The Attention Flywheel (fomoball) implements a novel, on-chain mechanism linking social attention—via theses posted on fomo—to automated buybacks and burns. Its core supply logic is transparent: every coin charges a fixed 4% fee, deposits proceeds into a dedicated vault, and triggers self-buybacks either on thesis detection (skipping cooldown) or via a 20m clock floor. The mechanism’s design explicitly avoids centralized discretion—the trigger lives off-chain on fomo, the fee and vault are immutable at deployment, and all parameters (impact cap: 2%, spend share: 75%, dip threshold: 12%) are published and hardcoded. Supply data from source_2 reports circulating and total supply as identical (≈836.4M), with max supply capped at 1B, implying no active minting—but no evidence confirms whether the remaining 163.6M is reserved, burned, or unissued. Vesting, allocation, or unlock schedules are entirely absent from all sources. While the mechanism demonstrably constrains dilution risk by fixing fee routing and disabling creator overrides, the lack of any disclosed token distribution plan prevents concentration or dilution analysis per rubric criterion specialist_2. The project’s utility is coherently articulated across web_1, web_2, and web_3, and operational dependencies (keeper, fomo API, Robinhood Chain) are named—but no audit, runtime verification, or independent confirmation of live engine execution is supplied. Confidence is medium: evidence is current (retrieved 2026-09-18), covers core mechanics and supply figures, but lacks independence (all documentation is self-published) and omits critical allocation context.

Overall score: 7/10 Confidence: Medium

Attention Flywheel (fomoball) proposes a novel venture thesis: attention — specifically, public theses posted on the fomo app — becomes a direct, on-chain market signal that triggers automated buybacks and burns for tokens launched on its platform. The business model is clear and self-contained: every trade pays a fixed 4% fee, held in a dedicated vault per token, and spent exclusively on self-buybacks when triggered either by a qualifying thesis or by an underlying clock-and-dip mechanism. This creates a closed-loop economic engine where the paying customer is the trader, the budget owner is the token’s own fee vault, and willingness to pay is evidenced by live trading activity across 36 listed coins — including real-time burn metrics like ‘Burned 158.44M tokens’ and fee-recycling rates of 39.8% (web_1). The docs and mechanism pages detail how the keeper executes verifiable, on-chain sweeps, claims, and snowballs — with all parameters (impact cap: 2%, spend share: 75%, cooldown: 10m) published and immutable post-deployment (web_2, web_3). However, no evidence confirms who operates the keeper, whether the vault keys are multi-sig or audited, or how the fomo API integration is validated — leaving operational control unverified. While the thesis is coherent and the product is live, the absence of independent verification of custody, execution fidelity, or third-party usage data limits confidence to medium. The model does not rely on token appreciation; revenue is strictly fee-derived and redeployed, satisfying specialist criteria 1–2. Scale assumptions hinge on sustained thesis volume and trading depth — plausible but unproven beyond current snapshot data.

Overall score: 7/10 Confidence: Medium

Fomoball’s core thesis — that social attention, when translated into on-chain buybacks via a verifiable thesis trigger, creates measurable demand pressure — is concretely implemented and observable. The live dashboard at fomoball.tech shows 36 launched coins, real-time burn metrics (e.g., 158.44M tokens burned), and live fee recycling (39.8% of fees reused), all tied to actual on-chain activity on Robinhood Chain. Crucially, the mechanism is not theoretical: docs and mechanism pages detail how each thesis on fomo triggers an immediate, cooldown-skipping buyback funded only by that coin’s earned fees — no external treasury, no admin override. This directly addresses specialist criterion 1: demand isn’t projected; it’s measured in thesis-triggered burns and fee recycling rates. For timing and substitutes (criterion 2), the model competes with manual market-making, liquidity mining, and centralized launchpads — but its friction is higher (requires fomo integration, no cross-chain support yet) and adoption hinges on fomo’s user base growth, not general crypto sentiment. Demand persistence (criterion 3) remains unproven: no data shows whether coins sustain thesis volume or buyback activity beyond initial hype, and the docs explicitly state “a quiet coin… is a small buy into a falling market.” Product completeness (complement_business_product) is strong — live contracts, working explorer links, and transparent vault mechanics — but token financials lack clarity on long-term fee sustainability across low-volume coins.

Overall score: 7/10 Confidence: Medium

The Attention Flywheel (fomoball) presents a coherent, differentiated thesis: attention — in the form of public theses on fomo — directly triggers on-chain buybacks and burns for tokens launched on its platform. This mechanism is explicitly defined across three static documentation pages (fomoball.tech, /docs, and /mechanism), with consistent technical detail on fee capture (4% fixed, immutable on-chain), vault isolation, thesis-triggered execution (bypassing cooldowns), and capped impact (2% of venue reserve). The design intentionally decouples trigger authority from platform control by sourcing theses exclusively from fomo’s unmoderated feed — a deliberate architectural constraint that strengthens the claim of decentralised social-market coupling. Token supply mechanics are transparent: circulating supply is ~836M out of 1B max, and burn data is visible per coin on the homepage. However, evidence of live, sustained organic traction remains thin: the homepage shows only 36 launched coins, most with negligible market cap (<$5K), minimal price movement, and no verifiable volume or user growth metrics. While the mechanism is technically specified and self-consistent, there is no evidence of third-party audits, real-time keeper operation logs, or independent verification of thesis-to-transaction linkage beyond static claims. The Robinhood Chain (EIP-155:4663) context adds ecosystem specificity but no cross-chain validation or external credibility signals.

Supports

• Clear, self-referential mechanism design with immutable on-chain parameters (fee, vault, impact cap) documented across multiple pages.
• Explicit downside constraints: budget-limited spend, gas-float dependency, and no floor guarantee — all acknowledged in docs.

Weakens

• No evidence of operational validation: no audit reports, no transaction-level proof of thesis-triggered burns, no traffic or engagement metrics.
• All documentation is static; no dynamic data (e.g., live thesis counts, vault balances, or keeper uptime) is verifiable from supplied sources.

On balance, fomoball delivers a novel, well-articulated thesis with strong internal logic and intentional trust minimisation — but its real-world efficacy remains uncorroborated by observable traction or independent verification. It is credible enough to continue evaluation, pending evidence of live execution and broader ecosystem adoption.

Overall score: 7/10 Confidence: Medium