
hrishikesh kamath retweeted
"We really do think that @HyperliquidX is the place that's gonna house all finance."
"We're huge @chameleon_jeff nerds. We think that he's a once in [a] generation founder."
"Building on top of a platform that has a founder like that is really, really important."
"The perps product, once the normal retail traders understand the power of it, is just gonna explode."
hrishikesh kamath retweeted
Now that Jensen is out, my pick for his successor would be Nikesh Arora (@nikesharora)
IMO Jensen was so important for the AI ecosystem because he was:
1) technical
2) aggressive
3) had the credibility required to be aggressive
What Nikesh did at Palo Alto the last 8 years is remarkable, and reflected in its stock (up 10x)
He’s one of only a handful of people who have the credibility required to be aggressive on day 1
Nikesh has the three attributes listed above while being a world class deal maker and having all the necessary relationships to keep NVDA at the center of the AI ecosystem
There’s no easy choice but he’d be my go to
We looked at existing copy trading behavior on Hyperliquid across a few apps and 170k wallets and found that only 33% of wallets are in profit on their copied trades. Article dives into why.
hrishikesh kamath retweeted
Our first research article dives into copy trading on Hyperliquid and informs how we are building the feature in Tailor.
We believe that starts with lower fees, helping users discover the best traders to copy trade using in house quantitative models, and building in risk controls to avoid just one bad copy trade from liquidating an account.
hrishikesh kamath retweeted
AWS as an RL environment 👀
when agents train in RL environments that actually look like the real world, they transfer better to the real world
this looks pretty amazing! the best way to have product adoption in the future is to make sure agents are good at using your product
so naturally every team will create copies if their product so agents rock at using them
We just shipped AWS Emulator, which gives coding agents a local copy of AWS to test against.
Early in my career, we built test environments like this by hand, one system at a time. Now, agents are writing code far faster than any team can check it in production. Matt Goldberg, a Senior Principal Engineer on our Developer Tools team, wanted to know whether agents could build and test those environments themselves. He gave @kirodotdev a single prompt, and about 15 minutes later it came back with a first version of an SQS emulator. Turing that into a robust solution took much more work
Here’s what I think this means for engineers today: allthingsdistributed.com/202…
hrishikesh kamath retweeted
hot take: most "autonomous trading agents" are a stranger with your wallet
tailor isn't a stranger. it remembers what you hold, how you trade, and what you'd never touch.
now it executes on its own, by your rules.
it doesn't trade for you. it trades like you
hrishikesh kamath retweeted
It's so weird that people don't understand that marketplaces just don't aggregate "demand" from users, they resolve disputes when something goes south and that's why they're valuable as an arbiter.
marketplaces will collapse
first - marketplaces that are just aggregators like airbnb in the next 365 days
second - then marketplaces with some ops like delivery, stores like amazon, zomato in the next 1000 days
from first principles, marketplaces aggregated demand for their users, but if an agent like muse, @iradotso or @instinct can do the same without charging you 17% , why would that survive?
@amazon @Airbnb and likes are on a downward slope and it's a steep slope
hrishikesh kamath retweeted
I love when AI makes mistakes; it reminds me why I still have a role
I love when AI doesn’t make mistakes; it lets me increase the stakes
hrishikesh kamath retweeted
One of those companies hit $50M ARR
(Some big retailers are their clients)
Reminder that just because Google can casually drop a “labs” feature doing exactly what your entire company does, doesn’t meant they will actually seriously compete with you in the industry.
hrishikesh kamath retweeted
The forward P/E obsession is absolutely bizarre.
Analyst earnings forecasts peak right before cyclical downturns. In tech/semis, hyperscaler capex has created one-time revenue spikes, masking real valuation risk and making concentrated indexes look deceptively cheap.
hrishikesh kamath retweeted
I wish there was a way to mark replies as AI slop, would improve the X experience significantly.
hrishikesh kamath retweeted
The single most important thing to understand deeply when building anything today is the sunk cost fallacy. You have to be ruthlessly dispassionate about what you did prior.
hrishikesh kamath retweeted
The next decade won't reward tourists. It will reward people who combine audacity with discipline. Who understand that technology cycles come and go, but that building enduring companies requires clarity of purpose and consistency of execution.
Venture has always been about backing outliers. That hasn't changed. If anything, it matters more now.
hrishikesh kamath retweeted
It seems likely that the risk of a supply chain attack will often exceed the risk of a coding agent making a new vulnerability.
Open source will shift away from helping you find already-working code to finding already-secured code, or finding new ideas altogether.
Nothing outs a newbie faster than valuing semis on P/E
A rare miss by Kobeissi
Just to put today's crash into perspective:
After today's crash, Nvidia, $NVDA, is now trading at a Forward P/E ratio of ~13x.
That's 7 turns below the S&P 500's ~20x Forward P/E and less than one-third of Walmart's ~43x.
Ironically, the name at the forefront of the AI buildout is now one of the "cheapest" stocks in the market.
Truly incredible.
What does it even mean to trade with agents? And why should agentic trading be built on top of decentralized networks like Hyperliquid?
Our team of quants, perp junkies and AI enjoyers have spent the last 6 months talking to users, building product experiences and iterating on these questions.
Our core beliefs that shape Tailor:
1. Every trader will have intelligence helping them trade
2. Agents are best at turning intents into actions
3. The future of agentic trading will be built on open networks
Traders today form a thesis, combine information across dozens of tabs and sources, need to understand the impact on their portfolio, and have to constantly reassess if the world still matches their beliefs.
We believe that intelligence should do that work for you. It should help you discover what actually matters, research your thesis, know how others are positioned, and help you decide what trades to put on. It should understand you. Your convictions, your goals, your risk appetite, and your blind spots. It should get better as you use it and it learns from you. It should tell you the mistakes you are making and how to improve as a trader.
Financial intelligence can only be effective if it operates over a large corpus of data that can be verified to be true. Every price, order, trade, position and participant can be verified to be true on decentralized networks like Hyperliquid. As the capabilities of agents improve, we believe that these networks are the natural home for agents to learn, reason and adapt.
That is what we are building with Tailor. If this vision resonates with you, please DM me and I will personally onboard you.
The end of the institutional advantage is here.
The last decade democratized access to markets. Anyone can trade almost anything, but we've done very little to help people make better decisions.
We’ve worked over the past year to change that and put intelligence in the hands of the people. Now introducing Tailor.
Follow the 🧶…
hrishikesh kamath retweeted
At these valuations, it's better for hyperscalers or SpaceX just to acquire CoreWeave or Nebius to expand their datacenter GWs rather than buy GPUs, lease datacenters, etc.
But not sure Jensen would let it happen. Not now.
hrishikesh kamath retweeted
One lesson from this launch: climb as far as you can with SFT before relying on expensive RL.
RL is still the cherry on top, as @ylecun once put it.
Today we release Ripple-65B Preview, our 65B dense LLM we pretrained from scratch.
Ripple sets a new state-of-the-art for U.S. open-weight models in agentic coding tasks, surpassing significantly larger models including Muse Spark Open-816B, Beam-501B, Inkling-975B, and Nemotron 3 Ultra-550B.
Learn more: ricursive.com/ripple
hrishikesh kamath retweeted
From now on if a reply is even remotely ai generated this is what my reply is going to be.
Replying to @QuantaCortex
The question gets sharper once you add time. Two people steering at once is one problem; two people steering an hour apart is another. The first instruction has already shaped the work by the time the second arrives, and “preserve both intentions” means something different when one of them has been partly executed. Does the agent treat the earlier direction as settled context that the later person is implicitly overriding, or as a standing constraint the later person may not have seen? Your history view answers “what happened,” but the agent still needs a rule for “what still binds.” There is also the quieter failure, where the instructions don’t visibly conflict but pull in different directions. One teammate says keep the onboarding under four steps, another adds a consent screen for a new region. Neither contradicts the other on its face, and the agent can satisfy both by compressing something else. The person reviewing the thread sees two reasonable requests and one reasonable result, and nobody notices that the skip link got folded into a footer. Readable history surfaces conflicts people would recognize; it does less for the ones that only show up in the output. Which raises the practical question of what the agent should do at the moment it notices. Pausing and asking is safe but turns every shared session into a queue of blocked questions. Picking one and noting the choice keeps things moving but means the losing instruction was overridden by software rather than by a colleague. Some teams will want role-based precedence, some will want last-writer-wins with a loud annotation, some will want the agent to branch and show both. Is that a setting the session owner configures up front, or something the agent infers from who is in the thread and how they’ve resolved similar collisions before? Suppose it is a setting. Then someone has to own it, and ownership of a session is itself a fuzzy idea once more than one person is typing into it. The person who opened the thread may have left it by the time the conflict arrives. The person who invited the second contributor may not have understood that inviting meant delegating. If the rule is “the creator decides,” the creator becomes a bottleneck for a conversation they are no longer part of. If the rule is “anyone present decides,” then presence has to be defined, and a tab left open in the background counts as present in every way the software can measure. Suppose instead it is inferred. The agent looks at past sessions, sees that when Priya and Dan disagreed, Priya’s instruction usually stood, and leans that way. This is efficient and almost certainly wrong in the way that matters. Priya’s instructions stood because she was closer to the customer on those tickets, not because she outranks Dan in general. The agent has learned a correlation and will apply it to the next conflict, which is about billing, where Dan is the one who knows. Inference from history produces a quiet hierarchy nobody chose and nobody can see. The reminders example makes the stakes concrete. Automatic reminders are a growth lever; a customer group that must never receive them is probably a legal or contractual constraint. These are not symmetric. One is a preference and the other is a prohibition, and a well-behaved agent should treat a prohibition as winning regardless of who said it or when. But the agent only knows it is a prohibition if someone told it so, or if the phrasing happened to carry the weight. “Please don’t send reminders to the enterprise tier” and “the enterprise tier must never receive reminders under the DPA” look similar to a model that is pattern-matching on intent. The second one has a reason attached; the first one may have the same reason and simply didn’t say it. So one answer is that instructions should carry metadata about their own strength. A person could mark something as a hard constraint versus a soft preference, and the agent could weigh accordingly. This is clean and nobody will do it. People do not annotate their messages in a chat thread with priority levels, and asking them to is asking them to do the agent’s job. The more realistic version is that the agent asks, once, when it detects a collision: “Is the no-reminders rule a hard constraint?” And now you are back to the blocked-question queue, just with a narrower question. There is a middle path where the agent proceeds but makes the conflict impossible to miss. It applies the more restrictive instruction, since that is the reversible choice, and it writes a visible note in the thread saying what it did and why. The person who wanted reminders sees that their instruction was shelved, and can escalate if they disagree. This has the virtue of defaulting toward caution and the cost of making the thread noisy. Every collision generates a note. In a session with four active contributors and a long-running task, the notes may outnumber the actual work. Noise is not a trivial objection. The whole case for the multiplayer thread is that the history is readable, that someone arriving late can scroll up and understand what happened. If the agent fills the history with its own commentary about conflicts it resolved, the history becomes a log, and logs are not readable in the sense you mean. They are searchable, which is different. The person who spots a mismatch now has to find it among forty annotations, most of which concern collisions that didn’t matter. Which suggests the agent needs a sense of which collisions matter. The reminders case matters because it touches an external party, a customer, and the consequence of getting it wrong is an email that cannot be unsent. The invite-step case does not matter in that way; if the invite step ends up in the wrong place, someone moves it. One instinct is to grade conflicts by reversibility and only surface the irreversible ones. This is a reasonable heuristic and it has a hole: the agent’s estimate of reversibility is only as good as its model of the world, and it does not know that the “move the invite step” change is going into a release that ships tonight. Maybe the grading should come from the environment rather than the agent. A session connected to a production mail sender is high stakes by construction; a session editing a design file is not. If the platform knows what the session can reach, it can set the default conflict behavior without anyone annotating anything. Sessions with irreversible side effects pause on collision; sessions with reversible ones proceed and note. This moves the decision from the people in the thread to the people who configured the integrations, which is probably where it belongs, since they are the ones who understood what the mail sender could do when they connected it. It also raises the question of whether the two contributors should even have equal authority over the mail sender. Permission to steer the session and permission to trigger its side effects are not the same thing, and most systems collapse them. If I can type into the thread, I can tell the agent to send the email. A more careful model would let the session owner grant steering rights broadly and effect rights narrowly, so that a teammate can redirect the work but cannot, through the agent, do anything they couldn’t do directly. Then the reminders conflict partly resolves itself: the person who asked for automatic reminders may not have the standing to cause them, and the agent can say so. But permissions are a blunt tool for a conversation. Most of what people say in a thread is not a command, it is context, and context from someone without effect rights is still useful. “The enterprise customers hate getting emails” is not an instruction and carries no permission, but the agent should hear it. The system has to distinguish between someone contributing information and someone issuing a directive, and people do not mark the difference when they type. They say “we should probably not email them” and mean either a fact about the world or an instruction, depending on who they are and what they’re thinking about. This is the point at which the problem stops looking like a conflict-resolution problem and starts looking like an interpretation problem. The agent is not adjudicating between two clear instructions; it is reconstructing what two people meant from fragments, and then discovering the reconstructions don’t fit together. The conflict may not even exist at the level of intent. The reminders person may have meant “reminders for trial users,” and the no-reminders person may have meant “the enterprise tier,” and the only reason they look like a collision is that neither of them specified the scope. A good agent would notice that the scope is unspecified and ask about that, rather than treating the collision as given. Asking about scope is cheaper than asking about precedence. “Which users should get reminders?” is a question either contributor can answer, and the answer often dissolves the conflict without anyone having to win. “Who gets to decide?” is a question that forces a hierarchy into the open, and people are reluctant to answer it in a shared thread where the other person can read the answer. If the agent has to interrupt, it should interrupt with the question that is easiest to answer and least likely to make anyone look overridden. There is still the hard case, where the scopes are clear and the instructions really do collide. Reminders to everyone; no reminders to the enterprise tier; the enterprise tier is part of everyone. Here the agent has to pick, pause, or branch. Branching is underrated. The agent could prepare both versions, the one with reminders and the one without, and present them side by side with the difference highlighted. Nobody has decided anything yet; the agent has simply done the work twice so that the decision can be made by looking rather than by arguing. This costs compute and buys clarity, and compute is cheap relative to a meeting.





























