Mighty – let AI agents use private data safely

Hacker News

Hey HN — I’m Johnny, co-founder of Mighty ( https://mightynetwork.ai ). The problem AI agents can think, but the moment they need your data they hit a wall — security teams won’t let PII, payroll, or customer records leak out. OAuth exists for humans, not code running on its own. Our take (super-hero style) • Headquarters — an end-to-end–encrypted data vault (client-side 256-bit) where you park anything private. • Sidekick — a tiny Python SDK that handles key exchange, token refresh, and per-resource checks. Drop it in like a trusty side-kick and your agent suddenly has clearance. • Confidential Compute — the “secret lair” where workloads run so we never see your plaintext. Together they give you a 1-click “Sign in with Google”-style flow for autonomous code. Today we support OAuth 2.0; SAML & full SSO are on the road-map. What you can build right now • A finance bot that reconciles bank transactions without exposing PII • A contract reviewer that only sees the folders it’s allowed to • An internal helper that can read HR data but nothing else Use Mighty SDK (pip install mighty-sdk-core) with your favorite AI frameworks. HN readers can start for free with personal account and for a limited time offer, we are offering compute credits with paid plans and we are throwing in a 30-day happiness guarantee. Why we’re posting We’ve spent a year talking to developers at startups and FAANG-size orgs; pilots are under way, but we want brutally honest feedback on the API, threat model, or any gaps you spot. Quick 3-min Loom demo: https://www.loom.com/share/ae1a4ea45f894648945ab859af578165 Thanks for taking a look! We’d love to hear if this unblocks something you’re building — or why it doesn’t.

Visit product View on Hacker NewsJun 25, 2025

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Product HuntOn track for Day 1 leaderboard · Strong signals: agents, agent, model · Missing: mac, macos, cursor

95%95% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: exist, ide, io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

TrustMRRLess likely to generate early MRR · Strong signals: personal, google, way · Missing: mobile apps, ios, entrepreneurs

50%50% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

24%24% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: paid · Missing: web3, chat, crypto

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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