The irony of this, is that Microsoft was trying to push CoPilot everywhere, however eventually Apple, Google and JetBrains have their own AI integrations, taking CoPilot out of the loop.
Slowly the AI craziness at Microsoft is taking the similar shape, of going all in at the begining and then losing to the competition, that they also had with Web (IE), mobile (Windows CE/Pocket PC/WP 7/WP 8/UWP), the BUILD sessions that used to be all about UWP with the same vigour as they are all AI nowadays, and then puff, competition took over even if they started later, because Microsoft messed up delivery among everyone trying to meet their KPIs and OKRs.
I also love the C++ security improvements on this release.
Microsoft owns 49% of OpenAI so why they should worry? JetBrains just proudly announce that they now use GPT-5 by default.
> going all in at the begining and then losing to the competition
Sure, but there are counter examples too. Microsoft went late to the party of cloud computing. Today Azure is their main money printing machine. At some point Visual Studio seemed to be a legacy app only used for Windows-specific app development. Then they released VSCode and boom! It became the most popular editor by a huge margin[0].
[0]: https://survey.stackoverflow.co/2025/technology#most-popular...
Anecdotally: Azure is the Teams of cloud services - nobody uses it voluntarily or because it's technically the best solution.
They use it because the corporation mandates it.
Partially, I still consider the Web shell and VSCode based editing experience the best of clound vendors as replacement from what started to me as telnet and X forwarding on the university DG/UX servers.
AWS is the worst of this experience, even IBM Cloud has better tooling in this regard, GCP is somehow in the middle, others like Vercel/Netlify naturally don't offer this kind of setup.
I’ve used Meet, Slack, Zoom and Teams extensively. Teams beats the others by miles in my opinion.
Zoom is pretty good for video meetings and especially for video conferences. I've never tried to use it for chat, but I imagine it's pretty lackluster. The really nice thing about teams is that it does both in one place.
But you know what's super underrated and I think could really take a hold on the business world? Discord! The video calls are so good! And multiple streams at the same time? Zoom can't do that!
The channels, too, just blow everything teams has out of the water. The video quality is better, its way faster, has more features, and they actually work. The audio filtering stuff actually works.
I really think with the right marketing they could take over the world. Honestly can't believe they haven't tried it yet.
is this sarcasm? Teams is by far the worst ive used out of those mentioned
One day we will be able to have threaded conversations....
> At some point Visual Studio seemed to be a legacy app only used for Windows-specific app development. Then they released VSCode and boom!
Indeed I heard directly from someone involved that the VS Code team understood the reputation of Visual Studio and wanted to call the product “Code” instead, and the compromise with marketing leadership was the the binary was called “code”.
The point is MS was so, so, so late to the party of cross-platform developer tools. And then suddenly they won the game.
Ah, it makes total sense to me now. Thanks.
> VS Code is not related to it in any shape or form Except they are made by the same company? and literally own the trademark for both?
Oracle literally own the trademark for both Java and JavaScript.
> Microsoft owns 49% of OpenAI
Power at OpenAI seems orthogonal to ownership, precedent or even frankly their legal documents.
CoPilot isn't anything Microsoft is trying to sell outside of their own products. And with GitHub Copilot there is no "copilot" model to choose, you can choose between Anthropic, OpenAI and Google models.
Sure UWP never caught on, but you know why? Win32, which by the way is also Microsoft, was way to popular and more flexible. Devs weren't going to re-write their apps to UWP in order to support phones.
UWP was more than just phones, https://blogs.windows.com/windowsdeveloper/2015/03/02/a-firs...
And Windows 11 was the reboot of Windows 10X,
Just because you can’t or won’t win the market with your opportunistic investment, doesn’t mean you should let your competitors completely annihilate you by taking that investment for themselves.
Google, Apple, FB or AWS would have been suitors for that licensing deal if MS didn’t bite.
About GitHub Copilot in specific: One big negative was how when GPT-4 became available that Microsoft didn't upgrade paying Copilot users to it, they simply branded this "coming soon"/"beta" Copilot X for a while. We simply cancelled the only Copilot subscription we had at work.
Copilot subscription?
I've been getting monthly emails that my free access for GitHub Copilot has been renewed for another month… for years. I've never used it, I thought that all GitHub users got it for free.
There's a free tier, and various paid tiers: https://github.com/features/copilot/plans
If you are a student or maintain a popular open source project, they give it to you for free. I’m guessing you might fall under that category.
Taking out of the loop? I have a feeling that vscode copilot has huge market share. It's more like competitors are slowly eating small piece of pie.
Microsoft mistook a product game for a distribution one. AI quality is heterogenous and advancing enough that people will make an effort to use the one they like best. And while CoPilot is excellently distributed, it’s a crap product, in large part due to the limits Microsoft put on GPT.
"Taking Copilot out of the loop" if you ignore the massive ecosystems of Github, Visual Studio, and Visual Studio Code.
Different CoPilot product. Typical Microsoft naming confusion.
I use IntelliJ with the Copilot plugin, using Claude. My employer has a big subscription for everything from Microsoft, and that includes Copilot, so that's free for me. But somehow Copilot also gives me access to Claude. No idea how that works.
What confuses me about MS Copilot is that there are (according to ChatGPT) 12 distinct services that are all Copilot:
Microsoft Copilot (formerly Bing Chat)
Microsoft 365 Copilot
Microsoft Copilot Studio
GitHub Copilot
Microsoft Security Copilot
Copilot for Azure
Copilot for Service
Sales Copilot
Copilot for Data & Analytics (Fabric)
Copilot Pro
Copilot Vision
Out of all of big tech, Microsoft is by far the worst at naming stuff. Its comically bad most of the time.
Also OpenAI pioneered but now the many competitors seem to have either caught up or surpassed them. They might still retain a significant brand recognition advantage as long as they don't fall too far behind, though.
Which competitor has alternative to ChatGPT Pro? I have Claude subscription and Opus 4.1 is not on the same level. ChatGPT Pro thinks for 5-10 minutes, while Opus either doesn't think at all or thinks very briefly. And response quality is absolutely different. ChatGPT Pro solves problems, Opus does not. Is there any competitor with "Pro" product which spends significant amount of computing for a single query?
umm I don't know what you are talking about, I use a Github Copilot 40 USD subscription in VSCode to code using various models, and this is the industry standard now in my region, as most employers are now giving employees the 10 USD subscription.
In what industry? Not where I am where unless customers actually sign a permission no one is allowed to come close to those tools in project delivery.
Maybe because Microsoft is a shit company and anything they do is sus af. And everyone knows it. And I'm tired of pretending like it's not. I wouldn't trust Microsoft to babysit my mortal enemy's kids.
Maybe if they weren't literally the borg people would open their hearts and wallets to Redmond. They saw that Windows 10 was a privacy nightmare and what did they do? They doubled down in Windows 11. Not that I care but it plays really poorly. Every nerd on the internet spouts off about Recall even though it's not even enabled if you install straight to the latest build.
They bought GitHub and now it's a honeypot. We live in a world where we have to assume GitHub is adversarial.
_NSAKEY???
Fuck you Microsoft.
Makes sense karma catches up to them. Maybe if their mission statement and vision were pure or at least convincing they would win hearts and minds.
Almost no one uses copilot unless they are not allowed to use anything else or don’t know any better. MS could have been a leader in this space but MS couldn’t understand why people didn’t like copilot but loved the competition.
Once co-pilot tendrils and icons began appearing in all of my orgs tools, they announced we would no longer be able to expense subscriptions for others. Only those who haven’t used ChatGPT Pro, Claude, Gemini, etc have anything good to say about copilot.
3 days ago I saw another Claude praising submission on HN, and finally I signed up for it, to compare it with copilot.
I asked 2 things.
1. Create a boilerplate Zephyr project skeleton, for Pi Pico with st7789 spi display drivers configured. It generated garbage devicetree which didn't even compile. When I pointed it out, it apologized and generated another one that didn't compile. It configured also non-existent drivers, and for some reason it enabled monkey test support (but not test support).
2. I asked it to create 7x10 monochromatic pixelmaps, as C integer arrays, for numeric characters, 0-9. I also gave an example. It generated them, but number eight looked like zero. (There was no cross in ether 0 nor 8, so it wasn't that. Both were just a ring)
What am I doing wrong? Or is this really the state of the art?
"What am I doing wrong?"
Your first prompt is testing Claude as an encyclopedia: has it somehow baked into its model weights the exactly correct skeleton for a "Zephyr project skeleton, for Pi Pico with st7789 spi display drivers configured"?
Frequent LLM users will not be surprised to see it fail that.
The way to solve this particular problem is to make a correct example available to it. Don't expect it to just know extremely specific facts like that - instead, treat it as a tool that can act on facts presented to it.
For your second example: treat interactions with LLMs as an ongoing conversation, don't expect them to give you exactly what you want first time. Here the thing to do next is a follow-up prompt where you say "number eight looked like zero, fix that".
> For your second example: treat interactions with LLMs as an ongoing conversation, don't expect them to give you exactly what you want first time. Here the thing to do next is a follow-up prompt where you say "number eight looked like zero, fix that".
Personally, I treat those sort of mistakes as "misunderstandings" where I wasn't clear enough with my first prompt, so instead of adding another message (and increasing context further, making the responses worse by each message), I rewrite my first one to be clearer about that thing, and regenerate the assistant message.
Basically, if the LLM cannot one-shot it, you weren't clear enough, and if you go beyond the total of two messages, be prepared for the quality of responses to really sink fast. Even by the second assistant message, you can tell it's having an harder time keeping up with everything. Many models brag about their long contexts, but I still feel like the quality of responses to be a lot worse even once you reach 10% of the "maximum context".
You also need to state your background somehow and at what level you want the answer to be. I often found LLM would give answer that what I ask is too complex and would take months to do. Then you have to say like ignore these constraints and assume I am already an expert in the field, outline a plan how to achieve this and that. Then drill down on the plan points. It's a bit of work, but its fascinating.
Or it would say to do X it involves very complex math, instead you could (and proceeds with stripped down solution that doesn't meet goals). So you can tell it to ignore the concerns about complexity and assume that I understand all of it and it is easy to me. Then it goes on creating the solution that actually has legs. But you need to refine it further.
It’s good at doing stuff like “host this all in Docker. Make a Postgres database with a Users table. Make a FastAPI CRUD endpoint for Users. Make a React site with a homepage, login page, and user dashboard”.
It’ll successfully produce _something_ like that, because there’s millions of examples of those technologies online. If you do anything remotely niche, you need to hold its hand far more.
The more complicated your requirements are, the closer you are to having “spicy autocomplete”. If you’re just making a crud react app, you can talk in high level natural language.
Did you try claude code and spend actual time going back and forth with it, reviewing it's code and providing suggestions; Instead of just expecting things to work first try with minimal requirements?
I see claude code as pair programming with a junior/mid dev that knows all fields of computer engineering. I still need to nudge it here and there, it will still make noob mistakes that I need to correct and I let it know how to properly do things when it gets them wrong. But coding sessions have been great and productive.
In the end, I use it when working with software that I barely know. Once I'm up and running, I rarely use it.
> Did you try claude code and spend actual time going back and forth with it, reviewing it's code and providing suggestions; Instead of just expecting things to work first try with minimal requirements?
I did, but I always approached LLM for coding this way and I have never been let down. You need to be as specific as possible, be a part of the whole process. I have no issues with it.
I agree, but I think there's an important distinction to be made.
In some cases, it just doesn't have the necessary information because the problem is too niche.
In other cases, it does have all the necessary information but fails to connect the dots, i.e. reasoning fails.
It is the latter issue that is affecting all LLMs to such a degree that I'm really becoming very sceptical of the current generation of LLMs for tasks that require reasoning.
They are still incredibly useful of course, but those reasoning claims are just false. There are no reasoning models.
FWIW, I used Gemini to write an Objective-C app for Apple Rhapsody (!) that would enumerate drivers currently loaded by the operating systems (more or less save level of difficulty as the OP, I'd say?), using the PDF manual of NextStep's DriverKit as context.
It... sort of worked well? I had to have a few back-and-forth because it tried to use Objective-C features that did not exist back then (e.g. ARC), but all in all it was a success.
So yeah, niche things are harder, but on the other hand I didn't have to read 300 pages of stuff just to do this...
I remember writing obj-c naturally by hand. Before swift was even a twinkle in tim cooks eye. One of my favorite languages to program in I had a lot of fun writing ios apps back in the day it seems like
I member obj c, using it was a profound experience, it was so different from other languages I felt like an anthropologist.
Also, fun names like `makeFunctionNameInCommentLongAndDescriptiveWithNaturalLanguage:(NSLanguage *)language`
In other words, the vibe coders of this world are just redundant noobs who don't really belong on the marketplace. They've written the same bullshit CRUD app every month for the past couple of years and now they've turned to AI to speed things up
Last week I asked Claude to improve a piece of code that downloads all AWS RDS certificates to just the ones needed for that AWS region. It figured out several ways to determine the correct region, made a nice tradeoff and suggested the most reliable way. It rewrote the logic to download the right set, did some research to figure out the right endpoint in between. It only made one mistake, it fallback mechanism was picking EU, which was not correct. Maybe 1 hour of work. On my own it would have taken me close to a working day to figure it all out.
This is just a thought experiment.
I don't mean to be treading on feet but I'm noticing this more and more in the debates around AI. Imagine if there are developers out there that could have done this task in 30 mins without AI.
The level of performanace of AI solutions is heavily related to the experience level of the developer and of the problem space being tackled - as this thread points out.
Unfortunately the marketing around AI ignores this and makes every developer not using AI for coding seem like a dinosauer, even though they might well be faster in solving their particular problems.
AI is moving problem solving skills from coding to writing the correct prompts and teaching AI to do the right thing - which, again, is subjective, since the "right thing" for one developer isn't the "right thing" for the another developer. "Right thing" being the correct solution, the understandable solution, the fastest solution, etc depending on the needs of the developer using the AI.
There are two ways to do this. One is to one-shot or maybe few-shot a solution. Maybe this works. Maybe it doesn't. Sometimes it works if you copy a solution from [Product 1] to [Product 2] and say "Fix this."
The other is to look at the non-working solution you get, read through it, and think "Oh, I didn't know about that framework/system/product/library, that's neat" and then do some combination of further research and more hand-holding to get to something that does work.
This is useful, more or less, no matter what your level.
It's also good for explaining core industry tooling you've maybe never used before. If you're new to Postgres/NoSQL/AWS/Docker/SwiftUI/whatever it can talk you through it and give you an instant bootcamp with entry-level examples and decent solutions.
And for providing fixes for widely known bugs and issues in products that may not be widely known to you (yet.)
IME ChatGPT5 is pretty solid with most science/tech up to undergrad. It gets hallucinatory past that, and it's still flattering, which is annoying, but you can tell it to cut that out.
Generally you can use it as a dumb offshore developer, or as an infinitely patient private tutor.
That latter option is very useful. The first, not always.
IMHO, the thirty minute developer would still save 10 minutes by vibe coding. That marketing's not wrong.
Spelling out exactly what you want and checking/fixing what you receive is still faster than typing out the code. Moreover, nobody's job involves nothing but brainiac coding, day after day. You have to clean up and lay foundations, whatever level you are at.
> IMHO, the thirty minute developer would still save 10 minutes by vibe coding. That marketing's not wrong.
For me, that's too general. Of course, perhaps for this particular, specific problem it might be true. But as this thread points out, anything niche and AI fails to help productively. Of course then comes the marketing: just wait, AI will be able to cover those niche cases also.
> want and checking/fixing what you receive is still faster than typing out the code
Then I do wonder why there are developers at all. After all that's what AI is so good at - if one believes the marketing - being precise and describing exactly what needs to be done. Surely it must be faster having two AIs talking to each and hammering out the code.
And even typing is subjective: ten fingers versus two, versus four .. etc. There are developers that can type faster than they can think - in certain cases.
There is also the developer in flow versus the stop and go using an AI prompts to get it just right. I dunno, if it comes true, then thankfully there won't be any humans to create bugs in code but somehow, I can't see it happening.
I think the majority of coders out there write the same CRUD app over and over again in different flavors. That's what the majority of businesses seem to pay for.
If a business needs the equivalent of a Toyota Corolla, why be upset about the factory workers making the millionth Toyota Corolla?
> I think the majority of coders out there write the same CRUD app over and over again in different flavors
In my experience, that's not entirely true. Sure, a lot of app are CRUD apps, but they are not the same. The spice lies in the business logic, not in programming the CRUD operations. And then of course, scaling, performance, security, organization, etc etc.
Good thing LLMs are really good at unique business logic, scaling, performance, security, organization, etc etc.!
(edit: /s to indicate sarcasm)
> What am I doing wrong
Trying two things and giving up. It's like opening a REPL for a new language, typing some common commands you're familiar with, getting some syntax errors, then giving up.
You need how to learn to use your tools to get the best out of them!
Start by thinking about what you'd need to tell a new Junior human dev you'd never met before about the task if you could only send a single email to spec it out. There are shortcuts, but that's a good starting place.
In this case, I'd specifically suggest:
1. Write a CLAUDE.md listing the toolchains you want to work with, giving context for your projects, and listing the specific build, test etc. commands you work with on your system (including any helpful scripts/aliases you use). Start simple; you can have claude add to it as you find new things that you need to tell it or that it spends time working out (so that you don't need to do that every time).
2. In your initial command, include a pointer to an example project using similar tech in a directory that claude can read
3. Ask it to come up with a plan and ask for your approval before starting
I guess many find comfort in being able to task an ai with assignments that it cannot complete. Most sr developers I work with take this approach. It's not really a good way of assessing the usefulness of a tool though.
He asked what he was doing wrong?
too big of tasks. break them down and then proceed from there. have it build out task lists in a TASKS.md. review those tasks. do you agree? no? work with it to refine. implement one by one. have it add the tests. refactor after awhile as {{model}} doesn't like to do utility functions a lot. right now, about +50k lines in to a project that's vibecoded. i sit back and direct and it plays.
Imagine the CS 100 class where they ask you to make a PB&J. saying for it to make it, there's a lot of steps, but determine known the steps. implement each step. progress.
Too big and requiring too much niche specific knowledge, you somehow have to inject that knowledge and allow it to iterate.
This is the way.
I run interviews at my company. We allow/encourage AI.
The number one failure method is people throwing all of the requirements in upfront. They get one good pass then fail.
I'm inclined to agree with this approach because someone not using AI who fails would likely fail for the same reasons. If you can't logically distill a problem into parts you can't obtain a solution.
Think of Claude as a typical software developer.
If you just selected a random developer do you think they're going to have any idea why your talking about?
The issue is LLMs will never say, sorry, IDK how to do this. Like a stressed out intern they just make up stuff and hope it passes review.
> It configured also non-existent drivers, and for some reason it enabled monkey test support (but not test support).
If it doesn't have the underlying base data, it tends to hallucinates. (It's getting a bit difficult to tell when it has underlying data, because some models autonomously search the web). The models are good at transforming data however, so give it access to whatever data it needs.
Also let it work in a feedback loop: tell it to compile and fix the compile errors. You have to monitor it because it will sometimes just silence warnings and use invalid casts.
> What am I doing wrong? Or is this really the state of the art?
It may sound silly, but it's simply not good at 2D
> It may sound silly, but it's simply not good at can2D.
It's not silly at all, it's not very good at layouts either, it can generally make layouts but there is a high chance for subtle errors, element overlaps, text overflows, etc.
Mostly because it's a language model, i.e it doesn't generally see what it makes, you can send screenshots apparently and it will use it's embedded vision model, but I have not tried that.
> What am I doing wrong?
Providing a woefully inadequate descriptions to others (Claude & us) and still expecting useful responses?
Try this prompt: Create a detailed step by step plan to implement a boilerplate Zephyr project skeleton for Pi Pico with configured st7789 SPI display drivers
Ask Opus or Gemini 2.5 Pro to write a plan. Then ask the other to critique it and fix mistakes. Then ask Sonnet to implement
I tried this myself and IMO, this might be basic and day-to-day for you, with unambiguous correct paths to follow, but this is pretty niche nevertheless. LLMs thrive when there's a wealth of examples and I struggle to Google what you asked myself, meaning that LLM will perform even worse than my try.
I found that second line works well for image prompts too. Tell one AI to help you with a prompt, and then take it back to the others to generate images.
> What am I doing wrong? Or is this really the state of the art?
You're treating the tool like it was an oracle. The correct way is to treat it as a somewhat autistic junior dev: give it examples and process to follow, tell it to search the web, read the docs, how to execute tests. Especially important is either directly linking or just copy pasting any and all relevant documentation.
The tool has a lossily compressed knowledge database of the public internet and lots of books. You want to fix the relevant lossy parts in the context. The less popular something is, the more context will be needed to fill the gaps.
> The correct way is to treat it as a somewhat autistic junior dev: give it examples and process to follow, tell it to search the web, read the docs, how to execute tests. Especially important is either directly linking or just copy pasting any and all relevant documentation.
Like "Translate this pdf to html using X as a templating language". It shines at stuff like that.
As a dev, I encounter tons of one-off scenarios like this.
Real vibe coding is fake, especially for something niche like what you asked it to do. Imagine a hyperactive eidetic fresh out of high school was literally sitting in the other room. What would you tell her? That’s a good rule of thumb for the level of detail and guidance
You can no longer answer "what is the state of the art” by pointing to a model.
Generating a state-of-the-art response to your request involves a back-and-forth with the agent about your requirements, having a agent generate and carry out a deep research plan to collect documentation, then having the agent generate and carry out a development plan to carry it out.
So while Claude is not the best model in terms of raw IQ, the reason why it's considered the best coding model is because of its ability to execute all these steps in one go which, in aggregate, generates a much better result (and is less likely to lose its mind).
> So while Claude is not the best model in terms of raw IQ
Which one is, and by what metric? I always end up back at Claude after trying other models because it is so much better at real world applications.
I think you need play around with some of the early codegen models so you can get a better intuition for how LLMs work/fail.
Ok. several tips I can give. 1. Setup a sub-agent to do RESEARCH. It is important that it only has read-only and web access tools. 2. Use planning mode and also ask the agent to use the subagent to research best pratices with the tech that you are wanting to do, before it builds a plan. 3. When ever it gets hung up.. tell it to use the sub-agent to research the solution.
That will get you a lot better initial solution. I typically use Sonnet for the sub-agents and Opus for the main agent, but sonnet all around should be fine too for the most part.
The only way I manage to get any benefits from LLMs is to use them as an interactive rubber duck.
Dump your thoughts in a somewhat arranged manner, tell it about your plan, the current status, the end goal, &c. After that tell it to write 0 code for now but to ask questions and find gaps in your plan. 30% of it will be bullshit but the rest is somewhat useable. Then you can ask for some code but if you care about quality or consistency with you existing code base you probably will have to rewrite half of it, and that's if the code works in the first place
Garbage in garbage out is true for training but it's also true for interactions
I've had similar experiences when working on non-web tech.
There are parts in the codebase I'd love some help such as overly complex C++ templates and it almost never works out. Sometimes I get useful pointers (no pun intended) what the problem actually is but even that seems a bit random. I wonder if it's actually faster or slower than traditional reading & thinking myself.
There's a lot of people caricaturing the obvious fact that any model works best in distribution.
The more esoteric your stack, and the more complex the request, the more information it needs to have. The information can be given either through doing research separately (personally, I haven't had good results when asking Claude itself to do research, but I did have success using the web chat UI to create an implementation plan), or being more specific with your prompt.
As an aside, I have more than 10 years of experience, mostly with backend Python, and I'd have no idea what your prompts mean. I could probably figure it out after some google searches, tho. That's also true of Claude.
Here's an example of a prompt that I used recently when working on a new codebase. The code is not great, the math involved is non trivial (it's research-level code that's been productionized in hurry). This literally saved 4 hours of extremely boring work, digging through the code to find various hardcoded filenames, downloading them, scp'ing them, and using them to do what I want. It one-shotted it.
> The X pipeline is defined in @airflow/dags/x.py, and Y in `airflow/dags/y.py` and the relevant task is `compute_X`, and `compute_Y`, respectively. Your task is to:
> 1. Analyze the X and Y DAGs and and how `compute_X` functions are called in that particular context, including it's arguments. If we're missing any files (we're probably missing at least one), generate a .sh file with aws cli or curl commands necessary for downloading any missing data (I don't have access to S3 from this machine, but I do have in a remote host). Use, say, `~/home` as the remote target folder.
> 2. If we needed to download anything from S3, i.e. from the remote host, output rsync/scp commands I can use to copy them to my local folder, keeping the correct/expected directory structure. Note that direct inputs reside under `data/input`, while auxiliary data resides in other folders under `data`. Do not run them, simply output them. You can use for example `scp user@server.org ...`
> 3. Write another snapshot test for X under `tests/snapshot`, and one for Y. Use a pattern as similar as possible to the other tests there. Do not attempt to run the tests yet, since I'll need to download the data first.
> If you need any information from Airflow, such as logs or output values, just ask and I can provide them. Think hard.
In my experience Claude is quite good at the popular stacks in the JavaScript, Python and PHP world. It struggled quite a bit when I asked it non-trivial questions in C or Rust for example. For smaller languages (e.g., Crystal) it seems to hallucinate a lot. I think since a lot of people work in JS, Python and PHP, that’s where Claude is very valuable and that’s where a lot of the praise feel justified too.
I have had no problems with using Claude on large rust projects. The compiler errors usually point it towards fixing its mistakes (just like they do for me).
Feed it Crystal documentation and example code. That is what I did with more obscure programming languages and it worked out well in the end.
You didn't specify any architecture design. Your prompts are about 10% of what would be needed to one shot this. This is what you do wrong.
One of the things you can do is provide a guidance file like CLAUDE.md including not only style preferences but also domain knowledge so it has greater context and knows where to look. Just ask it make one and then update and change as needed.
Sounds like you picked some obscure tasks to test it that would obviously have low representation in the data set? That is not to say it can't be helpful augmenting some lower represented frameworks/tools - just you'll need to equip it with better context (MCPs/Docs/Instruction files)
A key skill in using an LLM agentic tool is being discerning in which tasks to delegate to it and which to take on yourself. Try develop that skill and maybe you will have better luck.
I find it useful to ask it to build a design document first and push to add details where i see it lacking.
After a few iteration i then ask it to implement the design doc to mostly-better results.
What an odd thing to ask it. I installed claude code and ran it from my terminal. Just asked it to simply give me a node based rest API with X endpoints with these jobs, and then I told it to write the unreal engine c++ to consume those endpoints. 2500 lines of code later, it worked.
Tbh dawg, those tasks sound intentionally obtuse. It looks like u are doing more esoteric work than the crud react slop us mortals play in on the daily which is where ai shines.
I work almost exclusively with embedded devices, with low level code (mostly C, Rust, Assembly and related frameworks) - and that's where I also ask for help from LLMs.
Did you intentionally pick your career to make the AI look bad?
It works fine in those domains. I speak from experience. You need CI tools the agent can access, and lots of tests.
I managed to get most AIs to generate C# code when I ask for Java stuff, so it is always a kind of template generator that still isn't quite there.
That's interesting. I use it mainly for C# and Javascript/Frontend stuff.
I wonder if it's because there are maybe millions of MSDN articles, but I don't know if a Java analog to MSDN exists.
If you ask more than a single function, its more trouble than worth
> What am I doing wrong?
My coding ranges from "exotic" to "boiler plate" on any given day.
> Create a boilerplate Zephyr project skeleton, for Pi Pico
Yea... Asking Claude to help you with a low documentation build root system is going to go about the same way, I know first hand about how this works.
> I asked it to create 7x10 monochromatic pixelmaps
Wrong tool for the job here. I dont think IDE and Pixelmaps have as large of an intersection as you think they do. Claude thinks in tokens not pixels.
Pick a common language (js, python, rust, golang) pick something easy (web page, command line script, data ingestion) and start there. See what it can do and does well, then start pushing into harder things.
The thing you are doing wrong is asking it to solve hard problems. Claude Code excels at solving fairly easy, but tedious stuff. Refactors that are brainless but take an hour. It will knock those out of the park. Fire up a git worktree and let it spin on your tedious API changes and stuff while you do the hard stuff. Unfortunately, you'll still need to use your brain for that.
What you're doing wrong is that you're asking it for something more complicated than babby's first webapp in javascript/python.
When people say things like "I told Claude what I wanted and it did it all on the first try!", that's what they mean. Basic web stuff that that is already present in the model's training data in massive volumes, so it has no issue recreating it.
No matter how much AI fanatics try to convince you otherwise, LLMs are not actually capable of software engineering and never will be. They are largely incapable of performing novel tasks that are not already well represented in their weights, like the ones you tried.
What they are not capable of is replacing YOU, the human who is supposed to be part of the whole process (incl. architectural). I do not think that this is a limitation. In fact, I like being part of the process.
So I've used Zephyr. The thing you're doing wrong is expecting LLMs to scaffold you a bunch of files from a relatively niche domain. Zephyr is also a mess of complexity with poor documentation. You should ask it to consult official docs and ask it to use existing tools (west etc) and board defs to do the scaffolding.
I just had AI write me a scraper and download 5TB of invaluable data which I had been eyeing for a long time. All in ten days. At the end of it, I still don’t know anything about python. It’s a bliss for people like me. All dependencies installed themselves. I look forward to using it even more.
One frustration was the code changed so much in ChatGPT so had to be lots of prompts. But I had no idea what the code was anyways. Understood vibe coding. Just used ChatGPT on a whim. Liked the end result.
Write some hooks dawg
Its not shipping the model in Xcode. You are still sending your data off to a remote provider, hoping that this provider behaves nicely with all this data and that the government will never force the provider to reveal your data.
They are already forcing OpenAI to keep all logs. Go figure.
And people talk to GPT about very private things, using it as a shrink. What can go wrong.
Interesting to think about how Apple get to make product decisions based on Gatekeeper OCSP analytics now that every app launch phones home. They must know exactly how popular VSCode is.
Facebook got excoriated for doing that with Onavo but I guess it's Good Actually when it's done in the name of protecting my computer from myself lol
Apple doesn't need telemetry to send emails about their favorite coding AI to the 2 Xcode users
Off by about 33,999,998 users, but still a decent dunk.
https://appleinsider.com/articles/22/06/06/apple-now-has-ove...
34 million developers? That number doesn't even pass a basic sniff test. Are there 34 million people that have Xcode installed? That I can believe.
If my experience is anything to go by - a good proportion of this will be people accidentally double clicking a .md (or other random text suffix), and cursing whilst they wait for XCode to slowly load enough that they can quit it and open the file in a proper lightweight editor..
I feel like the #1 reason to install Xcode is to get Git working on macOS. Yours is probably #2. I wouldn't bet money on iOS/macOS development sitting at #3.
> At Apple's World Wide Developer Conference on Monday, Tim Cook mentioned that there are now 34 million registered developers with the company's platform.
I think that means either:
* they have revenues of $3.4b/year just from the $100 annual fees, or
* some decent percentage of people have signed up for a free developer account and then never done anything with it (like me)This won't make a dent. It still doesn't support any agentic operation.
The real news is when Codex CLI / Claude Code get integrated, or Apple introduces a competitor offering to them.
Until then this is a toy and should not be used for any serious work while these far better tools exist.
I just installed it—definitely not a toy.
Compared to stock Claude Code, this version of Claude knows a lot more about SwiftUI and related technologies. The following is output from Claude in Xcode on an empty project. Claude Code gives a generic response when it looked at the same project:
What I Can Help You With
• SwiftUI Development: Layout, state management, animations, etc.
• iOS/macOS App Architecture: MVVM, data flow, navigation
• Apple Frameworks: Core Data, CloudKit, MapKit, etc.
• Testing: Both traditional XCTest and the new Swift Testing framework
• Performance & Best Practices: Swift concurrency, memory management
Example of What We Could Do Right Now
Looking at your current ContentView.swift, I could help you:
• Transform this basic "Hello World" into a recovery tracking interface
• Add navigation, data models, or user interface components
• Implement proper architecture patterns for your Recovery Tracker appIf a bunch of markdown files forced into the context is “knowing”, then yes. They are usually located at /Applications/Xcode-beta.app/Contents/PlugIns/IDEIntelligenceChat.framework/Versions/A/Resources/AdditionalDocumentation
You are free to point Claude Code to that folder, or make a slash command that loads their contents. Or, start CC with -p where the prompt is the content of all those files.
Claude Code integration in Xcode would be very cool indeed, but I might still stick with VSCode for pure coding.
> Claude Code integration in Xcode would be very cool indeed, but I might still stick with VSCode for pure coding.
I'm sticking with VSCode too, but it's a bit silly to suggest that anyone is using XCode because it's their preferred IDE. It's just the one that's necessary for any non-trivial Apple platform development.
Adding a code generator isn't a marketing ploy to get people to switch editors, it's just a small concession to the many hapless souls stuck dealing with Apple on the professional side, or masochistically building mac SwiftUI apps just to remind themselves what pain feels like.
I mean you can stay in VSCode for most activities if you hate Xcode that much (I can relate btw). Plugins like Sweetpad make this possible. My approach now is to develop all logic in small Swift packages and run swift test in VSCode (or Claude Code), so I only absolutely need Xcode for debugging and building releases. Every once in a while I try SwiftUI previews, but those are usually broken anyways.
Isn’t that easy to add with some rules and guidelines documents? I usually ask Claude code to research modern best practices for SwiftUI apps and to summarize the learnings in a rules file that will be part of the SwiftUI project.
Yes and no. Proper Agentic coding tools like Claude Code are a bit more than just a bunch of markdown rulesets.
For example: it uses Haiku as a model to run tools and most likely has automatic translations for when the model signals it wants to search or find something -> either use the built-in search or run find/fd/grep/rg
All that _can_ be done by prompting, but - as always with LLMS - prompts are more like suggestions.
I'm as crazy about AI as the next dev, but that has to be the weakest example of AI capability that I have ever seen.
It seems every IDE now has AI built-in. That's a problem if you're working on highly confidential code. You never know when the AI is going to upload code snippets to the server for analysis.
Not trying to be mean but I would expect comments on HN on these kind of stories to be from people who have used AI in IDEs at this point. There is no AI integration that runs automatically on a codebase.
This could change on a daily basis, and it's a valid concern anyway.
There is automatic code indexing from Cursor.
Autocomple is also automatically triggered when you place your cursor inside the code.
Yes, Cursor, “The AI Code Editor.”
Cursor is an AI IDE and not what they are describing.
> There is no AI integration that runs automatically on a codebase.
Don't be naive.
This is HN. 10 years ago that would be true, but now I expect 99% of commenters to have newer used the thing they are talking about or used it once 20 years ago for 10 minutes, or even nkt read the article.
Gitkraken does
> "add their existing paid Claude account to Xcode and start using Claude Sonnet 4"
Wont work by default if I'm reading this correctly
This is not a realistic concern. If you're working on highly confidential code (in a serious meaning of that phrase), your while environment is already either offline or connecting only through a tightly controlled corporate proxy. There's no accidental leaks to AI from those environments.
thanks for giving the security department more reasons to think that way.
I spent the last 6 months trying to convince them not to block all outbound traffic by default.
The right middle ground is running Little Snitch in alert mode. The initial phase of training the filters and manually approving requests is painful, but it's a lot better than an air gap.
that’s what I do, but since it’s in my control the security teams don’t like it. ;)
There are ranges of security concerns and high confidentiality.
For most corporate code (that is highly confidential) you still have proper internet access, but you sure as hell can't just send your code to all AI providers just because you want to, just because it's built into your IDE.
Neovim and Emacs don’t have it built in. Use open source tools.
They both support it via plugins. Xcode doesn’t enable it by default, you need to enable it and sign into an account. It’s not really all that different.
Well that depends on whether you give it access or not, apple’s track record with privacy gives me some hope
On IDEA the organisation who controls the license can disable the build in (remote) AI. (Not the local auto complete one)
But I guess the user could still get a 3rd party plugin.
Most of the big corporations will have a special contract with the AI labs with 0 retention policies.
I do not think this will be an issue for big companies.
No. It’s always something you have to turn on or log into.
Also, there are plenty of editors and IDEs that don’t.
Let’s stop pretending like you’re being forced into this. You aren’t.
Sublime Text doesn't by default.
People working on highly confidential code will NOT have access to the public internet.
There is a gulf and many shades between "this code should never be on an internet-connected device" and "it doesn't matter if this code is copied everywhere by absolutely anyone".
To me "highly confidential" would mean "isolated from the internet" or else it isn't going to be "highly confidential" for very long.
Have you seen a lot of code from Klarna, Storytel, Spotify (companies I've worked at)?
None of these companies are isolated from the internet.
If it's that confidential you should be on an airgapped network.
There's simply no way to properly secure network connected developer systems.
You do know: when it's enabled.
many enterprises store their code on GitHub, owned by Microsoft, operator of Copilot
you can use Claude via bedrock and benefit from AWS trust
Gemini? Google owns your e-mail. Maybe you're even one of those weirdos who doesn't use Google for e-mail - I bet your recipient does.
so... they have your code, your secrets, etc.
> In the OpenAI API, “GPT-5” corresponds to the “minimal” reasoning level, and “GPT-5 (Reasoning)” corresponds to the “low” reasoning level. (159135374)
It's interesting that the highest level of reasoning that GPT-5 in XCode supports is actually the "low" reasoning level. Wonder why.
Yeah I don't get why they don't support Opus given that you're bringing your own API key.
you can use the API key, and it’ll give you access to all the model.
This is Claude sign in using your account. If you’ve signed up for Claude Pro or Max then you can use it directly. But, they should give access to Opus as well.
They should document it that way.
It's available now. Here’s short but more complete context than the submitted title or the Xcode release note: https://sixcolors.com/link/2025/08/apples-new-xcode-beta-add...
But they won't fix the infinite number of bugs Xcode has, its slowness and subpar ux
It's getting harder to find IDEs that properly boycott LLMs.
In a similar vein I can barely find an OS that refuses to connect to the internet
Wouldn't the more correct analogy be a text editor without "Klippy?"
too many of them these days: https://kakoune.org/
They don’t think it be like it is, but it do.
I hate that most browsers are willing to render React SPAs.
lynx, elinks, and w3m don't
Really?
“Boycott” is a pretty strong term. I’m sensing a strong dislike of ai from you which is fine but if you dislike a feature most people like it shouldn’t be surprising to you that you’ll find yourself mostly catered to by more niche editors.
I think it's a pretty good word, let's not forget how LLMs learned about code in the first place... by "stealing" all the snippets they can get their curl hands on.
And by reading the docs, and by autogenerating code samples and testing them against verifiers, and by paying a lot of people to write sample code for sample questions.
Yeah, none of that happened with LLMs
https://openai.com/index/prover-verifier-games-improve-legib... OpenAI has been doing verifier-guided training since last year. No SOTA model was trained without verified reward training for math and programming.
Your claim: "by reading the docs, and by autogenerating code samples and testing them against verifiers, and by paying a lot of people to write sample code for sample questions."
Your link: "Grade school math problems from a hardcoded dataset with hardcoded answers" [1]
It really is the same thing.
[1] https://openai.com/index/solving-math-word-problems/
--- start quote ---
GSM8K consists of 8.5K high quality grade school math word problems. Each problem takes between 2 and 8 steps to solve, and solutions primarily involve performing a sequence of elementary calculations using basic arithmetic operations (+ − × ÷) to reach the final answer.
--- end quote ---
My two claims:
1. OpenAI has been doing verifier-guided training since last year.
2. No SOTA model was trained without verified reward training for math and programming.
I supported the first claim with a document describing what OpenAI was doing last year; the extrapolation should have been straightforward, but it's easy for people who aren't tracking AI progress to underestimate the rate at which it occurs. So, here's some support for my second claim:
https://arxiv.org/abs/2507.06920 https://arxiv.org/abs/2506.11425 https://arxiv.org/abs/2502.06807
> the extrapolation should have been straightforward,
Indeed."By late next month you'll have opver four dozen husbands" https://xkcd.com/605/
> So, here's some support for my second claim:
I don't think any of these links support the claim that "No SOTA model was trained without verified reward training for math and programming"
https://arxiv.org/abs/2507.06920: "We hope this work contributes to building a scalable foundation for reliable LLM code evaluation"
https://arxiv.org/abs/2506.11425: A custom agent with a custom environment and a custom training dataset on ~800 predetermined problems. Also "Our work is limited to Python"
https://arxiv.org/abs/2502.06807: The only one that somewhat obliquely refers to you claim
Just don't use the features.
I couldn't get it to properly syntax highlight and autosuggest even after spending over an hour hunting through all sorts of terrible documentation for kate, clangd, etc. It also completely hides all project files that aren't in source control, and the only way to stop it is to disable the git plugin. What a nightmare. Maybe I'll try VSCodium next.
I thought vscodium was just vscode but open source. Won't any issues in vscode also be present in vscodium?
It can't access most Microsoft online services including Copilot, which happens to disable most of the features I don't want. (I understand this is both by design, as well as because Microsoft forbids unofficial forks from doing so.)
However, MS do everything they can to make plugins not work in VSCodium. And the plugin marketplaces are separate now.
Many of the popular features in VS Code are provided by plugins that are not open source and thus not provided with VSCodium.
Kate is brilliant.
Just disable the feature/plugin in your IDE of choice. Android Studio/IntelliJ: https://i.imgur.com/RvRMvvK.png
If you're on macOS there's Code Edit as a native solution (fully open source, not VC backed, MIT licensed), but it's currently in active development: https://www.codeedit.app/.
Otherwise there's VSCodium which is what I'm using until I can make the jump to Code Edit.
Okay dann lass die Ablage erst laufen ohne Teig dann kannst du mit Teig machen wenn du übergaben machst zwischen 13:30 und 14:00 Uhr dann bitte schichtführer/in Bescheid sagen bzw. geben tschüss
How about Sublime Text (not really an IDE, just text editor)
Neovim, emacs?
Amusing that Emacs that came out of the MIT AI lab, and heavily uses Lisp, a language that used to be en vogue for AI research.
Amusing is one word for it. Expert systems were all the rage until they weren't. We'll see how LLMs do by comparison.
The so-called "guardrails" used for LLM are very close to expert systems, imo.
Since the landscape of potentially malicious inputs in plain english is practically infinite, without any particular enforced structure for the queries you make of it, means that those "guardrails" are, in effect, an expert system. An ever growing pile of if-then statements. Didn't work then, won't work now.
neovim will support llms natively (though a language server) https://github.com/neovim/neovim/pull/33972
Neovim already supports LSP servers. The fact that a language server exists for anything, doesn't make neovim (or any other editor) "support" the technology. It doesn't, what it does support is LSP, and it doesn't and couldn't care less what language/slop the LSP is working with.
That’s not really native support for LLMs? It’s supporting some LSP feature for completions.
LSP != LLM
You have to enable it and install a language server, that's not the same as an LLM being baked in.
It’s not baked in, in that sense. You still have to enable it in XCode and link it to a Claude account. It’s basically the same.
At the level of "Having to configure something to use it", they're the same, but then that's the same as the hundreds of other config options then. I think we can be slightly more precise than that.
In Neovim the choice of language server and the choice of LLM is up to the user, (possibly even the choice of this API, I believe, having only skimmed the PR) while both of those choices are baked in to XCode, so they're not the same thing.
That's fair enough, but it's the opposite complaint, that XCode's LLM support is more limited because it is proprietary. That's a perfectly valid and reasonable objection, of course.
Gosh, it's almost like a proper IDE has synonymous features with LLMs
Ironically, you could probably vibe code your own.
Good luck getting just scroll bar right with vibe coding. You'll be surprised how much engineering is done to get that part work smoothly.
If enough examples are in-distribution, the model's scroll bar implementation will work just fine. (Eventually, after the human learns what to ask for and how to ask for it.)
Why wouldn't it?
Most programs today regularly have bugs with scrolling. Thus, an LLM will produce for you... A buggy piece of code.
LLMs are not Xerox machines. They can, in fact, produce better code than is in their training set.
That is funny for how much is wrong. Ask the LLMs to vibe code a text editor and you'll get a React app using Supabase. Engineering !== Token prediction
Non sequitur?
I have used agentic coding tools to solve problems that have literally never been solved before, and it was the AI, not me, that came up with the answer.
If you look under the hood, the multi-layered percqptratrons in the attention heads of the LLM are able to encode quite complex world models, derived from compressing its training set in a which which is formally as powerful as reasoning. These compressed model representations are accessible when prompted correctly, which express as genuinely new and innovative thoughts NOT in the training set.
> I have used agentic coding tools to solve problems that have literally never been solved before, and it was the AI, not me, that came up with the answer.
Would you show us? Genuinely asking
Do you really think so? Have you ever explored the source of something like:
Doesn't have to. The LLM will do it! We're done with code, aren't we?
Code is still there, but humans are done dealing with it. We're at a higher level of abstraction now. LLMs are like compilers, operating at a higher level. Nobody programs assembly language any more, much less machine language, even though the machine language is still down there in the end.
> Nobody programs assembly language
They certainly do, and I can't really follow the analogy you are building.
> We're at a higher level of abstraction now.
To me, an abstraction higher than a programming language would be natural language or some DSL that approximates it.
At the moment, I don't think most people using LLMs are reading paragraphs to maintain code. And LLMs aren't producing code in natural language.
That isn't abstraction over language, it is an abstraction over your computer use to make the code in language. If anything, you are abstracting yourself away.
Furthermore, if I am following you, you are basically saying, you have to make a call to a (free or paid) model to explain your code every time you want to alter it.
I don't know how insane that sounds to most people, but to me, it sounds bat-shit.
even nvim is getting native support for llms
It doesn't matter how they feel about LLMs, ignoring their battle hardened plugin system and going native would be bad architecture.
It’s just native support for ghost text. It’s not llm specific
You have to opt in and set up a language server
Is it? Link?
Of course it is, because that would be an aggressively stupid thing to do. Like boycotting syntax highlighting, spellckecking, VCS integration or a dozen other features that are th whole pint of IDEs.
If you don’t want to use LLM coding assistants – or if you can’t, or it’s not a technology suitable for your work – nobody cares. It’s totally fine. You don’t need to get performatively enraged about it.
"Claude in Xcode is now available in the Intelligence settings panel, allowing users to seamlessly add their existing paid Claude account to Xcode and start using Claude Sonnet 4"
Headline quite misleading. So not exactly that it will ship in Xcode but will allow connect to paid account.
This is great. I've been using Xcode with a separate terminal to run Claude Code, which has been a painful setup.
Agreed. Claude Code is an amazing experience with Jetbrains IDEs, but for some reason Xcode just hates having claude directly edit the files.
How do you use it with Jetbrains? Junie? Or just as a separate CLI session?
They might be referring to the plugin https://plugins.jetbrains.com/plugin/27310-claude-code-beta-
I use VS Code with Claude Code, then I just use Xcode to build and launch
The annoying thing is the official Swift extension can sometimes flag errors in perfectly fine code with zero problem in Xcode. So I’m forced to live with persistent “errors” when editing in VS Code/Cursor.
I’m building my first iOS app ever so I know it has much more to do with me not understanding Xcode but getting builds to succeed after making changes with Claude code has been a nightmare. If you or anyone have any tips, guides, prayers, incantations for how to get changes in one to not clobber the other and leave me in xproj symlink hell I would be so grateful.
Same, only it's Zed for me and Claude Code in a terminal
What was your problem with it? I see it running in a terminal more convenient (can point it to read local files outside of a project folder, for example)
You can use VSCode and XCode will automatically update when the files change.
if i could just get claude to properly remember it can directly edit the xcode project file, that'd be great.
for whatever reason it ignores my directive that it can from the CLAUDE file at least half the time. one time it even decided it needed to generate a fancy python script to do it. bizarre.
How so? I don't use xcode, but I much prefer having an agent in its own "app" so to speak.
Likely so it can auto suggest, directly edit code, integrate properly etc
Still shocked Apple has not created thier own LLM, they have bought so many AI companies and have a rich talent pool and money so what's stopping them ?
Terrible, terrible leadership at the top of the AI org. Plus a fundamental commitment to being a 'product first' company.
This same commitment would be why I wouldn't count them out on the AI side, btw. It's not clear that a private internal foundation model is any kind of required competitive moat. It's also not clear that having one is useless, and all the cool kids do have one or want one, but from a product view it might be that integrating makes sense.
Llama 4 (a terrible release) shows also that making such a model is still really hard. There are not enough ML leads at the pointy tip of the spear to support even 10 high quality foundation model teams globally.
If you have billions of dollars in cash and you are secure in your customer base, and you don't believe AGI liftoff will happen or change your business model, maybe you work out the kinks on product integration now using best in breed providers, not getting locked in on one of them, keep spending 1/10 to 1/100th to stay relevant and on it internally, use your incredibly powerful silicon buying power to get a next gen version of TPUs done, and wait until you know for sure you can spend under $10bn in cash on getting a great proprietary model done, one that you are certain will serve your needs.
Also this will give you time to get better leadership in the AI org.
Upshot - I think it's a mix of reasons, but not fatal, I'm not sure being slow erodes their product customer base, personally I'd like much better and more private AI out of Apple ASAP. We'll see what we get. I predict they'll move internal by 2031.
They have their Foundational Models [1], so I think they’re focusing more on device-level LLMs than larger ones.
[1] https://developer.apple.com/documentation/foundationmodels
Maybe they figure it’s just going to be hot swappable models and they’d rather save the 10s of billions on training for a commodified service.
Anything’s better than the current Xcode autocomplete.
My pet peeve is it will try to autocomplete any string you start typing with just random crap it thinks you might want in a string.
I think all autocomplete solution are crappy, no matter how sophisticated the AI. It is surprising how often the obvious choice is wrong, but it often just is. I deactivated it.
Generating some code is fine, but I now prefer the deterministic autocomplete for my types I have available in my current context.
Maybe you have not used a good one. Even the small locall only 100MB models Jetbrains uses are fine. Codeium/Windsurf one is good.
I haven't opened up xcode in years (thank god) I suppose this was inevitable. They have to keep up
The “Cursor for Xcode” startups just got Sherlocked…
Were there really such startups? It's so obviously a bad idea..
There's Alex (https://www.alexcodes.app), YC-backed.
Apple really should open it up to any model provider that has an “OpenAI-style API” by letting the user put in a base URL, api key, model id, and a few params like context limit as needed.
Xcode (26) already has that.
Weren’t the AI API’s converging? Why not let the users use whatever LLM they like.
Why would you limit users to Sonnet and not allow Opus when they are paying for their own account? I mean sure some people say Sonnet is good for coding but it seems needless to limit it in this way. Or they are just really slow to catch up… oh, right.
Another decade, another claim Apple’s behind and struggling to catch up
"Be ready for AIpple Revolution! We are making programming something different that hasn’t happen before! We are the first to introduce AI assisted agent coding with full integration with Siri, visionOS and so much more. New, holistic approach to creativity and efficiency"
>> Coding intelligence provides inconsistent results when modifying files that contain thousands of lines.
Under the known issues
This is true of all LLM agents. It’s a context window problem.
Sonnet only?
Does anybody know why Anthropic doesn't let you remove your payment info from your account, or how to get support from them?
I bought a Pro subscription, the send button on their dumb chatbot box is disabled for me (on Safari), and I still get "capacity constraints' limits. Filed a chargeback with my bank just because of the audacity of their post-purchase experience. ChatGPT-5 works good enough for coding too.
From my experience with Claude Opus it seems like it tries to be "too smart" and doesn't seem to keep up with the latest APIs. It suggested some code for a iOS/macOS project that was only valid on tvOS, and other gaffs.
The Pro plan ($20/mo?) is not and never was unlimited.
Does it have agent mode? Copilot for XCode has it and provides both GPT and Claude models, free or paid
They also upgraded the GPT-4.1 (actually a special Apple variant) to GPT-5 by default, with the option to use GPT-5-thinking, using your ChatGPT subscription. I don't know if it's a special Apple variant of GPT-5 but this is a big upgrade and more exciting than Sonnet 3.7.
I also wonder if it will have separate rate limits from ChatGPT (app/web) and Codex CLI (which currently has its own rate limits).
I have been trying to make iOS/macOS apps for years, but god, every time I have a go at it, Apple's documentation regime is still hot garbage. Eons ago I gave up Windows development because of Microsoft's inconsistent and uncertain APIs, but MS had great documentation. Apple is the opposite.
The "best" way to get the "latest" details on Apple's APIs is to suffer through mind-numbingly vapid WWDC videos with their reverse uncanny valley presenters (where humans pretend to be robots) and keep your full attention on them to catch a fleeting glimpse of a single method or property that does what you were looking for. Even 1.5x/2x speed is torture. I tried to get AIs to sift through the transcripts of their videos, and may Skynet forgive me for this cruelty.
Then when you go try to use that API, oops it's been changed in the current beta and there's no further documentation on it except auto-generated headers.
They also removed bookmarks from Xcode's built-in documentation browser years ago, and it doesn't retain a memory of previously open tabs, and often seems to be behind the docs on their websites.
I wish they would just provide open-source sample apps of each type (document-based, single-window etc.) for each of their platforms that fully use the latest APIs. At least that would be easier to ask AIs on, since that is what they seem to be going for now anyway.
I pretty much had the same experience recently when I had to deal with their Screen Time APIs. Had to go through the wwdc videos because the documentation was lack lustre.
Can't each of these companies with IDE integrations slurp up the network traffic and distill Anthropic's models?
If you can listen to billions of tokens a day, you can basically capture all the magic.
Terms of service specifically prohibits this.
How much of the training set comes from websites with "no automated scraping" in their terms?
The companies stole that data from the world, so I don't see why we couldn't take it back.
It's a nice sentiment. The companies with the integrations are the ones that could take it back, but they don't have the incentive to break legal agreements and share with the world.
Meanwhile the creative output of humanity is distilled into black boxes to benefit those who can scrape it the most and burn the most power, but this impact is distributed amongst everyone, so again there's little incentive among those who could create (likely legal) change.
That is not how training works…
Apple.com advertising a Mac Mini:
> Built for Apple Intelligence.
> 16-core Neural Engine
These Xcode release notes:
> Claude in Xcode is now available in the Intelligence settings panel, allowing users to seamlessly add their existing paid Claude account to Xcode and start using Claude Sonnet 4
All that dedicated silicon taking up space on their SoC and yet you still have to input your credit card in order to use their IDE. Come on...
To run a model locally, they would need to release the weights to the public and their competitors. Those are flagship models.
They would also need to shrink them way down to even fit. And even then, generating tokens on an apple neural chip would be waaaaaay slower than an HTTP request to a monster GPU in the sky. Local llms in my experience are either painfully dumb or painfully slow.
Hence the "come on".
I bet Apple are working on it, it’s just not ready yet and they want to see how much people actually use it.
It’s the Apple way to screw the 3rd party and replace with their own thing once the ROI is proven (not a criticism, this is a good approach for any business where the capex is large…)
"Apple Intelligence", at least the part that's available to developers via the Foundation Models framework is a tiny ~3B model [0] with very limited context window. It's mainly good for simple things like tagging/classification and small snippets of text.
[0] https://github.com/fguzman82/apple-foundation-model-analysis
Yes, but the Foundation Model framework can seamlessly use Apple's much larger models via Private Cloud Compute or switch to ChatGPT.
When macOS 26 is officially announced on September 9, I expect Apple to announce support for Anthropic and Google models.
Local models and any OpenAI-compatible APIs are available to the Xcode Beta assistant. This is just a dedicated “sign in with x” rather than manual configuration.
Trust me, you wouldn’t want to use a model for agentic code editing that could fit on a Mac mini at this stage.
A 128GB Mac Mini M5 would be sweet.
Wow they're finally getting it. The AI breakthrough will not come from procedural generation of memojis - but rather enabling developers to use your platform. But with the nearly hostile stance of your 30% take, we will see how far this goes.
What’s the ‘30% take’?
30% of all AppStore sales go right to Apple
15% if you’re part of the small business program.
What program do I have to join for 0%?
The one where you create your own mobile operating system.
Being in the EU and releasing in an alternative marketplace.
The one where you collect cash directly from users, and magically make handling that have zero overhead.
Credit card processing is hard... Go price out stripe + customer service + dealing with charge backs and tell me if you really want to do processing your self.
Well seeing that the most popular apps aside from games don’t have in app purchases and another subset of that has means to do payments subscriptions outside of the App Store, the 30% (actually 15% for small developers) is a boogeymen
