
Gryffi
Build interactive training journeys on a visual canvas
What it does
Gryffi transforms documentation into interactive journeys. It features a visual drag and drop builder for linear or branching training paths. The integrated AI Guides use retrieval augmented generation to answer questions in 14 languages with source citations. Unlike traditional platforms, Gryffi offers 360 degree views for location training and is 100 percent EU-hosted. It simplifies access through password-free magic links for end users and syncs with Microsoft 365.
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DocentPro — Explore the world2024 · ▲280Your AI Travel Guide to discover stories of your surrounding

- VAVisual A* pathfinding and maze generation in Python2024 · github.com · ▲135
I was fascinated reading through another recent HN submission about a highly efficient implementation of A* in Lisp, which got me thinking about how I could do something similar in Python. However, these kinds of pathfinding algorithms really need complex terrain/mazes with interesting obstructions to showcase what they can do and how they work. So, I started thinking about how I could generate cool and diverse random "mazes" (they aren't really mazes, but I'm not sure what the best term is). I got a bit carried away thinking of lots of different cool ways to generate these mazes, such…
- IMI made a better Perplexity for developers2024 · devv.ai · ▲185
Hi HN, I am Jiayuan, and I'm here to introduce a tool we've been building over the past few months: Devv (https://devv.ai). In simple terms, it is an AI-powered search engine specifically designed for developers. Now, you might ask, with so many AI search engines already available—Perplexity, You.com, Phind, and several open-source projects—why do we need another one? We all know that Generative Search Engines are built on RAG (Retrieval-Augmented Generation)[1] combined with Large Language Models (LLMs). Most of the products mentioned above use indexes from general search engines…

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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, March 2026
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Switch from ChatGPT to Claude with import memory feature
AI · Mar 2026 · claude.com


