101–110 of 196 posts
I’d just like to put a pitch in here for someone to do “smart rebase+merge” with AI. Now THAT would really speed up development, if my AI was intelligently merging code from different users in the background, based on understanding the intent behind each conflicting change.
How do I start using this on a codebase on my local computer? I'm quite confused by the quickstart. Do I use a VSCode extension? One of the Claude Code like clones but with this as a custom model?
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.
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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.
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 mi…
Intensely sticky user experience
> 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.
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I've never had so much fun coding in my life - you should definitely give it a try again!
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.
Have been using morph for a while (I am one of the authors of goose) and was surprised when introduced at the boost it gave me (much less iteration with the main expensive LLM, and I can even make the editing process simpler to take a load off the agent). Used it with claude 3.5, 3.7, 4 and currently with a o3/openai and anthropic/claude4 + morphllm combo today.
"Let's be wrong faster! It's a feature, we swear!"