I refuse to believe that unlimited spend on something that cannot be measured, and multiplies potential negative productivity, is not in the best interest of the company
This is the valley after all, LoC IS THE ONLY MEANING TO LIFE
NEW: Elon Musk wants a SpaceX IPO valuing the company at upwards of $1.75 trillion.
To get there he got the rules changed so that index funds, with millions of Americans' retirement savings, are forced to buy in.
Retirees could take huge losses, while insiders cash out.
Why is the creator of OpenCode pretty skeptical about AI productivity gains, and the hype around AI? A very conversation @thdxr (and lots of truth bombs:)
Timestamps:
00:00 Intro
07:03 Dax’s path into tech
09:04 Early startup experience
13:16 Getting involved with open source
16:13 OpenCode
23:17 Anthropic banning OpenCode
30:34 From terminal to GUI
32:34 OpenCode’s business model
36:33 Why inference is profitable
39:11 GPU bottlenecks
40:54 AI hype
45:50 AI spending
48:47 Dax’s memo
55:41 Dax’s skepticism of predictions
58:58 Engineering culture at OpenCode
1:02:38 How building works at OpenCode
1:05:36 Taste and quality
1:11:32 Dax’s work setup
1:12:35 The role of engineers and EMs
1:15:50 Advice for engineers
1:18:12 Book recommendation
Brought to you by:
• @AntithesisHQ – verify your system’s correctness without human review or traditional integration tests – and avoid bugs or outages https://t.co/AKYm4cbVCU
• @WorkOS – everything you need to make your app enterprise ready https://t.co/aiAee0oF5h
• @turbopuffer – a vector and full-text search engine built on object storage. It’s fast, cheap, and extremely scalable https://t.co/w9y67Gs8ab
Three interesting thoughts from Dax:
1. No AI-native coding agent company is “winning” by being better with AI.
Dax says that none of OpenCode’s competitors are crushing them, and that nobody is using AI so well that others cannot compete.
2. Most software engineers profit from AI as time gained, not increased output — unless you change incentives!
Dax says the natural way for software engineers to “cash out” their AI tooling gains is with time savings, by doing the same work as before, but faster. Until compensation and motivation structures change, most teams should expect output to stay flat while engineers go home earlier. There’s nothing wrong with this, but AI vendors sell a different outcome to CFOs: increased output.
3. AI code generation mutes the “guilt” of doing the wrong thing, but this builds up tech debt.
Pre-AI, writing a hack felt bad, the second time it felt really bad, and by the third time you’d often just refactor in order to fix up the code. Now, the agent hides the hack, which skews devs’ judgment and results in less tech debt being cleaned up.
Built a tiny GitHub Action so you can load @Proton_Pass secrets directly into GitHub Actions 👀
No more manually copying secrets into GitHub.
DATABASE_URL: "pass://Production/Database/connection_string"
Basically a Proton Pass version of 1Password’s load-secrets-action 🔐
Repo:
https://t.co/hdxL4epidq
🛠️ Maybe you've heard about WebMCP, but if you'd like to see it in action and learn how to work with it? I made a small site to demo how it works, how agents use it, what the spec looks like, and some important links.
It's interactive! Use the extension to actuate the page:
Or maybe we could use some of that wealth to fund universal health care, then use AI to make the system more efficient for everyone.
Also, how does that argument work when many of these companies are subsidized by taxpayer dollars?
Jeff Bezos: "If I do my job right, the value to society and civilization from my for-profit companies will be much, much larger than the good that I do with my charitable giving."
Agentic coding is great until you realize you became the project manager for a very confident intern who works at light speed, forgets context, and charges by the token.
Use AI like a power tool, not like a replacement brain.
https://t.co/AhK3Ic2EN4