cudgy

Retrain for what?

How much net after all costs, driving time, fuel, tools, equipment, truck, dues, and company’s take?

Don’t gangs almost always run political discourse?

Seems like a good role for a drone rescue ship that targets the EPIRB location.

Even when humans used to do the calculating?

People that do fundamental research typically require a bachelor degree.

Perhaps it is a perversion of logic to think that someone who has not been trained yet as a philosopher would think like a philosopher.

Claude code includes a large system prompt with every request while tool like pi does not. This could save tokens resulting in lower costs.

Deportations are down from my understanding. Most people think deportations are up due to the “shock and awe” ice operations. The current administration, as with prior administrations, has no intentions of actually reducing the amount of cheap labor whether they are undocumented or not.

The main impact is from fewer workers entering the country clandestinely from Mexico due to the current administration’s enforcement of the border and the general impression of immigrants being treated poorly by administration. Illegal border crossings are now at a 50 year low.

> You have to actually do the hard part of interacting with people and understanding their needs and so on within the context of work.

How many human managers actually do that, though? How many websites performed satisfactorily before AI arrived? How well has technology matched what consumers really need or want? Maybe, as a society we have underperformed and nescient AI performs well enough (or even better) in comparison.

Most companies only need a subset of the features that these mega-platforms offer, as they operate within single industry, targeting specific customers many times in a single country with a simpler legal landscape.

I have no idea for sure, but odds are 80% of the revenue of these current saas providers is generated from 20% of the features they offer. Lightweight newcomers can just focus on that 20% and ignore the other 80%.

3 cashiers per location and 9 developers per company is not a win/win for the 1,000s of cashiers replaced by the 9 developers.

Iron and fiber are durable and last for decades. Data centers (where the current flows for inference) consist of hardware that becomes obsolete within 5 to 10 years.

The question is can improvements in the hardware both in cost and performance outpace the increased demands on the LLMs and their future derivatives.

Well said. This also aptly describes the emergence of “script kiddies“ in the early 2000’s (now comically referred to as “engineers“). Promotion of simply combining libraries that are not understood by amateurish developers into mostly poorly implemented solutions thrust upon the end users. Corporations loved the lower salaries of web developers and the efficiency of utilizing open source libraries, thereby devaluing the skilled developers whose original intent was to share their knowledge via these oss libraries. Web development was the most affected by this trend initially, and as we see now is mostly impacted by the emergence of LLM‘s.