A good tool is invisible such that it is highly opinionated and nails the 80, 90, 99% use cases in terms of making assumptions regarding logical defaults.
Restated, the tool understands the process and what a “good” result/outcome looks like. It correctly presumes relevant and important information (especially given the current stage of your full task), and can “fill the gaps” between start and finish.
A good tool can distinguish between what you wanted, what you thought you wanted, and what you “should have” wanted.
A good tool makes it easy to do the “right” thing and hard to do the “wrong” thing.
A good tool doesn’t function to be understood, but to not be misunderstood.
I have spent a lot of time sitting in this question, having spent years creating design and drafting automation tools for architects, designers, and engineers in the building-design industry.
Nice to hear Google is thinking about this. Was always of the belief that they SHOULD be considering things like scarcity in the various recommendations the apps in their offerings provide.
Also reminds me of that guy in Berlin who caused traffic jams on Google Maps by walking around with a wagon filled with phones:
Personally, for any kind of serious reading, I have to go to a place/room with zero opportunities for distraction. No noise, no surrounding commotion, even slightly dimming lights helps. My retention rate drops significantly if there are any distractions.
For lighter reading or docs, I’ve got to take almost diagrammatic-like notes. whiteboard, paper/pen, or ipad work best for me - typed notes don’t work well. I may not even revisit the notes later, but actually writing them helps retention.
If I’m out of luck and must read in a non conducive environment, just resolving that I’ll have to re-read once or twice is the only way. Not aware of any magic solution unfortunately.
In my experience, boilerplate sql isn’t even something I would consider a big pain point. The biggest challenge (to me) is explicit data contract enforcement across the stack.
I haven’t personally had third-party ORM frameworks succinctly encourage synchronization and help me build against long-term divergence of data models across the stack. “Make it easy to do the right thing and hard to do the wrong thing” still leaves a lot of room for ‘gotchas’ as apps evolve over time.
Finally, I don’t understand the aversion in learning even a bit of sql. As topics go, it’s a very good (maybe even the best, if I were being provocative) effort:payoff ratio. Not sure I’d call myself a sql expert, but am always pleasantly surprised how much functionality is within reach by knowing even the very basics of sql.
As someone who has historically spent a lot of my time with C#, and now spend most of my days writing python… LINQ is typically what I miss most from C#… (obviously aside from static types and compiled binaries).
Most upscaling and super-resolution techniques I’ve seen use various implementations of interpolation; typically nearest-neighbor approaches. Although I don’t work in the medical field and haven’t checked in on the research at least since ViTs overtook CNNs for other areas of computer vision.
I’ve always found that most books within or similar to Ferris’ genre hold a nice message but are 90 fluff.
He picks on summarization as the sort circuit and claims the lived experience imbued within his books is the real draw. Not disagreeing here. LLMs unpack the intentional content stretching rigmarole authors go through as a function of publishers requirements to produce something that is minimally viable to publish.
Some seemingly important questions that come to my mind in a future where LLMs, ad-based revenue, and attention-as-currency prevail:
how do you create a knowledge-based product valuable/compelling enough to make people want to pay for it? And what is the best value-prop way to monetize it? (For BOTH producer and consumer)
Is length a necessary requirement for substantiveness?
If so, where on the spectrum of long-content_vs_short-content should it live?
Unfortunately, I im not confident the internet can fundamentally diverge from an ad-based or subscription monetization model in its current form. IMO, Michael I Jordan has a lot of refreshing ideas on how to build better marketplaces that sustainably benefit producers and consumers (as opposed to mostly just platform-maintaining-rent-seekers).
Could it be possible that Zuck has a Llama-shaped magic 8-ball to which he has fully committed himself to “dogfooding” an AI-only strategy to his responsibilities in the company?
I can also relate here, seeking a product review on Sony wh1000x_, Google wrote a nice seeming summary, but scrolling down to some Reddit discussions, stumbled upon a single comment that was very nearly verbatim what the “AI Summary” said, only the ai summary phrased the summary as if it were a sentiment aggregated over many users’ experience.
i.e.”users say…”
I agree with the sentiment, but native ads i.e. blogs, reviews, articles, etc. that do their best to hide that they’re a sponsored product review have been around for a long time. Admittedly, LLMs WILL make it even more difficult to discern the difference.
Is “the goal of Search” really: “to help you ask _anything_ on your mind”?
If “reimagined Search” is “designed to anticipate your intent”,
Would it correctly infer my intent to not utilize an agentic approach? Is there an “off switch”?
As for “Search agents”
“operating in the background 24/7”,
What is the carbon footprint of that? How do I turn it off? How do I ask it to stop phoning home my every keystroke?
These questions are asked partly rhetorically because it’s likely I don’t need a team of “24/7 Search agents” to help me guess the answers…
Historically, I scoffed when someone said “here’s the difference between a google search and asking ChatGPT”, or when people said that ChatGPT would “kill search”, but Google sure seems to be in a hurry to burry the original feature all by themselves.
THANK YOU, 1000% agree. I call this style “contrastive language”, and find it viscerally unpleasant. It is a pithy attempt to be overly attention-grabbing and “punchy”. Everything that is (was?) wrong with social media (sic LinkedIn) posts, even in the immediate lead up to when it became 100% LLM slop.
It came to my attention recently how many TOTAL objects currently exist in LEO. And that a study said that due to light deflection of these objects, that the earth’s night sky is an average of 10% brighter than it was in 1980s… although I generally am excited by technological advancement, that fact (if true) made me feel somewhat melancholy.
That’s fair, the “practically plagiarize” comment was admittedly harsh and quickly written.It’s obviously an allusion to the paper.
Nevertheless, referencing the LFR work and merely including a link where the paper is discussed feels a little like beating around the bush. The primary article doesn’t seem to use the words “self-similar” nor “fractal” - if you’re only interested in LFR work, why reference Mandelbrot at all?
How on earth can you write an article that practically plagiarizes the title, mention the paradox, and neither mention mandelbrot nor cite the original paper anywhere!?
Restated, the tool understands the process and what a “good” result/outcome looks like. It correctly presumes relevant and important information (especially given the current stage of your full task), and can “fill the gaps” between start and finish.
A good tool can distinguish between what you wanted, what you thought you wanted, and what you “should have” wanted.
A good tool makes it easy to do the “right” thing and hard to do the “wrong” thing.
A good tool doesn’t function to be understood, but to not be misunderstood.
I have spent a lot of time sitting in this question, having spent years creating design and drafting automation tools for architects, designers, and engineers in the building-design industry.