Is this similar to Gemini Diffusion? Thanks
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
#62[flagged]
Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec
#63Last 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.
the power of hacker news! New models are listed there now
Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec
#64Is this similar to Gemini Diffusion? Thanks
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
Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec
#65Earlier quoted context omitted.
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.
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.
I've never had so much fun coding in my life - you should definitely give it a try again!
Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec
#66Would 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
I think an MCP would do the job. We're shipping one out as we speak
+1 hyped for an mcp that I might be able to plug zed into
Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec
#67> 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.
This is a code editing model. 10 tokens per second editing may as well not exist for any interactive use case.
Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec
#68[deleted]
Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec
#69Really 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:…
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
Re: Launch HN: Morph (YC S23) – Apply AI code edits at 4,500 tokens/sec
#70Earlier quoted context omitted.
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.
yeah even claude is well over 11% error rates with search and replace