An Effective Altruism ethos provides moral/ethical cover for trampling individual privacy and property rights. Consider their recent decision to provide services for military projects.

As others have pointed out, Claude was trained using data expressly forbidden for commercial reuse.

The only feedback Anthropic will heed is financial and the impact must be large enough to destroy their investors willingness to cover the losses. This type of financial feedback can come from three places: termination of a large fraction of their b2b contracts, software devs organizing a persistent mass migration to an open source model for software development. Neither of these are likely to happen in the next 3 months. Finally, a mass filing of data deletion requests from California and EU residents and corporations that repeats every week.

Here are a few questions that should be part of an evaluation of the Parachute platform to pressure test the claims made on the website and this post: 1) How many Parachute customers have passed regulatory audits by CMS, OCR, CLIA/CLAP, and the FDA? 2) What high quality peer-reviewed scientific evidence supports the claims of increased safety and detection of hallucinations and bias? 3) What liability does Parachute assume during production deployment? What are the SLAs? 4) How many years of regulatory experience does the team have with HIPPA, ISO, CFR, FDA, CMS, and state medical board compliance?

If you're a current Windsurf customer, I hope you consider an alternative product and migrate quickly.

I'm Erick, founder of cStructure. After 18 months of building in stealth, we're opening our causal inference platform for public beta. Last week we launched in the US, this week we are expanding to Canada.

Our story started when I was leading data science teams and kept hitting the same wall: causal analyses would fall apart in stakeholder meetings. Subject matter experts would ask why their favorite variable wasn't in the analysis. Data scientists would latch on to their favorite correlation-based approach (e.g. XGBoost + SHAP, even though SHAP's docs describe why it's the wrong tool for causal inference*). Leaders would question assumptions. What should have been knowledge-driven decisions turned into gridlock.

Everything changed when we started using causal diagrams. These simple visual maps of cause-and-effect became a shared language between experts, analysts, and decision-makers. Domain knowledge could be captured precisely. Assumptions became explicit. Teams could focus on the right questions and controls.

But building and validating these models was painful - scattered across whiteboards, papers, and custom code. We built cStructure to make rigorous causal inference collaborative and accessible.

Try it: https://cstructure.dev (free during public beta + simple demo canvas + guided tutorials by the first Sign Up button) Features: https://cstructure.dev/#features

Our team comes from life sciences, energy, and tech, where we've seen these methods scale from startups to major healthcare systems. The platform lets teams: - Kickstart and revise a causal diagram with AI assistance - Collaboratively build and validate causal models - Detect potential biases and validate assumptions - Connect models directly to data with automated checks - Run analyses in-browser using JupyterLite (no server needed) or export to your private environment

Technical details: - Built with React + Yjs + WASM

Our roadmap includes federated privacy-preserving learning, FAIR causal models, extensible analysis modules, and integration with knowledge graphs and scientific evidence.

Yes, there are still rough edges. But we're sharing now because we want to build this with feedback from the community. We want to understand what teams actually need to move beyond correlation-machines to doing real science.

Would love feedback from researchers, data scientists, and anyone interested in bridging the gap between domain expertise and statistical rigor. What would help your team adopt causal inference?

- Erick (at cstructure.io)

* https://shap.readthedocs.io/en/latest/example_notebooks/over...

Comments URL: https://news.ycombinator.com/item?id=42378059

Points: 3

# Comments: 0

Consider the type of confounding that occurs in studies of people losing a lot of lean mass: cachexia, restriction to bed, famine.

Traditional weight loss methods have not shown the magnitude of survival benefits wrt cardiovascular disease, joint pain, diabetic complications. Exercise is wonderful, but as a public health intervention it is not sufficient.

If anyone looks at the totality of the high quality GLP1 clinical evidence and concludes these drugs are going to cause a net reduction in longevity and quality of life, then they should step back and assess their process for evaluating information.

Most hospitals have a compliance office where this type of issue would be handled.

Your comment is focused on buying a home, but there can be dramatic consequences for home owners, the stakeholders forced into paying both agents.

When a seller has little equity and a poor housing market, exiting a primary mortgage can be financially impossible. A recent survey found a large fraction of the US can't handle a surprise $400 expense. So the follow-on effects of these commissions are not trivial for a large swath of the country.

I also agree that depending on location (looking at you SF metro) there are much bigger factors, e.g. constrained supply, inflating home prices.

With consolidation of real estate brokerage firms, many home buyers may have overpaid due to a single firm representing both home seller and buyer, known as dual agency: https://therealdeal.com/2019/05/01/houlihan-lawrence-fails-t...

There is a third issue of large private equity firms buying large volumes of houses that may, in the future, warrant an antitrust investigation.