SeYoon Yoon retweeted
I am never leaving this app LMFAO
>be Don Wilson
>predict compute markets will be > than oil within 10 years
>anons call you delusional
>lmao.exe
>fyi this is your track record
>1992: found legendary prop firm DRW
>name it after your own initials (omega based)
>pioneer quant and electronic trading
>your firm trades Bitcoin in 2010 (bruh)
>launch Cumberland in 2014
>become one of the biggest institutional LP in crypto
>co-found Compute Exchange and back Silicon Data to build GPU markets and pricing indices
>literally became a billionaire by being early for over 30 years
like bruh you’re literally arguing about markets with a man whose racing sailboats are called “Convexity” LMFAO
you can’t make this shit up
incredible stuff thank you for the laughs
SeYoon Yoon retweeted
Andrej Karpathy:
"Prompting is going away. Delete everything, keep Graph."
In just 69 minutes, he shows how to build graphs and why they're the only layer that survives in the end
Prompts → Agents → Loops → Graphs
a loop keeps one agent running until the job is done
a graph decides which agents exist and what each one passes to the next
anyone can build an agent, almost nobody builds the graph that runs them
watch it today, then save the full guide on graphs below before everyone starts catching up ↓
SeYoon Yoon retweeted
a few of my predictions for the next 6 months of ai data:
- a monopolistic human data buy side layer emerges. right now you can use zap mail, heyreach, instantly and claude max plan to reach out to practically every potential human data buyer on the world in 7 days and (even with the worst strategy) at least come out with roughly 20 buyers spanning rl builders, neo labs and (if lucky) one or two major labs, who you could easily offload $20-$50m cumulatively from the very start. it is not going to be this easy to find buyers forever. as labs inevitably get f*cked by bad apples on rights, de-id, QC and the rest, a select few will emerge as “transaction gates” in which every corpora must flow them in order for the labs to deem as safe to buy. analogous to how a few labs already don’t touch corpora if it was de-id by Tonic Textual.
- the waterline continues to rise. ai labs are buying real company data to build artificial workspaces to teach their models how to do an office job. using a headcount as a metric, 3 months ago they bought company internals with 10+ headcount, now they almost exclusively purchase from companies with 50 or more employees. as models saturate these smaller workspaces, demand for smaller company corpora plummets whilst demand for the already dwindling supply of large sprawling company internals increases. yet doesn’t skyrocket as at a certain point if the price becomes too high then labs fall back on synthetic expert work. it goes without saying that small companies in a esoteric domain models underperform in that hold vertical depth (e.g a 30-man freight brokerage) will still hold immense value.
- reality gets graded more and more. as the most knowledgeable domain experts begin to fail in writing valid rubrics that aren’t instantly saturated we are going to have to get reality to play the verifier, domain expert and environment, if we want progress to continue. rubrics shift from the attributes of a perfect NDA to a lab measured critical tempurature.
- atom environments. why hasn’t the vertiginous increase of ai capabilities led to huge economic growth? how can we get non-tech people to understand how intelligent ai is already? how do we find the best value/token ratio? to “solve” these questions I believe need to give ai a “body” in which it can interact with the real world at extremely low bandwidth. giving ai a body sounds like a humanoid which is obviously just one small slice of real world interaction, a little bit like how literally none of the ways we interact with in the real world is only via our real body but via tools instead like handles, vehicles, instruments and cutlery. maybe I should reframe it from giving ai a body to giving the world ai
- protocols for company data emerge. for such a fuzzy yet crucial area of ai, it is insane how unorganized certain steps in company data brokerage are, such as giving context on what your companies internals actually hold.
SeYoon Yoon retweeted
Running an experiment.
How much will this dataset sell for? Sold on a non-exclusive basis.
Right answer gets slightly worn @TryBrickroad swag and a broccoli.
SeYoon Yoon retweeted
everybody on my timeline is chatting about data brokering and only one person has acc been paid so instead of larping about it i built the whole thing myself in a day for $111
@troyaitken_ has $30M to buy company data for the AI labs so i asked him if my old @ImportYeti spreadsheet from when i was selling PVC hoses in china was worth anything and he said yes so i went full side quest general
first i built a timeline map that pulled every single post about data brokering since the 1st of october and had @subquadratic read all 882 of them to figure out who is hyped who is doubtful and who is just selling a course
then i built a leaders pull that read 12,468 posts from the people acc closing deals and had subq pull out the rules that decide who gets paid and who gets ignored (first one in wins and written beats phone every time)
then i dumped 6 years of shipping records into postgres and built a seller pipeline that took 30,996 records down to 4,946 companies and had subq score every one of them on the buyers 16 questions then @firecrawl found 1,304 named bosses and 2,588 ways to reach them then @treg_ai checked the phone numbers and 103 out of 104 acc work then blitz checked the top 30 were the right person and @p0 parallel did deep research on every single boss so subq could pull one true story out of each to open the call with
and 2,470 warm ways in through the suppliers they already ship with
then i built a call matrix that tested 224 different ways to open the call and only one won and when it broke i fixed it with @claudeai opus 5.5
21,000 firecrawl credits gone in 2 hours and $111 all in and the whole thing is opensource and free and GOATED and free and opensource
25 dials tonight lets see what happens
Pewterdata.com has a $30M mandate to purchase business data over the next 8 weeks.
We're actively buying from companies with 20 or more employees and at least 5 years in business across six verticals:
Service. Manufacturing. Logistics. Healthcare. Ecommerce. Accounting.
If your company holds data in any of these spaces, we want to talk.
We also pay referral fees. 10% of the deal value, up to $100k, to anyone who connects us with a seller that closes.
If you know someone who fits, tag them below or send me a DM.
More at pewterdata.com
SeYoon Yoon retweeted
the kled marketplace is now live: kled.ai/datasets
labs can request and browse datasets from the kled contributor network and a curated network of enterprise partners.
SeYoon Yoon retweeted
Every week someone tells me they vibe-coded their app over the weekend and don't need developers anymore.
Before it touches a paying customer, make sure it includes:
> a schema someone designed, instead of one the AI improvised table by table
> login that still works when someone puts a plus sign in their email
> password reset links that expire and work once
> MFA, plus a recovery path for the user who lost their phone
> sessions that time out, and a way to kill every session for one user
> SSO for the enterprise customer who won't sign without it
> SAML, because that customer's IT team doesn't do magic links
> SCIM, so a fired employee loses access the moment IT offboards them
> roles and permissions deeper than "admin" and "everyone else"
> checks that stop user A from reading user B's data by changing one number in the URL
> tenant isolation, so one customer's bug never shows another customer's records
> an admin "log in as this user" button for support, with an audit trail on every use
> rate limits on login, so nobody can guess passwords at scale
> lockouts that stop attackers without locking out your CFO the night before the board meeting
> database migrations that run forward and roll back
> migrations that don't lock the main table at 2pm on a Tuesday
> indexes, because the query that was instant on 50 test rows times out on 5 million real ones
> connection pooling, so a traffic spike doesn't exhaust the database
> backups every night
> a restore from those backups, tested, because a backup you've never restored is a hope
> point-in-time recovery for the day someone runs a delete without a where clause
> soft deletes, so "I deleted it by accident" is a support ticket and not an incident
> validation on the server, not just in the form
> timezones stored right, so the 9am reminder doesn't fire at 4am
> the customer with an apostrophe in their last name
> emoji, accents and right-to-left text in every field
> money stored as integers, because 0.1 + 0.2 doesn't equal 0.3 in floating point
> currency, rounding and tax rules for every country you sell into
> a data model that survives your second customer asking for "one small change"
> idempotency keys, so a retried payment doesn't charge the card twice
> deduplication for the user who clicks submit four times
> pagination that still works on page 400
> exports, because sooner or later every customer wants their data in a spreadsheet
> imports that survive the CSV someone saved in 2009
> retention rules, and code that deletes what your contract says you'll delete
> secrets out of the code, out of the repo and out of the AI chat history
> rotating every key the AI pasted into a config file last month
> confirming every package the AI installed exists and is the one you meant
> dependency scanning, because the AI pulled in libraries nobody reviewed
> input sanitization everywhere a user can type
> protection against SQL injection, XSS, CSRF and the rest of the OWASP list
> file uploads that get scanned, size-limited and stored where they can't execute
> CORS rules that aren't "allow everything"
> security headers on every response
> encryption at rest and in transit
> least-privilege access for everyone, including the AI agent holding production credentials
> logs that record who did what, without recording passwords, tokens or card numbers
> a penetration test before your enterprise customer's security team runs their own
> answers to the security questionnaire every enterprise buyer sends, with evidence behind each one
> a disclosure process for the day a stranger emails you a vulnerability
> a plan for the day a laptop with production access gets stolen
> SOC 2, which means policies, evidence and an auditor, not a checkbox
> HIPAA, the moment a single patient record touches your system
> a BAA with every vendor that touches that data, including your AI model provider
> audit trails that show who viewed a record, not just who changed it
> PCI scope, and card numbers kept out of your database
> GDPR deletion requests that reach every table, backup and log
> data residency for the customer whose contract says EU only
> quarterly access reviews someone signs off on
> dev, staging and production environments that match each other
> infrastructure written as code, so the setup lives somewhere other than one person's memory
> a deploy pipeline that runs the tests before anything reaches customers
> one-click rollbacks, because Friday deploys happen
> zero-downtime deploys, because your customers don't have maintenance windows
> feature flags, so you can ship the code without shipping the feature
> autoscaling for the Monday morning spike
> a CDN, so the app loads fast in Singapore too
> DNS, SSL certificates, and renewals that don't expire on a Sunday
> background job queues, so slow work doesn't freeze the page
> retries with backoff, so one flaky API call doesn't become a lost order
> a dead-letter queue for the jobs that fail every retry
> cron jobs that don't run twice the day you add a second server
> caching, and clearing that cache when the data changes
> SPF, DKIM and DMARC, so your password resets don't land in spam
> bounce and complaint handling on every transactional email
> storage rules, so you stop paying to keep 2021's temp files
> cost alerts, so a runaway agent loop shows up the same day and not on the invoice (we learned this one for $1,382.59)
> a disaster recovery plan, written down and tested once a year
> a second region, if your contract promises uptime one region can't deliver
> structured logs you can search at 3am
> error tracking that groups the same bug instead of sending you 4,000 emails
> uptime checks from outside your own network
> alerts that page a human, tuned until they stop paging for nothing
> dashboards for latency, error rate and throughput
> tracing for one slow request across every service it touches
> an on-call rotation, and a person who picks up
> runbooks, so the engineer on call knows what "queue depth critical" means
> postmortems that change something
> a status page customers check before they email you
> unit tests written by someone who knows what the logic should do
> integration tests for every third-party API you depend on
> end-to-end tests for signup, checkout and the other flows that make you money
> load testing before the launch email goes out
> test data that looks like production without being production data
> regression tests, so fixing bug 41 doesn't bring back bug 12
> testing on the five-year-old laptop your biggest customer's ops team still uses
> accessibility compliance, because the enterprise contract requires it
> subscriptions, upgrades, downgrades and proration
> failed-payment retries and dunning emails
> invoices with the customer's PO number on them, because procurement won't pay without it
> sales tax and VAT
> refunds, credits and partial refunds
> usage metering that matches what you bill, to the cent
> annual contracts with custom terms your billing logic never planned for
> revenue reports your finance team can reconcile
> webhooks that verify signatures
> webhooks that retry, with a log of every failed delivery
> a versioned API, so you can change it without breaking your customers' integrations
> API keys customers can create and revoke
> API rate limits that protect you without blocking real integrations
> a plan for the day a third-party API changes its response format without telling you
> OAuth token refresh, because tokens expire at the worst possible moment
> a sandbox where customers can test their integration
> protection against prompt injection in every field that reaches the model
> customer and patient data kept out of prompts that leave your network
> a model spend cap per customer, so one heavy user can't run up your bill
> evals, so you find out when a model update makes your answers worse
> a fallback for the afternoon your model provider goes down
> a migration plan for the day your model gets deprecated
> prompt and output logs for debugging, minus whatever you're not allowed to keep
> a human approval step for anything the AI does that can't be undone
> pages that load on hotel wifi
> empty states, loading states and error states on every screen
> error messages a customer can act on, instead of "something went wrong"
> forms that keep what people typed when one field fails validation
> emails that render in every mail client your customers use
> notifications people can turn off
> undo
> keyboard navigation and screen reader support
> translations, with dates and numbers formatted for each locale
> a back button that does what people expect
> code a human can read
> code review on every change
> a branching strategy, so two people can work without overwriting each other
> monthly dependency upgrades, so you're never two major versions behind
> framework upgrades before the old version stops getting security patches
> deleting the dead code the AI left behind from its first three attempts
> splitting up the 2,000-line file the AI kept appending to
> architecture decisions written down, with the reasons
> a README that gets a new engineer running locally in under an hour
> documentation for whoever inherits this
> an admin panel for your ops team
> support tooling, so customer success can answer questions without pinging engineering
> a changelog customers can read
> terms and a privacy policy that match what the code does
> every account, domain and repo in the company's name, not the name of whoever built the prototype
> a bus factor higher than one
Oh, and besides keeping all of this working every day, forever… your vibe-coded app will still need people who know what they're doing:
> reading every line the AI wrote before it ships (a senior engineer)
> designing the data model before your second customer breaks it (a backend engineer)
> explaining why the AI's fix for one bug created two new ones (a senior engineer, again)
> deploys, rollbacks, servers and the cloud bill (a DevOps engineer)
> answering the page at 3am when payments stop (whoever's on call)
> patching the framework the week a vulnerability goes public (security)
> walking an auditor through how patient data moves (security and compliance)
> testing what the AI says it tested (QA)
> running a migration on a live database without losing a row (a backend engineer)
> saying no to the feature that breaks three others (a tech lead)
> turning "works on my laptop" into "works for every customer at once" (an architect)
> debugging the code when the AI that wrote it can't (a senior engineer)
> remembering why it's built this way, a year from now (someone who was there)
…wishing them the best 🫡
SeYoon Yoon 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!
SeYoon Yoon retweeted
just hit $545k MRR... still the only full-time dev
and the app is over 1m lines of code...
in the last 7 days i merged 1,089 PRs
here's the tech stack..
1. over 50 oh-my-pi agents running in parallel on my home linux server
2. 8 Max Claude plans, 1 SuperGrok Heavy plan, and 5 max Codex plans
3. Slack & Telegram to communicate with my bots. I no longer use a terminal or IDE
4. Linear, the central PM tool my bots use for issue tracking. Every engineering request, bug report, alert and error gets posted here
5. Supabase staging database + Railway preview environments for testing
6. Ubicloud runners for GitHub Actions
7. 4 Mac minis as self-hosted runners for iOS builds and 15 end-to-end iPhone journey tests
8. Sentry for error monitoring
9. pstack by @poteto, my main agent uses her orchestrate playbook to manage the other 50 sessions. it never touches code.
10. a custom 5-stage planning harness before any code gets written.
11. customized pr-af and codex for independent code reviews, so that the model that wrote the code never passes it
12. greptile for final code reviews
13. a watcher that tracks every web merge in production for 24 hours, and reverts it on its own if real users start failing
SeYoon Yoon retweeted
Anthropic engineer:
"90% of our engineers were already running self-improving loops
Now everyone is building agentic graphs"
"Prompting is basically over"
In just 10 minutes, she builds her whole Claude Code system live from an empty terminal
Prompts → Agents → Loops → Graphs
skip the loop and the same mistake ships again next week
skip the graph and you're still the one wiring every agent together by hand
prompting was the old workflow, graph engineering is the new one
watch it today
then save the full guide below before everyone catches up ↓
SeYoon Yoon retweeted
Claude Code tip, part 2: before you ship a vibe-coded site, have Opus 5.5 send out 𝟱 𝘀𝘂𝗯𝗮𝗴𝗲𝗻𝘁𝘀 to check 𝘁𝗵𝗲𝘀𝗲 𝟮𝟱 𝘁𝗵𝗶𝗻𝗴𝘀 𝗯𝗲𝗵𝗶𝗻𝗱 𝘁𝗵𝗲 𝗽𝗮𝗴𝗲𝘀, then fix them all in one pass:
Keys and access
→ Keep secret keys like your OpenAI key and Stripe secret key on the server only. Search your frontend files and git history, and replace any that show up
→ Check login and permissions on the server for every endpoint, and validate what users submit there too
→ Turn on RLS or write proper security rules for your database, so users can only read and write their own data
→ Keep the bucket that holds user uploads private, and share files through links that expire
→ Set HttpOnly and Secure on your login cookies
→ Keep passwords, tokens and API keys out of your logs
Don't let anyone run up your bill
→ Set spending caps or alerts on pay-per-use services like AI, SMS and email
→ Rate-limit sign-up, login and verification-code endpoints, and cap AI calls per user per day
→ Limit upload size and file types
→ Add a CAPTCHA to sign-up, and hold free credits until the email is verified
Where the money comes in
→ Verify the signature on every payment webhook
→ If the same payment webhook arrives twice, record it once
→ Only trust amounts your server calculates
→ In test mode, run through a refund, a canceled subscription and a failed charge, and make sure the statuses line up
→ When a trial ends or a subscription lapses, remove access on time
When something breaks
→ Hook up error monitoring so you're the first to know when something fails
→ Back up the database daily, and restore a backup into a fresh database once to test it
→ Get notified when a scheduled job fails, before users notice stale data
→ Add an external uptime monitor that alerts you the moment the site goes down
→ Make sure you can ban an account that's hammering your API with one click from your admin panel
Launch day
→ Turn off debug mode and delete test accounts and test data
→ Switch payments from test to live, and set up the live keys and webhook signing secret again
→ Turn on two-factor auth for Stripe, your hosting platform and your database dashboard
→ Turn on auto-renew for your domain and your HTTPS certificate
→ Run a security check on your dependencies (like npm audit) and upgrade anything with known vulnerabilities
For effort, follow Anthropic's cost guide. The default medium is enough for well-scoped checks like these. The keys and payments groups are expensive to get wrong, so raise those to high. Thariq tested it: turning effort up mostly buys more verification and edge-case testing.
Send this whole post to Claude Code. If you need to, add this line at the end 👇
"Send one subagent per group to check it without editing anything, and note which file or dashboard each issue is in. Run the keys and payments subagents at high effort. When you find a secret, report only where it is and never paste its value. Then fix everything yourself from their lists, so no two agents edit the same file. For things I have to do in a dashboard, like rotating keys, turning on two-factor auth or setting spending caps, give me the steps and I'll do them. For things only I know, like what the caps should be or how many AI calls per day to allow, ask me first and don't make them up. Don't rewrite git history, don't touch the production database, and only test payments in test mode. Show me the list of issues first, don't change anything until I confirm, and when you're done, tell me what you changed and what's left for me to do."
Claude Code tip: before you ship a vibe-coded site, have Opus 5.5 send out 𝟱 𝘀𝘂𝗯𝗮𝗴𝗲𝗻𝘁𝘀 to check 𝘁𝗵𝗲𝘀𝗲 𝟮𝟬 𝘁𝗵𝗶𝗻𝗴𝘀, then fix them all in one pass:
Consistent design
→ Write a DESIGN.md first, and make every color, font size, spacing and corner radius follow it
→ Keep the palette restrained and the type hierarchy clear
→ Make every button, card and input of the same kind look alike
→ Too flat? Add emphasis. Too crowded? Cut
→ Keep text readable on both dark and light backgrounds
On mobile
→ No sideways scrolling, and nothing spilling off the screen
→ Add a mobile menu
→ Make buttons big enough to tap
→ Keep the layout intact when the text is enlarged
Every state
→ Give loading, empty and error states a proper screen
→ Give every button feedback, with its own hover, pressed and disabled look
→ Show form errors right where they happen, plus submitting, success and failure messages
→ Add transitions to modals, dropdowns and tab switches
Use it like a real user
→ Click through the core flows, like sign-up and checkout, from start to finish
→ Find buttons that do nothing and links that are broken
→ Make sure the whole thing works with just a keyboard
Before launch
→ Say what you do in one line on the home page
→ Give each page one main button you want people to click
→ Give every page a title, a description and a favicon
→ Delete all placeholder text
For effort, follow Anthropic's cost guide from yesterday: medium is for well-scoped daily work. The default medium is enough for these 20 fixes; raise it for the harder ones. Thariq tested it: turning effort up mostly buys more verification and edge-case testing.
Send this whole post to Claude Code. If you need to, add this line at the end 👇
"Send one subagent per group to check it without editing anything. Then fix everything yourself from their lists, so no two agents edit the same file. If a group is more complex, raise that subagent's effort. Show me the list of issues first, don't change anything until I confirm, and when you're done, give me before-and-after screenshots."
SeYoon Yoon retweeted
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
Judging by my tl there is a growing gap in understanding of AI capability.
The first issue I think is around recency and tier of use. I think a lot of people tried the free tier of ChatGPT somewhere last year and allowed it to inform their views on AI a little too much. This is a group of reactions laughing at various quirks of the models, hallucinations, etc. Yes I also saw the viral videos of OpenAI's Advanced Voice mode fumbling simple queries like "should I drive or walk to the carwash". The thing is that these free and old/deprecated models don't reflect the capability in the latest round of state of the art agentic models of this year, especially OpenAI Codex and Claude Code.
But that brings me to the second issue. Even if people paid $200/month to use the state of the art models, a lot of the capabilities are relatively "peaky" in highly technical areas. Typical queries around search, writing, advice, etc. are *not* the domain that has made the most noticeable and dramatic strides in capability. Partly, this is due to the technical details of reinforcement learning and its use of verifiable rewards. But partly, it's also because these use cases are not sufficiently prioritized by the companies in their hillclimbing because they don't lead to as much $$$ value. The goldmines are elsewhere, and the focus comes along.
So that brings me to the second group of people, who *both* 1) pay for and use the state of the art frontier agentic models (OpenAI Codex / Claude Code) and 2) do so professionally in technical domains like programming, math and research. This group of people is subject to the highest amount of "AI Psychosis" because the recent improvements in these domains as of this year have been nothing short of staggering. When you hand a computer terminal to one of these models, you can now watch them melt programming problems that you'd normally expect to take days/weeks of work. It's this second group of people that assigns a much greater gravity to the capabilities, their slope, and various cyber-related repercussions.
TLDR the people in these two groups are speaking past each other. It really is simultaneously the case that OpenAI's free and I think slightly orphaned (?) "Advanced Voice Mode" will fumble the dumbest questions in your Instagram's reels and *at the same time*, OpenAI's highest-tier and paid Codex model will go off for 1 hour to coherently restructure an entire code base, or find and exploit vulnerabilities in computer systems. This part really works and has made dramatic strides because 2 properties: 1) these domains offer explicit reward functions that are verifiable meaning they are easily amenable to reinforcement learning training (e.g. unit tests passed yes or no, in contrast to writing, which is much harder to explicitly judge), but also 2) they are a lot more valuable in b2b settings, meaning that the biggest fraction of the team is focused on improving them. So here we are.
SeYoon Yoon retweeted
I'd frame it as follows: The data industry has taken off in recent years, but the quality of our net output is still far too low (amplifying behaviors like reward hacking).
We're working to build scalable methods for creating sample-efficient data for RL. In order to keep this pipeline going, we need to push the frontier of evaluation science, while building specific benchmarks to hillclimb on areas of clear economic value.
I've been advising Mercor on how to build this research direction effectively. These are my views, but I'm confident we're going to see a major investment from economy around the frontier labs (open inference, open post-training, and data) orient around expertise in building real-world representative evals and synthetic data methods to scaling training data around them.
I’m personally very excited about this, it is the research that will make more of the economy “feel the AGI” for the first time.
(And, there’s a big opportunity to build this on open models.)
Mercor is building a world-class research team.
As the leading AI data provider, we are uniquely positioned to combine benchmarks, data production, model training, and economics research to advance model productivity.
We are committed to sharing our findings with the world. DM me if this sounds exciting.
mercor.com/blog/why-mercor-i…
SeYoon Yoon retweeted
the future of enterprise ai is a sovereign ontology, deployed on prem, that grows into the last & final system of record for every company
one final git-backed folder called <my-company.exe>
your entire enterprise as code
SeYoon Yoon retweeted
30 things for Securitymaxxing your vibe coded app:
- Move every API key out of your frontend
- Add .env to .gitignore before your first commit
- Rotate any key that ever touched GitHub
- Turn on Row Level Security for every table
- Never trust the client. Validate on the server
- Check auth on every API route not just the UI
- Check ownership: can user A read user B's data?
- Rate limit login, signup & password reset
- Rate limit your AI endpoints before someone drains your credits
- Set hard spending caps on OpenAI, Anthropic & your cloud
- Use parameterized queries. No string-built SQL
- Sanitize user input before rendering it (XSS)
- Lock CORS to your own domains, never "*"
- Use HTTPS everywhere with HSTS on
- Set secure, httpOnly, sameSite cookies
- Hash passwords with bcrypt or argon2 or use a real auth provider
- Expire sessions and tokens
- Add CSRF protection on state-changing requests
- Validate file uploads by type and size & store them outside your app server
- Make storage buckets private by default
- Verify webhook signatures (Stripe, GitHub, etc.)
- Never return stack traces or raw errors to users
- Strip secrets and PII from your logs
- Run npm audit and update vulnerable packages
- Check that the packages your AI suggested actually exist
- Guard LLM features against prompt injection
- Never let the model run tools or SQL without limits
- Add security headers (CSP, X-Frame-Options)
- Back up your database and test a restore
- Ask your AI to attack your app before someone else does
Shipping fast is a skill.
Shipping safe is what keeps you in business.
Bookmark this before your next deploy + send it to a vibe coder.
SeYoon Yoon retweeted
One of my friend makes $1.2 million a year as an Anthropic engineer.
I asked him how he learned prompting so well. He sent me a video that was never supposed to get out. Their core team's prompting playbook.
You won’t find anything better about prompting than this 30 minutes video.
I watched it last night. Halfway through, I realized I've been using Claude completely wrong for two years.
Watch it, then read the article below.
SeYoon Yoon retweeted
Hiring in London 🇬🇧 for my Applied AI for Startups team at @OpenAI
This role is Codex focussed, combining work with top startups + ecosystem engagement
Team has MLEs, ex-founder/CTOs, research engineers. And it's pretty fun. Apply below ⬇️
SeYoon Yoon retweeted
Google CEO Sundar Pichai:
"If you don't learn how to orchestrate agents now, you'll spend 2027 catching up to people who started today"
In 30 minutes, he explains why the best engineers are moving from writing code to running agent graphs
Researcher → Writer → Tester → Reviewer → Fixer
The human becomes the operator, not the bottleneck
Agents → Orchestration → Graphs → Autonomous Systems
This interview is worth more than most $500 agent engineering courses
Bookmark and watch it today
Then read the full article on graph engineering below ↓
SeYoon Yoon retweeted
This guy literally explained why some people become successful… while others stay average.
And the reason is uncomfortable.
It’s not just hard work.
It’s not luck.
It’s Game Theory.
Once you understand this, you’ll start seeing the “game” behind success differently.
Watch this. 👀
