Curious by design | Shipping & learning in public
Denver, CO
Joined November 2020
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What is the local-business equivalent of Railway’s one-click deployment? 👇
Someone asks AI who can solve their problem. It finds a suitable business. Then what?
If the next step is “call during business hours, leave a voicemail and wait,” there’s a gap between getting recommended and getting hired.
Imagine the next step instead:
→ Check whether your address is in the service area
→ Upload photos of the problem
→ See the minimum charge and what affects pricing
→ Request an appropriate appointment
→ Get a clear confirmation of what happens next
That gives a local business two things to work on: becoming easy to recommend, and becoming easy to hire.
The raw material for the first part already exists in everyday work: customer questions, estimates, job photos, explanations of trade-offs and genuine reviews. The challenge is getting that knowledge out of the owner’s head and into useful public information.
The second part requires an intake process that can turn interest into an actual job.
My hypothesis: for many local businesses, improving that handoff could be more valuable than publishing another 50 pages. – at Denver, CO
Railway hacked AI SEO, and it took them from $20M to ~$100M ARR in 1 year:
1/ Claude Code picks Railway for 82% of Python apps
-> Ask it "how do I deploy this" and it installs Railway and puts your app live there
2/ The founder treats AI recommendations as a growth channel
-> His go-to-market team works on getting the models to "recommend us more and more"
3/ 6,653 how-to pages, one for every popular free tool people want to run themselves
-> Someone asks Google or ChatGPT "how do I set up n8n?" and Railway's page comes up. It explains the tool and sets it up on Railway in one click
4/ A step-by-step guide for every framework developers build with
-> 127 guides: Next.js, Django, Rails, FastAPI, Laravel, Go and more. Each one answers "how do I deploy X" and ends with the app live on Railway
5/ A comparison page against every competitor
-> Heroku, Vercel, Render, Replit, Lovable and 5 more, each with a migration guide. AI loves quoting comparisons
6/ Railway rewrote its help pages so AI can read them easily
-> One file lists all 414 pages with a short summary each, and every page has a plain-text version. When someone asks ChatGPT how to use Railway, it finds the right answer fast and gets it right
7/ A docs section written for agents
-> One step installs everything Claude Code or Cursor needs to deploy to Railway on its own
8/ Plugins inside ChatGPT and Claude Code
-> You ask the chat to deploy and it deploys, without ever opening Railway's website
9/ A server with one command and no signup
-> ssh railway new gives an agent the shortest path to "your app is live"
10/ A new changelog page every Friday since 2021
-> 312 pages of fresh content that keep Railway in front of Google and the models
11/ Every new AI model brings a new jump in signups
-> More code gets written, all of it needs to run somewhere, and this year the summer slowdown disappeared
12/ A go-to-market team of 7 runs all of it
-> The pages, docs and plugins keep selling while they sleep
13/ The one hole: front-end apps
-> Claude Code picks Vercel 100% of the time for Next.js and React apps. Railway wins Python, Vercel wins everything that runs in a browser
Full guide on how to win AI search in the first comment
👀 CurveBond
Solana launchpad. Starts coins at ~$20k FDV, graduates at ~$50k with roughly double the usual LP. Fees fund $CURVE buybacks and burns.
Why it exists:
- Most pads start tiny so bundlers can grab supply cheap and pads can farm pre-bond fees.
- Curve prices out cheap bundles: 20% at launch costs $4,500+.
How it makes money:
- 1% on bonding-curve trades.
- 5% of the creator’s tax.
- 75% of graduated-pool fees locked to the treasury forever.
- 50% of protocol revenue buys and burns CURVE.
Early numbers (founder-claimed):
- ~$87k volume day 1, ~$1m by day 3.
- Live since ~Sept 25.
Main risk:
- Traders may reject the $20k start, and fee accrual only matters if coins actually bond and keep trading. – at Denver, CO
"You type 'modern landing page for my app, make it pop', get something generic, and then burn twenty generations nudging it toward what you actually wanted. Each nudge drifts the design a little further from the last one, and by generation fifteen your buttons have three different corner radii." – at Denver, CO
J 👋 retweeted
There is an ENDLESS supply of intelligent people on the internet explaining why every opportunity is dead, every successful person got lucky, and everyone trying something new is too late, wrong or dumb.
Don't listen.
You deserve to share your ideas with the world.
So it appears the mobile app I planned for @RevenueCat #shipaton was built on a false understanding of the YouTube API agreement. May be able to release it for free but damn, I am gutted. – at Denver, CO
J 👋 retweeted
NEW: SEC staff clarifies when live tokens, buybacks, and staking receipts are not securities.
Absolutely huge for onchain finance.
Rumor mill is saying the SEC is gearing up to revamp KYC requirements so users only need to KYC once to access tokenized securities onchain.
Our sources are saying this would enable composability across different onchain venues & tokenization platforms to let users clear more volume by splitting orders across venues using a zk based identity system.
J 👋 retweeted
INTEL: Apple and Google are both hiring stablecoin experts as Big Tech moves toward crypto payment
Another day, another gizmo a.k.a. AI mini experience. @Meta
madewithpocket.com/p/GTVVqDS… – at Centennial, CO
J 👋 retweeted
Was really interesting to hear John, Beren, and Charlie speculate about why Sonnet 5 and Opus 5 feel like worse models than GLM 5.3
(despite the fact that Anthropic can do raw logit distillation from Fable, and can also train Sonnet/Opus on the environments from which Fable was trained).
Led to some interesting thoughts about value of distillation, what it takes to do distillation effectively, and what kinds of model behaviors are hard to extract from distillation.
6/ Once I understood that, the opportunity looked different.
The question isn't where to add a chatbot. It's where people are stuck carrying information between tools, repeating context and working out what happens next.
Those half-manual processes are everywhere. – at Denver, CO
4/ That's where this becomes meaningful for a vibe coder.
An AI could build that infrastructure for me before, but it was still custom code to design and maintain.
Using a managed foundation could mean fewer development steps, fewer fragile pieces and less invisible work. – at Denver, CO
1/ When OpenAI announced the Agents API, I found myself asking a fairly basic question:
I already vibe-code inside a Work project where an agent edits files, runs tests and keeps the project moving. Isn't that what this is?
The answer took a couple of layers to unpack. 🧵 – at Denver, CO
OpenAI just made it stupidly easy to build real AI agents.
The new Agents API basically gives developers the Codex harness as an API:
• One call → cloud agent
• Long-running sessions
• Automatic context compaction
• Parallel subagents
• MCP + built-in tools
• OpenAI-hosted sandboxes
• Or run on your own infra
This is way more than "call a model and hope."
You give it a task, model, tools + environment.
It can work across files, run code, use tools, delegate work to subagents and keep going across multiple context windows.
Public beta is live for all developers.
No extra Agents API fee - you pay for the models + tools your agent uses.
This is basically Codex infrastructure for anyone building agents.
