NightCity Labs Institute program
A three-month cycle for learning, building, and designing how science works in the post-AGI world.
NightCity Labs is an AI-native research institute building the cognitive architecture for scientific discovery: agents, simulations, interfaces, and physical-world deployments that turn research into a scalable software process.
Science is about to change shape. As agents become capable collaborators, the frontier opens into new scientific methods, new interface layers, and new institutional forms. The central question is how research evolves: how ideas are generated, how decisions are made, how experiments are orchestrated, and how institutions learn to think with machine collaborators.
The NightCity Fellowship is for people who want to work inside that transition. It is a place to learn new research rhythms, develop new habits of human-agent collaboration, and help design the practices, interfaces, and environments that post-AGI science will actually run on.
It is a focused collaboration cycle inside an operating institute: shared reviews, working notebooks, simulations, public experiments, and long build loops where fellows contribute to real NightCity work while sharpening their own direction.
Fellows plug into the institute's operating rhythm: structured research reviews, shared analysis sessions, and long-running notebooks that humans and agents update together.
The fellowship is about learning by doing. That means entering an active research environment and moving through short build loops with the institute team.
Work tends to move through cycles like:
The goal is to finish strong work and come away with a deeper feel for how science can be practiced when agents participate in the loop as researchers, operators, and collaborators.
Fellows choose an emphasis and combine paths as the work evolves.
Join an existing NightCity Labs direction and push it forward.
Examples include:
Links: Research / Simulations
Build the tooling that makes the discovery loop run.
Projects here focus on:
Link: Technology
Work on how AI reshapes scientific practice: what changes, what scales, and what becomes possible.
Typical outputs take the form of:
Each cycle usually resolves into two concrete outputs: a draft and a demo. The draft might be a manuscript, technical report, or essay prepared for submission; the demo might be a repo, simulation, interface, benchmark, or deployment slice.
Work is collaborative by default. Co-authorship is standard when a result is shared across the fellow + institute workstream.
The fellowship supports remote collaboration across time zones.
When it helps the work, fellows can also do focused in-person sprints in Lisbon: deep work weeks, build jams, and demo consolidation.
Projects vary in what they can share and when. Release and publication plans are set per project, balancing openness, rigor, and partner constraints.
If you want to join a future cycle, send a signal with:
Ping Papa Legba and include "Fellowship" in the subject.