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.
Same. I was very skeptical when Macrofactor introduced this feature, but have since been incredibly impressed. The ability to give it text alongside a photo and then adjust the results (broken out by ingredients) are critical. I’ve also been taking pictures of food sitting on scales and it will take the measurement into account.
Seems like the Macrofactor team took their time developing this feature, as it felt like they were one of the last to roll it out, but the extra polish definitely shows and was worth the wait.
I think the key difference is that they perform a search of the foods in Macrofactor's database which means that you're more likely to get a good estimate.
From someone who weighed and scanned a lot of foods, it has really improved the workflow
> 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 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.
Part of this is because VCs are hoping for a repeat of the Web 2.0 boom, where marginal costs were zero and billions of people were buying smartphones. If you check YC’s RFS (just an example), they’re all software: https://www.ycombinator.com/rfs
Everyone asks “what if this is like the internet” but what if it’s actually like the smartphone, which took decades of small innovations to make work? If in 1980 you predicted that in 30 years handheld computers would be a trillion dollar industry you’d be right but it still required billions in R&D.
There are a ton of non-software innovations out there, they just require more than a million dollar seed to get working. For example making better batteries, better solar panels, fusion power, innovations in modular housing, etc.
Hopefully you are right, but I fear this is a very naive and premature judgement. At one point the internet was a $50B industry insisting it would be a $1T industry. It even had a bubble, bursting and burying entire companies.
Yet, $1T was nevertheless a profound underestimation.
Same could be said about all the hype and trend that died, blockchain(ignore cryptocurrency part), IoT(not sure what happened), bigData(foundation of current AI regardless of whag anyone says) , app for everything(we indeed have more apps now and everything is junk) were also considered the new water/air/electricity/revolution/disruption.
We in aggregate seem to have developed a collective amnesia due to how fast these trends move and how much is burned in keeping the hype machine going to keep us on the edge. We also need to stop calling LLMs different just like every kid wants to claim mark zuck was diff or bill gates was diff so dropping out like them would make these kids owner of next infinite riches.
After a long decade of fast moving “this will truly revolutionize everything” speech every so often, we need to keep some skepticism. Additionally, the AI bubble is more devastating than the previous as previous money was being spread into multiple hypes from which some emerged silent victors of current trends but now everything is consolidated into one thing, all eggs in one basket. If the eggs break, a large population and industry will metaphorically starve and suffer.
Is it? There's certainly $1T of other businesses built with the internet, but the internet business, itself, was rapidly commoditized. The valuable things were the applications built on it, not the network. The argument here is that nobody's found the $1T applications built on AI foundation models yet, but OpenAI is valued as if they have, because their demo chatbot took off out of peoples' curiosity and people are extrapolating that accident exponentially into the future.
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.
Almost all of the internet build out happened between 1998 and 2008, and cost about $1T and was adding $1T to the economy annually by the end of that buildout.
This latest AI hype cycle is also about 10 years old and about $1T invested, and yet it's still a super-massive financial black hole with no economy-wide trillion dollar boost anywhere in sight.
The internet broadband, fiber, and cellular buildout changed the world significantly. This LLM buildout is doing no such thing and is unlikely to ever do so.
Approximately no one gave a flying fuck about “AI” at anything close to this scale and level of funding and hype before ChatGPT was released in Nov 2022. My non-tech friends and relatives couldn’t have named a single AI product, now they all use ChaptGPT, many of them daily and with paid accounts.
Let’s circle back in 2032 and see how much of this was “hype”.
> Approximately no one gave a flying fuck about “AI” at anything close to this scale and level of funding and hype before ChatGPT was released in Nov 2022.
I think that google image search is a really good example of useful results from the overall AI boom.
I do remember talking to someone in 2016 about the possibility of an AI winter if the image stuff didn't work out, so clearly I'm not the right person to talk to about that.
How much is "the internet" an industry? It's an enabler and a commodity as much as electricity or road networks are. Are you counting everything using the internet as contributing a sizable share to the internet industry's value?
By the time we finished pouring a trillion dollars into the global broadband, fiber, and cellular network buildout between 1998 and 2008, the Internet was already adding a trillion dollars a year to the economy.
We've now got 10 years and about a trillion dollars invested in this latest AI bubble, and it's still a super-massive financial black hole.
Ten years and a trillion dollars can make great things like happen. AI ain't that.
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.