cowanon2222

Sometimes there isn't a good guy, just a bad guy and a worse guy.

I use MQTT daily. I'm not sure why the commenter suggested it; it is orthogonal to queueing or log streams.

MQTT is a publish/subscribe protocol for large scale distributed messaging, often used in small embedded devices or factories. It is made for efficient transfer of small, often byte sized payloads of IoT device data. It does not replace Kafka or RabbitMQ - messages should be read off of the MQTT broker as quickly as possible. ( I know this from experience - MQTT brokers get bogged down rapidly if there are too many messages "in flight")

A very common pattern is to use MQTT for communications, and then Kafka or RabbitMq for large scale queuing of those messages for downstream applications.

I've had pretty good results using the AI features in Macrofactor. It's certainly not perfect, but it does a pretty good job with mixed text and photos and allows you to easily fine-tune the results.

Macrofactor is also the only app I've seen that actually estimates your underlying metabolic rate and adjusts accordingly. It predates the recent AI surge, and seems to have a team that's studied nutrition science behind it.

> if a perfectly symmetrical interior (& exterior - anything contributing to resonances) wouldn't sound better

I'm guessing it would likely look more pure on a frequency plot, but sound sterile if things were perfectly symmetrical. The little imperfections, materials, and design tradeoffs give each instrument its unique tone color (timbre). Often, musicians will chase a certain builder and year, and even within that, only a few instruments will be considered "great". For example, guitarists chasing the perfect Les Paul or most classical violinists chasing a Stradivarius.

The internet bubble is probably a good analogy. It took almost 20 years and several rounds of failed businesses for the internet to have the impact that was originally promised. The big internet companies of the 90s are not where the money was ultimately made.

Similarly, the current LLM vendors and cloud providers are likely not where the money will ultimately be made. Some startup 10-15 years from now will likely stack a cheaply hosted or distributed LLM with several other technologies, and create a whole new category of use cases we haven't even thought of yet, and that will actually create the new value.

It's basically the Gartner hype cycle in action.

The author's tone is over the top, but I think this quote is true:

> Large Language Models and their associated businesses are a $50 billion industry masquerading as a trillion-dollar panacea for a tech industry that’s lost the plot.

There are very real use cases with high value, but it's not an economy-defining technology, and the total value is going to fall far short of projections. On bottom lines, the overall productivity gain from AI for most companies is almost a rounding error compared to other factors.