viktour19

US healthcare is expensive because prices aren’t real and complexity is profitable. Hospitals and pharma have monopoly power, insurers add massive administrative drag, and nobody sets hard price limits. ~25–30% is spent on billing and paperwork alone. Hospitals consolidate into regional monopolies. Drug companies charge whatever they want. Insurance act as a middleman for routine care instead of risk protection. Outcomes don’t justify the cost. The system is optimized to extract revenue, not deliver care efficiently.

Fixing it doesn’t require utopia: cap prices for common services and drugs, enforce antitrust to break hospital monopolies, standardize billing nationally, and let Medicare (and others) negotiate drug prices. Shift insurance back to catastrophic coverage while covering primary care publicly, and cut hospital admin while paying clinicians more. These are boring, proven levers that haven’t happened because every inefficiency has a powerful lobby defending it.

The world’s first hydrogen exploration company was founded in Mali. No one knows for a fact what the development pathway is. Solar might turn out to be inconsequential.

https://news.mit.edu/2024/iwnetim-abate-aims-extract-hydroge...

> In 1987, well-diggers drilling for water in Mali in Western Africa uncovered a natural hydrogen deposit, causing an explosion. Decades later, Malian entrepreneur Aliou Diallo and his Canadian oil and gas company tapped the well and used an engine to burn hydrogen and power electricity in the nearby village.

> Ditching oil and gas, Diallo launched Hydroma, the world’s first hydrogen exploration enterprise. The company is drilling wells near the original site that have yielded high concentrations of the gas.

The effective parameters of the model are the parameters of the original model + lora parameters i.e lora updates only lora parameters, and full finetuning updates only original model parameters.

> LoRA and full fine-tuning, with equal performance on the fine-tuning task, can have solutions with very different generalization behaviors outside the fine-tuning task distribution.

The ability for nnets to generalize is inherently tied to their trainable parameter count via mechanisms we don't understand but the we know parameter count is the key. When you finetune with lora, you're updating maybe 5% of the parameters, I really don't think there is an illusion of equivalence in the field.

Diffusion models are already being evaluated using pretrained SSL models à la CLIP Score [1]. So it makes sense that one would incorporate that directly into training the model from scratch.

[1] https://huggingface.co/docs/diffusers/en/conceptual/evaluati...

What are details of the technique?

It's great how we went from "wait.. this model is too powerful to open source" to everyone trying to shove down their 1% improved model down the throats of developers

If hallucination is inevitable? What should developers do?

Design user experiences that align users with this behaviour!

Relatedly, I built a game to demonstrate how one might calibrate users to the responses of LLMs:

https://news.ycombinator.com/item?id=39255583

Combine this with something like Copilot and you get the 10x programmer :)

Yes please!

I love this.

I wonder how much of the annoyance with the term "Imbox" has to do with the dichotomy between System 1 and System 2 thinking. When I see "Inbox", I put almost no thought into what it means admittedly because I've been exposed to the term so many times much less so with "Imbox".

Interesting. I wonder what the dataset used to train the machine learning model looks like. Books and conversation style query pairs?

This is awesome. some new ideas for my drone.