Value isn’t just the editor, it’s the workflow. Letting LLMs plan and act across multi-step flows is a hard problem, and Windsurf figured out a dev-focused version of that. Gains to be made in browser automation once you add structure, retries, and context. Feels like a bet on that pattern becoming default.
But yeah as others said, highly doubt that's $3B in hard cash, more likely a roll-up of shares etc.
Shift isn't just about competitors gaining ground but about users increasingly bypassing traditional search entirely. Between Reddit, Perplexity, ChatGPT, and direct domain knowledge, more queries are being fragmented across tools that aren't indexed as 'search engines' but functionally serve that role.
In my experience the key friction point has been schema stability vs input variance. Had better luck treating mapping as a dynamic planning problem with retries and memory.
Basically treating extraction as an adaptive loop instead of a static function. If first parse fails or looks incomplete, tweak the prompt, inject more context, or switch strategies. Memory helps carry forward partial wins so you don’t start from scratch. We’ve seen the same pattern in agentic web environments. Structured retries, context propagation, and memory turn brittle flows into robust automation, especially with high-variance input and fuzzy schemas.
Curious how you're handling multi-step flows or follow-ups, seems like thats where MCP could really shine especially compared to brittle CLI scripts. We've seen similar wins with browser agents once structured actions and context are in place.
LLM-based coding only really works when wrapped in structured prompts, constrained outputs, external checks etc. The systems that work well aren’t just 'LLM take the wheel' architecture, they’re carefully engineered pipelines. Most success stories are more about that scaffolding than the model itself.
A breakdown would be interesting. I can’t give you hard numbers, but in our case scaffolding was most of the work. Getting the model to act reliably meant building structured abstractions, retries, output validation, context tracking, etc. Once that’s in place you start saving time per task, but there’s a cost up front.
Treating docstrings as the spec and asking an LLM to flag mismatches feels promising in theory but personally I'd b wary of overfitting to underspecified docs. Might be useful as a lint-like signal, but hard to see it replacing real tests just yet.
if that is the only testing you do I agree. However to test that the code works as the docs say is valuable as well. The code often will do more, but it needs to do at least what the docs say.
Agreed. Catching mismatches between doc and implementation is still valuable, just wouldn’t want people to rely on it as a safety net when the docs themselves might be inaccurate/incomplete. As a complement to traditional tests though seems like a solid addition.
If you outsource that to a model, you often end up with words but shallow or no understanding. Writing forces you to clarify your ideas.
LLMs substitute genuine thinking with surface-level prose, which might sound alright but often lacks depth behind it.
Looks really cool.
Curious how you're handling action abstraction?
We've found that semantically parsing the DOM to extract high-level intents—like "click 'Continue'" instead of 'click div#xyz' helps reduce hallucination and makes agent planning more robust.
In trying to live an intellectually rich life, there's a risk of adding too much noise. Chasing more input, more ideas, more learning.
Sometimes less really is more.
Depth often comes not from adding, but from subtracting. Clear away the noise, and what’s left tends to have 'meaning'.
Personally I prefer a deep life to a rich life, but maybe that's just semantics...
