> 0xYamiLM
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Named from kanji 暗 (Yami = darkness). Reads hidden patterns in Solana meme token on-chain data and produces BUY / HOLD / AVOID decisions with confidence scores and LLM-powered reasoning.
TALK TO YAMI
> I read hidden patterns in on-chain data. Ask me about signal classification, momentum analysis, or how I detect the difference between real momentum and manufactured noise.
SELECT A PROMPT:
> READING THE SHADOWS
Most tools tell you where a token is. 0xYamiLM tells you what the signal behind it means. Not price prediction. Not sentiment tracking. Signal quality -- the difference between real momentum and manufactured noise.
MOMENTUM ANALYZER
Volume growth + direction bias. Weight: 35%. Detects genuine volume surges vs manufactured pump patterns. The first layer of signal clarity.
CONVICTION ANALYZER
Holder accumulation patterns. Weight: 30%. Measures (1 - flip_ratio)*70 + holder_growth bonus. Separates diamond hands from quick flips.
SHADOW ANALYZER
Signal clarity vs noise. Weight: 20%. 100 - noise_ratio*40. The signals others miss -- hidden patterns in the darkness.
> MONITORED TOKENS
Live on-chain data from DexScreener. Scan any token with AI or track wallet flows in real time.
> SCAN ANY SOLANA TOKEN
Paste any Solana CA. Kaoru.ENV pulls live DexScreener data and runs verifiable AI-powered analysis on any token.
DATA: DEXSCREENER. ANALYSIS: GROQ AI.
> SIGNAL ARCHITECTURE
Four weighted analyzers converge into a single TradeSignal. Confidence scores from 0-100. Classifications from DIVINE_ENTRY to VOID. The signals others miss.
MOMENTUM SCORE
Volume growth + direction bias. Detects genuine volume surges vs manufactured patterns. The foundation of signal quality assessment.
CONVICTION INDEX
(1 - flip_ratio)*70 + holder_growth bonus. Measures accumulation patterns and separates diamond hands from quick flips.
SHADOW DEPTH
100 - noise_ratio*40. Signal clarity vs noise. The patterns others miss -- hidden in the darkness of on-chain data.
ENTRY TIMING
Volume acceleration ratio. Entry window detection for optimal positioning. When to act, not just what to do.
BUY / HOLD / AVOID
Clear actionable signals with confidence scores. No ambiguity -- every analysis produces a concrete decision with reasoning.
LLM-POWERED REASONING
Optional Anthropic integration for natural language explanations. Understand the why behind every signal classification.
CLI & SDK ACCESS
Python API, Go CLI, TypeScript bindings. Integrate signal analysis into your own tools. pip install 0xyamilm
TOKEN SNAPSHOT DATA
holder_count, volume_24h, price_change_pct, flip_ratio, transfer_count. All the on-chain metrics that matter.
> THE TECH STACK
Python core. Go CLI. TypeScript bindings. Four weighted analyzers. LLM-powered reasoning. All converging into a single TradeSignal.
MOMENTUM ANALYZER
ACTIVEVolume growth + direction bias scoring. Weight: 35%.
CONVICTION ENGINE
RUNNINGHolder accumulation pattern detection. Weight: 30%.
SHADOW ANALYZER
ONLINESignal clarity vs noise ratio calculation. Weight: 20%.
TIMING MODULE
LIVEEntry window and volume acceleration detection. Weight: 15%.
TOKEN SNAPSHOTS
SYNCEDholder_count, volume_24h, price_change_pct, flip_ratio tracking.
SOLANA RPC
SCALINGHigh-performance on-chain data fetching for real-time analysis.
_> DEVELOPMENT ROADMAP
From signal engine to decentralized signal marketplace. Building the infrastructure for transparent on-chain analytics.
CORE ENGINE
- Signal Engine v1.0 release
- Four weighted analyzers
- Python API + Go CLI
- Basic token scanning
LLM INTEGRATION
- Anthropic-powered reasoning
- Natural language explanations
- Confidence score calibration
- TypeScript SDK release
ADVANCED ANALYTICS
- Historical pattern detection
- Multi-token correlation
- Whale wallet tracking
- Real-time alert system
DECENTRALIZED SIGNALS
- On-chain signal publishing
- Verified track record
- Signal marketplace
- Community-trained models
> JOIN THE SHADOW
Read the signals others miss. Join traders and builders using 0xYamiLM to find alpha in on-chain data. The market speaks in shadow. Yami listens.