
SOWA Privacy
AI Privacy. Simple.
What it does
Every prompt you send to a AI Chatbot may contain personal data - names, case numbers, client details - sent straight to servers outside your control. SOWA Privacy intercepts it before it ever leaves your browser. Three-layer PII detection running entirely on-premise: Zero cloud. Zero infrastructure changes. Zero disruption to your workflow. Open source, fully auditable, and GDPR-conscious out of the box. Download the free Chrome Extension and make sure none of your data is yours to lose.
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- LPLocal Privacy Firewall-blocks PII and secrets before ChatGPT sees themDec 2025 · github.com · ▲111
OP here. I built this because I recently caught myself almost pasting a block of logs containing AWS keys into Claude. The Problem: I need the reasoning capabilities of cloud models (GPT/Claude/Gemini), but I can't trust myself not to accidentally leak PII or secrets. The Solution: A Chrome extension that acts as a local middleware. It intercepts the prompt and runs a local BERT model (via a Python FastAPI backend) to scrub names, emails, and keys before the request leaves the browser. A few notes up front (to set expectations clearly): Everything runs 100% locally. Regex detection…

- ALA local-first, reversible PII scrubber for AI workflowsDec 2025 · medium.com · ▲38
Hi HN, I’m one of the maintainers of Bridge Anonymization. We built this because the existing solutions for translating sensitive user content are insufficient for many of our privacy-concious clients (Governments, Banks, Healthcare, etc.). We couldn't send PII to third-party APIs, but standard redaction destroyed the translation quality. If you scrub "John" to "[PERSON]", the translation engine loses gender context (often defaulting to masculine), which breaks grammatical agreement in languages like French or German. So we built a reversible, local-first pipeline for Node.js/Bun. Here…
- LBLocal Browser AIOct 2025 · blog.alexewerlof.com · ▲15
Using the new Prompt API to build a free open source privacy-first chat extension that runs on Chrome or Edge on Linux, Mac, Windows and ChromeOS
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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