Doing open science gets you more citations (arxiv.org/pdf/2607.00546) However, does it also result in less individualistic innovation (less notoriety, more scooping, worse tenure outcomes)? #openscience #publishorperish

Leaders are responsible for vision and strategy, i.e., prioritizing problems and giving insights into avenues for solutions. If you're handing off fully-baked solution specs to your engineers you're wasting their talent and holding them back from growing.

There's an interesting distinction between "this can run again" and "I can prove that I ran this and here's the output" and the latter is becoming more important with increasing AI usage #agenticai #aiforscience #reproducibility

How many more times can I hear "agentic AI" this week before I quit using computers for the rest of my life challenge

AI agents, like humans, should be creating pipelines. They should not be parts of pipelines.

Moving steadily towards a free, open-source, vertically-integrated research platform, calkit.io now has a project-based reference manager that can sync bidirectionally with Zotero. #openscience #opensource #reproducibility

New feature on calkit.io: attach evidence to answers to research questions and quickly verify that they're still up-to-date given their provenance. Example project: calkit.io/calkit/examp... #reproducibility #openscience #opensource

Unifying development and operations in the same team/repository was a boon for software. Would science see similar gains from moving its various stages/functions closer together? #devops #openscience #opendata

"utils" modules are bad because they show you value DRY (don't repeat yourself) more than modularity, and you end up with tight coupling and low cohesion #softwareengineering #swe #softwarearchitecture

There are lots of great computational tools out there for doing different stages of research, but they're poorly integrated with each other, which is a source of errors and inefficiency.

How well does failure to reproduce correlate with failure to replicate? #metascience #reproducibility

If your collaborators don't want to learn Git/GitHub but you don't want to silo paper writing away from your code and data, calkit.io now has a LaTeX editor and WASM compiler :) #opensource #openscience #reproducibility

If you're building a writing tool for scientists, it's pretty important to make it easy to connect to visualization/analysis tools. That is, if you make users manually upload their data or figures, that's bad.

New blog post: "Conviviality in computational science" blog.khinsen.net/posts/2026/0... "Conviviality matters for science ... if you want to derive knowledge from your work, you need to know exactly what you are doing, and that includes a detailed understanding of your tools." 🧪 #metascience

Instead of compiling a replication package or repro pack at journal article submission time, imagine if you worked inside of a repro pack the whole project... #reproducibility #openscience

@plos.org's concept of the "knowledge stack" in science is interesting from an architectural perspective. Is any layer truly valuable on its own? I'd argue the full stack is the most valuable unit, which means you should be shipping the whole thing with every study. #openscience #opensource

If you know what individual to ask for help in a separate team, you are probably tightly-coupled, and maybe should be part of the same team

The Calkit VS Code extension now allows viewing interactive @plotly.com figures, and includes a gallery view.

If a human is supposed to read it, it shouldn't be written by AI

For those who manage research groups: Do you tend to be more individual-focused, with each member getting their own independent project, or team-focused, where individuals need to find their niche to contribute to a larger collective goal?

Been spending some time on the Calkit VS Code extension. Here's a screenshot using it with Claude to profile CUDA kernels in SLURM jobs and keep track of Nsight reports for each code change: #aiagents #cuda #gpu #claude

AI agents will happily create figures for you. They'll even export some source code. But can you trust that they did what they said they did? Here's a tutorial on how to get your AI agents to work reproducibly: docs.calkit.org/tutorials/ai...

I'm not a fan of splitting up code into a bunch of tiny functions as a default practice. It's like creating a bunch of minor characters in a story. It should only be done if those characters really add something, because each one will take away some attention. #swe #softwareengineering #dev

Made a little poster to "sell" my free software #opensource #openscience #reproducibility

The golden rule of architecture: Avoid moving horizontal slices away from each other, either into different repos or different teams

Maybe we can build more human interfaces for our computing tools without making them nondeterministic and extremely computationally expensive

On one side, you have literate programming, which views code and prose as belonging to a monolithic artifact. On the other, you have total fragmentation with code in one silo, data in another, writing in another, and no real interface between them. I think we need modularity with real interfaces.

Tightly coupled code should not be split across multiple repos #softwareengineering #swe #softwaredesign

IMO, the most important rule for using AI agents to do scientific research: Don't allow them to create artifacts like figures or numerical results on their own. Have them create, save, and run pipelines that create artifacts so provenance is preserved and traceable. #openscience #agentic

Success boils down to properly defining how to measure it then doing as many iterations as possible.