Experiments demonstrating an external source of consciousness would be very interesting.

Not a teapot in this case!

It’s funny, I’m often tempted to fact check data or lookup jargon, etc. and comment to save someone else the trouble. I once did this on the seriouseats subreddit with copy paste from a relatively reliable source and met with an insane heated argument over what amounted to semantics and a flurry of downvotes. I wonder if attribution to ChatGPT increases civility towards the commenter or if HN is just generally more civilized.

Further research supports these findings, showing that trucks and SUVs with hood heights greater than 40 inches are about 45% more likely to cause fatalities in pedestrian crashes than shorter vehicles with sloped hoods. The study, also by the IIHS, used data from nearly 18,000 crashes and noted that tall, squared-up hoods, characteristic of many best-selling SUVs and trucks, contribute significantly to the risk. The number of pedestrian deaths has significantly increased, with pedestrian fatalities jumping 13% to 7,342 in 2021, marking the highest number since 1981. This rise in pedestrian deaths has outpaced the increase in overall U.S. traffic deaths, highlighting a growing crisis in road safety related to larger vehicles[1].

These findings underscore the need for vehicle design changes to improve pedestrian safety, particularly as the proportion of SUVs on U.S. roads continues to rise. Despite advancements in vehicle safety that have reduced overall motor vehicle crash fatalities, the increased lethality of SUVs to pedestrians poses a significant challenge that requires attention from both manufacturers and regulatory bodies.

[0] https://www.iihs.org/news/detail/new-study-suggests-todays-s...

[1] https://www.reuters.com/world/us/tall-trucks-suvs-are-45-dea...

(ChatGPT 4)

https://www.dsm.city/departments/engineering_-_division/skyw...

1. Business users aren’t prepared to talk to their data in meaningful ways and this is an opportunity for LLMs to enhance the way users ask questions.

2. SQL modeling languages exist (although I’m not sure there are well maintained open source good ones and this is the biggest obstacle to what I’m working on now) and LLMs can extend these effectively by adding components such as dimensions, metrics, relationships, filters, etc. with less chance of hallucination

3. Deterministic SQL generation from a modeling repository is easier to troubleshoot and provide guarantees than end-to-end generation.

4. Existing SQL can be parsed to generate modeling components that can be committed to the model repository with LLM assistance

5. Much of the richness of going to data to answer questions is context, e.g., how does this dept compare to others, this month to same month last year, etc. Modeling languages are good at expressing these transformations, but business users and often analysts aren’t good at capturing all the permutations of these types of comparisons. Another area where LLMs can help apply tooling.

IMO, LLMs are more effective at using tools than generating complex outputs.

I buy the CO2 aspect. Many cases where Uber is now more convenient than public transport vs. El is better than a taxi

I’ve also lived in areas where taxi service was essentially nonexistent. I wonder how many DUIs and related accidents have been prevented by ride share apps.

Traveling abroad in Europe the apps work simply regardless of my command of the local language to explain my destination and keeps the drivers honest so they aren’t taking “the long way”.

How is anyone supporting taxis as superior to this? There was absolutely no accountability.

From an business model perspective, I would wager that eventually you could get this to a point of sustainability that doesn’t require armies of engineers and various support staff, a la Twitter.

Just don’t try to say taxis were better.

The concept seems more related to Thermal Mass than geothermal energy. https://en.wikipedia.org/wiki/Thermal_mass

I’m fascinated by Eathships which rely on this along with convection to regulate air temperature. https://en.wikipedia.org/wiki/Earthship

You get lots of at bats to do “something big” as opposed to 9-5 keep the lights on work that most engineers do for years in stagnant, highly politicized cultures year after year waiting for their boss to quit to get a promotion. It’s also a good way to level up a stagnant career.

The downsides include always “living in someone else’s house”, having to adapt to the clients tech and culture, having to leave your work behind and start from scratch.

Agreed that these type of shops are in the minority and once they scale, they exit to the big guys who then kill the culture and drive away the talent.

Palantir (from the outside) seems like a good example of this dynamic scaling along with the advantages of maintaining their own stack. Could you imagine what it would be like to be an engineer employed by the customers they serve?