RL unlocked AI for coding + math can it do the same for science? we're hosting a casual drinks+networking session to discuss challenges and oppos on the 27th of August Luma link in the comments!

A lot of scientific work is slower than it should be for a surprisingly simple reason: powerful tools still need people to move the work between them. In bioanalysis, teams already have tools they trust. SCIEX OS, Skyline, Analyst, Thermo, Waters, internal scripts. They are often very good at the specific jobs they were built for. The problem becomes obvious when you zoom out from any one tool and look at the whole workflow: a file gets moved from one place to another. A folder has to be prepared in the right format. A method has to be selected. A run has to be configured. Outputs need to be checked, copied, reviewed, and turned into something useful for the next step. None of this is exactly glamorous work. But it has to be done and done carefully. And when you watch highly trained scientists spend hours on it, you start to feel how much human attention is being spent on work that is necessary, but not very creative. We recently built an integration between ScienceMachine and SCIEX OS for one of our customers. The idea was simple: do not replace the software the team already trusts. Instead, make it part of a larger workflow that ScienceMachine can help orchestrate. Read the full technical write-up in the comments 📷 ScienceMachine can now reach into a real customer environment, work with SCIEX OS in the place it already runs, and turn a manual desktop workflow into a secure, repeatable step in a larger scientific process. The best part is that every integration like this compounds. Once ScienceMachine can work with one important tool in the workflow, it becomes easier to connect the next one, and then the next one, until more of the scientific process starts to feel continuous instead of fragmented.