I know you are trying to generate some controversy/visibility, but i think if we are being transparent here, you know this is wrong. People prefer using larger (or reasoning) models, with much bigger diff in tok/sec just for quality in coding, it comes first. Even if i have a big edit to apply, like 5k tokens, 200-300ms of difference in edit time are nothing. Edit speed is definitely not a bottleneck for dev UX, quality is. A dev who wants to save 200ms every code change over quality is someone who well, i cannot relate. If im using 1-2 agents in parallel, most of the time the edits are already applied while im reviewing code from the other agents. But again maybe that's just me.

Speaking of quality, how do you measure it? Do you have any benchmarks? How big is the difference in error rate between the fast and large model?

Request: please provide a system prompt in the docs to help the llm generate the diff format that performs best w/ your models. LLMs frequently change the way they present diffs on upgrades and I don't want to be guessing which format is best.

EDIT: Please clarify your privacy policy. If my interpretation is correct, paying users will have their data retained and trained on? Is there any way to pay to use the service (w/o picking up the phone) and not have my data trained on?

  4.1 Use of Service Data

  Depending on your subscription tier:

  Free Tier: We may use your submitted code data to train our models, improve our Services, and develop new features.
  Engineer Tier: We may use your submitted code data to train our models, improve our Services, and develop new features, subject to the confidentiality provisions in your service agreement.
  Enterprise Tier: We do not use your submitted code data for any purpose other than processing your immediate request. Your code data is never used for model training or service improvement.

I used the provided HTML example on https://morphllm.com/dashboard/playground/apply. Without editing anything at all, I pressed apply.

Your model added a bunch of CSS even though that wasn't in the update instructions at all. It also added a contact section, which again, wasn't in the update instructions that your demo provided.

Morph is a tool for integrating the output of other LLMs and not an LLM itself? It doesn't generate 4500 tok/sec, it can edit 4500 tok/sec?

Morph v3 fast: Input: $1.20 / M tokens, Output $2.70 / M tokens

Gemini 2.5 Flash: $0.30 / M tokens, Output $2.50 / M tokens

(Source: OpenRouter)

Also, are there any benchmarks comparing your fast apply models to others like Relace or even Llama via Cerebras? I’m particularly interested in output accuracy.

[0] https://www.relace.ai/

I am assuming your models are not opensource/openweights?

https://fireworks.ai/blog/cursor

> 1) Raw inference speed matters [most] for dev UX—agree or disagree?

Or maybe incremental content-assist and full-file problem-solving are two significantly different uses, though they're both dev UX use cases.

Because they're confusingly similar, comparing them (and denigrate full-file solutions) wastes time/energy. You muddy your own message.

Just concentrate on showing the value of what you do where and when. To wit...

In the inference case, you're really using context to provide affordances -- next steps. In the full-file case, you're starting instead from a goal statement, with context providing constraints.

I think where you want to go is to show when the tool anticipates where you *should* go; i.e., the extent to which it can lead junior developers to the next step, and senior developers to the next constraint/issue they're ignoring.

I believe just as "attention is all you need" surprised people, this kind of bottom-up approach has more legs than people expect.

I understand the naked probability model is trained on world code corpus; what would interest me is whether you can also create a model that learns the developer's biases.

Then the work is to see the issues in the context, but address them in the order and manner that the developer would. Lock-in would occur because, well, the system understands me. And it would be particularly nice when Programmer A wants to code like Programmer B. If your assistant has a model of Programmer B, the assistant could guide Programmer A in that direction.

Now I can be wrong, faster!

2- I'm confused. Claude Code and a Neovim plugin I used both do edits/diffs. Are you saying they're actually rewriting entire files instead?

3- Aren't "simple tasks" just things you train the model on? If so, are you solving a bunch of simple tasks or offering custom training?

> No more slow full-file rewrites or brittle search-and-replace hacks.

Here's the thing - LLMs are already blazing fast. I commented the other day that you could probably write Chrome's entire code base in a couple months at average speed. The bottleneck isn't speed, it's accuracy; that's of course my opinion.

+: https://omarabid.com/claude-magic

First I added their models to my ~/Library/Application Support/io.datasette.llm/extra-openai-models.yaml file:

  - model_id: morph-auto
    model_name: auto
    api_base: https://api.morphllm.com/v1
    api_key_name: morph

  llm keys set morph
  # Paste in API key from https://morphllm.com/api-keys

  llm -m morph-auto '<code>$code</code><update>$update</update>' --save morph

  llm -t morph -p code "$(cat orig.txt)" -p update "$(cat update.txt)"

Example transcript here: https://gist.github.com/simonw/de67818603d448a3fee788ace2976...

One thing that worries me: since it's using XML-style tags <code> and <update>, if my own source code contains those tags I expect it may get confused.

I'm also really curious about the XML tool calls in the documentation. I have not heard of this being the norm for tools like Cursor. Is that still the case? I feel like I'm pretty in the know about this stuff but must have missed that trend.

Very few companies can or are willing to answer that.

https://man7.org/linux/man-pages/man1/patch.1.html

Yeah, I love reviewing and debugging thousands of lines of buggy and dirty AI generated code. Who cannot love it?

Would love to chat about integrating the models into Kilo Code if you’re interested

You can contact me at brendan [at] kilocode [dot] ai

because ruby no need corecting. It works.