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RE: Witness Update: @themarkymark

4 days ago(edited)

as I already stated in the original analysis multiple times and after that recently, I grouped the 100000 wallets into 250 groups of 400 wallets each in order to make an comparision of decentralization, I thought it was obvious from the start, as I also stated there are many ways to do statistics, but I think this was the most earnest way to do it, to compare LasseCash with bigger coins/tokens.

You did nothing like this and your analysis was based on 10 wallets, which is complete garbage and therefore grok 3's words were based on huge lies. My analysis shows that LasseCash is more decentralized than all of the coins I compare with, if you dont understand it by now I cant help you.

Try to read my full analysis before you write all kinds of new shit.

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1 comments

@holovision.cash59

4 days ago

O.k. Fine. You don't consider me credible. Let's turn you on and get you hot and bothered with your AI fetish. You'll only trust your chatbot lover and I respect that.

Let's ask Grok 3. Here's the chat link to prove I am not making this up. : https://grok.com/share/bGVnYWN5_ec98bf4c-e3a5-48ad-ba25-d15d1b14c5d6

Can you accurately compare the distribution of a Hive-Engine token with a market cap of less than $15,000 to Bitcoin or Ethereum's distribution? Why or why not?

No, it is not possible to accurately compare the distribution of a Hive-Engine token with a market cap under $15,000 to that of Bitcoin or Ethereum in a reliable, standardized way.

Why Not?

Data Availability and Quality for Bitcoin and Ethereum: Both have extensive, real-time on-chain analytics from established platforms. For Bitcoin, sites like BitInfoCharts provide detailed rich lists and percentage breakdowns of holdings by address size ranges (e.g., ~14% of BTC held in addresses with 0.01–0.1 BTC). Blockchain.com offers cycle indicators and mining data for broader context. For Ethereum, Token Terminal tracks unique token holders, Etherscan provides ERC-20 holder lists, and reports like those from Cointelegraph detail top addresses (e.g., ~61% of ETH in just 10 addresses, mostly staking contracts and exchanges). These sources use verified, aggregated data with historical trends, enabling precise comparisons (e.g., Gini coefficients for concentration).
Challenges for Small Hive-Engine Tokens: Hive-Engine tokens are on-chain and transparent via explorers like hive-engine.com, but low-cap ones (<$15k) lack aggregated analytics or rich lists from reputable third parties—no equivalents to BitInfoCharts exist. Market caps are often based on thin liquidity pools, leading to volatile or unrepresentative pricing (e.g., total daily airdrops or pools under $100k across tokens). Holder data requires manual API queries (e.g., via Hive-Engine's endpoints for balances), but for obscure tokens, this yields raw, unverified lists with potential for errors like dust accounts or unlinked identities. Examples like Hivelist Token (LIST) or Hive Game Token (HGT) show market caps around $38k–$198k with sparse public distribution info, and smaller ones are even harder to surface reliably. Without standardized tools, comparisons risk inaccuracy due to incomplete snapshots or manipulation in low-volume environments.

In short, BTC and ETH distributions can be benchmarked with high fidelity, but small Hive-Engine tokens demand custom, error-prone analysis that's not feasible for "accurate" side-by-side evaluation. If you specify a particular token, I could attempt a rough on-chain pull for illustration.

Since comparing a Hive-Engine with such a low market cap to Bitcoin or Ethereum is not feasible is it possible to compare one Hive-Engine token's distribution to another Hive-Engine token's distribution?

Yes, it is possible to compare the distribution of one low market cap Hive-Engine token (under $15,000) to another, and it's more feasible than comparing to Bitcoin or Ethereum because both tokens operate on the same blockchain with identical data transparency and accessibility via the Hive-Engine explorer.

Why Possible?

Unified Data Source: Hive-Engine provides on-chain holder data for all tokens through its explorer (hive-engine.com). For any token, the "/holders" tab displays the total number of holders, a list of the top 100 holders with their balance percentages, and overall supply details. This standardization allows direct, apples-to-apples comparisons without cross-chain or platform discrepancies.
Key Metrics for Comparison: You can extract and analyze distribution using simple, consistent metrics like:
- Total number of holders (indicating broad vs. narrow adoption).
- Percentage of supply held by top holders (e.g., top 1, top 10, top 100) to assess concentration.
- Estimated Gini coefficient (a measure of inequality, from 0 for perfect equality to 1 for total concentration), which can be approximated from the top holders' data using basic formulas.
- Average holdings per holder (total supply ÷ number of holders).
Handling Low-Cap Specifics: Even for tiny market caps, the data exists and is verifiable via API endpoints (e.g., https://api.hive-engine.com/rpc/contracts) or the web interface. Low liquidity might mean fewer holders (e.g., 10–500) and higher concentration, but this is comparable across tokens. Issues like dust accounts (tiny holdings) can be filtered similarly for both.

Example Comparison Structure

If you provide two specific token symbols (e.g., from the tokens list at hive-engine.com/tokens), I could pull current data and compute this. Here's a hypothetical table based on typical low-cap Hive-Engine tokens (e.g., niche community or meme tokens with ~$5k–$12k caps):

Metric	Token A (e.g., Hypothetical Meme Token, ~$8k cap)	Token B (e.g., Hypothetical Utility Token, ~$12k cap)	Interpretation
Total Holders	42	156	Token B has broader distribution.
Top 1 Holder %	28.5%	12.3%	Token A is more centralized.
Top 10 Holders %	72.1%	48.7%	Token A shows higher whale dominance.
Top 100 Holders %	98.4% (covers nearly all)	89.2%	Both are concentrated, but Token B less so.
Estimated Gini Coefficient	0.82	0.65	Token A is more unequal (closer to 1).
Avg. Holdings per Holder	~2,380 tokens	~640 tokens	Token A has larger average stakes.

Limitations

Manual Effort: No automated rich lists like for BTC/ETH, so pulling full data requires querying the explorer or API for each token—feasible but not instant for deep analysis.
Volatility and Completeness: Low-cap tokens can have manipulated pools or inactive holders, but since both are affected similarly, relative comparisons hold up.
Scalability: For tokens with <50 holders, it's essentially a full census; for more, approximations from top holders suffice.

This approach enables meaningful insights into relative centralization or decentralization within the Hive ecosystem. Specify two tokens if you'd like a real-time example!

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