Industry Context — Common BS Fingerprints in Crypto, Blockchain & Web3
Gavin Wood
(https://gavwood.com) 📸 Data Snapshot: May 26, 2026Analyze the raw signals below. How would a machine score this business’s credibility?
Here are the exact signals captured from up to six pages of the site — the same raw inputs the evaluation engine analyzed. They are grouped by signal type so you can weigh each the way the machine does.
🏗️ Semantic Structure — heading hierarchy & page identity (Info Density · Commodity Fingerprint)
HOMEPAGE Gavin Wood (https://gavwood.com)
Gavin Wood
📝 The Narrative — clean text per page (Info Density · Semantic Coherence)
HOMEPAGE (https://gavwood.com) Gavin Wood
Since my childhood economics and game theory have always interested me, even to the point of co-publishing a strategy board game of my own design. When I first read about Bitcoin in 2011, I was largely uninterested, focusing too much on the currency aspect rather than the technology. However, when I revisited it in early 2013, I began to realise new possibilities opening up between the fields of ICT and game theory, and the inevitable social change to which this would lead. A mutual friend made the introduction to Vitalik that year and blockchain/crypto has dominated my life since. I coded the first functional Ethereum client in January 2014 released as "PoC-1" (i.e. the first proof of concept) and co-founded the project. Shortly after, I authored the Yellow Paper, the first formal specification of any blockchain protocol and one of the key ways Ethereum distinguished itself from other blockchain-based systems. I went on to co-design much of the "1.0" Ethereum protocol including the EVM, gas and the caller-pays account model. I also conceived/invented and designed much of what would become the Ethereum technology stack including the Solidity contract language, the RPC, the Whisper/Swarm protocols and the Javascript API. My original ideas for a decentralised web date back to early 2013, but my first post on the topic was in April 2014, later followed by a less-techy version. Prior to Ethereum, I accrued a masters degree and doctorate in computer science. I consulted for Microsoft Research on technical aspects of embedded domain-specific languages, designed and implemented the first truly smart lighting controller for one of London's top nightclubs, designed and implemented most of the world's first C++ language workbench, and built the software systems of OxLegal, a smart text contract-editor. Next
🛡️ Trust Signals — reviews, proof links, trust-theatre flag (Trust & Proof)
| Page | Reviews | Proof links |
|---|---|---|
| / (home) | 0 | 0 |
🔗 Identity & Technical Layer — schema JSON-LD: identity chains, entity gaps (Identity & Authority)
Your Diagnosis
Before revealing the machine’s verdict, predict the BS score for each signal. Higher = more BS (more fluff, less verifiable substance). Drag each slider, then submit to compare your judgment against the engine.
Stuck? Reveal the heuristic lens — how the deterministic page-auditor reads each signal (no AI, pure pattern rules)
These are the structural rules a local, deterministic auditor applies — the same lens you can use to judge each signal. They describe what to look for, not this company’s result.
Classify each sentence as substantive or hollow. Grounding markers — numbers, currencies, dates, technical units, named entities — outweigh marketing adjectives. When fluff sits right next to hard evidence, the fluff is forgiven.
Pull the main entities out of the H1, then check whether they actually recur through the body. A page that announces one thing and then talks about another drifts. Headings with no real sentences underneath read as pseudo-substance.
Count trust words (review, testimonial, rating, verified) against real outbound proof links (Google, Trustpilot, Clutch, G2, Yelp). Lots of trust language with zero verification links is trust theatre. Unlinked logo galleries count against it.
Look at how much sentence length varies. Natural writing varies its rhythm; templated or mass-produced copy is statistically uniform. Very low variation reads as commodity content — unless unique named entities break the pattern.
Inspect the JSON-LD. Is there an Organization or Person schema, and does it carry sameAs links to real external profiles (LinkedIn, socials)? Missing schema or no identity declaration signals an anonymous entity.
Want to apply this lens yourself? The free BS Indicator Chrome extension runs these heuristic checks live on any page. Bear in mind it is a single-page, deterministic tool — it relies only on pattern rules for the page in front of it and does not perform the cross-page semantic correlation this audit uses, so its readout is a starting lens, not the full verdict.
Based on 366 businesses audited.
Gavin Wood has 34.7 points less BS than the average for Crypto, Blockchain & Web3.
Crypto, Blockchain & Web3 BS: Gavin Wood (gavwood.com)
This site is a rare example of a near-zero BS technical portfolio. It prioritizes specific historical achievements and technical contributions over modern marketing tropes and ‘Web3’ buzzwords.
Integrate Person schema including sameAs properties linking to GitHub, LinkedIn, and official project sites to bridge the technical authority gap. Add outbound hyperlink paths to the mentioned ‘Yellow Paper’ and ‘Solidity’ documentation to increase proof_links_count. Update the heading hierarchy to include more descriptive, keyword-rich nouns to improve structural clarity.
The content is a precise match for the Crypto, Blockchain & Web3 industry. It details the foundational development of the Ethereum protocol, smart contract languages, and decentralized web concepts.
“The score of 11 is driven primarily by the lack of structured data (Identity and Authority) and the absence of direct outbound links (Trust and Proof). The Information Density and Semantic Coherence pillars scored near-perfectly due to the total absence of marketing fluff and high technical specificity.”
This training module utilizes a snapshot of public data from Gavin Wood, captured on May 26, 2026, to demonstrate how machine logic evaluates different types of business narratives.
Purpose: This data is presented under “Fair Use” / “Educational Exception” for the purpose of forensic semantic analysis, allowing users to compare human intuition against machine-generated evaluations.
Notice to Gavin Wood: This analysis is part of a non-adversarial audit conducted by 1 Euro SEO. The results provided by 1EuroSEO are intended as professional feedback to help improve any website’s machine-readability and authority signals. The 1EuroSEO BS Detection Tool is a free tool, and anyone can test any company to see how their content is interpreted by AI models.
Any company can use the insights for free and improve its voice by comparing it to industry clichés or competitors. When a company has updated its content, it can always submit a new audit request, which will be reflected in a new current score.
To all users: You are encouraged to visit the live site at https://gavwood.com to view the most current version of its content and learn from the source what this company is about and what it offers.