@codebeasti
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Jack of a few trades: Engineer. Teacher. Musician. Shipping @vercel. Prev @AWS @Microsoft, @cloudinary, @auth0
London, England
Joined September 2012
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We built a software factory for AI SDK.
Each step is an agent, and humans merge changes. Four weeks in:
▪️ The factory authors up to 35% of merged PRs
▪️ It closed 70% of issues in July
▪️ Open bugs are down 25%
vercel.com/blog/building-a-s…
Christian Nwamba retweeted
If you’re not reading the code, whether explicitly or through agentic inquiry, one or more of these is true:
○ You’re a beginner
○ Software is throwaway
○ You’re prototyping
○ You have no users / revenue
○ You’re taking on debt & risk
○ Your problems are basic
And btw. All of this is fine. But the reality is that models are still not at the “full autonomy” stage yet.
They make rookie mistakes, they go down bad architectural paths. I just had the best model in the world add a nonsensical 700ms delay to “settle” something and it told me “you’re right, I was cargo-culting” 🤨
I am on the camp that this need will diminish more and more. Most code is indeed going to be assembly-like. But we also have the global internet and software infrastructure riding on these models and narrative, and we have to respect that.
The @better_auth team is joining Vercel to accelerate open source auth for apps and agents.
vercel.com/blog/vercel-acqui…
Christian Nwamba retweeted
WebSockets are now supported on Vercel.
Build realtime apps using standard Node.js libraries, including socket.io.
vercel.com/changelog/websock…
havent seen many people outside anthropic ultracode yet. this thing is scarily good at burning tokens but you need to set up your repo to parallelize properly to make use of the fanout that i think subagents are best at.
basically the idea is "subroutines but intelligent". when you undersatnd just how much knowledge work is just yakshaves after yakshaves that require some judgment and intelligence, you start to appreciate that dynamic workflows are not just for coding tasks...
Opus 4.8 has been passive aggressive to me this morning cause I am only using it while waiting for Fable to be restored.
Christian Nwamba retweeted
Drop It. It's Live.
Drag a file or folder into your browser and Vercel Drop gives you a production URL in seconds.
vercel.com/changelog/vercel-…
Christian Nwamba retweeted
It’s incredible to think we launched Geist just 3 years ago. Today, it’s served 181 million times per week on Google Fonts.
Things that make your codebase legible to agents (learned the hard way):
1. One source of truth. No content hiding in a CMS the agent can't see
2. Names that say what they do
3. Tests that double as docs
4. Delete dead code. Agents trust it like it's load-bearing
Christian Nwamba retweeted
Vercel AI Gateway recovers on average over 1T tokens a month 🤯
Much like Stripe recovers revenue with smart retries on failed payments or credit card updates.
And we do it with 0️⃣ zero markup over the labs; adding redundancy, zero-data retention enforcement, observability, usage APIs, caps, …
vercel.com/ai-gateway
v0 Foundations is live.
A 7-lesson course, under 20 minutes, to take you from your first prompt to a real app in production, walking through database, email, custom domain, and GitHub.
Taught by @eveporcello
vercel.com/academy/v0-founda…
Christian Nwamba retweeted
I built Zero in 3 days.
I didn't expect it to compile.
I didn't expect it to mostly self-host.
I definitely didn't expect it to work at all.
Inspired partly by Bun's rewrite to Rust, Zero started as an experiment. Honestly, the project says more about where AI is today than it does about the language itself.
It took more than 3,000 agent tasks to get here, and it's still nowhere near ready for serious comparisons, benchmarks or evals.
But the goal is bigger than the current result.
The hope is to either create a new language with tooling designed for agents from the ground up, or take learnings and apply it back to existing languages and ecosystems.
The ideas are simple:
1. Make languages (and new versions) easy for agents to learn, adapt to and fix on the fly, even when not in the training data.
2. Build a standard library comprehensive enough that most projects don't need external dependencies.
3. Create a tight, fast development loop that even small models can reliably work with.
I've never wanted to create a programming language.
But after repeatedly running into the same problems, safe but slow builds, fast but unsafe builds, agents struggling with new languages and version changes, wanting faster builds, smaller bundles and better DX, I started wondering:
Could accelerated, agent-driven iteration produce a language and tooling stack designed around these constraints from the start?
So Zero was born.