Mangrove: Matchmaking for AI Safety

Software to match people with shared AIS interests into teams that complete projects, increasing the odds that those beyond narrow talent pipelines upskill, develop credibility, and turn their ideas into output.

Endorsed by

Marcus+1

Endorsed by

Marcus+1

Project Details

We’re running a pilot of 56 people live at www.mangrove.one and are discovering that providing peer accountability via online matchmaking is effective at motivating people to produce AI safety output rather than just reading about it.

In the next month, we would like to

Scale these findings to onboard a beta cohort of 500 people.
Host hackathons on Mangrove to stress-test Mangrove’s infrastructure, so that Apart Research can feel confident about using Mangrove to facilitate its hackathon matchmaking.
Build out a track-record system that compiles project outputs, attendance records, and peer reviews. This would both improve the quality of Mangrove's matchmaking for repeat participants and create a datasource that employers and grantmakers can use to find high quality candidates to extend opportunities to.

Our team has been self-funded since April and currently includes

Zach Hsu (Stanford, BCG)
Jayani Srinivasan (UC Berkeley, Apart Research hackathons, Blue Dot)
Josh Peng (UBC Alumni, AI Safety Camp, Blue Dot, 2x Mangrove participant)

Theory of Impact

Joining a peer-group to complete AI safety projects increases the odds that newcomers contribute to the field. However it rarely happens. Most people interested in AI safety default to exploring alone, or worse, returning to life as usual. Finding peers with shared interests, availability, and motivation to collaborate for a sustained period of time is difficult.

By removing this friction—via software that matches people into volunteer teams that share interest and availability—people stay motivated to upskill outside of structured fellowships. This enables the talent pipeline to expand beyond the capacity of AI safety's MATSs, Generators, or Horizons.

That's what Mangrove does. Members share their interests, upload their weekly availability, and then post project ideas or apply to existing ones. When enough people commit to an idea and their schedules overlap, Mangrove forms their team and provisions everything they need to begin producing output: a private Slack channel, a Drive, and a scheduling tool that identifies their common availability (no-more endless email threads).

Mangrove also provides incentives to keep them going: peer accountability and reviews, compiled into publishable track records that can help individuals find employment. Since teams collaborate on shared deliverables, skipping meetings means letting real people down, keeping members committed to their journey when life events vie for people’s time.

People

Zach Hsu

Founder

Funding Details

Start Date: Apr 15, 2026
End Date: -
Expected Duration: -
Funding Raised to Date: -
Annual Budget: -
Monthly Burn Rate: -
Current Runway: -
Funding Goal: -
Funding Stage: Seeking first grant
Fiscal Sponsor: -

Grants Received– no grants recorded

Add grant

Funding Asks

grantmaking.ai Launch Round

Applied

Software to match people with shared AIS interests into teams that complete projects, increasing the odds that people beyond scarce talent pipelines upskill, develop credibility, and turn their ideas into output.

Minimum

$17,500

Ideal

$33,500

What this ask is

In the next month, we would like to

Scale these findings to onboard a beta cohort of 500 people.
Host hackathons on Mangrove to stress-test Mangrove’s infrastructure, so that Apart Research can feel confident about using Mangrove to facilitate its hackathon matchmaking.
Build out a track-record system that compiles project outputs, attendance records, and peer reviews. This would both improve the quality of Mangrove's matchmaking for repeat participants and create a datasource that employers and grantmakers can use to decide who to extend opportunities to.

Our team has been self-funded since April and currently includes

Zach Hsu (Stanford, BCG)
Jayani Srinivasan (UC Berkeley, Apart Research hackathons, Blue Dot)
Josh Peng (AI Safety Camp, Blue Dot, 2x Mangrove participant)

Theory of impact

Joining a peer-group to upskill into AI safety increases the odds that newcomers contribute to the field. However it rarely happens. Most people interested in AI safety default to exploring alone, or worse, returning to life as usual. Finding peers with shared interests, availability, and motivation to collaborate for a sustained period of time is difficult.

By removing this friction—via software that matches people into volunteer teams—people gain motivation to upskill outside of structured fellowships. This enables the talent pipeline to expand beyond the capacity of AI safety's MATSs, Generators, or Horizons.

That's what Mangrove does. Members share their interests and upload their weekly availability, then post project ideas or apply to existing ones. When enough people commit to an idea and their schedules overlap, Mangrove forms the team and provisions everything it needs to start: Slack, Drive, and suggested meeting times.

Since people collaborate on one shared deliverable, skipping a meeting means letting real people down. This keeps people committed even when life events vie for people’s time.

Mangrove projects also produce something the field currently lacks: data. Every project generates artifacts, attendance records, and peer reviews. If compiled, this could help fellowships, employers, and grantmakers vet talent beyond their immediate networks.

Furthermore, teams do not have to stop when a project ends. Projects that proves viable can keep growing, staffing more volunteers off Mangrove as the work expands, until they become standing initiatives.

How the money will be spent

Minimum ($17,500):

Hackathon prize pools (1.75k x2): $3,500
Project compute fund: $3,000
Development and infrastructure: $9,500
Beta cohort outreach: $500
Legal: $1,000

Ideal ($35,000), everything above plus:

Third hackathon prize pool: $1,750
Project compute fund, expanded: $3,000
Community manager stipend: $3,000
Track-record system, deeper: completion diagnostic (never-started vs stalled-after-kickoff), full track-record rollup: $4,250
Growth, expanded: $2,000
Infrastructure headroom: $1,000

Discussion

Private comments in this thread are only shown to approved funders and grant reviewers. Email hi@grantmaking.ai or to request access.

Ammara DurraniEndorsement11d

Proud to be an AI Safety researcher working with a Mangrove project team led by Josh Peng on "(Re)designing Middle Powers: Investigating Sovereign AI Initiatives and Socio-economic Realities".

Grant ReviewerPrivate5d?