Minds Eye the Gem of Bulls Eye
Implementation of the AI Algorithm
We develop the system that is going to use an AI model to gather information from all data points, construct successful launches and bring structure (from now on nomenclature) of when, which types of applications and what type of process might be successfully launched. The AI algorithm will then carry out consequent steps for each of the five main categories (CA -Contract Analysis, Marketing, Deployer, Buyers and Impression).
Contract Analysis (CA Eye)
Data Inputs:
Smart contract code to compare with other contracts
Taxes (initial and post-launch)
Code exceptions or exposures
AI Implementation:
Smart Contract Similarity: Minds Eye will look for other similar contracts, including those which were rug-pulled or scam-flagged by using natural language processing (NLP) and code comparison algorithms.
Tax Analysis: Minds Eye will compare initial and post-launch tax percentages and monitor any code updates that affect taxes. It will cross-reference with successful projects to spot abnormal tax structures.
Anomaly Detection: Using pattern recognition, Minds Eye compares pre and post-launch tax percentages and witnesses any changes in taxes related to code. It will compare against successful projects to identify any abnormal tax structures.
Outcome: A trust score for each contract based on the history of similar contracts and irregularities.
2. Marketing
Data Inputs:
KOLs (Key Opinion Leaders): Organic vs. paid, follower count, engagement, and exposure.
Community Analysis: community analytics for organic growth or bots, large or small and visibility to the outside world and level of engagement.
Raids on X.com (formerly Twitter): Frequency, success rates, and impact on exposure.
Ads and Calls: Number of ads, channels used, reach, and frequency of exposure.
Narrative: Fit within specific or multiple narratives, relevance, and trend alignment.
AI Implementation:
KOL Influence: This is how Minds Eye will decide which of the KOLs and influencers have good engagement metrics (likes, shares, comments, reach). The Minds Eye automatically measures these metrics against previously successful projects to determine if the KOLs are influential or have overinflated engagement.
Community Quality: The Minds Eye will go through Telegram and social media groups to look at the activity of the members and engagement rates. For instance, it can tell if a social account is growing due to bots or not with sentiment analysis and activity patterns.
Ad Efficiency: Tracking ads and how they affect following or community growth. It can link ad campaigns with token price movements and exposure to evaluate efficiency.
Narrative Fit: Leveraging trend analysis and natural language processing, the Minds Eye will assess how well the project fits within prevailing crypto narratives (e.g., DeFi, NFTs, Layer 2s) and surface projects that align with multiple trending narratives for higher odds of success.
Outcome: A marketing score of the project based on the community activity and growth, KOLs support, advertising efficiency and narrative synchronization.
Deployer
Data Inputs:
Deployer Wallet: Where funds have come from, wallet history (previous rugs), and how much funds have left.
Team Wallets: Holdings, sales, fund movements, labelling (airdrops, marketing wallet, etc.).
Bundle Buys Big Purchases From Team or Deployer Wallets Or Moving Funds.
AI Implementation:
Deployer History: Minds Eye will monitor historical transaction analysis between deployer wallets, past projects, scam associations or recurring scammy behaviour. The integration could be accomplished by utilizing traditional blockchain transaction analysis and associating the address to other known rug-pull projects.
Team Activity Monitoring: The Minds Eye will monitor team wallets for selling patterns or fund movements, which could indicate potential market manipulation. It will track any sudden moves, such as transferring funds to external wallets, which could be red flags.
Fund Tracking: Bundle buys and fund flows will be analyzed to understand how the team is managing their assets. Minds Eye can check if these funds are being reinvested in the project or moved out for personal gain.
Outcome: A deployer and team tracking records of previous activity, funds flow and potential for market manipulation score.
Buyers
Data Inputs:
Snipers: Number of sniper wallets, how many hold vs. sold, and wallet reputations (linked to successful projects).
Bribes: The size and profitability of bribes, comparison to successful projects.
Holders: Total number of holders, wallet size distribution, holding trends (growth, stalling), and big wallet movements.
AI Implementation:
Sniper Wallet Analysis: Minds Eye will look into the swarms of sniper bots and inform you whether there is early interest in a project. It will be used to monitor success rate in sniper wallets (long-term holders or quick hawks wallets) determining if the project could have early pump and dump.
Bribe Analysis: The Minds Eye will compare the size and impact of bribes to successful projects. Bribes that lead to positive outcomes (growth, exposure) will be considered positive signs, while large bribes with negative results will be flagged.
Holder Distribution and Quality: The Minds Eye will keep an eye on where tokens are being distributed and whether there is too big a token distribution in terms of a small number of large wallets (as well as suspicious wallets). It will also break down the amount of time-holding patterns to help identify if early holders are dumping or accumulating.
Outcome: A holder and sniper score, providing insight into how stable or risky the holder distribution is, and whether there is a high concentration of tokens in risky wallets.
Impression
Data Inputs:
Website: Speed, origin or similarity to other sites,auto-generated content detection.
X.com Analysis: Number of followers, follower quality (organic or bot), follower growth, authenticity of posts, engagement metrics.
Telegram Analysis: Member count, growth, member activity, quality of shared information.
Validation: Project documentation (AI-generated vs. human-written), technical detail, copywriting quality.
AI Implementation:
Website Analysis: The Minds Eye will be able to find whether the website is copied or auto-generated using web scraping tools and NLP. It can track similarities with website patterns for the successful or scammy projects it is aware of.
X.com and Telegram Activity: Minds Eye will track activity, distinguishing between organic growth and artificial (bot) driven growth based on patterns of increases in followers.In pair with engagement levels and participation trends to determine what is truly of interest and which interactions are valuable
Documentation Validation: Minds Eye can process project documentation to evaluate its depth, quality, and whether it shows signs of being hastily generated by AI or human experts. It will flag projects with poor or suspicious documentation.
Outcome: An Impact score is given on the credibility of the website, its engagement on social media, and the quality of documentation for the project.
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