An upgraded dev experience in Google AI Studio | Hacker News Reader

An upgraded dev experience in Google AI Studio

developers.googleblog.com

197 pointsmeetpateltech110 comments

> Gemini 2.5 Pro is incredible at coding, so we’re excited to bring it to Google AI Studio’s native code editor. It’s tightly optimized with our Gen AI SDK so it’s easier to generate apps with a simple text, image, or video prompt. The new Build tab is now your gateway to quickly build and deploy AI-powered web apps. We’ve also launched new showcase examples to experiment with new models and more.

This is exactly what I see coming, between the marketing and reality of what the tool is actually able to deliver, eventually we will reach the next stage of compiler evolution, directly from AI tools into applications.

We are living through a development jump like when Assembly developers got to witness the adoption of FORTRAN.

Language flamewars are going to be a thing of the past, replaced by model wars.

It migth take a few cycles, it will come nonetheless.

I agree. Until about 2005 it was code-on-device and run-on-device. The tools and languages were limited in absolute capabilities, but easy to understand and use. For about the past 20 years we've been in a total mess of code-on-device -> (nightmare of deployment complexity) -> run-on-cloud. We are finally entering the code-on-cloud and run-on-cloud stage.

I'm hoping this will allow domain experts to more easily create valuable tools instead of having to go through technicians with arcane knowledge of languages and deployment stacks.

Having worked on expert systems the difficulty in creating them is often the technical limitations of the end users. The sophistication of tooling needed to bridge that gap is immense and often insurmountable. I see the AI as the bridge to that gap.

That said it seems like both domain expertise and the ability to create expert systems will be commoditized at roughly the same time. While domain experts may be happy that they don’t need devs they’ll find themselves competing against other domain experts who don’t need devs either.

AI as the bridge to fix expert systems. Now I've heard it all!

Obligatory video (sound familiar?): https://www.youtube.com/watch?v=oLHc7rlac2s

I think I’m missing your point. Perhaps we don’t share the same understanding of what I mean by AI and Expert Systems.

I wouldn’t call expert systems AI even though the early use of AI referred to symbolic reasoners used in expert systems.

If you are capturing domain knowledge from an expert and creating a system around it, what would you call that? I think modern AI will help deliver on the promise of expert systems, and I don’t think modern AI obviates the utility of such systems. Instead of a decision support system for human users it’s a decision support system for an AI agent. The same AI agent can interface with human users with a more familiar chat interface - hence acting as a bridge.

Most users will not be able to write Multidimensional Expressions or SPARQL queries and with an AI intermediary they won’t need to.

Expert Systems (TM) was trademarked (abandoned now) way way back in 1982. It was a licensed term that meant something very specific to those with a long background in technology. You sound new to the space and thus want to reinvent the term for your own purposes, which is a fine thing really, but an understanding of the history behind it may save you a great deal of grief.

tl;dr, expert systems as a concept was hot! As an actual implementation, it was a colossal failure. The new AI hype train has always contained echoes of expert systems, and I was giddy with excitement to see someone complete the loop. The serpent eats its own tail after all. Which just goes to show that folks would be considerably more enlightened if they just read a bit more history.

I’m not new to the space, I’ve been working in tech for a very long time. I was into ontologies since the late 90s. And I agree they were in general a colossal failure, or at least didn’t live up to the general hype, they did find success in various niches and did have a big impact on the world. Tim Bernes-Lee was coming out with the semantic web stuff in 99, OWL ontologies is 2004. I did some interesting things with Freebase. I built out huge entity models and created an entirety extraction system to a help power a search engine for large scale cataloging which saw real world use. While not my work Bing and Wolfram Alpha did use such systems to improve their search/utility. Google bought Freebase and incorporated its functionality. The DoD did have success with it, and for a while Boeing had an interesting system but AFAIK they lost a lot of expertise and it atrophied somewhat.

It was in general a library science thing, like search engines were, even today I wish I could disambiguate my search queries to be able to specify which word overloading I’m referring to, be it Java the language, the city, or the coffee.

I’ve spent a decent amount of time trying to introduce these concepts to regular people and have long considered it generally hopeless. I went on to work in ML and I’ve long thought it would be easier to teach a computer to use these systems than regular people. At least AI is at a point now where it can act as a bridge.

I made a lot of money selling expert system tools between 1982 and 1986. Good times.

>We are finally entering the code-on-cloud and run-on-cloud stage.

Sounds like an absolute nightmare for freedom and autonomy.

but only because it is

SWE will be renamed to AIOps :)

You joke, but it is already here you only got the name wrong, MLOps.

SWE will be renamed to whatever the abbreviation is in an overseas language when AI can just be made to replace domestic experts with the cheapest college grads overseas. The problem doing so was always a lack of solid technical ability and experience for the cost cutting being done, but now? They just need to know basic english and block diagrams.

I have never seen an entire profession race to make itself entirely unemployable and celebrate it.

Too many people are hoping they'll be one of the lucky ones still employed and doing little work while talking to a LLM ;)

How so? It's already status quo at companies with massive monorepos like Google and Meta. Your IDE connects to a remote computer and you write and run code on it. No more fiddling around with arcane dev env setup processes it's honestly really refreshing and doesn't feel restrictive at all. On the contrary I can nuke my cloud dev environment and bring it up again in minutes without worrying about losing anything.

To be fair, before Linux taking off, most UNIX shops where code-on-development-server and run-on-deployment-server stage.

Cloud is only the rebranding of timesharing, clear with different technology stacks, however the approach to development is exactly the same as working on an UNIX shop back in 1975 - 1990's.

So back to the dark ages before the tech boom basically.

Finally, companies can wrench back control from those pesky users. Only Google should have root; any other interaction should be routed through their AI! You wouldn't want to own your own device anyways, just rent it!

This is why I think Rabbit is one of the most interesting startups around. If I could wave a wand and go pick any startup to go work at, it would be Rabbit.

Which Rabbit are you meaning? When I search for Rabbit AI I get a few hits and none of them seem like the most interesting startup around.

https://www.rabbit.tech/

They're developing some super interesting ways of the os developing itself as you use the device, apps building themselves, stuff like that. Super early days, but I have a really really good feeling about them (I know, everyone else doesn't and I'm sure thinks I'm nuts saying this).

You're not explaining why you have such a good feeling - is their team uniquely good, far ahead? Is there something specific in how they architected it? I think a lot of people are headed in this direction, they have a bad brand, the need to totally restructure their team, and probably bad equity structure now and a need for a down round, it'll be hard to get good talent.

The rabbit OS project is literally the only correct path forward for AI. Hopefully they go for local on device inference, as they removes cloud costs, solving the burning pile of cash problem most AI companies have.

Directly driving a user's device (or a device hooked up to a user's account at least) means an AI can do any task that a user can do, tearing down walled gardens. No more "my car doesn't allow programmatic access so I can't heat it up in the morning without opening the app."

Suddenly telling an agent "if it is below 50 outside preheat my car so it is warm when I leave at 8am" becomes a simple to solve problem.

Maybe. But everyone else here is celebrating Google being firmly inserted between them and any cognitive work they might do.

I feel like I am experiencing so peak level trolling right now or am completely out of the loop. Are you guys seriously trying to make the point that that rabbit R1 deceive is the best think to happen to AI?

Not their device, look at their OS work.

Complete AI control over a personal phone. Anything a user can do the AI can figure out how to do.

That is the end game for everyone right now - a new class of ambient AI powered personal computing.

Do you guys really think these obvious marketing comments will work here?

I'm not related to them at all. I've written about this field independently - https://meanderingthoughts.hashnode.dev/lets-do-some-actual-...

The idea is a fully personal AI that can control ones devices to accomplish complex tasks. Rabbit is working on this through their rabbitOS project, lots of other players are doing the same thing. OpenAI is trying, and lots of open source projects. Even homekit has initial support for LLM integration.

IMHO controlling a phone directly is the best path forward. Google and Apple are best situated to exploit this, but they may be unable to do so due to structural issues within the companies.

Sounds like the old days of Windows where you just need to format and reload every so often to get everything working the way it should. You have to reset your AI sessions to get them back on track, why would an AI OS be any different?

I feel that the lower level you go the more you want knowledgeable human experts in the loop. There is so much nuance in OS development that I think it'll be a while before I trust AI do have free rein over my devices.

But at the current speed of AI innovation I won't be that surprised if that day comes faster than I expect.

... that little AI assistant gadget thing that bombed? Them?

Yes, I think people wrote them off WAY too quickly, I don't really want to get into a back and forth on if they should have done tech reviews even at all blah blah blah, yeah I agree wasn't an ideal way to introduce yourself to the world, but if you listen to their CEO, use their product, and pay attention to the team they've put together... I feel strongly they're onto something big.

Are you being wrote off too quickly when you blatantly lie about your product capabilities?

Keep in mind that the company the CEO last founded before working on Rabbit was a crypto scam, though. They’re really not giving people much reason to trust them.

Plus, why a separate device and not a mobile app?

I didn't know about their crypto stuff, but the R1 is still my fav thing to play with. I'm older and I don't want a phone with me all the time, I like to go for walks without the phone, but sometimes I still want something, camera + a bit of intelligence in the pocket is great, and the R1 is fun.

R1 is rebadge android phone

"Listen to their CEO"

"No, not the scam part"

Isn’t rabbit a scam? https://paulogpd.bearblog.dev/en-rabbit-r1-its-a-scam/

I use the R1 daily, it doesn't feel like a scam to me.

> This is exactly what I see coming, between the marketing and reality of what the tool is actually able to deliver, eventually we will reach the next stage of compiler evolution, directly from AI tools into applications.

Is this different from other recent models trained eg for tool calling? Sounds like they fine tuned on their SDK. Maybe someday, but it's still going to be limited in what it can zero shot without you needing to edit the code.

> Language flamewars are going to be a thing of the past, replaced by model wars.

This does seem funny coming from you. I feel like you'll still find a way :P

Gemini 2.5 will write a whole Linux kernel from scratch! We are seeing a paradigm shift! This is bigger than the invention of electricity! Awesome times to be alive!

I think there will still need to be some kind of translation layer besides natural language. It's just not succinct enough (especially English, ew), especially where it matters like a rules engine. The thought of building something like an adjudication or payment system with a LLM sounds terrible.

You don't need to use natural language to write your rules engine. LLMs speak every language under the sun, real or made up.

You could define your rules in Prolog if you wanted - that's just as effective a way to communicate them to an LLM as English.

Or briefly describe some made-up DSL and then use that.

For coding LLMs the goal is to have the LLM represent the logic clearly in whatever programming language it's using. You can communicate with it however you want.

I've dropped in screenshots of things related to what I'm building before, that works too.

> describe some made-up DSL

Ironically, for something like the parent suggested i.e. a rules engine, this is the main work.

...as long as your application is only a few thousand lines of code.

Context windows are still tiny by "real world app" standards, and this doesn't seem to be changing significantly.

I regularly put 50k LoC codebases in gemini, it has a 1M context window and actually uses it well.

I've had the opposite experience. If I give it that much context it starts to hallucinate parts of the application that it very much has access to look up. This only starts happening at large context windows.

Depends on what you're doing. Too much context and code generation gets sloppy, but it does a decent job attending to the large context to answer questions, analyze control flow, investigate bugs, review style consistency and guideline violations, etc.

Let me know when it handles 1.5M lines.

Let me know when you meet a person that can handle 1.5M lines, because most people I've worked with can't. Certainly I've never worked on something that required even reading that much... Especially when targeted search options exist to find just specific function/class implementations as needed.

I have a large team that works in a 1.5M line codebase. Not everyone is familiar with every line, but we regularly make changes that would blow through the usable context window of LLMs (which is, in fact, much smaller than advertised).

Is a messy 1.5M lines of tightly coupled code best practice? Of course not. But it evolved over about 20 years and processes tens of billions of dollars of financial transactions. In my experience, it is archetypical of real-world software for a large successful company.

I use LLMs where I can and they're incredibly useful. But their limits are severe compared to a good human software developer and the shortcomings mostly revolve around their tiny context. Human neuroplasticity is still champion.

Sure, they work in that huge codebase, but for any specific task, the number of lines your team keeps in their short/long term memory is surely much much smaller than that.

> 1.5m line codebase

I work regularly with AOSP code (~3mn). While LLMs (Copilot w/ Claude Sonnet 3.7, in my case) cannot gobble all of it up, they have no trouble answering my queries, for the most part, from submodules. If nothing else, using LLMs has dramatically reduced the time it takes to understand a new code file / submodule / a range of commits.

I'm going to wildly guess that AOSP is part of every LLMs training set, so that feels like cheating somehow :-)

he will probably get back to you in a year then

Remind me in a couple of years when this product is abandoned.

It will fail where all other tools fail. Migrating databases, scaling issues, ..

Presumably Google AI Studio[1] and Google Firebase Studio[2] are made by different teams with very similar pitches, and Google is perfectly happy to have both of them exist, until it isn't:

- AI Studio: "the fastest place to start building with the Gemini API"

- Firebase Studio: "Prototype, build, deploy, and run full-stack, AI apps quickly"

[1] https://aistudio.google.com/apps

[2] https://firebase.google.com/

Wait until you hear about Google Vertex AI Studio.

I recently tried to understand the AI products listed in the cloud console. That was not an easy task, despite them clearly having made great pains to clean it up.

Bosses reading this:

"this is brilliant! I'll assign multiple teams to the same project. Let the best team win! And then the other teams get PIP'd"

That's what Telegram does. They have multiple separate teams working on their own version of the same kind of project, and whoever does best wins.

Technically it’s actually a good idea

…if you do it before publicly releasing and spending marketing budget on both products, giving them a full software lifecycle and a dedicated user-base that no longer trusts you to keep things running.

Honestly, even in that case it sucks to be a developer there knowing there’s a 50% chance that the work you did meant nothing.

> Honestly, even in that case it sucks to be a developer there knowing there’s a 50% chance that the work you did meant nothing.

Does it have to mean nothing? If there is a review at the end of the exercise, good parts from each of the teams can be explored for integration to build the best final product. Of course all these things are probably as much political as technical so it is always complicated.

I was certainly thinking of the more political case where one team simply gets laid off with no review. That case would be perfectly alright, to be honest.

Canvas: "the fastest place to start building with the Gemini APP"

Also, did you hear about Jules?

Oh crap I thought they were talking od firebase studio, which I was playing with yesterday and was curious to see if there would be improvements.

Why does Google suck so much at product management?

I've used a bunch of these, and they're different with different use-cases, but your point still stands about them being confusing. It seems many AI companies see a world where we have lots of small mini-apps written by LLMs, so why not integrate that into a variety of tools, I guess?

AI Studio: Mostly a playground for building mini-apps that integrate with the Gemini APIs. A big sell seems to be that you don't need an API key, instead you just build your app for testing, and the access is injected somehow. The UI is more stripped down than an IDE and I assume you'd only use it to prototype basic things. I don't know why there are "deployment" options in the UI, frankly.

Firebase Studio: Mostly a sales funnel for firebase I assume, but this is a "tradition" prototyping/development tool that uses AI to make a product. It supports front end and backend code. This also has a chat bot, but it's more of a web-IDE than a chat-first interface.

Gemini Canvas: This is gemini-the-chatbot writing mini-web-apps in a side-panel. The use case seems to be visualization and super basic prototyping. I've used it to make super simple bespoke tools like a visualizer for structured JSON objects for debugging, or an API tester. The HTML is served statically from a google domain, and you can "remix" versions created by others with your own prompts.

Jules - Experimental tool that writes code in existing codebases by handling full "tickets" or tasks in one go. Never used it, so i don't know the interface. I think it's similar to Codex though.

Gemini Code Assist - their version of a copilot. I think its also integrated or cross-branded with

Vertex AI API, Gemini API - these are just APIs for models.

The ability to seamlessly integrate generated images is fascinating. Although it currently takes too long to really work in a game or educational context.

As an experiment I just asked it to "recreate the early RPG game Pedit5 (https://en.wikipedia.org/wiki/Pedit5), but make it better, with a 1970s terminal aesthetic and use Imagen to dynamically generate relevant game artwork" and it did in fact make a playable, rogue-type RPG, but it has been stuck on "loading art" for the past minute as I try to do battle with a giant bat.

This kind of thing is going to be interesting for teaching. It will be a whole new category of assignment - "design a playable, interactive simulation of the 17th century spice trade, and explain your design choices in detail. Cite 6 relevant secondary sources" and that sort of thing. Ethan Mollick has been doing these types of experiments with LLMs for some time now and I think it's an underrated aspect of what they can be used for. I.e., no one is going to want to actually pay for or play a production version of my Gemini-made copy of Pedit5, but it opens up a new modality for student assignments, prototyping, and learning.

Doesn't do anything for the problem of AI-assisted cheating, which is still kind of a disaster for educators, but the possibilities for genuinely new types of assignments are at least now starting to come into focus.

I love this, and as for AI-assisted cheating, I would make it such that the student can use any tool whatsoever under the sun, but then needs to do a live in-person presentation on it followed by 10 minutes of Q&A. Some are better bullshitters than others, but you'll still see a very clear difference between those who actually worked and those who had the work done for them.

Yes, I think this kind of combination is where higher ed is going to land. I've been talking to a colleague lately about how social skills and public speaking just got more important (and are things we need to focus on actually teaching). Likewise, I think self-directed, individualized humanistic research is currently not replicable by AI nor likely to be - for instance, generating an entirely new historical archive by conducting oral history interviews. Basically anything that involves operating in the physical world and deploying human emotional skills.

The unsolved issue is scale. 5-10 minute Q&As work well, but are not really doable in a 120 student class like the one I'll be teaching in the fall, let alone the 300-400 student classes some colleagues have.

AI could help with scale. Schools need to build SCIFs for their students to complete evaluations in an environment guaranteed to be free of AI assistance.

Just as a side note, I ended up turning the 1970s RPG type game it originally made into a text-based RPG where you play as Henry James in 1889, kind of fun! Curious if the link actually works:

https://aistudio.google.com/app/prompts?state=%7B%22ids%22:%...

Why did they hide the model thought details? Thoughts look like this now:

Analyzing TypeScript Errors

I'm currently focused on the actions/user.ts file, where I've identified a few issues. Specifically, I need to address a "Cannot find name" error related to UpdateUserDetailsFormState, and confirm that the intended target is UpdateUserDetailsFormInput. There might be some other discrepancies in the file that I need to resolve.

Debugging the Import

I've been trying to pinpoint the Cannot find name 'UpdateUserDetailsFormState' error. The type is definitely defined and imported, but it's not being recognized in the updateUserDetails function's arguments. I'm suspecting a scoping issue with the import statement within actions/user.ts. I also need to verify that UpdateUserDetailsFormState is correctly defined with the fieldValues property as optional as per the schema.

My understanding is that the ability to watch the chain of thought is no walled behind the ultra subscription?

It’s also rate-limited, which means the model will silently start skipping the chain of thought after a certain number of daily requests.

Definitely a downgrade over the old version, though really it’s just Google deciding to offer less for free.

Are you sure it's rate limited? I've been using it for quite a while today and it still appears to be thinking.

Hmm, maybe it depends? I was kinda far into a long conversation and it stopped thinking midway through. Maybe it’s based on context length and not a direct rate limit

I feel like it doesn't think when it doesn't need to. It always been like that.

It allows you to use your own api ke.

Their new 'thoughts' smack of "reticulating splines"

Someone else said in this case that you can access it with premium subscription but I have also heard that some products are hiding the CoT because of concerns about distillation

Seeing these announcements make me nervous. I feel like I found some sort of cheat code by using AI Studio for free. Seeing them build it out, makes me wonder when they are going to start charging for it. Though Grok has been very generous as an alternate. I guess there's a lot of good options out there. I'm just used to hitting limits most places, and not as good models.

Agree. And for some reason I find responses from AI Studio is much better than Gemini for the same models. I _already have_ Gemini advanced, bit still mostly use AI studio just for the quality of the responses.

> Though Grok has been very generous as an alternate.

I don't need it inserting console.logs and alert popups with holocaust denials and splash screens with fake videos of white genocide in my apps.

Wasn't it Google's models that showed America's founding father's as being black women? They all have their issues. I just want to get things done, before AI just takes over everything.

They are very clearly different and grok is really indefensible (as is anything associated with Musk these days)

Starlink is indefensible?

I'm not going to waste my time looking up sources, but I remember that Starlink actively worked against Ukraine at one point - so yes, indefensible.

Yes stop polluting our skies.

I care much more about text quality in May 2025 than image generation accuracy in Feb 2024. Generated images are not advertised as 'accurate', whatever that even means.

It's just a copy of all other models in terms of functionality. I didn't find anything controversial nor extra-ordinary in it. Image generation sucked.

Some Indian twitterers found a way to get it to utter Hindi profane words, that's probably the most controversial thing I know about it.

Just a few days ago Grok started inserting paragraphs about the contested nature of white genocide in South Africa and the 'kill the boer' chant into almost every response it gave, with the vast majority of prompts having nothing to do with South Africa, white genocide, or boers.

https://www.theguardian.com/technology/2025/may/14/elon-musk...

https://x.com/xai/status/1923183620606619649

Why is it even a question of "when they are going to start".

Running LLMs costs a stupid amount of money, beyond just the stupid amount of money to train them. They have to recoup that money somewhere.

Did anyone else notice the weird subtle typos in the output?

"Te harsh jolt of the cryopod cycling down rips you"

"ou carefully swing your legs out"

I find this really interesting that it's like 99% there, and the thing runs and executes, yet the copy has typos.

Maybe typos are non-ai signal until we have a typo-fidelity ai to redefine that.

They don't mention it in the demo but they should really take a page from Lovable and add a bidirectional sync to a git repository.

Get outta here! You can do that in AI Studio now? If so, I need to run, not walk, to the nearest computer. Too bad I am sitting on the toilet right now..

You can’t in AI studio, it’s available on loveable.

What, your toilet isn't computerized?

https://www.youtube.com/watch?v=Dd06Md1xOd0

More than anything fancy I just want better ways to get files out of Gemini.

All the copying and pasting is killing me.

I use https://www.cursor.com/ and it can use Gemini and write straight to files in your project. Its free for a month.

Google's been playing catch-up with OpenAI for a couple of years, but now they seem to be hitting their stride.

Using Google Studio AI as a developer will give you a better understanding of the challenges from a product point of view, and why clear, succinct and unambiguous communication is so important. Everyone should try it. But yes, it’s really good for web apps (not so sure for low level programming), not meant as an IDE, purely for AI prompting, which is interesting.

I spent a few minutes playing with Studio and the model and agent are very impressive.

But be sure to connect Studio to Google Drive, or else you will lose all your progress.

Finally an AI story here so I can post about leaving Anthropic.

Sorry guys, yes, Claude is the best model, but your lack of support for structured responses left me no choice.

I had been using Claude in my Saas, but the API was so unreliable I'd frequently get overloaded responses.

So then I put in fallbacks to other providers. Gemini flash was pretty good for my needs (and significantly cheaper), but failed to follow the XML schema in the prompt that Claude could follow. Instead I could just give a pydantic schema to constrain it.

The trouble is the Anthropic APIs just don't support that. I tried using litellm to paper over the cracks but no joy. However, OpenAI does support pydantic.

So i was left with literally needing twice as many prompts to support Gemini and Anthropic, or dropping Anthropic and using Gemini with OpenAI as a fallback.

It's a no-brainer.

So you guys need to pull your fingers out and get with the programme. Claude being good but also more expensive and not being compatible with other APIs like this is costing you customers.

Shame, but so long for now...

AI studio, Gemini, Labs ... There are far too many entry points for Google's AI work.

Promotion driven culture

We also have https://websim.com/ for a while now which takes a prompt and makes your web app. Nothing as fancy, but it has existed for a long time (in AI terms) now.

Still feel like you’d have to be an ignoramus to use this beyond toy bullshit projects since they train AI on your work, humans read your work, and “You may not use the Services to develop [ML|business|mental] models that compete with the Services (e.g., Gemini API or Google AI Studio).”

What kind of smooth brain hears, “they train AI on your ideas and code, humans read your ideas and code, and you agree not to compete back against this thing a multi trillion dollar company just said can do everything, which competes with you,” and says yes? Oh, the smooth brain who doesn’t even realize, because it’s all buried in “additional” legal CYA documents.

ChatGPT still dominates the reach test since I can at least opt out of model training without losing chat logs, even though I have to agree not to compete with the thing that competes with me. Google is like a corporate version of a gross nerd you tolerate because they’re smart, even though they stalk you weirdly.

What a disgrace, we all ought to be sick about creating a legalese-infused black mirror dystopia, racing to replace our own minds with the latest slop-o-matic 9000 and benefit the overlords for the brief instant they count their money while the whole ecosphere is replaced by data centers

It’s like somehow the most magical tech in history (LLMs) comes along and gets completely shafted by elite grifter-tier scumbaggery. Chat bot speech is more protected than human speech these days, Google doesn’t give a crap about privacy, it’s all about the slimy hidden-opt-out of getting everything fed into the slop machine, and break everything if you do.

“Gee, should the app break if the user doesn’t want us to read their shit?” “Yeah, that sounds good, ain’t nobody got time to categorize data into two whole buckets, ship it!”

“How about we make a free better app that doesn’t even have the option of us not reading their shit?” “Oh, yeah, that’ll really help the KPIs!”

Finally, Google is utilizing their cloud

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