ThomAub

Next question: why are you still using branches? We talked about PRs. I don't think they make sense anymore, if you rethink them from first principles. Yes, if you only use them to trigger CI, then sure, go ahead. That's a fat commit, not a PR. But for mandatory code review? Knowledge sharing? C'mon. But some of you said: "we use PRs because we use branches! I love to keep my stuff on branches." And I'm here to tell you: you don't need branches! You can just push to main. It's called trunk-based development and the idea behind it is true continuous integration: you continuously integrate your changes with the trunk, the main branch. It's been around for a while, it works. Just yesterday I talked with someone who said they've used it in an org with 400 engineers, without any issues. Ruby, the project, uses trunk-based development. The main idea is that you want to avoid long-lived branches. They're bad, because branches can diverge and merging can turn into a thing. And you don't want routine operations to become a thing. Even when I worked in teams that used PRs and branches, I considered it a smell when a branch lived for longer than a day or two. But when I shared this with others, I'd often get something back like this: "Well, good for you, but no. MY thing, what I'm working on, that can't be done on main. It needs to be on a branch! It has a database migration, it changes the underlying shape, the architecture, it refactors this and changes that. It can't go on main yet! I need to merge it all at the end." And then I'd say: "Nah, I can merge nearly anything continuously in small commits to main. You split things up. Add the column first, merge that, deploy it, now it's out, works, safe. Add the helpers first, merge them, get them out. Want to migrate from legacy pattern A to new pattern B? Don't do it all at once. Add the dependency, then migrate A to B, one by one, safe." If it can be merged in a large change, I bet it can be done in small increments that you merge to main. Yes, there are exceptions, but it's 0.5% of cases, not 50%. But there was a real problem with this approach: it was hard! There's a lot of "meta programming" you have to do: make sure this small first change compiles and runs and the tests work, merge it, get it out, monitor it; then do the next small piece, make sure that works and get it out; then maybe push some still-unused dead code, add a feature flag, add a config variable; or keep both configurations working at the same time, etc. That meant sometimes it was just easier to do it in a big fat branch. But big fat branches are risky! A system that continuously flows is safer than a system that starts and stops in spurts. But -- you guessed it -- here's the good news: Now, with agents, all of these chores are no chores at all! Agents are great at adding feature flags, they're great at splitting up code, they're great at keeping track of three changes and merging them one by one and watching them roll out. They're great at keeping two configurations working for a day. They're great at migrating one by one. In other words: it's now 10x easier to just work on main and make sure everything is continuously integrated and working. It's now easy to make sure the system constantly flows. And look, ... I'm not here to convince you to switch to trunk-based development. Just a ruse to get you to read this. (But also, hey: try it! Pushing to main is amazing.) I'm here to make you RETHINK how you develop software. Don't just use agents to replace your typing. Use agents as the magic code-writing machines they are. Everything that was hard about software development because it involved writing code -- even the meta programming -- has become easier. You can now reach for the stars! But you need to look up.

Every company's data is one of a kind. We treat it that way. Here's how: → You pick the scope → We anonymize every name, face and logo → You review a sample first → Raw data are always deleted, by contract

We just raised our ceiling: Ooak Data now licenses a company's data for up to $1M. Your Slack, docs and tickets are worth more than you think. What's yours worth? Get a live estimate in a few clicks: ooakdata.com/partner

we dont do premium economy, only bus

2026 jobs: Pretraining Post training Human as LLM as Judge Kubernetes Rust migrations GH actions sr manager Jev API compatibility migration Security Making your own Slack Sharepoint admin

asked an rl eng candidate what his fav benchmarks were - TBench 4 - TBench 3 - Real SWE

a 50-person company’s slack and email data is selling for $300-400K right now, and the labs are calling brokers on fridays saying they don’t care what it costs. here’s everything from our conversation with data broker ryan lock (sponsored by Ahrefs): ryan sold his solar company ($35M revenue, 1,000 reps) last year and stumbled into data brokering when someone told his co-founder he could license his company data. the first offer gave him two days to accept or it exploded. his co-founder negotiated it up 4x. that gap is the whole business: the labs are desperate, business owners have no idea what they’re holding, and nobody’s advising the sell side. he launched in july, went viral once, and was doing 15 meetings a day with business owners. team of 13 now. every single lab reached out to him inbound through X. the model is simple: buy data assets from companies, package them into training sets for frontier and mid-tier labs. what they want is operational data: email, slack, drive, accounting, shopify, klaviyo. raw text, not photos or video. the part nobody understands: it’s not volume, it’s context. a customer email that gets pinged into slack that gets resolved in a ticket is a complete story a model can learn from. switch from slack to discord two years ago and lose the chain? the data’s worth a fraction. ryan’s own solar data was nearly worthless because everyone just picked up the phone. his co-founder’s remote slack-native team got the full ask. the numbers: average deal is $300-400K for a 50-100 person company, deals go up to $2-4M for the big ones. quick valuation proxies are number of software tools, years of full-context data, and headcount, because a hundred people generate idea-discussion-approval-feedback loops that five people in one office never write down. the de-identification alone costs $10-50K per terabyte, done by a third party who never lets ryan touch the raw files. and don’t try to scrub it yourself: tony becomes “employee six” everywhere consistently, or the whole set is destroyed. ryan’s analogy for margins: raw data is oil at $100 a barrel, the labs turn it into jet fuel at $2,000, and the broker takes the risk that the barrel has no engine inside. the spirit airlines bankruptcy sold its data to google for $10M, which he thinks is low for the largest public deal. the data companies are all talked about as very profitable, but the real edge is procurement on demand: he forecasts what the labs will want next and ranks his pipeline by acquisition priority. current requests: circuit board CAD files, architecture firms, heavy construction, and video game 3D development. the gotchas before you sell: never sign a perpetual exclusivity. perpetual use is standard (nobody can track files down the chain), but exclusivity should be termed, usually two years, or you’re locked out of a future revenue stream forever. and if you’re selling your business in the next year or two, a $500K data deal can become a due diligence landmine on a $20M exit. tom’s referrals keep saying exactly that. micro one pays $50-75K referral fees because they’re playing a different game. ryan pays 6% and tells brokers to charge the seller for running the process instead. where it goes next: ryan thinks the buyer pool expands past the frontiers to enterprises training their own models. a 20-person HVAC company only knows what it knows, but add 150 years of industry data and you’ve got a vertical model, the harvey-for-law playbook in every trade. the video side is wilder: labs pay $10-20 an hour for people to wear headcams doing manual work, and there are state-sponsored warehouses in china and every low-cost country where workers rotate through sewing and dishwashing stations for robot training. jacky’s fraud prediction: 500 fake chinese companies with active linkedin pages. ryan’s answer: if the simulation is that good, the data’s probably valuable anyway. bonus: jacky’s own slack community, 500 people and 20K messages a month, has already gotten offers. “no amount of money, well, that’s a lie, I’d take my pants off for seven figures.” the over/under for ryan’s next 12 months was set at $300M gross, and micro one just passed a billion annualized. tom asked to become a master affiliate and jacky immediately called it master and slave. tony’s response: “the white guy’s still on top. we went back a hundred years.” the industry is a gold rush and the shovels are already being sold. NGMI with @itstonyyu and @tomwang24, guest @RyanLockAI. watch/listen ↓

Companies are sitting on years of operational data. AI labs need exactly that to keep improving. At @Ooakdata we turn your workflows into a new revenue line for your business: earn up to $1M while keeping your IP. How it works: 1- Get a live estimate of what your data is worth on ooakdata.com/partner 2- Fill a short form about your company 3- Our team reviews your request and gets back to you in 24h.

If you want in on the data broker thing, this is literally all it is > sign up as a micro1 referral partner and a Polyshares partner so you have your own tracked links > find a business with 30+ employees and years of records sitting in their systems > check the Wayback Machine for 3+ years of company history before you email anyone > send the owner your link > let the buyer evaluate the company and price the deal > get paid up to $50K per completed micro1 referral, or a share of deal value on Polyshares We submitted 46 data deals in 3 weeks doing exactly this and 13 are moving to next steps The article has the whole process, the email I send owners and the earnings math, so read it next if you want to capitalize on this. Links in the comments

Three months, one batch, a lot of late nights in SF. We came to @ycombinator with @ooakdata to bring company workflows to AI training. Turns out a lot of companies are ready for it. Now the real work starts, and we're hiring in SF and Paris. 👇 link in comments