Codiff, a local diff review tool
Nowadays I review a lot of code locally that was written by llms. I used to review my own code using git + delta. It started to feel limiting with the amount of code written by llms. When looking at a large diff on Friday I pointed an llm at diffs.com and trees.software and told it to build an app. It only took 16 minutes, is extremely fast for large diffs, beautiful and minimal. Today I polished it up and added all the features that I need. It has file filters, search, an llm walkthrough mode, and review comments that you can paste back into your llm. I will be using Codiff a lot, and can…
In plain words
Codiff is a local diff review tool designed for developers who need to examine large code changes, particularly those generated by language models. It offers file filtering, search functionality, an LLM walkthrough mode for guided reviews, and the ability to add review comments that can be exported back to AI tools. The tool prioritizes speed and minimal design while handling substantial diffs more effectively than traditional git-based review workflows.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Nowadays I review a lot of code locally that was written by llms. I used to review my own code using git + delta. It started to feel limiting with the amount of code written by llms. When looking at a large diff on Friday I pointed an llm at diffs.com and trees.software and told it to build an app. It only took 16 minutes, is extremely fast for large diffs, beautiful and minimal. Today I polished it up and added all the features that I need. It has file filters, search, an llm walkthrough mode, and review comments that you can paste back into your llm. I will be using Codiff a lot, and can finally review the large diff from Friday that led me to build this If you like it, fork it!
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.
AI · 16d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com


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 · 26d 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