Literature Review Synthesizer
Synthesize academic notes into lit reviews with your LLM
What it does
PhD students read 50 to 200 papers for a single review. Notes in Obsidian? Fine. The synthesis part, where you actually figure out what the field is saying, takes weeks of manual work. This plugin handles it inside your vault, using your own API key. Nothing leaves your machine. No server, no account. Pick a folder of notes, run a synthesis, get a structured note back with frontmatter and backlinks.
Does the same job
all alternatives →- IBI built an Obsidian plugin to create notes from BibTeX2024 · github.com · ▲95
With this plugin you can create literature notes from BibTeX entries, display formatted reference lists, and instantly generate citations.
- OTOdin – the integration of LLMs with Obsidian note taking2023 · github.com · ▲160
Reading Inbox SynthesizerJun 2026 · community.obsidian.md · ▲4Turn your Obsidian web clipping backlog into reading memory
- NINotesOllama – I added local LLM support to Apple Notes (through Ollama)2024 · smallest.app · ▲156
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).
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