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Limitless – AI OSINT search and interactive intelligence sandboxes

Hacker News

Limitless – AI OSINT search and interactive intelligence sandboxes

Hi HN, I built Limitless because I wanted to create a unified, high-fidelity platform for Open Source Intelligence (OSINT), dark web collection, and interactive threat training. Most cybersecurity training systems are either standard slide decks or text-heavy questionnaires. At the same time, actual analyst tools are scattered across hundreds of disconnected command-line scripts and API wrappers. Limitless merges real analyst tools with a reactive, browser-based training simulator. The Ecosystem Components: 1. *Feynman (OSINT search): An intelligence search engine that maps digital footprints, uncovers hidden connections, and aggregates intelligence across 200+ online sources instantly. 2. *Sentinel (Darknet Agent):* An autonomous AI agent designed for deep-web collection, learning, and reasoning across darknet forums. 3. *Interactive Training Sandboxes:* Scenario-driven tutorials that teach cybersecurity operations through visual tools: * GEOINT Simulator: Uses Leaflet and coordinates to calculate physical proximity (Haversine formula), supporting tolerance radii and partial scoring. * Steganography Lab: Toggles Red/Green/Blue color channels and bitplane depths on canvas elements dynamically in the browser. * Draggable Chronology & Classification: Drags event timelines and classifies logs using `@dnd-kit`. * Audio & Video Intel: Custom playback speed modulators and zoom canvas overlays. Localization: Reactive, real-time translations supporting 9 languages (English, Spanish, Portuguese, French, Russian, Hebrew, Arabic, Japanese, and Hindi). You can explore the interactive intelligence based sandboxes for learning directly https://limitless-osint.com/ I’d love to hear your feedback on the investigative tools (Feynman & Sentinel), the workspace user experience, or what intelligence tools you would like to see us build next. Thanks!

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
91%91% 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: agent, user, visual · Missing: mac, agents, macos
72%72% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: platform · Missing: plus, intuitive, reviews
48%48% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: open source, ide, io · Missing: https docs, excited, just released
29%29% 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 · Strong signals: training, active · 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 · Strong signals: audio · 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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