building ai underwriting to finance all human ingenuity starting with farmers. ex-affirm, ex-granular. family and good quests
San Francisco, CA
Joined December 2012
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Daniel Tebes retweeted
Weekend hack: HackerTube. I was annoyed with YouTube's recommendations so I had Claude filter down 43M comments over 20 years of HN to 6,454 videos that keep being recommended. *Significantly* better viewing experience, ~2 hours with Opus 5.5:
hackertube.org/
- Excellent recs without any logins (state is kept in local browser session)
- Thumbnail is a representative frame from middle of the video instead of the super duper A/B test hyper-optimized user uploads
- Underlying data from the comment analysis as one zip file, point your Claude at it and ask for your own version
- HackerTube Longs ✊
A bit more on the process:
I use Claude Code (desktop app for Mac), Opus 5.5 on Medium effort as my default. I only talk to Claude and have it do everything, the only "manual work" I helped with in tandem was in the browser (reminiscent of MenuGen): 1) I had to create some kind of API key from Google Cloud UI to query YouTube for video metadata, 2) I booted up a VPS box for serving and 3) I bought the domain name. Once the box was up I gave Claude the IP and told it update, secure, harden, deploy and optimize the code, and it did the whole thing.
Many awesome projects that would have normally taken weeks/months even can now be few-shotted for breakfast. What else are we not thinking of.
Daniel Tebes retweeted
pretty sick.
Surprise 1: Anthropic acquired a one-person biotech
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.
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Surprise 2: that person is me
Surprise 3: I’m here to tell you all about it
Yes, this is what you think it is; after 6 yrs of increasingly loose anonymity: the Professor Oak reveal post
…and yes, it has gotten so boring around here that I’m literally doxxing myself to spice up the timeline
-------
Ok, allow me to introduce myself. My name is Cody Tranbarger. I mostly tweet, but occasionally find time to dabble in drug dev & starting/building biotechs. My past lives span the VC, crossover, startup continuum; Prof Oak has been my way of keeping a digital foot in those worlds, despite having my physical feet planted elsewhere.
The past 18 mo, ‘elsewhere’ was Mithril Medicine, a co. I founded to develop bsAbs for LAMA2-CMD & other ECM-related genetic diseases.
Mithril was born out of hands-on building with @MikeNGladstone, @JasonPaulRhodes, @LifeSciVC, & team @atlasventure. Somehow, I made enough half-decent calls along the way (even a broken clock is right twice a day, eh?) to convince them Mithril/I warranted a small seed. While I can hardly rationalize such an outrageously cavalier bet, I’m grateful for it, & I'd partner with Atlas again in a heartbeat.
In typical AV fashion, Mithril’s primordial form was virtual, decentralized, & ultra-lean. This stage tends to be short-lived, but for Mithril, it worked surprisingly well, for surprisingly long, thanks in part to the rapid maturation of the AI tools I used liberally.
Eventually, Mithril became a bit of an experiment itself - to find the upper bound of how far it could scale as a single operator + a bunch of Claude (+ a stellar bench of part-time consultants).
In parallel, I started spending time with @nc_frey, @an1lam, & @CoefficientBio team; and promptly experienced, for the 1st time, AI at the frontier. Soon thereafter, Mithril stopped looking like an experiment, & started looking like a preview of an entirely new operating model.
From that spark came “Pipeline-in-a-Person.” The idea immediately resonated when we shared it here back in April; and it was central to the shared vision that led to Anthropic’s acquisition of Mithril, & my joining the team.
Why join Anthropic? Three reasons.
(1) For Mithril, the programs continue, with more resources & infrastructure behind them, and as much Claude as I can put to work.
(2) For me, the chance to start several more programs, & find out how many one person can actually run in parallel - an almost unprecedented luxury for a curious drug hunter.
(3) For our mission, a path to institutionalize & scale this new way of working, then put it in the hands of all of biopharma, all over the world - which, candidly, I find the most energizing.
Will AI x Bio cure diseases? Unequivocally yes, imo… but not by the route that gets the most airtime. Rather than timeline acceleration & compression, I see orchestration & parallelization as today's low-hanging fruit.
Most drug hunters see a handful of go/no-go calls in a career, & spend entirely too much of the rest trading emails (my inbox can attest).
Give them the bandwidth to run several programs at once, and the time to focus on scientific judgment, and I’m willing to bet they’ll make better calls. And at scale, better calls translate to more drugs, higher hit rates, broader patient impact, & likely, a disease or 10 or 100 cured. I couldn't be more excited to build toward that future @AnthropicAI.
Finally, a heartfelt thank you to BioTwitter/X. The end of anonymity is bittersweet, but if anyone’s earned a proper introduction, you all have.
It’s difficult to overstate how much this app has done for me: a decade of education, plenty of friends, many job opportunities, several mentors, a few investors, even a co-founder (fmr). It was the generosity of this community that inspired me to start posting 6 years ago, after just as many years lurking. I hope I’ve been able to pay it forward, in my own small way.
But, to be clear, Prof Oak isn’t going anywhere. Same beat, same themes, and now, a front-row seat to drug development at the frontier. Stay tuned for dispatches from the trenches; and remember...
Gotta Cure 'Em All
Daniel Tebes retweeted
These were three of the biggest science results I’ve seen in my lifetime
Just absolutely jaw-dropping results
Within ~5 years it seems like any scientific result a human is capable of understanding will be knowable
I find this incredibly exciting
We are announcing three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory.
openai.com/index/building-sc…
Daniel Tebes retweeted
Announcing three major scientific discoveries, one each in mathematics (harmonic analysis), computer science (complexity theory), and physics (quantum field theory).
AI starting to help us improve our understanding of the universe! openai.com/index/building-sc…
Daniel Tebes retweeted
I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
Daniel Tebes retweeted
Assorted things I've been mulling over regarding agents in the economy:
• Companies are in some sense the original superintelligence. It's often the case that they can use their amortized cognitive surplus to advance their interests against those of certain consumers: relative to the optimization capacity of companies, consumers can be inattentive, underinformed, irrational, etc. (Think, say, of some of the classic insurance upsells at the end of a booking process, which is typically priced substantially above what a regular policy would cost.) Herbert Simon introduced the idea of bounded rationality, and firms often end up benefiting from it. Thanks to personal agents, we will probably get somewhat more rational in how we engage with companies.
• How exactly will outcomes change? Well, certain kinds of price discrimination will work less well (how do coupons work if every agent hunts fastidiously for them?), and certain kinds of customer cross-subsidy will change (today, people who forget to cancel subscriptions subsidize those who don't). Taken across the economy as a whole, I'm not sure what the incidence of all of this will be.
• In corporate strategy, people have long debated the relative importance of distribution and product quality. Perhaps you've made something better, but does anyone know about it? Won't people just continue to buy the ACME Corp Mousetraps? Personal agents will, I think, act as a kind of structural subsidy for product quality. How would markets look different if individuals always spent at least 10 hours researching their purchases? On the margin, focusing on making a product better is, I think, going to become a more effective strategy for companies. This is good!
• It true, a corollary is that information about product quality will become more important and impactful. Today, most people have very limited ability to aggregate and integrate such data, but agents will have insatiable thirst for such signals. (It's often said that data is becoming more valuable because it helps in the training of models. That's obviously true, but I think there's a secondary way in which it's more valuable simply as a complement to cheaper intelligence.)
• A big question is whether agents will increase superstar effects (rational agents all settle on the same product) or increase dispersion (because everyone has slightly different tastes and preferences, which agents are good at eliciting, finally overcoming the artificial clustering we've been subjected to).
• Many internet companies have monetized demand routing, which is in turn often predicated on short attention spans. "I'll click on the first thing I see." This has led to some amount of inefficiency, as companies try to propitiate capricious ranking algorithms and incur various placement fees. It's not all deadweight loss (willingness to pay is itself a kind of signal, which is valuable to buyers), but it's not great. How will the nature and value of this routing change in a world of personal agents? I don't know the answer, but I think that the effects might be pretty big.
• Much of what I'm writing hinges on the assumption that personal agents will be on the consumer's side. I suspect that that will be the winning strategy, but whether it's true, and what it means to be true, involves a lot of other questions.
Taken as a whole, the market is a massively parallelized, decentralized, and highly imperfect value attribution machine, and, taking stock of all of the effects, it seems to me that the quality point may well be the biggest: that agents will change the nature of the backpropagation such that the rewards for better products increase. Maybe this is too optimistic, but I suspect (and hope) that companies that make things that they're proud of will feel that the universe is a little more partisan in their favor.
Daniel Tebes retweeted
The most insane paid ads program in Silicon Valley, behind Wispr Flow's $0 to $2B run
1/ "10 really good creators can get you to $50M a year"
-> The model: brands recruit creators to film their ads and pay them a percent of the ad spend
2/ A year ago Wispr Flow stopped organic UGC completely and put everything into paid
-> 500 to 700 new UGC ads a month now, aiming for 1,000
3/ They took the paid ads playbook of killer ecom brands
-> SF finally figured out what the best DTC brands have been doing for years
4/ Their view: organic only works with real talent on the team, while paid works for anyone who can ship enough different ads
-> You don't need a genius to win on paid, you need lots of ads that each look and sound different and a system that keeps them coming
5/ Ecom brands pay creators a cut of each sale. SaaS brands pay a cut of the ad spend, since a software sale can take weeks to close
6/ Wispr pays 7% of ad spend, uncapped, paid out every 7 days
7/ New creators get a guaranteed minimum, which is how Wispr signed good ones before it had a track record
-> $1,000 for 20 to 25 videos a month. A creator who earns $500 in commissions gets the other $500, and once they earn more than $1,000 on their own the guarantee stops
8/ Each creator works on one customer segment
-> A customer no ad was speaking to, a format nobody could shoot, a country with nothing running. One creator per gap
9/ No two videos are allowed to look alike
-> Meta's Andromeda clusters videos that are 50 to 60% similar and suppresses them, so 1,000 talking heads get treated as one ad
10/ They recruit on Instagram with keyword search
-> Search "UGC creator", "tech", "SaaS", mine the followers and comments of every good creator, send 100 to 200 DMs in a week
11/ 50 creators accepted out of 5,000+ applications
-> $220k paid out so far, top creator makes $12k a month
12/ Creators get $100 for referring someone at their level or above
-> No beginners on the team
13/ They scaled 5 creators, then 10, then 20, then 40, and stopped at 40 to 60
-> The whole climb from 5 to 50 took four months
14/ Creators are run like a sales team
-> 30-minute onboarding each, a weekly call with the media buyer, 1:1s, creator coaches
15/ Creators see the ad account
-> Every Thursday the media buyer walks them through what's working, which customers to target next and what to test
16/ One media buyer runs all of it: 50 creators, 700 ads a month
17/ The same creator ads run on Meta, AppLovin and TikTok
18/ The group stays small on purpose
-> $100k paid to 20 creators looks better to creators than $200k paid to 1,000
19/ Creators see which ad is winning and start making their own version of it. Wispr stops this and pushes them to film something new instead
-> Meta treats the copy as the same ad as the original, so the copy never gets shown
20/ Why other creator programs fail: brands accept everyone who applies, nobody coaches the creators, and creators join without understanding they only get paid when their ads perform
21/ Meta charges more every year for the same amount of reach, and that cost is out of Wispr's hands. The one thing they control is how good and how different their ads are, so that's where the money goes
-> Last quarter Meta showed 14% more ads than the quarter before and still raised the price of each one by 12%
22/ The guy who built it joined Wispr Flow at 19, before the product existed, with a 2.5 GPA
-> 25 months later: $280M Series B at a $2B valuation
Daniel Tebes retweeted
All seven Databricks co-founders began our journey together at UC Berkeley. We started the company in a room in Soda Hall, moved to a small office on Addison Street, and spent our first few years in Berkeley before eventually heading to SF. Two of our co-founders are still on the faculty.
Today we announced Databricks Field at California Memorial Stadium, our first collegiate athletics sponsorship.
Berkeley shaped everything about how this company thinks, and this is our way of investing in the next generation of students and builders who'll do their best work on that campus.
Go Bears!
databricks.com/company/newsr…
Daniel Tebes retweeted
they just killed like 1000 startups
Now available: ChatGPT for Financial Services.
This is a tailored ChatGPT Work experience that combines built-in financial data with GPT-6 Astra’s reasoning.
Teams can develop research, build financial models, and create customized client materials.
openai.com/index/introducing…
Daniel Tebes retweeted
Today we're releasing data on models accelerating research at OpenAI.
Recursive self-improvement could be the most important contributor to AI capabilities over the next few years, but by default it will only be seen inside a few frontier AI labs. Being transparent is more urgent than ever, so we can inform the public discussion on whether and how to pace model development. I ask other AI companies to do the same.
openai.com/index/research-ac…
Daniel Tebes retweeted
I just launched an AI model based on sleep data… and it accurately predicts your age.
I teamed up with @m_franceschetti, and it's now available on their platforms.
It turns out you have a sleep fingerprint. This research can identify you out of thousands of users from one night's signal with 92.5% accuracy.
It also detects…
+ biological age within 3.3 years
+ diabetes better than Apple’s model
+ speed of aging
+ heart failure at 0.822
This Eight Sleep model is possibly the most accurate contactless bioage estimate ever reported.
What we did:
#1 What data was it built on?
This model was built on the largest raw biosignal dataset ever used to train an AI, from any device, including every wearable on the market.
> 2.04 million hours
> 136,575 participants
> 498k sessions
> 122 million segments
#2 What can it predict?
It can predict your biological age (the age your body acts like) within 3.3 years.
It also predicts heart-related and metabolic conditions. Here are the detection scores (AUROC):
> diabetes (0.852)
> heart failure (0.82)
> hypertension (0.810)
> sleep apnea (0.792)
> snoring (0.751)
> general heart conditions (0.734)
> cancer (0.678)
> hot flashes (0.671)
> migraines (0.673)
#3 How was it built?
Interestingly, the pretraining task was not “predict someone’s age”. The model was tasked with comparing two 60 second windows across different nights to figure out if the nights belonged to the same person.
To do that, it had to find someone’s ‘sleep fingerprint’. These are biological signals that the data is coming from the same person. Things like how forcefully your heart contracts, your breathing depth and rhythm, and the timing of the recoil wave each heartbeat sends through your body.
Those signals are age-predictive. It learned to estimate age, detect diabetes, and flag heart failure as a downstream readout.
The whole pretraining run was ~four days.
#4 Why is it good at age?
The reason aging prediction is accurate is mechanical. Aging stiffens arteries, reduces cardiac compliance, changes autonomic tone, HRV declines. Aging also alters sleep architecture. Deep sleep shrinks and fragmentation rises.
Every one of those changes the recoil waveform and its overnight dynamics.
Said differently, the heart of a 65 year old mechanically pushes the body differently than a 25 year old's.
#5 Why a bed vs wearables?
A bed is an elegant solution. It makes a high fidelity uninterrupted 5 to 10 hour recording every single night possible. And session-level sequence modeling becomes viable.
Whereas wearables get fragmented data: battery limits, sparse snippets, people taking the watch off, adherence dropping over weeks.
#6 More data, better prediction
The bigger the training batches (the more people the model compares at once) the better it got, log-linearly (R²=0.982).
That means the recipe is predictable: you can forecast improvement with more compute, the same way scaling laws work for language models.
The current model only ever compares two nights at a time, and the average training user contributed under 4 nights.
The team's stated next step is modeling 30+ consecutive nights per person. You can imagine how this will improve with the constant stream of data Eight Sleep gets every night.
—
It’s worth noting some limitations. Internal labels are self-reported and external cohorts are small, and that this is a research milestone, not a diagnostic device.
What makes this exciting: a passive, daily activity like sleep can now provide meaningful insight into your well-being.
I speak often of Autonomous Health, a world where the things around us take care of us without our knowing or asking.
Eight Sleep is a great example of this in practice and a major reason I maintain so much optimism for the future of health.
🤝 Paid partnership
Daniel Tebes retweeted
Extremely proud of this article I wrote for the legendary @lennysan! This is the culmination of months of research into why most AI designs are slop, and my top techniques for fixing this.
LLMs can be super creative, but as we post-train them more and more, we stifle that creativity. We train them to predict tokens that balance everyone’s preferences. But good design does the opposite: it’s risky, bold, and opinionated. You have to put your LLM in a different state of mind to get this kind of design from it. You have to break out of the average slop, into the fringes of the bell curve where unseen ideas live.
This post is all about how to do that. Hope it helps, and let me know if I should go deeper on any of this for future posts!
open.substack.com/pub/lenny/…
Daniel Tebes retweeted
Regret the tone of my post on data centers yesterday.
What I should have said:
There were reasonable concerns about data centers 18ish months ago: water, taxes, jobs, electricity prices, the environment and what they would do to small towns. Well-structured data center projects have largely addressed these concerns today and we should be celebrating this.
On balance, data centers are awesome for America in every way.
On water: U.S. data centers use a fraction of what golf courses use. A lot of the numbers from 18 months ago were off by over 1000x. Newer data centers use closed-loop systems or recycled water. Should be required by every town approving a data center project.
On taxes: looking only at sales-tax exemptions, as Ronan Farrow did, is the wrong way to evaluate this. Data centers pay significant property taxes. Loudoun County, which is the wealthiest county in America, now collects on the order of $1 billion a year from data centers. In Quincy, WA, data centers are more than half the property-tax roll. Over time, property taxes can go to zero while government spending increases in these towns.
On jobs: this has been unambiguously awesome for blue collar Americans. Demand for electricians, plumbers, welders, HVAC techs, and contractors has gone vertical, and it is not a one-time construction job. These buildings get upgraded and expanded over time. That is why the building trades are fighting for them, and why some unions are now treating opposition to data centers as a reason not to endorse politicians.
On power: the original fear was that households would pay for the incremental electricity demand in the form of higher prices. That is why the ratepayer-protection deals and the new large-load tariffs exist. The right structure is: the data center brings or pays for new generation and signs a contract long enough that existing customers are protected. Where that is happening, utilities are cutting or freezing residential rates and saying so on the record. Where it is not, people are right to object. Electricity prices are going down *today* in a number of large states because of data centers.
On the environment: data centers overwhelming use natural gas today, which is the cleanest power source outside of nuclear, solar and wind. And the companies that are building the data centers are committed to carbon neutrality such that an equivalent amount of solar will likely be built. Maybe more importantly, the data centers need batteries to function effectively and these batteries can also sell energy back into the grid (which recently prevented blackouts in Texas). Over time, data centers will run on solar plus batteries.
On the towns: Poverty in Quincy, WA fell from 29% to 6%. Data center taxes paid for a new high school, a hospital, a library, police and fire stations. This is happening in many left for dead former mill and farm towns that had no other bidder for the land.
Data centers are actually reindustrializing parts of America and creating the kind of working-class jobs both parties have spent decades claiming to support. That should not be a partisan issue. Data centers can and should be awesome for America and they increasingly, overwhelmingly are. Supporting the outsourcing of data centers to China will likely age just as well as support for the outsourcing of high quality, blue collar manufacturing jobs to China has aged.
When the facts change, I change my mind. I hope that reasonable people who had good faith reasons to oppose data centers at least consider updating their beliefs given the change in the facts over the last 18 months. This really matters for America.
I will say I also think the idea of making data centers beautiful is a good one that has yet to be implemented. Data centers should be just as beautiful as Grand Central Station. We can learn a lot from the railroad buildout. Neoclassical revival ftw.
Might write up open-weight AI tomorrow as this is equally essential to America.
Daniel Tebes retweeted
I’m Noah, the founder of Instinct.
Instinct is a personal agent that we’ve been building for the past few months. The interface is simple: there are no new interfaces. You can text or call it. It's trained to use a phone and a computer in the same way that humans do. Instinct combines simplicity with extreme capability.
I’m thrilled with everything our early users are doing with Instinct. They’ve told us they’ve planned cross-country road trips, bought weekly groceries and concert tickets, and cancelled hundreds of dollars of subscriptions. Someone’s even planning their wedding with Instinct.
We want to make Instinct the best personal agent for all of you. It’s available in an invite-only beta program while we’re actively bringing up more compute. I’m excited to see what you all do with it.
instinct.co
Daniel Tebes retweeted
If you care about outcomes you care about evals.
Similar as having OKRs or NPS - agents require evals.
This is why we are constantly looking at how accurate, how fast, and at what cost our agents are performing
AI diffusion is far more rate limited by having good evals than most realize. The kind of evals that you see for every model release are incredibly helpful, but only tell you the shape of general AI progress and the relative capability level of models.
The far bigger space over time are evals on all the major workflows that enterprises do, down to the specifics of an individual company.
This will be a huge space over time because you can’t automate what you can’t assess the progress on. Enterprises will not be able to go just on vibes.
Daniel Tebes retweeted
Great article by @sonyatweetybird, esp on the importance of high-quality evals/benchmarks.
"Most evals start as a founder squinting at outputs and vibe-checking whether they feel right."
^we started here but have since invested an enormous amount of time, effort, and resources into building Legal Agent Bench and other internal eval sets to turn legal judgment into a hill-climable signal. It's created a powerful flywheel for us @harvey.
h/t @ItsJulioPereyra's team of legal researchers who bring their much needed domain expertise to the equation and at @BrendanFoody @mercor for helping us scale it up.
Daniel Tebes retweeted
We teach kids things that have no connection to the world around them and wonder why they’re not engaged.
So I made this list of books covering 18 core pillars of “boring” infrastructure modern society is built on.
There are cathedrals everywhere for those with eyes to see.
Daniel Tebes retweeted
Having an AI product you trust to execute transactions on your behalf and navigate websites is amazing, even better than I thought. 💜 Instinct
Daniel Tebes retweeted
Just gonna leave this here…
colossus.com/article/joshua-…
