sfaist

superglue (yc w25) | Founding Engineer | Onsite in Munich, Germany, 70-100k + 0.5-2% Equity

superglue is an integration layer for developers, agents, and enterprises. Our open source product enables users to integrate and orchestrate APIs via natural language at a higher reliability (91%) than any general purpose model. We are making agents actionable.

We can move from first contact to decision in less than two weeks.

Reach out to [email protected]

We would've assumed that the llms are much better at writing working code since it's not random APIs but rather established API patterns which they should be able to one-shot (e.g. Stripe). Bad error messages are a problem indeed. We will release another one with retries very soon.

Quite interesting actually. not sure why, I assume it just overthinks. What suprised me even more is how bad o4-mini performed, after taking up hours of evaluation time and more credits than all other llms combined. More thinking != better (integration) coding performance

The reason we think this would be interesting to share here is that these llm benchmarks seem increasingly disconnected from reality. idc if the llm can solve a PhD math question or make scientific discoveries, I care if it can solve our problems, which in our case is automating API integrations. Turns out it mostly can't, which tracks well with our experience using cursor.

Hi folks, I'm Stefan, founder of superglue. To be upfront, we are building an integration agent and had this mildly weird idea of exposing the capabilities through MCP and see what happens if an agent can build its own tools. Beyond using this in Cursor to build data pipelines and integrations faster, we have limited ideas on what to do with this, but it is interesting enough to post. So let us know if you can think of something useful or fun.

Hey HN, I'm Stefan, co-founder of superglue. Adina and I started working on automating data pipelines and transformations about a year ago. Folks love our agent for building and maintaining data pipelines, and we thought it would be fun to let another agent do all the work that's left. Our goal here is to make this process as easy and painless as possible, and we'd love to hear how you deal with building and maintaining data transformations right now, and if this is a value add.

Sure, email me: [email protected]

Thanks!

my personal understanding (anyone feel free to correct me here) of MCP is that it is basically a standardized interface for tool use. So, if you as an API provider (e.g. stripe) want agents to connect to your API, you can offer an MCP server that serves as a middleman between you and the agent. What we fundamentally do is serve also as a middleman, but not (primarily, yet) for agents, but for normal (non-AI) applications that would otherwise need to use the REST/SOAP/whatever API with a bunch of integration code. Also, MCP does not do any data transformation, that would be on the agent to do.