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Crypto-Recovery

Python-based cryptocurrency wallet forensics suite supporting 40+ wallet types on Windows, Linux, and macOS. Features encrypted vault analysis, wallet detection, seed recovery, multi-chain address derivation, threaded processing, and TXT/CSV/JSON export. Designed for security research, compliance, incident response, and blockchain investigations.

Details

External ID
1371730939
Source
GITHUB
Company
—
Product
Crypto-Wallet-Recovery
Website domain
github.com
Launched
Sept. 15, 2026
Cohort
—
Upvotes
35
Upvotes percentile
0.7558288496028696
Tags
bip39-recovery, bip39-wallet, bip44-recovery, bitcoin-address-recovery, btc-recovery, btc-wallet, crypto-fund-recovery, crypto-wallet-recover, lost-bitcoin-wallet, lost-bitcoins-wallet, lost-cryptocurrency-recovery, lost-wallet-recovery, recover-btc
Fetched at
Sept. 18, 2026, 4:41 a.m.
Updated at
Sept. 18, 2026, 4:41 a.m.

Enrichment

Theme
cryptocurrency utilities and wallet tools
Vertical
Security
Function
Compliance & governance
Audience
B2B
AI stance
Not AI
Project type
Hobby / open-source project
Normalized one-liner
cryptocurrency wallet forensics and recovery suite for blockchain investigations
Manually corrected
False

Could you build this?

Partial The orchestrating Python CLI and standard BIP derivation logic are vibe-codeable, but analyzing encrypted vault formats and parsing proprietary wallet structures across 40+ wallet types requires deep cryptographic and forensics knowledge.

What it would actually take: The project requires custom parsers for dozens of distinct wallet file formats (e.g., Berkeley DB, LevelDB, custom encrypted JSON formats from Electrum, MetaMask, Core wallets, etc.). The difficult part is reverse-engineering key-derivation functions (KDFs like scrypt, Argon2, PBKDF2) and specific memory/file carving techniques to extract partial seeds or plaintext descriptors from unallocated disk space or encrypted blobs. Implementing this demands deep domain expertise in digital forensics, low-level data extraction, and wallet architecture.

Competitors

Other products that read as similar to this one — 199 launches clear the similarity bar, closest 8 shown.

Attention rank: #47 of 200 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).

Launched 320 days after the earliest competitor.

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