jneen collectivehow to make programming terrible for everyone
https://jneen.ca/posts/2026-03-27-how-to-make-programming-terrible-for-everyone/
you can tell i have adhd by the 17 footnotes
arclight@arclight that's how i feel too lol
@jneen My most recent project was a Python implementation of an aerosol scrubbing model that is implemented as part of larger R code. The R code is not documented to the level we need so the Python implementation was mainly an excuse to chase down references for everything left undocumented or unattributed in the R application. The original documentation was about 50 pages for the entire application; my documentation was about 200 pages for just one model (though strip out the example plots and source code and there's still at least 50 pages of design info and technical basis). In this case the documentation was far more valuable than the code because it cited chapter and verse where every equation, correlation, and piece of data came from. It's good having a pedantic technical reviewer that holds you accountable.
So yeah, I appreciate the work that went into your post and the background detail. :)
Aside: Is a commercial chatbot even capable of providing references for its work? My understanding is that all the attribution is laundered away when the LLM is constructed; all it can produce is obsequious hearsay...
@arclight i'm glad you had the documentation for that project, sounds like a life-saver. human communication >>>>
@arclight to your other question, part of the reason there's ambiguity on this is that LLMs can *claim* to provide references and introspect about its output, but that introspection and those references are still just... output
@jneen We've seen with the number of legal cases where lawyers have been caught out with fabricated cases as well as journal papers with fabricated references that the system will simply stick tokens together to meet its optimization threshold. So even if attribution hadn't been intentionally bleached away, nothing the chatbot emitted could be trusted unless there's some trustworthy deterministic (non-LLM) system that can verify the existence of citations and assess their relevance. *Everything* is a fabrication.
What concerns me is not the LLM part of the chatbot - that's just a pile of linear algebra - it's the cobbled-together UI that responds like an obsequious servile intern, Stepford ELIZA on Prozac. That part of the system is built on 30+ years of dark pattern research to keep people spending tokens. Right, wrong, as long as users keep spending, the system is operating as designed. The only acceptance test is that line goes up.
@arclight exactly! i've seen someone ask a chatbot "would you hallucinate if i asked you X" and it's just... not how it works.
Tiota Sram@jneen @arclight a point I like to emphasize in these discussions is that if you gave me the same kind of money currently being lit on fire, I could build you a system that would take a natural-language query about code you want you write and then come up with a short list of open-source projects (along with their licenses) that have code that does that thing, along with a pointer to that exact snippet of code. It would of course have some results that weren't great sometimes and there would be an art to querying it effectively, like any search engine... But it would be a much better programming assistant than code LLMs.
Jeff Robot 🤖@arclight @jneen — this! Keeping engagement, keeping one hoping that the next response will be the right one. Feels like gambling. A recent JA Westenberg piece goes well with this: https://mastodon.social/@Daojoan/116219554271259845.
@jneen I get extra twitchy about chatbot use because my job is software QA on nuclear safety analysis code (here's a decent technical basis for an earlier related code https://www.osti.gov/biblio/10200672/) We have enough problems with coarse models and missing or uncertain data, we don't need a machine confidently fabricating nonsense. I'm not going in front of a regulator to explain our answers are bullshit because someone trusted a chatbot to fill in the blanks. Health & safety of the people comes first, then environmental protection, then protection of equipment. It's simply unethical to use these systems in any part of the safety analysis or design or licensing process. There's too much at stake.
An assessment of the potential for in-vessel fission product scrubbing following a core damage event in IFR (Technical Report) | OSTI.GOV
A model has been developed to analyze fission product scrubbing in sodium pools. The modeling approach is to apply classical theories of aerosol scrubbing, developed for the case of isolated bubbles rising through water, to the decontamination of gases produced as a result of a postulated core damage event in the liquid metal-cooled IFR. The modeling considers aerosol capture by Brownian diffusion, inertial deposition, and gravitational sedimentation. In addition, the effect of sodium vapor condensation on aerosol scrubbing is treated using both approximate and detailed transient models derived from the literature. The modeling currently does not address thermophoresis or diffusiophoresis scrubbing mechanisms, and is also limited to the scrubbing of discrete aerosol particulate; i.e., the decontamination of volatile gaseous fission products through vapor-phase condensation is not addressed in this study. The model is applied to IFR through a set of parametric calculations focused on determining key modeling uncertainties and sensitivities. Although the design of IFR is not firmly established, representative parameters for the calculations were selected based on the design of the Large Pool Plant (LPP). The results of the parametric calculations regarding aerosol scrubbing in sodium for conditions relevant to the LPP during a fuel pin failure incident are summarized as follows. The overall decontamination (DF) for the reference case (8.2 m pool depth, 770 K pool temperature, 2.4 cm initial bubble diameter, 0.1 pm aerosol particle diameter, 1573 K initial gas phase temperature, and 72.9 mole % initial sodium vapor fraction) is predicted to be 36. The overall DF may fall as low as 15 for aerosol particle diameters in the range 0.2-0.3 pm. For particle diameters of <0.06 pm or >1 pm, the overall DF is predicted to be >100. Factors which strongly influence the overall DF include the inlet sodium vapor fraction, inlet gas bubble diameter, and aerosol particle diameter. The sodium pool depth also plays a significant role in determining the overall DF, but the inlet gas phase temperature has a negligible effect on the DF. | OSTI.GOV