Who needs it first
Requests show which aI safety labs, eval teams, and funders need the first working loop.
Improves: A tighter request path for AI safety eval benchmark workbench, with the next owner and outcome visible.
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AI safety
Organizes benchmarks, model runs, findings, and funder-ready evidence packets.
In build
App status
In build
Access
AI safety
App field
What makes it useful
The app is useful when the people, evidence, action, and follow-up are clear enough for someone to move without a private explanation from the Rhiz team.
Useful inputs
The people this should help first
The outcome that would make the app worth using
Current blockers, manual work, or missed follow-through
Documents, receipts, claims, source links, and open questions
App flow
The app starts with a concrete need, turns it into a usable next move, and keeps the useful result close enough to reuse.
Define benchmark
Run eval
Review failures
Attach provenance
Escalate risk
What gets better
Who needs it first
Requests show which aI safety labs, eval teams, and funders need the first working loop.
Improves: A tighter request path for AI safety eval benchmark workbench, with the next owner and outcome visible.
Where people get stuck
Completed and blocked actions show which step is confusing, slow, or missing proof.
Improves: One clearer checklist item, prompt, handoff, or follow-up reminder inside the app.
What becomes reusable
Impact proof patterns strengthen Proof ledger.
Improves: A marketplace primitive that can improve related apps instead of staying trapped in one request.
How Rhiz helps
Keep shipped work, evidence, receipts, outcomes, claims, source links, confidence, and change history together.
Run benchmarks, rubrics, scorecards, model tests, readiness checks, and reviewer decisions with provenance.
Build faster
Rhiz starts from proven open-source patterns where they fit, then wires the trust, access, and follow-through layer around the people using the app.
Evaluation workbench
Standard language-model benchmark task execution.
integrateEvaluation workbench
LLM app evaluation, red-team tests, and comparison workflows.
integrateEvaluation workbench
Large language model evaluation tasks, scorers, solvers, and logs.
integrateProof ledger
Append-only, tamper-evident ledger patterns for receipts and claims.
studyProof ledger
Document signing, audit trails, and receipt-style artifact flows.
studyCompounds with
Related apps share primitives, outcomes, or operating lanes, so useful signal from one request can strengthen the next surface instead of disappearing into a separate backlog.
Proof ledger + Evaluation workbench
Shares Proof ledger and Evaluation workbench.
In buildProof ledger + Evaluation workbench
Shares Proof ledger and Evaluation workbench.
In buildProof ledger + Evaluation workbench
Shares Proof ledger and Evaluation workbench.
In buildEvaluation workbench + Proof ledger
Shares Evaluation workbench and Proof ledger.
In build