Well, yea. Everyone agrees with these points. Who wants to ruin things for the sake of it?
The reason why things get worse is because of systemic problems: misaligned incentives, information asymmetry, diffuse responsibility, etc. It's actually hard to improve these aspects of large systems. Hence, why the world is an imperfect place.
Suddenly turning off services when the billing cap is reached is a big reliability risk for customers. All workloads using that billing account would be impacted immediately.
If they weren't turned off at the billing cap, but were given some leeway instead, either that becomes the new hard limit, or GCP will have to give away the difference.
And there's no "middle ground" you could implement that makes sense either - like a "frozen" state. Preventing new writes to a GCS bucket breaks the writer app. Freezing VMs serving web traffic takes the site down.
Even if the service was shut down once the billing limit is hit, how long would GCP wait for the user to add funds or raise the limit? GCP would need to either keep the services in a hidden/frozen state or not turn them off / freeze them at all (in which case GCP would be giving away resources for free).
Maybe GCP can give users a heads-up when they're about to hit the limit? GCP already does - billing alerts do exist. It's just possible to blow past them if your usage is a massive spike.
Moreover, getting the hundreds of GCP services to implement a "frozen" state is difficult. It's hard enough getting everyone to listen to the "billing account disabled" signal, and (soft-)delete the resources (based on the resource, after some time interval). Given these billing overruns happen for smaller customers, it's not really worth solving the problem - which I don't think has a great solution to begin with.
How much do existing services trust new email service providers? It would seem to be an uphill battle for Cloudflare to start a new service. It's easy to automate from the start, meaning it's easier to send spam. I suppose reputation is not simply based on the domain the email comes from?
1) Pulls you in with a catchy title, that at first glance seems like a dunk on Cal.com (whatever that is).
2) Takes the "we understand your pain" approach to empathize w/ Cal.com, so you feel like you're on the good vibes side.
3) Provides a genuine response to the actual problem Cal.com is dealing with. Something you can't dismiss out of hand.
4) But in the end of the day, the response aligns perfectly with the product they're promoting (a click away to the homepage!)
This mix of genuine ideas and marketing is quite potent. Not saying this is all bad or anything, just found it a bit funny. The mixed-up-ness is the point!
A blog post like this is half the story. I’d like to see the results. Did your brother get more business? What were the failure modes? Did customers care if it was a bot or not?
They have direct feedback that many people return them bc they’re too heavy, and yet… It’s just Apple being stubborn. I guess it’s not a big enough problem for them, and they don’t care about losing the market. One must laugh.
He came to give a lecture at UT Austin, where I did my undergrad. I had a chance to ask him a question: "what's the story behind inventing QuickSort?". He said something simple, like "first I thought of MergeSort, and then I thought of QuickSort" - as if it were just natural thought. He came across as a kind and humble person. Glad to have met one of the greats of the field!
The problem can be complex, which sometimes means the solution needs to be complex. Often, you can solve a complex problem in simple ways. There’s many ways to do that:
a) finding a new theoretical frame that simplifies the space or solutions, helps people think through it in a principled way
b) finding ways to use existing abstractions, that others may not have been able to find
c) using non-technical levers, like working at the org/business/UX level to cut scope, simplify requirements,
The way you can make complexity work for your career, is to make it clear why the problem is complex, and then what you did to simplify the problem. If you just present a simple solution, no one will get it. It’s like “showing your work”.
In some orgs, this is hopeless, as they really do reward complexity of implementation.
One of the odd things people do with tech is taking someone else's random projections at face value?
What does it mean to say "we were promised flying cars", or "every city would have micro-factories, that 3D printing would decentralize production"?
The people creating these narratives may a) truly believe it and tried to make it a reality, but failed b) never believed it at all, but failed anyway, c) or be somewhere else on this quadrant of belief vs actuality.
Why not just treat it as, "a prediction that went wrong". I suppose it's because a narrative of promise feels like a promise, and people don't like being lied to.
It's a strange narrative maneuver we keep doing with tech, which is more future-facing than most fields.
It's extremely painful that there's are free, OSS dictation tools that can run on-device, that are so much better than Apple's dictation, and yet it's quite difficult to use them on the iPhone. I'm referring to Whispr. Microphone access is a pain for custom keyboards -- for good reason, but still.
> When I was taken to the Tate Modern as a child I’d point at Mark Rothko pieces and say to my mother “I could do that”, and she would say “yes, but you didn’t.”
Actually, no you couldn't. The subtlety of the choice of colors, their shading, and their soft shaping, and the program of their creation over many years - you couldn't do that. They're lovely and sublime, and wonderful and an abyss. If you want to throw all that away and reduce it two boxes of paint, go ahead - but you'll be wasting a lifetime's engagement, of the joy of seeing with your intellect wide open.
> The value got extracted, but compensation isn't flowing back. That bothers me, and it deserves a broader policy conversation.
It bothers me, too. But, look at the history of the internet. There's no reason to expect we'll be able to fix this problem.
1. Search engines drove traffic to news/content sites, which monetized via ads. Humans barely tolerate these ad filled websites. And yet, local news went into steep decline, and the big national players got an ever-larger share of attention. The large, national sites were able to keep a subscriber-based paywall model. These were largely legacy media sites (ie: NYT).
2. News sites lost the local classifieds market, as the cost of advertising online went to zero (ie: Craigslist). This dynamic was a form of creative destruction - a better solution ate the business of an older solution.
3. Blog monetization was always tough, beyond ads. Unless you were a big blog, you couldn't make a living. What about getting a small amount of money per view from random visitors? The internet never developed a micro-payment or subscription model for the set of open sites - the blogosphere, etc. The best we got were closed platforms like Substack and Medium, which could control access via paywalls.
All this led to the internet being largely funded through the "attention economy": ads mostly, paywalls & subscriptions some.
The attention economy can't sustain itself when there are fewer eyeballs:
1. Tailwind docs have to be added just once to the training set for the AI to be proficient in that framework forever. So one HTTP request, more or less, to get the docs and docs are no longer required.
2. Tailwind does change, so an AI will want to access the docs for the version its working with. This will require access at inference time. This is more analogous to visiting a site.
A common pattern I see is data-plane nodes receiving versioned metadata updates from the control-plane. As long as the version is higher than the node's previous one, it's correct to use. So, the metadata is a sort of monotonic counter with a bag of data attached to it. This pattern produces a monotonic counter, which I assume is a naive sort of CRDT - though the data itself doesn't benefit from CRDT merge semantics. In this world, as long as a node gets an update, its going to incorporate it into its state. In the article's terms, the system has Strong Convergence.
I'm trying to figure out under what practical circumstances updates would result in Eventual Convergence, not Strong Convergence. Wouldn't a node incorporate an update as soon as you receive it? What's causes the "eventual" behavior even after a node gets an update?
It seems to me the trouble is actually getting the update, not the data model as such. Yes, I realize partial orders are possible, making it impossible to merge certain sequences of updates for certain models. CRDTs solve that, as they're designed to do. (Though I hear that, for some CRDTs, merges might result in bad "human" results even if the merge operation follows all the CRDT rules.)
The reason why things get worse is because of systemic problems: misaligned incentives, information asymmetry, diffuse responsibility, etc. It's actually hard to improve these aspects of large systems. Hence, why the world is an imperfect place.