be unlimited decisions on the same state. that's why output can be free. the future of finance and markets are one state with many gates - the optimal substrate for Jev
Jev is built for the decisions that software makes millions of times a day. Its pricing is also tuned for that:
- $0.042 per million input tokens, output tokens free
- Answers in 70 to 500 ms end to end
- On TypeSafe's published workflows, up to 190x faster and 440x cheaper than running the same logic through an LLM
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was chatting with an oversubbed yc company and asked specifics about how something was done that literally makes or breaks the business
founder: "sorry, i don't really have anything formal prepared because you're the first investor to ask me this"
VCcels frothing
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i'm bullish labs or models specializing in time-series
the frontier labs build modalities in order of consumer demand: text, image, voice, video, etc. horizontal enterprise demand follows the same demand structure (i.e., first eng, now knowledge work). time-series is typically low prio
but it shouldn't be. time-series is the hurdle to having a reasoning layer for the physical world's signals: every machine, patient, grid, market, etc emits time series. agents that operate physical systems need a model that reads time series natively and native signal encoding beats any generalized model reading the same data as text
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who are the leading event-based model labs today?
frame-based model are clearly inferior for physical ai. a frame arrives every ~30ms and then inference runs after so anything happening between that is invisible until the next frame: there's a floor on reaction time. also, every frame is a full picture whether or not anything actually changes in the environment. in order for a model to identify the change, the model has to compare frames which wastes compute at inference and data at training. it's highly energy inefficient to process full frames continuously if there's no env change.
the catch is that the internet is made of frames. every vision foundation model that exists was made possible bc trillions of frames already existed. event data doesn't have the same pre-training corpus and can only be generated with rare sensors. if you try to convert video to events you end up with the same limitation as frames bc you don't have the data in between each frame
most event-based model labs are building a dataset by hand but this playbook is super slow. anyone taking on a novel approach or about to land a mass-market design win with event-tracking sensors (i.e., event sensor sits exists next to a frame camera on all iphones, teslas, etc)?
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an index of ai cyber startups will probably outperform any other ai related software index on a relative basis over the next 5 years
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bullish on a company that makes deposits easier for apps
extra bullish when its ceo opts for the "customer support lead" title instead
.@BlinkCashX is seeing 95% user base retention across its earliest integrations.
"When we can convince a user to ditch deposit addresses and use Blink, 95% of those people never touch a deposit address again.
"They stay on Blink, and they continually redeposit with Blink."
"Across a lot of the onchain consumer apps that we're familiar with, only 14% of users will create an account and fund that account, which is very low. Traditional fintech is closer to 40."
"What would it do for an app if we can triple your deposit volume? Apps usually do a 10x inside the app on the deposit volume. So a dollar as a deposit is $10 in trading volume, for example. So if we can triple your deposit volume, what would that do for you as a business?"
Customer Support Lead, @jjjjacobx, on the live show today.
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biggest differentiator for ai chip design tooling co's is the prover imo bc the hard part of chip design is proving that rewriting the logic by the model still computes the same thing vs. the rewrite itself. a power/performance/area gain doesn't matter until we know which rewrites is proven correctly because the ones worth rewriting are typically hardest
issue is that rewrites like retiming a datapath or moving pipeline stages break the standard equivalence check. you need sequential equivalence but then those scale badly on big blocks. it's a genuinely non-trivial problem
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all training data today for physics models is just old simulations. proteins, weather, and materials all have decades of publicly available sims data so their models came first
automotives, aerospace, semis, etc. only have private sims data
massive commercial opp to unlock this
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before you proceed to rug one of my portfolio companies and not pay back the capital that is originated from @credifi themselves, happy to answer any questions you may have
LOL it really is a free $3000 airdrop w/ no KYC
im at maybe ~1840 reputation on ethos but im giga bearish on it
i might take the money and run cuz its +EV and its from @base
or i might do the funniest thing
here's the facts:
- where does the money come from?
presumably base incubator
think of it as penance (back again to the reputable KOLs..sigh) for all the times $BRIAN and/or similar rugged, coinbase has money so i dont feel bad for taking money from them
grok says that they were given 100k or something to jumpstart this by Base (all incubated Base projects do)
these 3000 were promised to me 3000 years ago
- they only ask for email which again u can dummy, and a base/ethos wallet, didnt even ask for X auth
- the founder is nice and i did enjoy looking at his blog going through shitholes in ukraine and he seems like a normal guy so thats maybe the biggest benefit of this whole thing
- "its a stain on reputation"
no its not lmfao
its literally yelp for crypto and currently i think ive lost around $1500 or so on various bs Ethos endorsed things (granted, they did airdrop the validator..but i didnt sell so its zero money)
- why not wait until they get exploited (everything does eventually)?
probably the best course of action?
- also, one more thing to note
If @base does airdrop (this year most likely) - then this will most likely disqualify you from any social/KOL/X airdrop
Again, they have RH to compete with and right now they're lagging behind hard, and nothing better than an airdrop to jumpstart an eco perhaps?
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Actually, to make this even more fun - I'll probably use $1500 of the $3000 to buy the @ethos_network presale tomorrow.
Not because I believe in it - but because if the token goes down, its proof that onchain reputation is worthless (or not as valuable as they'd like to sell you on it), so +EV.
If I don't do a 3x on it by year's end - then they don't get the loan back 👍
Very simple.
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Borrow up to $3,000 based on your Ethos Credibility Score. No collateral, no liquidation and no KYC.
@ethos_network users with a score of 1800+ are eligible.
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Alexander Lin retweeted
This is @BlinkCashX
It just won’t be obvious to you until it’s too late
dropping another billion dollar fintech idea:
someone should build OneKYC
KYC once and get instant access to neobanks, trading platforms, CEXs, etc
there’s massive consumer demand for this
everyone is tired of spending 30 minutes scanning their eyelids every time they sign up for a new app
there’s also massive demand from the platforms themselves
pre verified users = less onboarding friction
less friction = higher conversion rates, more funded accounts, and more volume
and boom 18 months later, Sumsub or Persona acquires you for a billion in cash
banger of an idea
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