We're building a CAD for drug design, we often have to handle large and highly varied file formats. Protein structures, compounds, python scripts, lab notebook entries, instrumentation data, etc.
From a data structure and file ergonomics perspective, think of it as similar to Unity or UE4 for drug design. We have a huge variety of assets to manage alongside their relationships to each other, and the project files are local on the user's machine (with a collaboration / sync over the network between scientists working on the same project, hence where something like this would come in for us).
Many of those files are fine with a winning side strategy, but some of them might not be that clean. Take a protein structure defined by an `mmcif` file for example, if we clean the file by removing hydrogen atoms and another scientist repairs a side chain on that same file then we'd need a way to reconcile those differences.
On the agent side, our agents will generate small python scripts that manipulate the proteins, then cache and re-use those scripts as tools when possible. So preserving those scripts alongside the mutated asset and conversation history is something we've been working on.
Does this provide gitflow to handle conflicts from multiple agents touching the same file system or is it purely for single-branch sequential iterations on the filesystem?
I have a use case that could use this if it supports handling branching and merging file systems.
People often use "non-deterministic" to imply "unreliable" when that's not always the case. You can have an LLM that's clearly non-deterministic but also reliable for tasks.
The hardest part about using agents to code for me has always been working in teams. When you can cut through huge parts of the code with a chainsaw, how do you review multi-thousand line PRs?
It's really hard to do surgical changes with an AI agent, and it's even harder to review those changes. Even if I'm reviewing the specs and the code, the cognitive load on reviews feel like they've ballooned from what used to be a few hours to now taking me days to review these PRs.
The problem with models like this is they're built on very little actual training data we can trace back to verifiable protein data. The protein data back, and other sources of training data for stuff like this, has a lot of broken structures in them and "creative liberties" taken to infer a structure from instrument data. It's a very complex process that leaves a lot for interpretation.
On top of that, we don't have a clear understanding on how certain positions (conformations) of a structure affect underlying biological mechanisms.
Yes, these models can predict surprisingly accurate structures and sequences. Do we know if these outputs are biologically useful? Not quite.
This technology is amazing, don't get me wrong, but to the average person they might see this and wonder why we can't go full futurism and solve every pathology with models like these.
We've come a long way, but there's still a very very long way to go.
This is awesome! The only limiter here is the resolution, I think this is fantastic for cellular level organelles but it doesn't quite get down to the same resolution something like x-ray diffraction does.
There's a huge trade off between resolution and scale that makes it hard to determine things like complex molecular dynamics and how those dynamics influence the broader functions of the cell.
That said, excited for more images like this! More data at that scale is always a good thing for researchers.
Honestly, this is a good thing. OpenClaw as a concept was rather silly to run such a heavy model for. If you want something like OpenClaw to work you really need to figure out how to do it with an economical model.
I'm not convinced people who are doing real work on production applications with any sizable user base is writing code through only agents. There's no way to get acceptable code from these models without really knowing your code base well and basically doing all the systems thinking for the model.
Your workflow is probably closer to what most SWEs are actually doing.
That's good to hear, I might have jumped a little too quickly in my opinion. It's a bit of a Pavlovian response at this point seeing a product I very much love embrace a giant chat window as a UX redesign haha.
I would love to see more features on the roadmap that are more aligned with users like us that really embrace the Cursor 2 style with the code itself being the focal point. I'm sure there's a lot you can do there to help preserve code mental models when working with agents that don't hide the code behind a chat interface.
Yeah that's the disconnect though right? Even with the best frontier models, you need to do a lot of system design work, planning, and reviewing before you can let these models run.
These models are infinitely more effective when piloted by a seasoned software engineer and that will always be the case so long as these models require some level of prompting to function.
Better prompts come from more knowledgeable users, and I don't think we can just make a better model to change that.
The idea we're going to completely replace software engineers with agents has always been delusional, so anchoring their roadmap to that future just seems silly from a product design perspective.
It's just frustrating Cursor had a good attitude towards AI coding agents then is seemingly abandoning that for what's likely a play to appease investors who are drunk on AI psychosis.
Edit: This comment might have come off more callous than I intended. I just really love Cursor as a product and don't want to see it get eaten by the "AI is going to replace everything!" crowd.
Man, I wish they'd keep the old philosophy of letting the developer drive and the agent assist.
I feel like this design direction is leaning more towards a chat interface as a first class citizen and the code itself as a secondary concern.
I really don't like that.
Even when I'm using AI agents to write code, I still find myself spending most of my time reading and reasoning about code. Showing me little snippets of my repo in a chat window and changes made by the agent in a PR type visual does not help with this. If anything, it makes it more confusing to keep the context of the code in my head.
It's why I use Cursor over Claude Code, I still want to _code_ not just vibe my way through tickets.
I wonder how much of this is also used for audience segmentation for their advertisements? Linkedin ads are some of the most expensive out of any social media platform, but they also tend to have the highest conversion since you can get pretty niche with your targeting.
>But if your workload is not shifting from write-heavy to read-heavy, you inevitably will be responsible for a major outage or quality issue.
I think that's actually a good way to look at it. I use AI to help produce code in my day to day, but I'm still taking quite a while to produce features and a lot of it is because of that. I'm spending most of my time reading code, adjusting specs, and general design work even if I'm not writing code myself.
There's no free lunch here, the workflow is just different.
Talk to me about computational chemistry, bioinformatics, and molecular biology!