
N-Drew On-Chain Intelligence
Governance intelligence that tells you what's coming
What it does
Most on-chain analysis stops at vote counts; N-Drew On-Chain Intelligence goes deeper. Using behavioral wallet clustering and a 4-layer framework, we expose structural decay—concentration risk, delegate dependency, and quorum fragility—obscured by aggregate stats. Our Uniswap brief reveals 4 clusters control 90.4% of power. We detect "event-driven" spikes that signal quorum failure 60–90 days early. Built for VCs and DAOs who need decision-grade intelligence, not just dashboards.
Does the same job
all alternatives →- LALedger-analytics – Analytics for ledger-cli2018 · github.com · ▲102
- IIInteractive implementation of the NIST Blockchain use case flow chart2018 · brucemacd.github.io · ▲82

- IHInterledger – How to Interconnect All Blockchains and Value Networks2018 · medium.com · ▲16

- TWTrack whether cryptocurrencies are being used and for what2024 · app.chainspy.net · ▲7
Hey HN, I'm Jake, and I created Chainspy, a website that aggregates and visualizes on-chain metrics across multiple blockchains like Bitcoin, Ethereum, and others. It's designed to give you insights beyond just price data. Why Chainspy? - On-Chain Metrics: Track blockchain activity (e.g., transactions, active users) across multiple networks, all in one place. - Advanced Charting: Visualize blockchain data with integrated TradingView charts. Ideal for comparing blockchain performance over time. - Wealth Distribution: See how wealth is distributed across wallets and spot centralization trends.…
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AstraPixels▲267A pixel-art solar system at its real current positions.
Growth · 30d ago · astrapixels.com
Launched alongside, May 2026
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Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai


- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com