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.

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.

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.

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.

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 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.

I am stealing that phrase haha

I don't think all means-to-end people are just in it for money, I'll use the anecdote of myself. My team is working on a CAD for drug discovery and the goal isn't to just siphon money from people, the goal is legitimately to improve computational modeling of drug interactions with targets.

With that in mind, I care about the quality of the code insofar as it lets me achieve that goal. If I vibe coded a bunch of incoherent garbage into the platform, it would help me ship faster but it would undermine my goal of building this tool since it wouldn't produce reliable or useful models.

I do think there's a huge problem with a subset of means-to-end people just cranking out slop, but it's not fair to categorize everyone in that camp this way ya'know?

But you can have an extremely well designed product that functions flawlessly from the perspective of the user, but under the hood it's all spaghetti code.

My point was that consuming software as a user of the product can be quite different from the experience of writing that software.

Facebook is a great example of this, there's some gnarly old spaghetti code under the hood just from the years of legacy code but those are largely invisible to the user and their experience of the product.

I'd just be careful to separate code elegance from product experience, since they are different. Related? Yeah, sure. But they're not the same thing.

1. You treat your code as a means to an end to make a product for a user.

2. You treat the code itself as your craft, with the product being a vector for your craft.

The people who typically have the most negative things to say about AI fall into camp #2 where AI is automating a large part of what they considered their art while enabling people in group #1 to iterate on their product faster.

Personally, I fall into the first camp.

No one has ever made a purchasing decision based on how good your code is.

The general public does not care about anything other than the capabilities and limitations of your product. Sure, if you vibe code a massive bug into your product then that'll manifest as an outcome that impacts the user negatively.

With that said, I do have respect for people in the latter camp. But they're generally best fit for projects where that level of craftsmanship is actually useful (think: mission critical software, libraries us other devs depend on, etc).

I just feel like it's hard to talk about this stuff if we're not clear on which types of projects we're talking about.

Like sure, is it a VS code fork with agents stapled to it? Yes. But are they on the same scale as most of the people mentioned? Ehh probably not.

It reads more like a hit piece from someone with a grudge against random SF companies than anything else.

It's focused on structural analysis right now, but the goal is to allow for biologists, crystallographers, chemists, etc to quickly analyze large samples of structural data for patterns and find where those patterns break down.

Our goal is to make it a platform to analyze the output of various papers, tools, and structures to build a single unified biological model of your druggable target. For example, what if your alphafold output disagrees with pre-existing literature? If Diffdock says your candidate can bind to a pocket on a protein that hasn't been validated yet, what's the implications of that on the underlying biological mechanism?

Biology is extremely complicated, so scientists create simplified lenses of the world to make sense of things. Biologists are looking at different things than crystallographers, crystallographers are looking at different things than computational chemists, etc, etc.

Finding disagreements in these simplified lenses early can save a lot of money before things move to lab experiments.

Most of these organizations are looking for customizations that B2B SaaS struggles to provide since they have to walk a line of catering to a market segment broadly then building customization for specific clients.

I've seen a huge surge in organizations investing in small software development teams to do internal builds for things that they just aren't getting from these tools. Technology is not the value center for these companies.

I work in healthcare, so my perspective is heavily contextualized by that, but I'm seeing providers (especially specialty providers) build internal engineering teams to create ancillary systems that sit on top of their EHR. They are doing this instead of buying similar modules that might be up sold by the EHR.

Anyway, I just feel like these market trends are deeper than what this article implies.