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Autobtc.ai – Your Personal AI Manager for Bitcoin and Lightning

Hacker News

Autobtc.ai – Your Personal AI Manager for Bitcoin and Lightning

Hi HN community, I'm excited to share AutoBTC.ai, a project close to my heart, conceived from a need to make Bitcoin and Lightning node operations more accessible and efficient. Recently, we've launched the beta version that currently enables several basic lightning node operations. AutoBTC.ai is designed to automate tasks, efficiently manage lightning node liquidity as per user needs, and handle payments securely. Importantly, all these operations are enforced by the node and the browser, making sure no harm can come even if an AI turns malicious. In the pursuit of bringing more people on board with Bitcoin and Lightning, AutoBTC.ai is built to be as user-friendly as possible. One of its key features is the direct encrypted connection from your browser to your Lightning node, making operations smooth and secure. In addition, it has robust security features that mask sensitive data in the browser and only unmask it when AI returns an answer, further enhancing trust and ease of use. We're looking forward to exciting developments, such as the integration of advanced features like autopilot for liquidity, accounting, security operations, cold storage, and a fiat on-off ramp. Here's the link to our beta version: https://autobtc.ai . I invite everyone to give it a try (At present, AutoBTC.ai operates exclusively with Lightning payments) We're truly eager for your insights and experiences. How could AutoBTC fit into your operations? What features would you find most beneficial?

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Indie HackersFits the IH revenue-focused audience · Strong signals: efficiently · Missing: supports, reddit linkedin, podcasting
90%90% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: user, tasks · Missing: mac, agents, macos
74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal · Missing: mobile apps, ios, entrepreneurs
41%41% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: exclusive, friendly, efficient · Missing: plus, platform, intuitive
41%41% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, io · Missing: https docs, just released, exist
35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
13%13% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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