kierangill
Joined 8 years ago
Joined 8 years ago
I recently read this William Zinsser quote that inspired a nickname for this: Clotted Claude [1].
> Nobody has made the point better than George Orwell in his translation into modern bureaucratic fuzz of this famous verse from Ecclesiastes:
> > I returned and saw under the sun, that the race is not to the swift, nor the battle to the strong, neither yet bread to the wise, nor yet riches to men of understanding, nor yet favor to men of skill; but time and chance happeneth to them all.
> Orwell's version goes:
> > Objective consideration of contemporary phenomena compels the conclusion that success or failure in competitive activities exhibits no tendency to be commensurate with innate capacity, but that a considerable element of the unpredictable must invariably be taken into account.
> First notice how the two passages look. The first one at the top invites us to read it. The words are short and have air around them; they convey the rhythms of human speech. The second one is clotted with long words. It tells us instantly that a ponderous mind is at work. We don't want to go anywhere with a mind that expresses itself in such suffocating language. We don't even start to read.
> Through the perturbation of patient messages, we evaluate whether LLM behavior remains consistent, accurate, and unbiased when non-clinical information is altered. […] Our findings reveal notable inconsistencies in LLM treatment recommendations and significant degradation of clinical accuracy in ways that reduce care allocation to patients. […] Our perturbations reflect realistic patient messages from electronic formatting errors and/or simulate patient groups that would be impacted by a wide adoption of patient-AI systems (female patients, non-binary patients or those who use gender-neutral pronouns, patients with health anxiety, patients with a more dramatic disposition, patients with less technological aptitude, and patients with limited English proficiency, etc.)
We’re all peering down the kaleidoscope of a trillion parameter model. It’s no surprise gentle nudges in inputs (grammar, language proficiency, cultural norms) yield different outcomes, despite the intent not changing. It’s one thing to generate crap code, it’s another to generate crap medical advice.
For example, sometimes context will be taken up by a side tangent, tool call outputs, or low-value codebase exploration.
Much of the time, I prefer to preserve the history of my conversation instead of summarizing it. I find summarized conversations lead to more frustrating future chats because the LLM misses intent and or context. (Or, the presence of paragraphs and paragraphs of LLM output makes the next token predictor dumber? Unsure.)
I know you can’t trust an LLM’s self-assessed “confidence” of a prediction, but I’ve found that confidence can at least be directionally correct for some tasks. For our benchmarks, however, confidence was poorly correlated. What’s worse is that binary classification models (“Do you see $diagnosis in this photo?”) highly influenced the LLM to confidently predict $diagnosis.
I’m concerned for those using LLMs for diagnostics, and getting confidently led to the wrong conclusion.
American healthcare is seldom affordable, accessible, or high-quality. We are fixing this for pediatrics. Blueberry is the most affordable option amongst our competitors. We practice the highest quality pediatric telemedicine, as evidenced by our exclusive hiring of board-certified pediatricians and the usage of at-home medical kits. And, we’re accessible 24 hours a day.
Our success is shown in the lives we save, the costs we save our insurers, and our exploding B2B and D2C business.
As you can imagine, pulling off affordable high-quality healthcare is a challenge. It requires a lot of engineering ingenuity, a C-suite aligned with positive patient outcomes above short-term profits, and a great product team.
We use Django, Hotwire Turbo (an HTMX-like framework), Pytorch, Sklearn, and Flutter. Experience in these technologies helps, but what’s more important is general full-stack knowledge, curiosity, and a strong work ethic.
Full-stack engineer: https://jobs.ashbyhq.com/blueberrypediatrics/dc8108f3-34ed-4...
Our homepage: https://blueberrypediatrics.com/
Our engineering blog: https://engineering.blueberrypediatrics.blog/
American healthcare is seldom affordable, accessible, or high-quality. We are fixing this for pediatrics. Blueberry is the most affordable option amongst our competitors. We practice the highest quality pediatric telemedicine, as evidenced by our exclusive hiring of board-certified pediatricians and the usage of at-home medical kits. And, we’re accessible 24 hours a day.
Our success is shown in the lives we save, the costs we save our insurers, and our exploding B2B and D2C business.
As you can imagine, pulling off affordable high-quality healthcare is a challenge. It requires a lot of engineering ingenuity, a C-suite aligned with positive patient outcomes above short-term profits, and a great product team.
We use Django, Hotwire Turbo (an HTMX-like framework), Pytorch, Sklearn, and Flutter. Experience in these technologies helps, but what’s more important is general full-stack knowledge, curiosity, and a strong work ethic.
Full-stack engineer: https://jobs.ashbyhq.com/blueberrypediatrics/dc8108f3-34ed-4...
Our homepage: https://blueberrypediatrics.com/
Our engineering blog: https://engineering.blueberrypediatrics.blog/
I’ve found that when my agent flies off the rails, it’s due to an underlying weakness in the construction of my program. The organization of the codebase doesn’t implicitly encode the “map”. Writing a prompt library helps to overcome this weakness, but I’ve found that the most enduring guidance comes from updating the codebase itself to be more discoverable.
https://www.blueberrypediatrics.com/
Reach me a <first name>.<last name>@blueberrymed.com
https://engineering.blueberrypediatrics.blog/