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

110•bhaktatejas922•5h ago

Hey HN, I’m Tejas at Morph. We’ve built a blazing-fast model for applying AI-generated code edits directly into your files at 4,500+ tokens/sec. No more slow full-file rewrites or brittle search-and-replace hacks.

Here's a demo video: https://www.youtube.com/watch?v=LdT8epGHJPk.

Why? AI spits out code that can’t reliably be inserted into existing code. Full file rewrites, brittle search-and-replace hacks are too slow, expensive, or error-prone.

Morph's approach:

- Your agent outputs edits “lazily”, referencing unmodified lines in the existing file (ex: // ...existing code...)

- Morph instantly applies these edits to a file using our Fast Apply model + speculative decoding against the original file, making AI patches fast, reliable, and production-ready.

This approach was pioneered by Cursor last year, but their models aren’t available as APIs—so we built Morph for developers everywhere (with a large free tier!)

Live demo (no signup): https://morphllm.com/dashboard and docs: https://docs.morphllm.com/quickstart

We have 2 Fast Apply models: morph-v3-fast - 4500+ tok/sec, and morph-v3-large - 2500+ tok/sec. These models power Fast Apply at create.xyz, databutton, continue.dev, and more!

We also provide retrieval models for embedding + reranking. Next Up: Inline Edit Model (Cmd-K): Extremely fast inline edits - keep dev flow state; and Morph Tab API: Our Next Edit Prediction model guesses your next code edit + action with sub-500ms latency. It's currently in private beta, but you can request early access here: https://morphllm.com/tab

Hot takes:

1) Raw inference speed matters more than incremental accuracy gains for dev UX—agree or disagree?

2) Full-file rewrites by frontier models are legacy—Fast Apply edits win on speed, cost, reliability.

3) As benchmarks on narrow tasks saturate to 99%+, complexity is shifting from single frontier models to specialized inference-optimized models. As frontier models move upmarket, they'll leave simple tasks behind, and they'll be used to do tasks only frontier models can do

We’d love to hear your ideas and experiences with coding agents!

Comments

handfuloflight•5h ago

Is there anyway to bring this into Claude Code?

bhaktatejas922•5h ago

There might be a way to using their new hooks commands, but out of the box, not yet. email us if you want to make it happen!

https://docs.anthropic.com/en/docs/claude-code/hooks

booli•4h ago

If this proves the way forward, it will be in Claude Code soon enough natively

koakuma-chan•4h ago

There is already https://www.relace.ai/, albeit not as blazing fast at mere 4300 tok/s

bhaktatejas922•4h ago

Perhaps. Boris from the Claude Code team shares a bit about their view here https://www.youtube.com/watch?v=Yf_1w00qIKc

My read is that despite Claude moving upmarket in what it can do, they are keen on clinging to all the (token heavy) tasks they're leaving behind

halfjoking•3h ago

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.

bhaktatejas922•3h ago

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

amelius•4h ago

Can't you ask these LLMs to simply output a patch file?

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

bhaktatejas922•4h ago

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/patches for single line edits:

https://github.com/x1xhlol/system-prompts-and-models-of-ai-t...

treyd•3h ago

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?

bhaktatejas922•2h ago

hm maybe but correction/issue detection is a much harder task for models. If you pipe back the errors in it could work, but personally still see Fast Apply as the better approach

deepdarkforest•4h ago

> 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, 200-300ms 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?

bigyabai•4h ago

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.

bhaktatejas922•3h ago

There's no amount of error rate that's acceptable to us - edits should always be correct. We've just found anecdotally the saving users time is just provably also very important for churn, retention and keeping developer flow state, right after accuracy.

bigyabai•3h ago

Then why are you using a custom model instead of an industry-leading option?

I don't mean to be rude, but I can't imagine you're selling a product on-par with Claude 3.7. Some level of performance tradeoff has to be acceptable if you prioritize latency this hard.

bhaktatejas922•3h ago

We're not - our model doesn't actually think up the code changes. Claude-4 or Gemini still writes the code, we're just the engine that merges it into the original file.

Our whole thesis is that Claude and Gemini are extremely good at reasoning/coding - so you should let them do that, and pass it to Morph Fast Apply to merge changes in.

johnfn•2h ago

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.

bhaktatejas922•1h ago

yeah even claude is well over 11% error rates with search and replace

IanCal•1h ago

This is a code editing model. 10 tokens per second editing may as well not exist for any interactive use case.

bhaktatejas922•4h ago

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 fast is around 2%. If you're doing code edits that are extremely complex or on obscure languages - large is the better option. There's also an auto option to route to the model we think is best for a task

deepdarkforest•3h ago

Glad to hear quality comes first! Then I assume you have some public benchmarks like the ones you mention that are reproducible? I could only find this graph https://docs.morphllm.com/guides/apply but there is no mention of what it refers to, what data it used etc.

candiddevmike•3h ago

I don't believe anyone can be in some kind of "flow state" while waiting on LLM responses. I think it's funny that we complained for years about C and others being slow to compile and now folks are fine waiting seconds++ everytime they want to change something.

bhaktatejas922•3h ago

how so? Is your view that flow state at all isnt a thing, or just with using LLMs?

candiddevmike•3h ago

Flow state is 100% a thing, it's just impossible with LLMs (at least, for me). I can't be blocked waiting on things during a flow state or my mind starts wondering to other places.

ada1981•3h ago

I've had the opposite experience.

bhaktatejas922•3h ago

same

bhaktatejas922•3h ago

Fast Apply definitely helps with keeping flow state and is a large part of Cursor's success

Personally I work on multiple repos at a time to solve for this

0x457•2h ago

I do it like simultaneous exhibition in chess:

- Multiple repos or independent changes in monorepo

- First round of changes idgaf about anything beyond public interface and unit tests

   - I review public interface and make changes if needed
  
   - I review unit tests it wrote to see that at least from the outside it looks alright.

 - here I either:
   
   - make more unit tests (features, edge cases and make it write code for it)

   - polish what it generate

klank•2h ago

Time really is a flat circle. My software career started with me archaically flipping characters in a file I vaguely understood with long pauses waiting on magic compilers to give me my actual output.

Now it's dying in the same place. Thankfully I got to spend the brunt of my career working through the fun, intermediate years.

bhaktatejas922•1h ago

I've never had so much fun coding in my life - you should definitely give it a try again!

klank•1h ago

Thanks, I appreciate the good vibes.

However, it's kind of a trope for me at this point that people assume a negative opinion of using generative AI in the development process is due to a lack of experience using it.

simonw•2h ago

Have you tried any of the ludicrously fast LLM demos yet?

https://inference.cerebras.ai/ and https://groq.com/ and https://deepmind.google/models/gemini-diffusion/ (waitlisted) are all 10 to 100x faster than regular models, which really does have a meaningful impact on how I interact with them because I don't have to disengage for 15+ seconds while I wait for a response.

I have video demos of a few of those: https://simonwillison.net/2024/Oct/25/llm-cerebras/ and https://simonwillison.net/2024/Oct/31/cerebras-coder/ and https://simonwillison.net/2025/May/21/gemini-diffusion/

Aurornis•3h ago

> 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 didn't get applied correctly somewhere.

bhaktatejas922•3h ago

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

ashwindharne•3h ago

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.

bhaktatejas922•2h ago

exactly. The point is that none of the users even realize a model is doing the apply - it should be so accurate and fast that it feels like its not there

smrtinsert•2h ago

Slow is smooth and smooth is fast.

bhaktatejas922•2h ago

and speculative edits is faster

Cort3z•2h ago

As far as i understand, this is not +-300ms. It is 300ms vs. 10 sec or something. That is a huge difference. I personally find the time to wait for these larger models a limiting factor. It’s also probably a resource waste for fairly simple task like this. (Compared to the general function approximation of the llms)

But I honestly feel like the task of smartly applying edits falls somewhat within traditional coding tasks. What about it is so difficult it could not be done with a smart diffing algorithm?

deepdarkforest•37m ago

you misunderstood. its 300ms just for the apply model, the model that takes your coding models output (eg sonnet) and figures out where the code should be changed in the file. Cursor has its own, and claude uses a different technique with strings as well. So its 10sec vs 10sec +300ms using your analogy

k__•1h ago

I have to admit, that using slow models is unbearable when I used fast one before.

I don't know if the quality and speed are linearly related, though.

AirMax98•58m ago

Seriously agree — try using something like Sonnet 3.7 and then switching to Gemini 2.5 Pro. The code that both output is fine enough — especially given that I mostly use LLMs as a fancy autocomplete. Generally a better prompt is going to get me closer to what I want than a more robust model. The speed hit with Gemini 2.5 Pro is just too substantial for me to use it as a daily driver.

I imagine the speed difference might not matter so much if you are performing seismic updates across a codebase though.

paulddraper•1h ago

I do not use Opus for coding, I much prefer Sonnet.

Many tasks work better with iteration/supervision and Sonnet makes that feasible.

rs186•4h ago

Sounds interesting, but I imagine all the big players (Cursor, Windsurf, and maybe even OpenAI/Anthropic) will achieve something similar very quickly in their tools first-party, which will decimate the company. And I don't get the API part of this -- at the end of the day people use those IDEs, and I don't see developers/companies want to send their code to yet another endpoint.

bhaktatejas922•3h ago

Perhaps - Cursor does this in house. I see the coding agent space being large as we shift into a market of on-demand software.

Sending code externally is meh especially for companies with tight security rules. We do self-hosting for them in their infra

Qerbz•3h ago

Heard some insane rumors of the efficacy increase of this in action even though I don't know how you do it

bhaktatejas922•3h ago

the rumors are true! learn how we do it by joining the team :)

bigyabai•3h ago

That's an interesting first comment to post from a 5-month old account.

zackangelo•3h ago

For anyone more curious about how this works, Fireworks wrote a blog post about it last year (I think):

https://fireworks.ai/blog/cursor

simonw•3h ago

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>$code</code><update>$update</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 <code> and <update>, if my own source code contains those tags I expect it may get confused.

bhaktatejas922•2h ago

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

seanw265•2h ago

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.

bhaktatejas922•2h ago

the v2 model listed currently points to morph-v3-large. We're working with them to get v3-large and v3-fast listed

bhaktatejas922•1h ago

the power of hacker news! New models are listed there now

bijection•2h ago

How does this compare to relace, which I believe is also a YC company? They seem to have very similar functionality [0]

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

Workaccount2•2h ago

Just for clarification here because I am a bit confused,

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?

bhaktatejas922•2h ago

Correct, but morph is a LLM as well. In practice its basically Big LLM using small LLM as a tool call

Workaccount2•2h ago

I see. How is this not going to get run over immediately by big players? Google's diffusion model is already in the wings, and it's both wicked fast and ~flash-lite intelligent.

bhaktatejas922•57m ago

you could make the argument about any startup really. To me its the same reason they don't build the foundational model for legal, for sales, etc.. - everything comes at a cost. Allocating researcher time to this is attention not spent on the general frontier model - losing 1-2% there is the difference of billions of dollars for them

eabeezxjc•2h ago

why not ruby?

because ruby no need corecting. It works.

nico•2h ago

Would be awesome to have a browser extension that could create a bridge between ChatGPT and VSCode, applying Morph in between (or Claude instead of ChatGPT). Essentially use the web interface, instead of the APIs for agentic coding

bhaktatejas922•2h ago

I think an MCP would do the job. We're shipping one out as we speak

sidgarimella•1h ago

+1 hyped for an mcp that I might be able to plug zed into

elzbardico•2h ago

1) Raw inference speed matters more than incremental accuracy gains for dev UX—agree or disagree?

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

bhaktatejas922•2h ago

key word incremental - for fast apply to be useful it should be so fast and accurate that most people don't realize there's a model there at all

callamdelaney•2h ago

Yeah sounds like exactly what we need

laborcontract•2h ago

Really like this. I've been trying microsoft's copilot and it's so clunky, particularly when applying edits. One would assume they have the resources to train the 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.

[0] https://morphllm.com/privacy

bhaktatejas922•1h ago

done! Yeah we have ZDR options as well, just email us to enable it info@morphllm.com

Morph via OpenRouter is always zero data retention

lastdong•2h ago

Is this similar to Gemini Diffusion? Thanks

bhaktatejas922•1h ago

No, we use autoregressive llms. Diffusion models would be super interesting here. Mercury is doing some interesting work with diffusion in code gen but still too early to tell if it'll get good enough for production usage

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

Comments