* Added `--repo` and `--name` flags for running only examples with a
specific name, or repo (substring matching)
* Fixed a race condition that caused hangs when running multiple
examples at the same repo and sha
* Fixed a bug where scoring was completely wrong because I had passed
the arguments to `apply_diff_to_string` in the wrong order
* The current evals now run quickly and without errors.
Release Notes:
- N/A
1. Handle diffs with no trailing new lines
2. ep: Don't assume workdir name in edit history paths
3. Fix `imitate_human_edits()` for pure insertions
Release Notes:
- N/A
---------
Co-authored-by: Agus Zubiaga <agus@zed.dev>
1. `apply_diff` will create a file if the diff says so (by starting with
`--- /dev/null`)
2. Update examples format to match recent changes
3. `ep split-commits` can work with a stream of inputs and generate `n`
samples per input
4. Unicode handling fixes
Release Notes:
- N/A
Generates a training or evaluation example from a
chronologically-ordered commit. This is a port from the Python codebase
(except for the reorder_patch.rs, which was originally written in Rust
in).
Release Notes:
- N/A
* Fix some bugs in capture of EP examples from running app
* Tweak markdown format for EP examples
* Store repo and revision in TOML front matter
* Represent cursor position using a comment line
* Allow multiple expected patches in evals
* Remove line-based scoring criteria for evals
* Add a `synthesize` subcommand to the EP cli that generates examples
from git commits
Release Notes:
- N/A
This PR improves the `edit prediction: Capture Example` in several ways:
* fixed bugs in how the uncommitted diff was calculated
* added a `edit_predictions.examples_dir` setting that can be set in
order to have the action automatically save examples into the given
folder
* moved the action into the `edit_predictions` crate, in preparation for
collecting this data passively from end users, when they have opted in
to data sharing, similar to what we did for Zeta 1
Release Notes:
- N/A
Follow-up of https://github.com/zed-industries/zed/pull/44887
Trims the worktree trust mechanism to the actual `worktree`s, so now
"global", workspace-level things like `prettier`, `NodeRuntime`,
`copilot` and global MCP servers are considered as "trusted" a priori.
In the future, a separate mechanism for those will be considered and
added.
Release Notes:
- N/A
Closes https://github.com/zed-industries/zed/issues/12589
Forces Zed to require user permissions before running any basic
potentially dangerous actions: parsing and synchronizing
`.zed/settings.json`, downloading and spawning any language and MCP
servers (includes `prettier` and `copilot` instances) and all
`NodeRuntime` interactions.
There are more we can add later, among the ideas: DAP downloads on
debugger start, Python virtual environment, etc.
By default, Zed starts in restricted mode and shows a `! Restricted
Mode` in the title bar, no aforementioned actions are executed.
Clicking it or calling `workspace::ToggleWorktreeSecurity` command will
bring a modal to trust worktrees or dismiss the modal:
<img width="1341" height="475" alt="1"
src="https://github.com/user-attachments/assets/4fabe63a-6494-42c7-b0ea-606abb1c0c20"
/>
Agent Panel shows a message too:
<img width="644" height="106" alt="2"
src="https://github.com/user-attachments/assets/0a4554bc-1f1e-455b-b97d-244d7d6a3259"
/>
This works on local, SSH and WSL remote projects, trusted worktrees are
persisted between Zed restarts.
There's a way to clear all persisted trust with
`workspace::ClearTrustedWorktrees`, this will restart Zed.
This mechanism can be turned off with settings:
```jsonc
"session": {
"trust_all_worktrees": true
}
```
in this mode, all worktrees will be trusted by default, allowing all
actions, but no auto trust will be persisted: hence, when the setting is
changed back, auto trusted worktrees will require another trust
confirmation.
This settings switch was added to the onboarding view also.
Release Notes:
- Introduced worktree trust mechanism, can be turned off with
`"session": { "trust_all_worktrees": true }`
---------
Co-authored-by: Matt Miller <mattrx@gmail.com>
Co-authored-by: Danilo Leal <daniloleal09@gmail.com>
Co-authored-by: John D. Swanson <swanson.john.d@gmail.com>
This PR adds a staff-only button to the edit prediction menu for
capturing your current editing session as edit prediction example file.
When you click that button, it opens a markdown tab with the example. By
default, the most recent change that you've made is used as the expected
patch, and all of the previous events are used as the editing history.
<img width="303" height="123" alt="Screenshot 2025-12-14 at 6 58 33 PM"
src="https://github.com/user-attachments/assets/600c7bf2-7cf4-4d27-8cd4-8bb70d0b20b0"
/>
Release Notes:
- N/A
We were panicking whenever something went wrong with an example in the
CLI. This can be very disruptive when running many examples, and e.g a
single request fails. Instead, if running more than one example, errors
will now be logged alongside instructions to explore and re-run the
example by itself.
<img width="1454" height="744" alt="CleanShot 2025-12-12 at 13 32 04@2x"
src="https://github.com/user-attachments/assets/87c59e64-08b9-4461-af5b-03af5de94152"></img>
You can still opt in to stop as soon as en error occurs with the new
`--failfast` argument.
Release Notes:
- N/A
- Limit status lines to 10 in case `max_parallelism` is specified with a
grater value
- Handle logging gracefully rather than writing over it when clearing
status lines
Release Notes:
- N/A
This PR restructures the commands of the Edit Prediction CLI (now called
`ep`), to support some flows that are important for the training
process:
* generating zeta2 prompt and expected output, without running
predictions
* scoring outputs that are generated by a system other than the
production code (to evaluate the model during training)
To achieve this, we've restructured the CLI commands so that they all
take as input, and produce as output, a consistent, uniform data format:
a set of one or more `Example` structs, expressible either as the
original markdown format, or as a JSON lines. The `Example` struct
starts with the basic fields that are in human-readable eval format, but
contain a number of optional fields that are filled in by different
steps in the processing pipeline (`context`, `predict`, `format-prompt`,
and `score`).
### To do
* [x] Adjust the teacher model output parsing to use the full buffer
contents
* [x] Move udiff to cli
* [x] Align `format-prompt` with Zeta2's production code
* [x] Change score output to assume same provider
* [x] Move pretty reporting to `eval` command
* [x] Store cursor point in addition to cursor offset
* [x] Rename `edit_prediction_cli2` -> `edit_prediction_cli` (nuke the
old one)
Release Notes:
- N/A
---------
Co-authored-by: Oleksiy Syvokon <oleksiy@zed.dev>
Co-authored-by: Agus Zubiaga <agus@zed.dev>
Co-authored-by: Ben Kunkle <ben@zed.dev>
This PR partially implements a knowledge distillation data pipeline.
`zeta distill` gets a dataset of chronologically ordered commits and
generates synthetic predictions with a teacher model (one-shot Claude
Sonnet).
`zeta distill --batches cache.db` will enable Message Batches API. Under
the first run, this command will collect all LLM requests and upload a
batch of them to Anthropic. On subsequent runs, it will check the batch
status. If ready, it will download the result and put them into the
local cache.
Release Notes:
- N/A
---------
Co-authored-by: Piotr Osiewicz <24362066+osiewicz@users.noreply.github.com>
Co-authored-by: Ben Kunkle <ben@zed.dev>
Max Brunsfeld