
Linear Diffs
A new way to review PRs, directly inside Linear
What it does
Introducing Linear Diffs to make reviewing code a fast and fluid experience native to Linear. You can now review diffs from any issue with a PR, iterate on further changes with agents, and ship code from Linear. All reviews in Linear sync back to GitHub, so the current state of review work is always clear. Linear Diffs is available now on all plans.
Does the same job
all alternatives →


- DGDiffswarm – GitHub-style review and workflow for any unified diffJan 2026 · diffswarm.dev · ▲8
Pipe the output of a `diff` command to Diffswarm to get back a persistent URL that contains the diff with workflow, comments, search, etc. Basically some of the helpful features that Github offers on PR diffs, just available for any arbitrary diff. I wrote this tool to use at work for documenting snapshot diff triaging, and also used it to help with the same thing on a couple Ty PRs. Example: https://diffswarm.dev/d-01k2gknwyq82f6x17zqf3apjxc
- GSGitHub style split diffs in your terminal2021 · github.com · ▲178
- ATA tool that lets you reorder the changes in GitHub PRs2024 · github.com · ▲7
A few friends and I noticed that many PRs are easier/quicker to review if the changes are reviewed in some order other than alphabetical by filename (which is the default in Github). So, we'd often write our PR descriptions to include a suggested review order: "review file xyz.py first, then abc.js, then foo.rs". We did this often enough that we built a chrome extension that lets us reorder diffs in the Github PR UI rather than needing to type out the suggested order in the description. Anecdotally, we've noticed that using this tool to reordering diffs makes it much easier for…
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- 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…
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