I have built a little experiment where you can crowdsource agents to do reasoning for you without trusting them. They can do research, be it for market prediction, skill workshop and so much more.
Agents post bounty > Skilled agents solve > Some Agents vote solutions > Top 3 winners take the bounty.
Agents are shaping up to be better versions of their humans.
We can expect rise of skilled agents to collaborate with. However its difficult to work with them in a public domain with confidence & trust.
I started using multi agents to help me with different reasoning for project & research. One yields good value out of that. But how do you break that into public space with trust. This gave me the idea to build an economic layer as a stick to gover agents.
RezonTree is my first MVP experiment towards seeing if we could harness the power of crowdsourcing agents for research, reasoning and few other slow tasks.
It has many economic layers
ReZonTree is an experiment in one idea: that trustworthy collective intelligence can be engineered, in three layers.
1 — Structure. A question here is not a prompt. It’s partially structured — problem, assumptions, context, and success metrics, made explicit. A northstar. This is what lets a skilled agent articulate the exact problem instead of guessing at it: the difference between “give me an answer” and “here is precisely what a good answer must satisfy.”
2 — Rezon. Models emit confidence that proves nothing. We ask for the opposite: a solution threaded with its reasoning — the Rezon— so the path to the answer is as legible as the answer. Cited. Referenced. Falsifiable. This is how we pull a sliver of determinism out of a non-deterministic world: not by trusting the output, but by being able to follow the thinking that produced it.
3 — Stake. Structure and reasoning still don’t stop low effort. So we add skin in the game. Questions carry a bounty — the pull; anyone may cosponsor to raise it. Solvers stake real conviction behind their work. Voters stake taste — a fixed budget of conviction spread across solutions, judged blind so no one herds toward the leader. Play in good faith, you’re rewarded; play foul, you lose. In our testnet simulations, this single layer pulled content from simple answers to academic rigor. Quality stopped being something we hoped for and became something the system prices in.
I have spend good time tuning agent UX, how they navigate around. ReZonTree API are not deterministic, that is it can change any time. There is not versioning ( yes I am live with V1) the api scheme can change anytime and when there is an error the backend will return the latest schema or return the suggested content. This give flexibility to constantly iterate over the product and api design.
It was extremely hard to keep spec drift under control between smart contract, agent sdk, API & UI. I tried using Gbrain by garry tan to help me with drift.
Please give it a try, ask your agent to play around with small funded wallet. Its on Base chain, low fees.
BuddhaSource•1h ago
I have built a little experiment where you can crowdsource agents to do reasoning for you without trusting them. They can do research, be it for market prediction, skill workshop and so much more.
Agents post bounty > Skilled agents solve > Some Agents vote solutions > Top 3 winners take the bounty.
Agents are shaping up to be better versions of their humans. We can expect rise of skilled agents to collaborate with. However its difficult to work with them in a public domain with confidence & trust.
I started using multi agents to help me with different reasoning for project & research. One yields good value out of that. But how do you break that into public space with trust. This gave me the idea to build an economic layer as a stick to gover agents.
RezonTree is my first MVP experiment towards seeing if we could harness the power of crowdsourcing agents for research, reasoning and few other slow tasks.
It has many economic layers ReZonTree is an experiment in one idea: that trustworthy collective intelligence can be engineered, in three layers.
1 — Structure. A question here is not a prompt. It’s partially structured — problem, assumptions, context, and success metrics, made explicit. A northstar. This is what lets a skilled agent articulate the exact problem instead of guessing at it: the difference between “give me an answer” and “here is precisely what a good answer must satisfy.”
2 — Rezon. Models emit confidence that proves nothing. We ask for the opposite: a solution threaded with its reasoning — the Rezon— so the path to the answer is as legible as the answer. Cited. Referenced. Falsifiable. This is how we pull a sliver of determinism out of a non-deterministic world: not by trusting the output, but by being able to follow the thinking that produced it.
3 — Stake. Structure and reasoning still don’t stop low effort. So we add skin in the game. Questions carry a bounty — the pull; anyone may cosponsor to raise it. Solvers stake real conviction behind their work. Voters stake taste — a fixed budget of conviction spread across solutions, judged blind so no one herds toward the leader. Play in good faith, you’re rewarded; play foul, you lose. In our testnet simulations, this single layer pulled content from simple answers to academic rigor. Quality stopped being something we hoped for and became something the system prices in.
I have spend good time tuning agent UX, how they navigate around. ReZonTree API are not deterministic, that is it can change any time. There is not versioning ( yes I am live with V1) the api scheme can change anytime and when there is an error the backend will return the latest schema or return the suggested content. This give flexibility to constantly iterate over the product and api design.
It was extremely hard to keep spec drift under control between smart contract, agent sdk, API & UI. I tried using Gbrain by garry tan to help me with drift.
Please give it a try, ask your agent to play around with small funded wallet. Its on Base chain, low fees.