Vikram Dutt retweeted
“The most valuable asset in any business is distribution.”
@RyanSerhant argues:
"You can have the greatest product of all time; if no one knows about it and no one buys it, you have an unknown product that does not sell, and that is a bankrupt business."
Are you pushing or pulling?
"When you buy advertising, you are pushing your message onto people, and that’s a push message, and you have to do that over and over and over and over and over before people pay attention. Social and the power of PR is pull, and so we were able to pull people into our stories day in and day out."
My guest today is @RyanSerhant, founder of SERHANT and star of Netflix’s Owning Manhattan.
We talk about the psychology of getting noticed, earning trust, and becoming the person people choose.
00:00 Intro
02:19 Get Chosen Before You’re Established
10:03 Why Sellers Should Take Acting Classes
11:11 What Is Embarrassment Costing You?
12:28 The 1,000 Minutes a Day Framework
16:26 Why Buyers Resist Your Pitch
20:57 Are You Giving Up Too Soon?
21:39 How to Use Google Alerts to Land Leads
23:21 The Craziest Deal in Serhant History
31:21 How to Handle Ego in Negotiations
33:02 Your Next Move at Rock Bottom
38:56 Why Attention Isn’t Making You Money
43:11 Find Clients Where Nobody Knows You
44:55 Why Strangers Ignore Your Offer
46:51 What Is Success Costing You?
52:03 The Excuse That Costs You Respect
First time I've seen someone understand space drugs on Twitter.
Worth a read:
Let me explain why is @VardaSpace is such an important company
A patient who has cancer has cells that have proteins that are activated leading to growth, replication and then damage.
Targeted drugs work on these cells in 2 ways:
1. Inhibit the receptor (the protein), essentially jamming its function until the drug wears off. An example is Tamoxifen, used in breast cancer to block oestrogen receptor binding.
2. Degrading the receptor altogether so signalling doesn't work and the cell needs to spend time recreating the receptor before it can work again, an example being Fulvestrant which degrades the receptor itself.
The second option here is important to understand because the effect of it significantly out lasts how long the drug lasts in your system. One type of these drugs is called PROTACs - it's a newer type of cancer drug that destroys these proteins (proteolysis) removing functions often related to cancer signalling.
Now these drugs are complex, 15+ chemical steps and can be upto 4x more heavy than normal drugs. With each molecule taking many shapes, when packed together, they often don't form a uniform lattice shape. On Earth, gravity leads to convection currents that feeds these molecules unevenly into the forming crystal, too fast resulting in non-uniform crystal formation.
These drugs:
- Dissolve poorly when ingested
- Have difficulties crossing cell membranes
- Lead to unpredictable bioavailability
- Require high doses in order to get enough drug in the system
Varda here by @WillBruey and @zebulgar aims to solves this with undisturbed crystal formation growth in microgravity.
With a new purer drug you get:
- Predictable bioavailability
- A more stable drug
- A better outcome for patients
This is Varda
Vikram Dutt retweeted
I learned how to set up Grok Bot from Peter.
If you’re into AI highly recommend following him.
Here's my updated review of each personal agent:
1. Grok Bot
Pros: A team of bots is the easiest to understand. Free X access is amazing for research. Voice mode and the new Pages feature are great.
Cons: Email feature doesn't really work.
2. Muse
Pros: Free, best consumer UX, most meme-worthy avatar, and business calling is amazing.
Cons: Muse Wonder is good but Opus/Astra is better.
3. Claude Duet
Pros: Most proactive out of the box. Feels polished and simple to use. Arguably the best model behind it.
Cons: Missing some features in research preview, like being able to drive its browser.
4. ChatGPT Dots
Pros: Works inside ChatGPT, so it can drive all your cloud and local threads.
Cons: Much better than at launch but still buggy. Voice mode is slow to connect.
I've also tried Instinct and some of the other agents, but I don't want more than 4 at a time connected to my email.
📌 Don't miss my full Claude Duet review here for everything that it can do: youtu.be/D-pz8L1orVI
Vikram Dutt retweeted
Dario Amodei says Anthropic held back broad access to Mythos-class models for four months to strengthen cyber safeguards. Mythos was originally scheduled for release in February this year.
He also says distillation poses no major commercial threat, but raises security concerns, and Anthropic has blocked suspected attempts to use its models for bioweapons.
Thank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic.
That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it.
I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits:
1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed.
2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it.
3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do.
4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this.
5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future.
6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent.
7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it.
8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company.
9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund.
All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking.
For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea.
Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option.
I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent.
But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it means to compete at the frontier, and we stand by them, both in the sense that they are transparently ordinary things to do as part of that strategy, and that we stand by the strategy as a whole.
To be fair, there are important questions worth confronting (and that we are confronting) about what to do when the AI-driven acceleration gets fast enough that it is hard for safety to keep up. We are nearing that point and that’s what’s driving some of our thinking about “pacing.” But 1-4 are actions taken before that point and should be perfectly expected based on Anthropic’s strategy as a company. Of course it is possible to disagree with that strategy, but that’s a more fundamental disagreement that you could have had any time over the last few years.
I agree we could have handled your items 7, 8, and 9 better. All fall into the category of “rhetoric or policy communication that wasn’t carefully written or resulting from fast-changing circumstances not propagating from one team to another fast enough.” This kind of thing happens a lot in large organizations (Anthropic is over 5,000 people), and doesn’t even necessarily represent a failure or mistake of any single person. I am of course the CEO, and I take responsibility. The volume of communication from Anthropic is enormous and if these are the worst examples, I think we’re frankly doing pretty well, though I will continue to work hard to make sure we do even better.
Your 5th item (the change to our RSP) is the most substantive item, and people can debate this, but I think that the old, more rigid RSP created a lot of situations where we were technically required to do something that required a lot of effort but in retrospect wasn’t very important for safety, as well as not covering some of the most important things. Our current RSP’s focus on transparency brought things like risk reports and roadmaps, and its emphasis on independent external review laid the groundwork for even more intensive embedded evaluation, all of which I think have been valuable in driving a race to the top. In the original announcement in September 2023, we described the RSP as an “early iteration that we [would] build on” and said that “rapid iteration…will almost certainly be necessary.” I understand that changing plans is always a move that creates justifiable scrutiny — I just don’t think we ever represented that we wouldn’t do so.
Relating to your 6th item (having an FCF in addition to our RSP), I know our intent is to meet the letter and spirit of our regulatory obligations (via our FCF) while also remaining able to experiment with more novel approaches to risk management that go beyond what the law currently requires (via our RSP). I promise to look into it further, as it’s a complex issue and it is possible we are not weighing the considerations in the right way.
Overall, although Anthropic has made plenty of mistakes as an organization — including some of the items on your list — I feel that we have repeatedly taken serious and substantive actions counter to our bottom line and that there is a significant negativity bias distorting assessments like the one you are making above. We’ll always try to do better but, again, I am extremely proud of our track record and stand by it. I will have more to say soon about how we are going to further live our values in this moment of rapid change.
Vikram Dutt retweeted
Hackers reportedly used Claude and Grok to automate attacks targeting over 400 banks and other financial targets in Latin America. Via the information
In one of the first documented cases of its kind, AI agents appear to have stolen data and accessed bank accounts with little human input.
This makes attacks much easier to scale, potentially overwhelming defenders. I guess this is just the beginning
Vikram Dutt retweeted
so hackers apparently used Claude + Grok + Kimi to automate a cyberattack on 400 banks.
but the crazy part is HOW they got around the safety guardrails:
> told models they’re in internal tests LOL
> Claude refused? → switch to Kimi
> Kimi refused too? → fall back to old-school malware
automated cyber attacks are becoming a norm apparently.
Hackers reportedly used Claude and Grok to automate attacks targeting over 400 banks and other financial targets in Latin America. Via the information
In one of the first documented cases of its kind, AI agents appear to have stolen data and accessed bank accounts with little human input.
This makes attacks much easier to scale, potentially overwhelming defenders. I guess this is just the beginning
Vikram Dutt retweeted
Whatever Gartner says related to AI or any modern technology, the opposite is true
Remember this is the company that listed IBM and AWS as the top coding harnesses a few months ago: above Cursor, Anthropic and never mentioned OpenAI lol
I am surprised anyone pays for Gartner
“At the moment, we have more compute capacity than we have demand [...]”
Ok @Gartner_inc, this is nonsense. We are *massively* compute constrained. Our portfolio companies are struggling to find capacity.
Full article here: cnbc.com/2026/10/19/gartner-…
Vikram Dutt retweeted
Estimating empowerment from offline data is useful, because it provides us with an unsupervised objective for learning the relative "value" of states for reaching diverse outcomes. But it's notoriously hard to make this tractable. In our new paper, we present a tractable estimator, as well as some applications!
How should an agent decide where to explore, given unlabeled offline data? 🤖
In our new paper, we show that empowerment, a measure of how much control an agent has at a state, can be estimated from offline data and used to guide exploration!
ishirgarg.github.io/offline-…
(1/7)
Vikram Dutt retweeted
Amazing, the e-mudhra thing was found out of the box via this repo!
Wonder how many more such latent opportunities exist; just waiting for an agent or a software to be pointed at it.
btw at 100k views in 24hrs, @jediahkatz's talk is an early candidate to be THE breakout talk of aie nyc
the bar is set very high for @poteto's keynote at AIE CODE next month hahaha
Inside Grok Bot: is the agent harness dead?
@jediahkatz of @SpaceXSI explains why smarter models let Cursor remove prompts and guardrails, and why Grok Bot is adding them back.
Watch his AI Engineer New York talk:
youtube.com/watch?v=7Y4sbZgB…
Vikram Dutt retweeted
Sharing new post-training research on self-distillation + RL (JOLT), done together with @NVIDIAAI.
In self-distillation, the same model performs the role of both a teacher and a student. The teacher gets extra information that the student doesn't, and the student learns from feedback provided by the teacher. However, the teacher could still use shortcuts in ways the student has no way to follow.
Our proposed approach, JOLT, trains the teacher as well, to learn from rewards on its own attempts, while penalizing any drift that's far off from what the student model would do. JOLT+ further supplements JOLT by providing the student model outcome-based rewards in addition to the teacher supervision signals.
On the MATH benchmark, we found that JOLT matches GRPO with 6.5x fewer training tokens, while JOLT+ convincingly beats GRPO across math, coding, terminal tasks, and tool use benchmarks.
New research: We developed JOLT, a post-training method where one model acts as both teacher and student, with the teacher trained to give feedback the student can use.
On MATH, it matched GRPO's accuracy with 6.5x fewer training tokens.
perplexity.ai/hub/blog/a-goo…
Let me explain what just happened.
OpenAI pointed thousands of AI agents at millions of mouse brain cells and got new leads on brain diseases like Parkinson’s in 32 minutes.
Each agent took one gene disruption, analyzed how the cells responded, searched the scientific literature, and proposed an explanation.
Then all the reports were stitched into one big map linking genes, cell effects, papers, and possible mechanisms.
Humans doing this one gene at a time would need months to a year.
Two things the agents found:
1. Two genes, Tsc1 and Tsc2, usually work as a team to control cell growth (and are linked to aging). But in the part of the brain that controls movement, switching off Tsc2 changed dozens of genes and switching off Tsc1 changed almost nothing. This matches a real disease called tuberous sclerosis, where TSC2 mutations are tied to problems in that part of the brain.
2. A gene called DCTN1 causes a Parkinson-like disease. When it was switched off, the cells made more cholesterol. The agent linked this to older research and suggested a test: does losing DCTN1 mess up cholesterol inside brain cells, and could fixing that protect them? Human experts called it new and worth testing.
Labs now produce more data than people can read, and the small, strange clues get skipped. Here, AI went through all of it and handed scientists a list of ideas to test.
The full map is public on GitHub too.
Still crazy!
GPT-Rosalind surfaced a new lead for drug discovery: could restoring cholesterol transport protect neurons from degeneration?
DCTN1 is a gene linked to Perry syndrome, a rare neurodegenerative disorder. In mouse-brain data, disrupting it increased expression of genes involved in making cholesterol. GPT-Rosalind suggested a possible explanation: cholesterol may not be reaching where neurons need it.
That gives scientists a specific mechanism to test and, if confirmed, a direction for developing new treatments for neurodegenerative disorders.
With @perturbai_tx and @scrippsresearch, a team of thousands of GPT-Rosalind agents worked in parallel, connecting gene-expression results from millions of mouse brain cells with scientific literature in just 32 minutes. We’re now expanding this collaboration to identify new drug targets.
openai.com/index/scaling-ai-…
Vikram Dutt retweeted
𝗦𝗲𝗹𝗳-𝗘𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗔𝗴𝗲𝗻𝘁 𝗛𝗮𝗿𝗻𝗲𝘀𝘀
The agents don’t fail loudly. They fail the same way, again and again, and nobody notices until a user does.
So the question I keep coming back to isn’t “how smart is the agent?” It’s “how does it get better without getting worse?”
This harness answers that.
The agent perceives, thinks, plans and acts like any other. The difference is what happens after a mistake.
It doesn’t just record that something failed. It compares the failed run against a good one and finds the exact step where things went wrong.
It doesn’t patch one-off errors either. It groups failures by root cause, so it fixes the pattern, not the symptom. No hardcoded answers. No sneaking test data into the prompt.
And nothing changes on trust alone. Every update the agent proposes for itself has to pass a quality gate and a performance gate before it’s allowed in. If it makes things worse, it’s rejected.
That last part is what matters most. Self-improvement without protection is just drift with extra steps.
Precise diagnosis. Real generalization. Regression protection. Validated updates.
Vikram Dutt retweeted
Dan Koe dropped the ultimate founder masterclass for turning your knowledge into a media + product EMPIRE:
Vikram Dutt retweeted
Quantum computers attack Bitcoin
Fusion can attack Gold value
Nowhere is safe – at San Francisco, CA
Replying to @MarathonFusion
In 2025, we published a bold proposal to use fusion reactors for gold production.
And now we’ve done it: Mercury transmuted into stable gold, using fusion neutrons, at 10x the record set by CERN in 2025.
We’ve reduced alchemy to a series of difficult but tractable engineering problems.
Vikram Dutt retweeted
SAVE THIS: vibe-coded an app? Run these 3 free scans before you push it.
1. TruffleHog (28.6K stars)
Finds leaked API keys, passwords and tokens. It knows 800+ secret types, from AWS and Stripe keys to database passwords, and can show only the keys that still work.
brew install trufflehog
trufflehog git file://./your-app --results=verified
2. Gitleaks (30.2K stars)
Scans every commit in your history, so it catches the key you "deleted" three commits ago that's still sitting in git.
brew install gitleaks
gitleaks git -v
3. OSV-Scanner by Google (11.2K stars)
Checks every package your app depends on against Google's vulnerability database, and can tell you whether the vulnerable code is actually used.
brew install osv-scanner
osv-scanner scan source -r .
Two minutes, three commands, and you'll know what you're about to ship.
Vikram Dutt retweeted
Nomad Letters: The Full Collection of the Nomad Investment Partnership Letters to Partners, 2001–2014, now available for preorder: press.stripe.com/nomad-lette…
i first read the Nomad letters when i was 17, and they changed my life. this is an embarrassing admission to make about a collection of hedge fund letters that primarily circulated as a stolen pdf in value-investor circles, and have now become enough of a meme to enjoy a second life of their own. but on the occasion of their republication in beautiful form by Tammy and the team at Stripe Press, i thought it was worth reflecting briefly on the impact these letters had on me.
Sleep and Zakaria’s fundamental truth (truke?) is often, and incorrectly, remembered as “scale matters.” everyone knew this when Sleep was writing.
the real insight was that scale is most powerful when its benefits are surrendered directly to the consumer.
the thing i remember most from Sleep is his wonderfully strange “robustness ratio,” which measured the amount the customer saved against the value retained by shareholders. value escaping the firm to the consumer, rather than being captured by it, was the metric of a great business. a great business is a lossy one.
this places Sleep in an interesting position opposite the other greatest anglo-investor of his generation, Chris Hohn. Nomad and TCI look similar from a distance: concentrated portfolios, long holding periods, indifference to benchmarks and, eventually, the conversion of their private fortunes into large philanthropic efforts.
they differ not merely in the fact that Hohn decided to continue (and even convert to Hinduism so that he could manage his portfolio in the next life, as reported in the FT), but in that their ideal businesses are exact mirror images. Hohn loves an irreversible asset that can extract a tax on its customer. in Hohn’s world, if a company possesses pricing power, it should, and must, exercise it.
rather Sleep’s ideal company possesses pricing power and repeatedly and consciously declines to use it.
the emergence and now total dominance of the labs makes this distinction newly urgent and would, on its own, be occasion enough to republish the work, quite apart from how beautiful and important the letters are.
our instinctive response to a discontinuity in technology is to invest at the frontier. Sleep’s letters are at least some reason for caution. technical leadership can be copied or simply made irrelevant by the next advance, and capital floods towards visible scarcity just before technology eviscerates that very same scarcity.
god knows Sleep was never hostile to technology. he made more money on Amazon than almost anyone else. but, at least in my reading, he was skeptical of technical novelty as a moat in its own right.
what mattered to him was whether that technological step had been converted into a self reinforcing relationship in which the customer privatized the gain.
in Nick’s frame, and he's welcome to correct me (although my sense is requiring him to lift a hand off of the wheel of a f450 in the hills of northumberland at this point would be a step too far) the enduring AI business may therefore be the one that passes declining costs on most aggressively, not one that preserves scarcity, bombs its own customers margins and tries to eat as much of the application layer as possible.
i find it increasingly useful to use the pair trade between totemic-hohn, and totemic-sleep actions as a frame in which to view the actions of the frontier. my guess is, in the fullness of time, or at the very least in this life (hohn is welcome to prove me wrong in the next), that Sleep might have won this bout.
very glad to see these letters in print.
Vikram Dutt retweeted
Madhu Guru (@realmadhuguru) led product for Gemini.
He thinks fewer than 75 PMs are qualified to do the work.
That's a pretty wild stat. Madhu is now a senior director of AI at Meta, and he identified 5 skills that every PM working on things like Gemini needs to be able to do:
1) Vision and opinionated UX design
2) Model intuition to extract max value
3) Going from pixels to evals to hill climbing
4) Agentic flows: tools, context and safety guardrails
5) Deep user understanding
He says it's scar tissue from hiring, so I'm inclined to believe him. He means AI-native PMs, but I really think it's fewer than 75 for model PMs.
I found 46 open model PM roles, oddly close to 75. I read all the postings, and they ask for similar things. Here's what they look for, by percentage of postings:
1) Vision and opinionated UX design: 61%
2) Model intuition to extract max value: 61%
3) Going from pixels to evals to hill climbing: 76%
4) Agentic flows: tools, context, safety guardrails: 59%
5) Deep user understanding: 50%
Now the last line of his tweet:
"It is a rare, but learnable skillset. Only way is to build, build, build."
I think that's the most important point, and the postings agree with him. Only 7 of the 46 require production experience. What they're really looking for is that you are building, and you can go build these things on the side.
What I recommend: pick a task your favorite model struggles with. Write down the failure modes. That's the start of your eval set, and that's the product you should build, something that solves them. Start with a behavior spec for it: "the model should..." Those lines become additional evals. Then ask Claude Code to build a product that passes those evals, and iterate. You'll learn how to build on top of models.
Model PMs are paid extremely well. The median posted base is $345K, and the range runs from $144.6K to $490K.
As he said, it's learnable. Because there's so little supply, why don't you become one? Why don't you get that pay bump? I think you can, and I'm here to help. Let me know any challenges you face in the comments.
I haven't talked to a single company that thinks it needs a better model to do what it wants.
I think we have far more "intelligence" than we can use, and the biggest problem today is organizing knowledge and using it with the models we have.
These guys are doing something cool:
They connect your code with your company's knowledge base. Basically, when you merge a pull request, Falconer detects which docs are now outdated and prepares updates.
Trillions are pouring into the race to build the first general superintelligence, but leveraging your own specialized know-it-all is now possible as of today.
I built the knowledge systems at @stripe and @uber. Then I helped Anthropic, Metronome, and Graphite build a killer developer experience. Everyone wants Stripe-level quality, but that bar is too low.
Falconer gives you and your customers complete, specialized intelligence: an internal knowledge base and external documentation in one beautiful package. Falconer is the ultimate trust engine to manage your context: self-driving, observable, and agent-friendly.
I co-founded @falconer_ai with @mbenedetto212 and assembled a team of know-it-all builders. We're backed by fellow know-it-alls @thegp, @BoxGroup, @svangel, @karimatiyeh, @benln, @zachlloydtweets, @CeciStalls, @MerrillLutsky, @scottmac, @malthe, and @adamjstevenson, with angels from Anthropic, Stripe, Meta, Cursor, Replit, and more.
Frontier intelligence companies like @FireworksAI_HQ and @⬛⬛⬛⬛⬛⬛; fintech companies like @payabli and @vori; and healthtech companies like @thatch_ai use @falconer_ai because they all share the same vision as us: the knowledge you earn should belong to you.
Now you can know everything, so you can build anything.






















































