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Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec

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Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec

#31

Post by ashwindharne »

deepdarkforest wrote: post #9

> 1) Raw inference speed matters more than incremental accuracy gains for dev UX—agree or disagree? 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, 200-300m…

I do find that having inference happen ~50% faster is much more valuable to my workflow than a single digit accuracy increase. If I'm going to have to check that the changes are correct anyways, getting more iterations in faster feels much better than incremental accuracy.

There's definitely a tipping point though. If the accuracy gains are so high that I can check its work less carefully or less often, the benefits of inference speed are effectively nil.

Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec

#33

Post by treyd »

bhaktatejas922 wrote: post #10
amelius wrote: post #8

Can't you ask these LLMs to simply output a patch file? https://man7.org/linux/man-pages/man1/patch.1.html

you can - but they dont work reliably in practice. Common issues include search match fails, missing commas in replaced items (model doesnt have surround context while replacing), and a few other error cases. This issues are much worse for scattered edits across a file from real world queries (ex: make this page look nicer). Patches tend to work fine for single line or extremely focused edits though - Cursor uses s&r…

I wonder if it'd be feasible to have a much smaller model that could go in and correct these meshing issues that require simpler reasoning?

Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec

#34

Post by simonw »

This uses an OpenAI-compatible endpoint, so got this working with my https://llm.datasette.io/ CLI tool.

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
Then I added the API key like this:

  llm keys set morph
  # Paste in API key from https://morphllm.com/api-keys
Then I saved an LLM template with their prompting pattern:

  llm -m morph-auto '$code$update' --save morph
Now I can run operations like this:

  llm -t morph -p code "$(cat orig.txt)" -p update "$(cat update.txt)"
The -t option is the template I named when I ran --save. The -p name value options then set the content for the template $code and $update variables.

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

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

Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec

#35

Post by bhaktatejas922 »

halfjoking wrote: post #28
handfuloflight wrote: post #2

Is there anyway to bring this into Claude Code?

Make an MCP server, and turn off the Write|Edit|MultiEdit tools? Actually - that's what this company should do. It should be an MCP server so anyone could plug it into any agent with a url and an API key.

great idea! we'll have one up soon :)

Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec

#36

Post by bhaktatejas922 »

Aurornis wrote: post #26
bhaktatejas922 wrote: post #12

Earlier quoted context omitted.

I think it depends - the actual thing to measure it to keep a developer in flow state. Many errors as well as latency break this. To be brief yes, accuracy comes first. Quality is measured 2 main ways: 1) End-to-end: User query -> to task resolution. These are aider style benchmarks answering the question of actual task completion 2) Apply Quality: Syntax correctness, character diff, etc.. The error rate for large vs…

> the actual thing to measure it to keep a developer in flow state. Personally, I find flow state hard to achieve when I constantly have to switch modes to debugging LLM output or an edit error that I missed. When the majority of time is spent waiting for the main LLM to think, I will always wait a few extra seconds for a better edit than risk having to spend multiple cycles playing find-the-bug because something did…

Like most things its a tradeoff. Developer tolerance for errors is extremely low - but the error rate for Fast Apply is even lower

Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec

#37

Post by [deleted] »

simonw wrote: post #34

This uses an OpenAI-compatible endpoint, so got this working with my https://llm.datasette.io/ CLI tool. 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 Then I added the API key like this: llm keys set morph # Paste in API key from https://morphllm.com/api-ke…

[deleted]

Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec

#38

Post by johnfn »

bigyabai wrote: post #11
deepdarkforest wrote: post #9

> 1) Raw inference speed matters more than incremental accuracy gains for dev UX—agree or disagree? 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, 200-300m…

The marketing language seems to suggest they're insecure over quality and want to promote quantity. But I'm in the same boat as you - I would happily take 10 tok/sec of a correct answer instead of wasting an hour curating 4500 tok/sec throwaway answers. Benchmark performance matters 100x more than your latency. If these "hot takes" extend into Morph's own development philosophy, then I can be glad to not be a user.

Anyone can get 10 tok/sec - just tell the model to output the entire file with changes, rather than just the delta.

Whatever LLM you're using will have a baseline error rate a lot higher than 2%, so you're going to be reviewing all the code it outputs regardless.

Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec

#39

Post by bhaktatejas922 »

simonw wrote: post #34

This uses an OpenAI-compatible endpoint, so got this working with my https://llm.datasette.io/ CLI tool. 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 Then I added the API key like this: llm keys set morph # Paste in API key from https://morphllm.com/api-ke…

Wow that was fast - this is awesome. it shouldnt be a problem unless your code has both and internally. 1 or the other should be fine

Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec

#40

Post by seanw265 »

Last time I looked into Morph, I noticed you weren’t yet on OpenRouter. I see that’s changed, but it looks like only an older model is listed. Any plans to be more active there?

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.

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