Alternatives
Products that do what Recall Local does
Offline"Corporate Brain" to organize files & chat with data
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Hi Hackers, Excited to share a macOS app I've been working on: https://recurse.chat/ for chatting with local AI. While it's amazing that you can run AI models locally quite easily these days (through llama.cpp / llamafile / ollama / llm CLI etc.), I missed feature complete chat interfaces. Tools like LMStudio are super powerful, but there's a learning curve to it. I'd like to hit a middleground of simplicity and customizability for advanced users. Here's what separates RecurseChat out from similar apps: - UX designed for you to use local AI as a daily driver.…
2024 · recurse.chat
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An open source approach to locally record everything you view on your Apple Silicon computer. Note: Relies on Apple Silicon, and configured to only produce Apple Silicon builds. I think the idea of recording everything you see has the potential to change how we interact with our computers, and believe it should be open source. Also, from a privacy / security perspective, this is like... pretty scary stuff, and I want the code open so we know for certain that nothing is leaving your laptop. Even logging to Sentry has the potential to leak private info.
2023 · github.com
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Hi folks, we're Debanjum and Saba. We created Khoj as a hobby project 2+ years ago because: (1) Search on the desktop sucked; we just had keyword search on the desktop vs google for the internet; and (2) Natural language search models had become good and easy to run on consumer hardware by this point. Once we made Khoj search incremental, I completely stopped using the default incremental search (C-s) in Emacs. Since then Khoj has grown to support more content types, deeper integrations and chat (using ChatGPT). With Llama 2 released last week, chat models are finally good and easy enough to…
2023 · github.com
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This lets you talk to local LLMs in Apple Notes. I saw Obsidian Ollama (https://github.com/hinterdupfinger/obsidian-ollama) and thought it was handy, but I'm too lazy to migrate away from the Apple ecosystem, so I quickly hacked this together. I tend to use Notes as a scratchpad for prompts, so it's nice to do some quick inference without leaving the app. Notes doesn't really support plugins so I'm using the macOS accessibility API for reading selections and then stream responses using the clipboard (not ideal but it works).
2024 · smallest.app
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I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…
2025 · github.com
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Stop wasting tokens and re-explaining your project every session. Recall gives Claude Code durable memory — entirely offline. - raiyanyahya/recall
Jun 2026 · github.com
- 16UL
Hi everyone, Just wanted to share a use case where local LLMs are genuinely helpful for daily workflows: file organization. I've been working on a C++ desktop app called AI File Sorter – it uses local LLMs via `llama.cpp` to help organize messy folders like `Downloads` or `Desktop`. Not sort files into folders solely based on extension or filename patterns, but based on what each file actually is supposed to do or does. Basically: what would normally take me a great deal of time for dragging and sorting can now be done in a few. It's cross-platform (Windows/macOS/Linux), and fully…
2025 · github.com
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I've been working on this tool that lets you build a personal knowledge graph from articles, blog posts, podcasts, YouTube videos, and other content you find interesting online. You can safely forget everything and trust that Recall will resurface it when something new that is related comes up. Looking forward to your thoughts and feedback on how it could be improved! The original version of Recall was posted last year nov on HN: https://news.ycombinator.com/item?id=33425947 Since then I have pivoted to a browser extension.
2023 · recall.wiki
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2022 · github.com
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How It Works - Offline Indexing: Docs are processed and embedded using the GTE-small model at build time. Browser-Based Magic: - SQLite database (stored in the browser) for vector search. - Local embedding model for query processing. - Local LLaMA model for response generation using WebLLM. - Everything Happens Locally: No data leaves the user’s device. Key Benefits - No API Costs: Everything runs in the browser—zero backend expenses. - Unlimited Chats: No rate limits or usage restrictions. - Privacy-First: Your data stays on your device, always. You can find the code here:…
2024 · docs.akiradocs.ai
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Recall Memory is a free Sandboxed Mac App which let's you scroll through time and allows you to "Recall" what you were doing earlier. Recall works by capturing the active window every second and only saves screenshots with significant changes. Making it easy to scroll through what you were doing earlier. All data is processed and always stored locally.
2024 · recallmemory.app
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2014 · cdn.rawgit.com
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Hello HN, I built Minute because I wanted searchable meeting notes without sending recordings or transcripts to a cloud service. It captures microphone and optional system audio, transcribes locally with Whisper, and generates summaries, decisions, and action items using a local LLM through llama.cpp. After the initial model download, everything runs on-device and in-process. There’s no account, server, or telemetry. The app is built with Tauri, Rust, React, and Metal acceleration. Notes are stored locally as plain text, JSON, and WAV files. I built the project end-to-end with Codex as a…
Jul 2026 · github.com
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I wrote a small local tool to transcribe audio notes (Whisper/Parakeet). Code: https://github.com/bilawalriaz/lazy-notes I wanted to process raw transcripts locally without OpenRouter. Llama 3.2 3B with a prompt was decent but incomplete, so I tried SFT. I fine-tuned Llama 3.2 3B to clean/analyze dictation and emit structured JSON (title, tags, entities, dates, actions). Data: 13 real memos → Kimi K2 gold JSON → ~40k synthetic + gold; keys canonicalized. Chutes.ai (5k req/day). Training: RTX 4090 24GB, ~4h, LoRA (r=128, α=128, dropout=0.05), max seq 2048,…
2025 · bilawal.net
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