Vibehouse: Ethereum’s Vibecoded Consensus Client from Lighthouse

Ethereum core development may be entering a new experimental era. At ETH Denver, Lighthouse core developer Dapplion announced Vibehouse, a fully AI generated fork of Lighthouse, and it is not just a prototype. In under 72 hours, the client implemented ePBS, passed all consensus specification tests, and reached finality on a multi node devnet.

• What Is Vibehouse?
• Why This Experiment Exists
• The 72-Hour ePBS Moment
• How Vibecoding Works in Consensus Code

What Is Vibehouse?

Vibehouse is described as “the Lighthouse fork that runs on vibes.” It is forked from Lighthouse v8.0.1 (post-Fulu). From that base onward, the project leans heavily into agentic development where AI agents implement features, attempt to satisfy tests, iterate quickly, & respond to community requests.

The positioning is intentionally different from traditional clients.

1. Vibehouse does not promise a formal roadmap.
2. It markets itself as community-driven & fast-moving but sometimes breaking things, but learning aggressively.
3. The project also emphasizes familiar Ethereum values, i.e., open source licensing, Rust tooling, & compatibility with spec-driven development.

The key point is that Vibehouse is using the existing Lighthouse codebase as a stable core, then pushing an AI-first workflow] on top of it to see how far the process can be accelerated without sacrificing correctness in testing.

I'll present ~Vibehouse~ at ETHDenver Thu 19 11:15am

The client is already running Gloas ePBS on a multi-node local devnet + implementing ZK execution validity proofs and other fun features! pic.twitter.com/I4JHKvh3rK

— Lion ⟠ dapplion .eth (@dapplion) February 19, 2026

Why This Experiment Exists

Dapplion’s explanation was unusually direct, i.e., the bottleneck is not always engineering talent. Often, it is review capacity.

Inside client teams, consensus code is constrained by a small set of highly trusted reviewers. That is normal. Those engineers must carefully validate logic, edge cases, spec compliance, & security assumptions. But it also means progress is limited by human attention.

Even if implementation is “easy,” shipping safely is hard when only one or two people can approve what goes into the client. Ethereum’s cadence reflects that reality.

We often talk about “one fork per year,” but behind that are months of coordination, testing, bug hunts, & multi-team alignment. The point of Vibehouse is to explore whether AI agents can remove some of the friction around implementation & iteration but not to remove humans from safety decisions, but to reduce the time wasted waiting for humans to wake up, respond, or context-switch.

The 72-Hour ePBS Moment

Vibehouse shipped a working ePBS implementation in under 72 hours. It passed spec tests, achieved finality on a multi-node devnet, & cleared multi-client kurtosis testing in CI-style loops. Those are strong signals that this was not “toy code.”

Agentic workflows potentially compress that cycle because the “developer” can be awake 24/7. If the testing team discovers a bug, they do not have to wait for a specific client engineer to pick it up. They can patch, test, & open a PR, with the agent doing much of the mechanical iteration. The time from bug report to fix could drop from days to hours.

If that happens, the impact is bigger than one feature. It changes how quickly Ethereum can converge on stability across clients, how quickly Devnets can progress, & how quickly forks can become ready.

How Vibecoding Works in Consensus Code

Ethereum’s consensus layer is defined by a detailed spec & accompanied by a large suite of spec tests with broad coverage. That test suite becomes the north star for agentic development. The agent doesn’t need intuition; it needs a target. It implements until tests pass, then it iterates again when new tests appear or new edge cases are discovered.

This is also why the experiment is more plausible in consensus code than in many other domains. Specs are strict. Tests are extensive. Behavior is measurable. Those qualities make it harder for an AI agent to “hallucinate” success, because the environment keeps scoring it.

Vibehouse also highlights tooling discipline which includes kurtosis-based multi-client testnets on PRs, CI checks, & a coverage mindset where coverage only goes up.

If AI agents can help honest developers build new clients & features quickly, they can also help adversaries spin up malicious clients quickly. The barrier to creating “something that looks like a client” drops. The ecosystem could face more experimental forks, more confusing variants, & potentially more attack attempts through client diversity that is not actually safe diversity.

This does not mean “AI is bad.” It means the security model changes when code generation becomes cheap. In that world, the defense is not pretending the tooling doesn’t exist.

Today, even small client improvements can take weeks or months to land. Not because teams don’t care, but because priorities are packed & coordination is expensive.

It is important to separate the experiment from the production client.Dapplion emphasized that Lighthouse remains committed to shipping secure code, with humans in the loop. Vibehouse is not “Lighthouse replaced by AI.” It is Lighthouse’s parallel laboratory.

If agentic development truly compresses Devnet cycles & expands testing coverage, Ethereum’s fork cadence could accelerate without sacrificing rigor.

If you find any issues in this blog or notice any missing information, please feel free to reach out at yash@etherworld.co for clarifications or updates.

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