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FLock.io

FLock.io (FLOCK) — a reference card for a crypto project: what the product does and how verifiable the project’s key claims are; also — alignment with the Decentralized AI thesis (DecAI Fit).

FLock.io — an AI/Training | Infra project about decentralized training and validation of AI models (AI Arena) and federated learning (FL Alliance) with onchain incentives (staking + rewards + slashing). Token (symbol: FLOCK).

Disclaimer

Important: this page is a reference overview of the project. Within DecAIHub, we separately record a verification based on open sources within the AI thesis (what is real AI/onchain/why a token, and which sources this is based on).
This is not financial advice and not a recommendation to buy/sell/hold any asset. We do not assess price, returns, or entry/exit timing.
Conclusions are based on publicly available materials found as of the verification date. The absence of confirmation does not imply the absence of the technology.

TL;DR (for readers who are not into AI)

  • What it is: a platform where participants train and validate models and receive rewards; there are the AI Arena (competitive training/validation), FL Alliance (federated learning) and AI Marketplace / API Platform layers.
  • Who it’s for: ML/AI developers and teams that want to run training/fine‑tuning tasks and obtain “winning models”; participant‑executors (training nodes/validators/delegators); application developers who consume models via the API.
  • Why onchain/token: the FLOCK token is used for staking/admission to roles, distribution of rewards and slashing, as well as for payments for access to models/limits and for DAO governance.
  • What you can actually touch (Tier‑1): the “Introduction to FLock” documentation https://docs.flock.io/, the whitepaper https://www.flock.io/whitepaper, the gmFLOCK guide (staking → participation in the AI Arena) https://docs.flock.io/flock-products/ai-arena/quickstart/gmflock.

How it works (very briefly)

  1. The user does: creates a training task or joins as a training node / validator / delegator.
  2. The system does: training nodes train models and send submissions; validators evaluate and reach consensus on scoring/ranking; for FL Alliance — participants train on local data and aggregate the updates.
  3. The user gets: a “winning model”/consensus model and infrastructure for deployment (marketplace/API).
  4. Where the blockchain is: staking, distribution of rewards and slashing, as well as issuance/management of incentive flows are described as onchain mechanisms (see the whitepaper + tokenomics docs).
  5. Token role: FLOCK — admission to roles via stake, payments and governance via the DAO.

Quick links: AI thesis fit

Project passport

Field Value
Project name FLock.io
Also known as (aliases) FLock
Logo (logo) ../assets/88_flock-io_logo.svg
Token symbol (ticker) FLOCK
Category AI/Training | Infra
Primary chain (chain) Base
Type EVM
Network status mainnet
Consensus (consensus) L2 (Optimistic Rollup, Base on Ethereum)
Launch date (launch_date) 2025-01-03
Project status (status) active
Token (standard) ERC-20
Token contract 0x5ab3d4c385b400f3abb49e80de2faf6a88a7b691
Token model (token_type) utility (payments + staking + governance)
Max supply 1 000 000 000 (cap)
Issuance capped + daily mint (management contract; emissions)
Tokenomics (tokenomics, brief) staking for roles + rewards/slashing; distribution: 66.7% community/ecosystem vs 33.3% team+investors (per the docs)
Governance DAO (token holders)
Admins/keys —
Audit —
Bug bounty —
Team (team) —
Funding (investors/funding) seed + strategic rounds (per the blog)
AI component (ai_component) decentralized training + federated learning
Use‑case (use_case) train/validate models with incentives
DecAI Fit — AI thesis fit 6/6 — high alignment with the Decentralized AI thesis
DecAI Fit component summary ai_score 3 / onchain_score 3 / token_score 3 / evidence_score 3
Last verified 2026-02-11

General project overview

FLock.io is a platform for decentralized training and validation of ML models that attempts to solve the problems of centralized control (bias/lack of public oversight, data protection, closedness) through a combination of an AI pipeline (training/evaluation/aggregation/deployment) and onchain incentives.

In Tier‑1 sources, the platform is described as an ecosystem of several interconnected modules: AI Arena (training/competition of models), FL Alliance (federated learning with preservation of data sovereignty) and a deployment/consumption layer (AI Marketplace and API Platform for access to models).

Features/modules

  • AI Arena: training nodes train models, validators evaluate and reach consensus on scoring/ranking; participants stake and receive rewards, slashing is possible.
  • FL Alliance: federated learning — local data is not transferred; participants upload updates/weights and receive rewards/slashing based on the results of voting/evaluation.
  • AI Marketplace / Model Hosts: hosting of “winning models” and provision of interfaces (API/SDK).
  • API Platform: an OpenAI‑SDK‑compatible API for integrating models into applications.

Typical scenarios

  • Scenario 1 (task creator): defines an ML task → stakes/posts a bounty → receives the best model (per validator consensus) and the ability to deploy it.
  • Scenario 2 (training node): joins a task → trains and submits a model → receives rewards or risks slashing (per the protocol rules).
  • Scenario 3 (validator/delegator): validates models or delegates stake → receives a share of the rewards.

Getting started (wallet/network)

  • What you need: an EVM wallet, the Base network, the FLOCK token for staking/participation.
  • Network/chain: Base mainnet (the sources also mention the Base Sepolia testnet for early phases).
  • Fees: Base fees for onchain operations (stake/unstake/claim/transfer).

Architecture/tech (non‑AI)

  • Components: smart contracts for the token/staking/emissions and task management; an offchain part for training/validating models; the AI Marketplace and API Platform interfaces.
  • Participant roles: task creators, training nodes, validators, delegators, FL clients, model hosts (the list and the roles are described in the whitepaper).
  • Where logic runs: training/evaluation of models — offchain (participants’ compute), while admission/incentives (stake, rewards, slashing, emissions, governance) — onchain (Base).
  • Dependencies: Base (L2), wallets/EVM infra; API integrations (OpenAI‑compatible SDK) for model consumers.

Token/economics (expanded, no forecasts)

  • Token utility: stake for participation in roles; bounties for speeding up/quality of tasks; payments for access to models/limits; governance.
  • Protocol necessity drivers: the protocol uses stake‑gating and reward/slash mechanics to defend against Sybil/DoS/free‑rider attacks and to incentivize quality models (described in the whitepaper).
  • Who pays fees: participants who perform onchain operations on Base.
  • Where fees go: Base network fees — to validators/the network; protocol payments/payouts depend on the chosen parameters (emissions/payments to model hosts) and are described in the tokenomics/whitepaper.
  • Issuance/burn: total supply capped at 1B; the tokenomics states that the day‑one unlock is minted initially, and then there is a daily mint via a management smart contract; the whitepaper describes emission + slashing.
  • Security/incentive mechanisms: staking + rewards; slashing for malicious behaviour; delegation (delegators) and gmFLOCK as a tool for participating in the AI Arena.
  • Distribution: the docs describe a model of 66.7% community/ecosystem and 33.3% team+investors; investors and the team — vesting (1y cliff + 2y linear) in the token allocations.
  • Treasury: —. No specific treasury contract/policies were found in Tier‑1 sources; in the whitepaper, governance is described conceptually.

Governance

  • Who makes decisions: DAO (token holders).
  • How protocol parameters change: via proposals and voting (described in the whitepaper as the governance model).
  • Where: — (within this card, the Tier‑1 whitepaper governance section is recorded; public governance tools/a forum were not added).
  • Keys/multisig: —.

Security and incidents

  • Audits: — (BaseScan in links_* shows “No audit submitted”).
  • Bug bounty: —.
  • Incidents: —.
  • Threat model: the whitepaper explicitly discusses classes of attacks (Sybil/DoS/free‑rider/poisoning) and motivates staking + slashing + rate limiting as mitigations.

Project status and timeline

  • Current status: mainnet.
  • Key milestones: the token contract on Base was created 2024-12-20 (BaseScan); mainnet launch + TGE on Base — 2025-01-03 (official blog).
  • Roadmap: see the Roadmap section in the docs (links_* currently contains “—”; the Tier‑1 docs roadmap index has been added to Evidence).

Ecosystem/integrations

  • Integrations: Base (L2), EVM wallets; API Platform (OpenAI‑compatible SDK) for applications.
  • Partners: the official blog mentions partnerships (e.g. Morpheus, IO.net, Aptos) and an EF grant.
  • Where it’s actually used: the product modules are described in the docs (AI Arena, FL Alliance, AI Marketplace, API Platform) and the whitepaper (the full pipeline).

AI component of the project

  • What is real AI here? AI Arena and FL Alliance describe a specific ML pipeline: training/validation/ranking of models and federated learning with aggregation; there is an API Platform for consuming models.
  • What is truly onchain? staking/admission to roles, distribution of rewards, slashing, emissions/task‑management contracts; the FLOCK token as an ERC‑20 on Base.
  • Why is a token needed? the token provides stake‑gating and economic incentives (rewards/slashing), bounties and payments for access to models/limits; it is claimed as the DAO governance asset.
  • Red flags (based on the information found): risks of validator quality/honesty and collusion; centralization risks in offchain components (UI/orchestration); risks of a proxy upgrade of the token contract (transparent proxy).

DecAI Fit component summary

  • DecAI Fit: 6/6 — high alignment with the Decentralized AI thesis
Component Level Score
AI reality high 3
Onchain necessity high 3
Token necessity high 3
Evidence Tier‑1 present 3
Details

AI reality

AI_0 — AI intent (Tier‑1 roadmap) (1/0)

  • AI_0 (decision): 0
  • Claim (one line): There are signs of AI reality per Tier‑1 (see AI_1..AI_4), so the AI_0 flag (intent‑only) does not apply.
  • Evidence: Tier‑1 — see Evidence/links in AI_1..AI_4.
  • Why sufficient / why not (one line): AI_0=1 is admissible only when AI_1..AI_4=0; here ai_score without AI_0 = 3.
  • Conflict note (if any): —

AI_1 — what the AI does (1/0)

  • AI_1 (decision): 1
  • Claim (one line): AI Arena trains and validates models (training nodes vs validators), FL Alliance performs federated learning and aggregation of updates.
  • Evidence: Tier‑1 — https://www.flock.io/whitepaper ; https://docs.flock.io/flock-products/api-platform
  • Why sufficient / why not (one line): the whitepaper describes the pipeline and roles, the docs describe the products and the API.
  • Conflict note (if any): —

AI_2 — demos/examples (1/0)

  • AI_2 (decision): 1
  • Claim (one line): there is a reproducible guide: stake FLOCK → obtain gmFLOCK → participate in the AI Arena; as well as a description of the API Platform for integrating models.
  • Evidence: Tier‑1 — https://docs.flock.io/flock-products/ai-arena/quickstart/gmflock ; https://docs.flock.io/flock-products/api-platform
  • Why sufficient / why not (one line): these are practical steps with interfaces/formulas and user actions.
  • Conflict note (if any): —

AI_3 — metrics/quality (1/0)

  • AI_3 (decision): 0
  • Metrics: —
  • Context (version/date/how measured): —
  • Evidence: Tier‑1 — — (within this card, no public benchmarks/quality metrics of specific models were recorded)
  • Why sufficient / why not (one line): there are evaluation/ranking mechanisms, but without a single public set of metrics for AI_3.
  • Conflict note (if any): —

AI_4 — tech description/architecture/versions (1/0)

  • AI_4 (decision): 1
  • Claim (one line): the whitepaper describes the layer architecture (blockchain layer + AI layer) and participant roles, while the docs describe the product modules and the API.
  • Evidence: Tier‑1 — https://www.flock.io/whitepaper ; https://docs.flock.io/
  • Why sufficient / why not (one line): there is a structured technical description and a formalization of incentives/consensus.
  • Conflict note (if any): —

Onchain necessity

OC_1 — contracts/addresses/chain (1/0)

  • OC_1 (decision): 1
  • Chain: Base
  • Addresses/identifiers: the FLOCK token (proxy) 0x5ab3d4c385b400f3abb49e80de2faf6a88a7b691, implementation 0x22754dad1b54ce707997afce9a78a3413045f784; AI Arena Task Manager CA 0x5813dEe58523EA082Ee80162527EfEed7799AaA0 (token allocations docs).
  • Evidence: Tier‑1 — https://basescan.org/token/0x5ab3d4c385b400f3abb49e80de2faf6a88a7b691 ; https://basescan.org/tx/0x4c1022b850601847d4f16db5355410132583cb681a339adbe8f50017d477f3a0 ; https://docs.flock.io/flock-tokenomics/token-allocations
  • Why sufficient / why not (one line): the explorer confirms the contract and the timestamp, the docs record the key CAs for emissions/task management.
  • Conflict note (if any): —

OC_2 — onchain beyond just a token (1/0)

  • OC_2 (decision): 1
  • What useful logic is onchain: stake‑gating of roles, emissions/rewards, delegation and slashing; gmFLOCK as the mechanism of participation in the AI Arena.
  • Evidence: Tier‑1 — https://www.flock.io/whitepaper ; https://docs.flock.io/flock-products/ai-arena/quickstart/gmflock ; https://docs.flock.io/flock-tokenomics/token-allocations
  • Why sufficient / why not (one line): onchain incentives are part of the protocol design, not just token issuance.
  • Conflict note (if any): —

OC_3 — onchain is critical (1/0)

  • OC_3 (decision): 1
  • “Replace the blockchain” test: without onchain stake/rewards/slashing, permissionless admission to roles and the distribution of incentives/penalties among participants cannot be implemented without trust in a centralized operator.
  • Evidence: Tier‑1 — https://www.flock.io/whitepaper
  • Why sufficient / why not (one line): the whitepaper directly links security and incentives to the onchain layer.
  • Conflict note (if any): —

Token necessity

TK_0 — no token (1/0)

  • TK_0 (decision): 0
  • Claim (one line): the project has a token, FLOCK (ERC‑20 on Base).
  • Evidence: Tier‑1 — https://basescan.org/token/0x5ab3d4c385b400f3abb49e80de2faf6a88a7b691
  • Why sufficient / why not (one line): the explorer confirms the token.
  • Conflict note (if any): —

TK_1..TK_4 (1/0)

TK_1 — payment for the service (1/0)

  • TK_1 (decision): 1
  • Claim (one line): access to “winning models” in the marketplace may be charged for; the whitepaper specifies payments in FLOCK for access beyond rate limits.
  • Evidence: Tier‑1 — https://www.flock.io/whitepaper
  • Why sufficient / why not (one line): the payment mechanics is described as part of token demand (access/payment).
  • Conflict note (if any): —

TK_2 — incentives/collateral/penalties (1/0)

  • TK_2 (decision): 1
  • Claim (one line): participants stake FLOCK for roles; there are rewards and slashing for malicious behaviour; gmFLOCK is issued for staking FLOCK to participate in the AI Arena.
  • Evidence: Tier‑1 — https://www.flock.io/whitepaper ; https://docs.flock.io/flock-products/ai-arena/quickstart/gmflock
  • Why sufficient / why not (one line): stake/reward/slash — the central part of the economy and security.
  • Conflict note (if any): —

TK_3 — governance (1/0)

  • TK_3 (decision): 1
  • Claim (one line): holding FLOCK grants participation in DAO governance (propose/debate/vote).
  • Evidence: Tier‑1 — https://www.flock.io/whitepaper
  • Why sufficient / why not (one line): the governance model is described in a separate section (FLock Governance).
  • Conflict note (if any): —

TK_4 — token is replaceable (1/0)

  • TK_4 (decision): 0
  • Claim (one line): the token can be replaced with another one without changing the mechanics.
  • Evidence: Tier‑1 — —.
  • Why sufficient / why not (one line): the token is embedded in the stake/reward/slash/payment/governance mechanics; a replacement is not proven to be equivalent.
  • Conflict note (if any): —

Evidence (table)

Tier Source What it proves
Tier‑1 https://docs.flock.io/ Description of the FLock platform and product modules (AI Arena / FL Alliance / AI Marketplace / API Platform).
Tier‑1 https://www.flock.io/whitepaper Architecture (blockchain layer + AI layer), participant roles, staking/rewards/slashing, payments and governance.
Tier‑1 https://docs.flock.io/flock-products/api-platform API Platform: an OpenAI‑SDK‑compatible API for integrating models.
Tier‑1 https://docs.flock.io/flock-products/ai-arena/quickstart/gmflock Reproducible guide: stake FLOCK → mint gmFLOCK → participation in AI Arena roles.
Tier‑1 https://docs.flock.io/flock-tokenomics/token-allocations Token allocations: supply cap 1B, daily mint, distribution and key CAs (Task Manager).
Tier‑1 https://basescan.org/token/0x5ab3d4c385b400f3abb49e80de2faf6a88a7b691 Onchain fact of the FLOCK token as an ERC‑20 on Base (proxy + implementation).
Tier‑1 https://basescan.org/tx/0x4c1022b850601847d4f16db5355410132583cb681a339adbe8f50017d477f3a0 Timestamp of the token contract creation (Dec‑20‑2024).
Tier‑1 https://www.flock.io/blog/mainnet-launch--token-generation-event-on-base Mainnet launch + TGE on Base (2025‑01‑03).

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