SN 5. Rank 28 of 32 by emission
Hone
Validators lease a public coding challenge, rotation-deal it to half of the serving miners, commit the signed responses, reveal the hidden tests, grade every response in a local Docker sandbox, and submit the resulting weights on chain.
In their own words · raw.githubusercontent.comSN5 Hone is a programming exam with sealed answers, where an exam board mails out a coding problem, the examiners lock in every candidate's answer before the hidden test cases are revealed, and full marks are the only marks.
The commodity, explained from zero
What the subnet produces is a record of coding problems, the programs written to solve them, and a verified pass or fail for each. The README says it holds "the validator for RLVR on Bittensor Finney, NETUID 5" and leaves RLVR unexpanded. Problems arrive from "a private problem server"; the README says plainly that "Problem construction is not part of this repository". The design document mentions "exported dataset labels", which is the nearest the materials come to naming an output (inferred from docs/DESIGN.md).
Why such a record would have a price, and who would pay, is not stated. There is no product page, no price, no customer and no contact address. The chain description is the two-word tag "Hone training". Here the exam comparison breaks: a real exam board exists to certify students for someone, while the materials do not say who reads Hone's graded papers or what they are for.
Why it is on Bittensor at all
The materials do not say what decentralization changes here; on that question the honest answer is nothing yet, or nothing stated. What the design document does describe is a trust model: each validator "accepts no remote grading claim" and runs every submitted program in its own sandbox, so neither the problem server nor any miner can set a score.
How the work gets done
A validator, the examiner, leases a public coding problem from the problem server and sends it, signed, to half of the miners currently serving. Miners, the candidates, return a Python or Rust program; the included demo miner simply forwards the problem to a hosted GLM-5.2 model. The validator commits the exact set of signed answers to the server, and only then receives the hidden test cases, so no test can leak into a prompt. It runs every answer in a locked-down container with no network access, and a program that passes the whole hidden suite scores between 0.95 and 1.0 with speed as the tiebreaker; anything else scores zero. Each miner's last 200 results feed the weights that split emission, the subnet's share of newly minted TAO, and the owner's cut is set to 0%.
How you would know it works
The only artifact is the dashboard at honedashboard.com, titled "Hone Network · Miner Performance", which promises median accuracy, response rate and latency per validator. To a fetcher it reads "Waiting for aggregate data" and "0 registrations"; a reader needs a browser. There is no dated benchmark, sample output or usage figure.
What is missing
There is no miner guide, only a demo that calls a paid third-party model and defaults to the test network, plus five sample challenges. No hardware or competitiveness statement exists for a miner that wants to win; the registration cost is not stated (about 0.14 TAO on chain on 2026-09-09, with 243 active miners). The chain contact and discord fields are empty. The README calls the project "RLVR subnet" while the chain and dashboard say Hone. There is no plain-language paragraph anywhere, and the design document marks copy-resistant scoring as a TODO.
Go deeper
Sources for this explainer
- https://honedashboard.com
- https://raw.githubusercontent.com/hone-subnet-org/hone-subnet/main/README.md
- https://raw.githubusercontent.com/hone-subnet-org/hone-subnet/main/docs/DESIGN.md
- https://raw.githubusercontent.com/hone-subnet-org/hone-subnet/main/docs/DEMO_MINER.md
- https://raw.githubusercontent.com/hone-subnet-org/hone-subnet/main/examples/sample_challen
Metaphor: a programming exam with sealed answers. Every claim is drawn from the evidence set or the subnet's own materials; "(inferred)" marks a conclusion rather than a quote. Corrections.
How we scored it
Four audiences, four questions each, scored on what a first-time reader can find in five minutes. Method in the rubric.
- subnet_name
- Hone
- github_repo
- github.com
- subnet_contact
- not set
- subnet_url
- honedashboard.com
- discord
- not set
- description
- Hone training
- additional
- not set
Stakers and validators●●●●● 1.8
Should I allocate here? · rank 32 of 32 for this audience
Q1What it is●●●●● 2
This repository contains the validator for RLVR on Bittensor Finney, NETUID 5.
output is graded coding solutions, inferable from the protocol diagram, no unit or buyer, chain says Hone training
Q2Who it is for●●●●● 0
no users, customers, revenue or usage anywhere in own materials, the dashboard shows miner accuracy only and renders Waiting for aggregate data to a fetcher, third-party pages describe an older Hone
Q3How it resists gaming or fails●●●●● 3
commit-before-reveal, sandboxed grading, zero for invalid or timed-out responses, hotkey-keyed history, a private problem server as a dependency, duplication-resistant scoring marked TODO
Q4Identity and documentation●●●●● 2
identity 4 of 7 (contact, discord, additional not set), github and url resolve, the README calls the project RLVR subnet while the chain and dashboard say Hone, no staker page, no about page
Miners●●●●● 2.5
Can I compete, and what wins? · rank 29 of 32 for this audience
Q1What it is●●●●● 3
accepts signed POST /solve requests from authorized validators
coding task in, a Python or Rust solution out, five sample challenges in examples, no miner repo, demo miner calls a hosted GLM-5.2
Q2Who it is for●●●●● 2
a Z.ai API key with access to glm-5.2
and a public port are the demo requirements, no hardware statement for a competitive miner, registration cost not stated (chain burn 0.14 TAO), 243 miners
Q3How it resists gaming or fails●●●●● 3
Reward and weights: a fully correct submission scores 0.95 to 1.0 with speed as tiebreaker, anything else zero, rolling window of 200 observations, history cleared on hotkey change, no immunity
Q4Identity and documentation●●●●● 2
no miner guide beyond the GLM demo miner and sample challenges, the README is a validator runbook, repo pushed 2026-09-05, no releases, the demo miner defaults to the test network
Buyers and enterprises●●●●● 0.3
Can I use this today? · rank 32 of 32 for this audience
Q1What it is●●●●● 1
nothing to buy or call, the README says problem construction is not part of this repository and names a private problem server, dataset labels mentioned in passing, no product statement, no date
Q2Who it is for●●●●● 0
no pricing and no customer type anywhere
Q3How it resists gaming or fails●●●●● 0
no status page, SLA, support address or support channel, discord and contact not set on chain
Q4Identity and documentation●●●●● 0
no API docs, no contact on the site or in the repo, subnet_contact not set
Newcomers●●●●● 1.5
What is this and why does it matter? · rank 32 of 32 for this audience
Q1What it is●●●●● 2
Validators lease a public coding challenge, rotation-deal it to half of the serving miners
technical README opening, chain description Hone training is a two word tag, no plain paragraph anywhere
Q2Who it is for●●●●● 0
no statement of what decentralization changes in own materials, third-party explainers describe an older pretraining design and were not fetched
Q3How it resists gaming or fails●●●●● 2
live dashboard of miner accuracy and latency exists but renders Loading and Waiting for aggregate data to a fetcher, no dated benchmark or sample output
Q4Identity and documentation●●●●● 2
subnet_name Hone matches the dashboard title Hone Network, description Hone training is not a sentence, url resolves, no about or learn page, the README names the project RLVR subnet
How this score came to be. Verified means the scorer fetched the page and the words are on it; inferred means concluded from code, absence, or a third party. The link badge is a separate automated check made before publication.
Something wrong? Corrections of fact are applied as they arrive during the window; score disputes are batched at its close. How to file one · GitHub issue · email.