Perma
In the AI era, your data isn't safe. Unless we cant see it.
What it does
The cloud storage you're using was designed for a threat model that no longer exists. Every breach now feeds an AI. Every provider can read your files. Every ransomware crew has better tooling than the teams fighting them. Perma can't read your data. Not won't - can't. Client-side encryption, immutable hardware, $1/GB paid once. Attackers see cypiertext. It's unreadable to everyone, including us. Forever. We're offering 2GB free for everyone who wants to see what REAL permanent storage is.
Does the same job
all alternatives →
Cynative Security Research AgentJul 2026 · github.com · ▲97Ask your cloud anything without breaking prod. Read-only.
- ETEnd-to-End Encrypted Cloud Storage. All Open Source2014 · disk42.com · ▲42
- SASecurefs, a better transparent encryption filesystem2015 · github.com · ▲18
- EDEllipticc Drive – open-source cloud drive with E2E and PQ encryptionOct 2025 · ellipticc.com · ▲21
Hey HN, I’m Ilias, 19, from Paris. I built Ellipticc Drive, an open-source cloud drive with true end-to-end encryption and post-quantum security, designed to be Dropbox-like in UX but with zero access to your data, even by the host. What’s unique: Free 10GB for every user, forever. Open-source frontend (audit or self-host if you want) Tech stack: Frontend: Next.js Crypto: WebCrypto (hashing) + Noble (core primitives) Encryption: XChaCha20-Poly1305 (file chunks) Key wrapping: Kyber (ML-KEM768) Signing: Ed25519 + Dilithium2 (ML-DSA65) Key derivation: Argon2id → Master Key → encrypts all…
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
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Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 27d ago · cactuscompute.com
Launched alongside, May 2026
the whole month →
Parallel agents, diff reviewer, and multi-model comparisons
Dev tools · May 2026 · kilo.ai
- NW
Hey HN, Henry here from Cactus. We open-sourced Needle, a 26M parameter function-calling (tool use) model. It runs at 6000 tok/s prefill and 1200 tok/s decode on consumer devices. We were always frustrated by the little effort made towards building agentic models that run on budget phones, so we conducted investigations that led to an observation: agentic experiences are built upon tool calling, and massive models are overkill for it. Tool calling is fundamentally retrieval-and-assembly (match query to tool name, extract argument values, emit JSON), not reasoning. Cross-attention…
Life & fun · May 2026 · github.com
- FM
Dev tools · May 2026 · github.com