Also it's just a Chrome / Blink derivative. They don't actually have an independent web stack like Mozilla does. That independent stack requires a lot of developer effort to maintain.
There is also Firefox Focus. Been using it on iOS since it came out a few years ago.
It integrates as an ad blocker for Safari, so I don't actually use Firefox itself (since as you mentioned, all browsers on iOS are just a wrapper to Safari anyways).
I just browse using Safari and ads are blocked by Firefox Focus. Pretty neat.
> The only thing that really worked was sending so many rat reports to the city that they had to keep coming out to bait the burrows in the neighbors small yard, until they likely talked to the owner directly to do something about it.
I was recently deputized into the "Rat Pack" of New York City[1].
The main thing I learned is that exactly what you said is true. When there are rat problems, you have to go to the source. Traps/poison in a localized area is not going to work, as the brown rat is easily able to reproduce faster than we can kill them with those methods.
In fact, certain methods have ended up helping the rats. At one point the city put out thousands of boxes with poison in them. The problem is the boxes were designed to be nice and cozy for the rats, so they'd be tempted to go in and eat the poison. Instead, they go in there and mate. (They also use the boxes to evade predators).
NYC's current strategy is to improve data collection on rats, and then use that data to better enforce standards (like garbage disposal), eradicate burrows, and plant different shrubs that aren't as friendly to rats. You have to fully eliminate the environments that sustain them, you can't exterminate your way out.
The job of "software engineer" as we know it will end.
Before the industrial revolution, shoemakers would make shoes. It was a specialized skill, meaning shoes were very expensive, so most people couldn't afford them.
Then factories were invented. Now shoes could be made cheaply and quickly, by machines, so more people could afford them. This meant that far more people could be employed in the shoe industry.
But those people were no longer shoemakers. Shoemakers were wiped out overnight.
Think of how huge the shoe industry is now. There are jobs ranging from factory worker to marketing manager. But there are zero shoemakers.
AI writing software doesn't mean it's the end of the industry. Humanity will benefit greatly, just like we did from getting cheaper shoes.
But those people were no longer shoemakers. Shoemakers were wiped out overnight.
Have you seen the cost and popularity of "Made in X" handmade boots though? Red Wing, Origin, Red Back. It's absolutely crazy
The difference is, all of a sudden we could make a lot of CHEAP shoes and yes I'm sure it wiped out a lot of shoe maker jobs, but there is still a lot of good shoe makers around and there is still a high demand for handmade shoes and boots.
Shoemaking is an interesting analogy, and it brings to mind a few other facts that might be relevant. I have zero experience in the industry of shoemaking, so these are my impressions, they could be wrong:
1. There are still many thousands of people in the US alone employed today as traditional shoemakers at boutique firms. It's a very niche career, but it does exist.
2. As the cost of shoes plummeted and our ability to create more complex designs exploded, we also got a huge proliferation of innovation and creativity in shoe design.
3. Yes, today's shoe industry has lots of factory workers and marketing managers...but it also has many tens of thousands of more specialized roles like shoe designers, materials specialists, process automation engineers, etc.
I can see a future where software is almost entirely created by AI, but we have many specialized roles of people who know how to apply AI tools to software creation, or who sit in the interface between the business and the AI in some way that it's hard to foresee now.
On the flip side, if we truly get ASI, then it is hard to see what exactly those specialized roles represent that can't be replaced.
What does Sam Altman do that an ASI won't be able to?
The shoe comparison is ridiculous. For let's say 99% of people, shoe requirements are the same (in function), with almost all variations being purely esthetic. There are, let's say, 10 kinds of shoes, or perhaps 100. Make it a thousand, for argument's sake.
Meanwhile, every single business has different workflows and therefore different needs. The most common ones (browsers, etc.) are answered by traditional software. If you can write in detail the business needs as pertaining to workflows - business rules, let's call them - you've effectively made the software already. The only difference being that telling ChatGPT to do something in English and telling the computer to do it in code is that one is non-deterministic.
Software is, primarily, a means to process information, which is to say reality (in a business setting). An AI that can replace software developers can, in effect, replace every job that happens on a computer, in every company on Earth. Apart from Jevon's paradox (which is much more applicable to software than shoes), this shift would be so gargantuan that it's barely worth thinking about, in the same way that it's not worth thinking about a supervolcanic eruption: the consequences would be earth-shattering, and finding employment would be the least of your worries.
To add to this: the author is missing a major aspect of the Jevons paradox.
They keep referencing "more efficient software developers," but the Jevons paradox isn't only about efficiency. The efficiency creates lower cost, which in turn increases demand.
The main cost of software is software engineers. It's a specialized skill, so it's a high-salary job.
With AI doing most of the work, salaries will begin to fall. It will no longer make sense to study computer science, or spend years learning to code, for such a low salary. There will no longer be people doing what we call software engineering today.
So the author is right, Jevons paradox will take effect. But like I said above, it will replace the current industry with a very different-looking industry.
I really don't see AI generating safe code for automotive embedded systems that is maintainable and MISRA and HIS compliant. And there will need to be software engineers who are trained to debug these systems.
The idea discussed in the article is how do we make a sign which can even be understood in 10,000 years? They need to understand we are saying "bad, stay away" in order for them to even know if they disrespect it. There is a risk they think we're saying "this is great, come and see!"
> That bound to create a religion/cult and that would probably cause much greater harm to that society.
Sure, it could start something harmful to society. But what if there is no warning at all? They're just as likely to start their own religion which says they should spread this stuff everywhere, far across the planet. Which could render much of Earth uninhabitable. Seems worth the risk of creating a "fear of radiation" religion, to save the Earth and life itself.
I work for a startup ISP, and I often wonder how we can better educate the end consumer about all this.
I'm a developer but sometimes take customer support calls. One time a consumer had just gotten our 1Gbps symmetrical fiber installed, and he was testing it against his crappy 200Mbps/10Mbps high-latency existing cable. He claimed that the cable was "far better" and wanted to cancel us. Took me about a half an hour of troubleshooting and trying to convince the guy that it's virtually impossible the other connection was better. Turned out he was standing close to the old router, far from our router, using a low-spec Android phone. I had him connect to our router via Ethernet with a Macbook, and the speed test blew his mind.
The problem is that ISPs, including the one I work for, advertise only one number: bandwidth. Consumer thinks "1,000Mbps is faster than 200Mbps, so I should buy that!" They then run a speed test and whoever has the higher number is the winner. But it's not their fault for not knowing the flaw in their method, because the only marketing or education about our product that any ISP does is around that one number.
I have a lot of ideas around how better marketing/education could be achieved. But I have a lot of code to write, so, maybe one day...
In the meantime I take every opportunity to explain to folks that 200Mbps fiber != 200Mbps cable. Latency is incredibly important to how "fast" your internet feels, and of course there are a million things about your Wifi router and how/where it is set up.
A lot of ISPs include a performance test built into the modem which the customer and employees can access via a portal. For high speed plans e.g. ATT will have the installer who brings the modem, connects it, runs the test, shows the customer the speed, and then asks the customer to test their device. From there they can troubleshoot/explain any differences. Particularly around the >1 gbps plans because so few people realize most of their wired devices have that limit.
Of course if you're a "light initial touch" ISP or are more open about letting them run their own modem then this kind of approach isn't as easy to do and you're stuck with trying to educate the customer on doing the whole shebang themselves.
Yes, we are doing all of the above except for the on-modem speed test, which we are working on.
Having the installer run a test and talk with the customer is helpful, but a lot of people don't trust the installer. The incumbent ISPs are so evil that they've convinced consumers that all ISPs are evil tricksters. So we'll get a customer service call 10 minutes after the installer has left: "I'm not getting the speeds I'm paying for!"
I've always thought it was Apple who dropped the ball with Siri.
When Siri came out in 2011 -- two years before Alexa -- all my coworkers and I had iPhones. I remember sitting in my office as people yelled at Siri all day trying to get her to be useful. "Hey Siri, what's the weather tomorrow? No... No SIRI, WHAT'S -- THE -- WEATHER -- TOMORROW!"
Even though it sucked, it seemed every hardcore Apple user was ready to jump onboard. Who cares if I'm in a crowded office with people trying to get work done while I spend 10x longer to perform a function in the noisiest possible way? I'm using this thing!!
The voice recognition has improved since then. But the functionality still sucks.
When I'm in private, there are a couple commands I'll use.
- "Hey Siri, call xyz" where xyz is someone in my contact list I have tested with Siri and is known to work. Not recommended to try without testing first.
- While cooking, "Hey Siri, set a timer for 10 minutes." Works great.
- While driving and navigating: "Hey Siri, take me to the nearest gas station." That one is pretty good, except the actual maps are not smart enough so sometimes you'll be turned around in the opposite direction you were going, since technically that's where the nearest gas station is.
I never understood why they couldn't make this tool better, even before LLMs and without any AI at all. Just hard-code a bunch of phrases, and ways to translate those phrases into some action.
"Hey Siri, how close is my UPS delivery?"
"Hey Siri, where can I get the best price on xyz cat food?"
"Hey Siri, what's my bank balance?"
"Hey Siri, how much is a Lyft to xyz?"
I bet if they had a single developer working on adding Siri commands full-time, they could announce something like 20-50 new Siri functions at every WWDC.
But it seems the goal now is just "Make it an LLM," instead of focusing on recognizing the task that the user wants to do, and connecting it to APIs that can do those tasks.
They could've dominated the "conversational system" market 13 years ago.
> But it seems the goal now is just "Make it an LLM," instead of focusing on recognizing the task that the user wants to do, and connecting it to APIs that can do those tasks.
I almost completely agreed with you, but this is not true! Apple is trying to solve the task & API problem with “task intents”, on which they go into more detail outside of the keynote: https://youtu.be/Lb89T7ybCBE
The new Siri models are trained on a large number of schemas. Apps can implement those schemas to say “I provide this action” (aka, the user intends to do this action). Siri can use the more advanced NLP that comes with GenAI to match what you say to a schema, and send that to an app.
These app intents are also available to spotlight and shortcuts, making them more powerful than just being Siri actions
Non LLM conversational agents are a dead end. It's a waste of that one programmer's time now that we have an imperfect but pretty good solution. There is zero discoverability in voice commands and the best you'll do is remember 3 to 10 commands if you can't actually ask the agent anything. Better to have that person work on the team to improve the LLM.
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