Ridges AI (SN62)

Overview

Ridges AI (ticker: SN62) is a decentralized network of AI software engineering agents built as Subnet 62 on Bittensor. The project’s goal is simple to state and ambitious to deliver: let autonomous agents take a software problem end‑to‑end—reading an issue, editing code, running tests, and proposing a fix—then reward the best agents on-chain. SN62 is the subnet’s native “alpha” token. It powers validator staking within the subnet, directs emissions to high‑performing participants, and serves as the economic signal for how much the market values the subnet’s work. Ridges operates its own agent platform and evaluation pipeline, while inheriting Bittensor’s on‑chain economics and staking model. (docs.ridges.ai)

What makes Ridges AI distinct

• Purpose‑built for coding tasks: the subnet focuses on real software engineering problems rather than generic chat prompts. Agents are evaluated on standardized coding benchmarks. (docs.ridges.ai)
• Open competition for quality: miners submit agent code, validators evaluate it, and the system routes emissions toward top results. (docs.ridges.ai)
• Native token on Bittensor: SN62 follows the alpha‑token design used by all Bittensor subnets, with a capped supply and emissions tied to validator and miner performance. (docs.bittensor.com)

History & Team

Ridges AI emerged in 2025 within the Bittensor ecosystem, evolving from earlier agent experiments and re‑focusing on end‑to‑end software engineering. The subnet is led by Shakeel (“Shak”) Hussein, who has publicly represented SN62 in community discussions and interviews about building fully autonomous “AI software engineers.” (x.com)

External coverage and investor disclosures help fill in details about the project’s early story. DSV Fund, a Bittensor‑focused hedge fund, announced a combined $300,000 commitment to Ridges AI (an OTC allocation plus on‑market purchases) and described Hussein’s background and the project’s thesis of open‑sourcing agent code to accelerate iteration. While Ridges has not published a full investor list, the DSV note is a concrete, on‑record example of outside capital taking a position in SN62’s ecosystem. (dsvfund.com)

The broader community has also heard the team’s plans through long‑form conversations. Interviews and recaps have highlighted a path from prototyping agents to packaging them into practical tools and interfaces that could run locally or behind enterprise firewalls. These discussions reinforce the project’s “thick agent layer” view: orchestrate many specialized steps, not just a single model call. (subnetalpha.ai)

Technology & How It Works

High‑level architecture

Ridges describes its network as a distributed evaluation platform for code‑solving agents. Five core components work together: a central platform (for submissions and orchestration), a proxy (for secure inference/embeddings), screener nodes (quick quality filters), validators (full evaluations), and miners (agent developers). The network runs agents in isolated containers, scores them with standardized tests, and uses those scores to drive on‑chain weights and rewards. (docs.ridges.ai)

• Platform: coordinates agent submissions, versioning, and validator queues.
• Proxy: gateways to model inference and embeddings with cost control and request validation.
• Screeners: light‑weight filters that keep obviously low‑quality agents from consuming validator resources.
• Validators: independent nodes that run full evaluations in sandboxed environments.
• Miners: agent authors competing to post the top‑performing open‑source agent. (docs.ridges.ai)

Agent submission and sandboxing

A miner publishes agent code that implements a simple Python entrypoint. When a submission arrives, validators run the agent inside a sandbox with strict controls: no arbitrary network access, limited libraries, timeouts, and only two external endpoints exposed via the Ridges proxy—one for model inference and one for embeddings. The goal is to make evaluations reproducible, secure, and comparable across many validator machines. (docs.ridges.ai)

Benchmarks and scoring

Ridges evaluates agents on SWE‑bench, a widely used suite of real‑world GitHub issues. Each time an agent update is submitted, validators test the agent on a set of problems and compute a score based on whether the generated patch passes the target project’s tests. This standardized approach makes it easier to compare agents and reduce subjective judgments. (docs.ridges.ai)

A competitive, open‑source incentive

Ridges’ incentive design is intentionally sharp‑edged: the top agent earns the miner’s reward until a better one appears. To avoid “copy‑and‑nudge” takeover with trivial edits, a new challenger must clear a defined improvement threshold before dethroning the leader. This winner‑takes‑all incentive is meant to push rapid innovation while keeping scoring simple and verifiable. (docs.ridges.ai)

Live network telemetry

Ridges operates a public dashboard showing agent queues, connected validators, and evaluation states in near real time. While the exact numbers change throughout the day, the interface gives a sense of network activity—from screeners to validator runs—so developers can track progress and iterate quickly. (ridges.ai)

Tokenomics & Utility

SN62 as a Bittensor “alpha” token

Every Bittensor subnet issues its own alpha token with a hard cap of 21 million units, mirroring TAO’s capped supply. SN62 is the alpha token for Subnet 62 (Ridges). Alpha tokens are emitted on a block schedule and distributed among subnet participants. (docs.bittensor.com)

Emissions on Bittensor follow a standardized split across roles: 41% to miners, 41% to validators and their stakers, and 18% to the subnet owner. Within that split, the exact share each miner or validator receives depends on performance scores (via Yuma Consensus) and stake weights. Ridges adopts this framework and then layers its own winner‑takes‑all miner logic on top, so the single best agent at any time captures the miner portion of emissions until surpassed. (docs.bittensor.com)

Pricing, staking, and redemption

Alpha tokens use a built‑in market function on Bittensor. Each subnet maintains a two‑asset reserve (TAO and its alpha), and the alpha price is the ratio of TAO in reserve to alpha in reserve. When a user stakes into a validator on a mining subnet, they effectively swap TAO for alpha; when they exit, they swap alpha back to TAO. This is implemented as a constant‑product AMM at the protocol level, so large trades may experience slippage. (docs.bittensor.com)

In practice, SN62’s main utility is economic: it represents stake within Ridges’ subnet. Holding and staking SN62 to validators affects validator weight and therefore how emissions flow. The token does not grant ownership of Ridges’ codebase; rather, it is the instrument for allocating rewards and signaling demand for this specific class of AI work inside Bittensor. The subnet can also configure whether alpha transfers are enabled between wallets, an option exposed through Bittensor’s “TransferToggle” parameter. (docs.bittensor.com)

Ecosystem & Use Cases

What the agents do

Ridges agents are designed for software engineering tasks that benefit from structure and repeatability. Typical workflows include:

• Reading an issue or failing test, locating relevant files, and proposing a diff.
• Writing or updating unit tests, fixing CI regressions, and patching edge cases.
• Producing clean code changes that run under project tests and can be reviewed as patches or pull requests. (docs.ridges.ai)

This focus aligns the subnet’s incentives with measurable outputs: either the patch fixes the problem under test, or it does not. Over time, the system pushes miners to harden their agent frameworks, improve tool use, and refine search strategies across large codebases. (docs.ridges.ai)

From research to products

Public interviews with the team sketch a path from pure benchmarking to revenue‑bearing tools—packaged agents, an IDE‑like interface, and self‑hosted options for enterprises that need privacy. The idea is to take the same agentic core that wins in open competitions and expose it through APIs and interfaces businesses can use. (subnetalpha.ai)

Advantages & Challenges

Advantages

• Measurable progress: Using a standard benchmark for coding tasks keeps the target clear and reduces hype‑driven scoring. Users can reason about agent quality from reproducible tests. (docs.ridges.ai)
• Open code, faster iteration: Requiring open‑sourced miner agents makes it easier for the whole field to improve quickly—ideas can be verified, copied, and pushed forward. (docs.ridges.ai)
• Strong incentive alignment: The emission split and constant‑product market give a transparent path from demand (staking) to rewards (emissions), tying the token economy to useful work. (docs.bittensor.com)

Challenges

• Winner‑takes‑all dynamics: Concentrating miner rewards on a single top agent may discourage smaller teams and can create rapid turnover as leaders leapfrog one another. Ridges tries to mitigate trivial “copy wins” with improvement thresholds, but the trade‑off remains. (docs.ridges.ai)
• Sandbox constraints vs. real‑world complexity: The secure, no‑network sandbox is great for fairness, but some real projects need external context or APIs. Designing agents that thrive under limits while still generalizing is non‑trivial. (docs.ridges.ai)
• Onboarding to Bittensor: Acquiring alpha tokens, setting up coldkeys/hotkeys, and understanding validator staking is different from typical L1/L2 flows and has a learning curve. (docs.bittensor.com)

Where to Buy & Wallets

Ridges AI can be purchased on Subnet Tokens, where SN62 trades against TAO as a Bittensor subnet pair. SN62 is available through Bittensor staking interfaces as well: stake TAO into Subnet 62 and receive SN62 in return, then stake that SN62 to a validator within the subnet. Subnet front‑ends and explorers provide UIs that abstract the protocol’s swap‑like staking, but under the hood you are exchanging TAO for the subnet’s alpha token and back. (geckoterminal.com)

For storage and signing, use the official Bittensor wallet options:

• Bittensor wallet browser extension or mobile app for managing TAO balances and staking.
• btcli (command‑line) and the Python SDK for advanced users, including hotkey management.
• Support for hardware wallets is available through the browser extension setup.

Bittensor wallets use a coldkey/hotkey model. The coldkey is your main wallet (for holding balances and governance). The hotkey is used for operational roles like mining or validating inside a subnet. If you are only holding or staking, you may not need a hotkey at all. (docs.bittensor.com)

Regulatory & Compliance

SN62 is an “alpha” token issued by a Bittensor subnet and inherits the network’s on‑chain economics. It functions as a staking and reward instrument tied to Subnet 62’s agent work rather than equity in a company. That said, there is no public indication that Ridges AI or SN62 has received formal regulatory approvals or classifications in major jurisdictions. In the United States, token projects are assessed under existing laws and case‑by‑case facts; alpha tokens like SN62 sit within a newer category where utility, staking mechanics, and governance often shape how a regulator views them. The Bittensor documentation sets the technical and economic context—fixed supply, protocol‑level AMM, and an emissions‑driven incentive split—but it does not serve as legal guidance. (docs.bittensor.com)

From a shariah perspective, Ridges AI is not described as shariah compliant. The project has not published an Islamic finance screening or certification, and the token’s economic design—market trading of an alpha token, emissions‑based rewards, and staking—would require a specialized review by qualified scholars. In the absence of an independent certification or a published screening framework, it is not considered shariah compliant. (docs.ridges.ai)

Future Outlook

Ridges AI is advancing along two tracks at once. On the network side, it is refining a competitive arena that rewards the best autonomous coding agents under clear rules. On the product side, public interviews and updates point toward packaging that agentic capability into tools and interfaces—an IDE‑like experience, APIs, or self‑hosted deployments—that enterprises can actually use. If those pieces come together, the subnet’s economic loop tightens: better products draw more demand, more demand leads to more staking, and more staking increases emissions flowing to the top‑performing agents and validators. (subnetalpha.ai)

The project’s open‑source policy and “winner‑takes‑all” scoring create a fast feedback cycle. Top agents must keep improving to stay on top; challengers can adopt and extend good ideas quickly. Combined with standard benchmarks and a secure sandbox, that competition could push the field of coding agents forward. Whether the subnet can convert benchmark wins into broad adoption will depend on ease of use, integration into developer workflows, and the quality of enterprise packaging. (docs.ridges.ai)

Summary

Ridges AI (SN62) is a Bittensor subnet devoted to one concrete goal: autonomous software engineering agents that can fix code and ship patches. It brings together a clear evaluation pipeline, live network telemetry, and a sharp incentive model that concentrates rewards on the best‑performing open‑source agent at any given moment. As a Bittensor alpha token, SN62 has a fixed supply, a transparent emission split across roles, and pricing defined by a protocol‑level reserve between TAO and the subnet’s alpha. The result is a system where measurable technical performance, not marketing claims, determines who earns. If Ridges can translate that competitive edge into usable developer tools and enterprise‑ready interfaces, it may become a key part of the growing decentralized AI ecosystem focused on real, testable software work. (docs.ridges.ai)