Field notes on on-chain forensics, trading intelligence, and the tools shaping both.
Long-form analysis from the people building the forensic stack. We cover the things we live inside every day — holder maps, cluster detection, the TON and Hyperliquid intelligence landscape, and where AI is actually useful (vs. where it's noise). No press releases. No paid mentions.
Why Intel Maps: the TON forensic stack in 2026
Three forensic platforms. Three philosophies about what an on-chain investigation should look like. We pull each one apart on the dimensions that actually decide a trade — chain coverage, cluster math, infrastructure labeling, and what it costs to keep using it past month one.
Landscape
The Best TON Analytics Tools in 2026
TonAPI, Tonviewer, DexScreener TON, STON.fi explorer, Intel Maps — each one is great at a different job. We map who you should reach for, and when.
Methodology
How to Spot a Cabal: A Forensic Field Guide
The signals we look for in every token before we'd let anyone we know touch it: cluster shape, co-funding pattern, KOL entry timing, infrastructure mislabels. With math.
Thought leadership
AI in On-Chain Forensics: What's Real, What's Hype in 2026
Every analytics tool now ships an "AI" pill. We separate the genuinely load-bearing applications (clustering, labeling, narrative summarization) from the marketing.
Engineering
Integrating Alpie: Notes from Wiring a Reasoning Model into a Forensic Product
We swapped our generic AI summarizer for Alpie's alpie-32b reasoning model. The
integration writeup — production gotchas, the cost shape, and where reasoning models
actually earn their compute.
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