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...

>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.

What is the irony? Microsoft integrated copilot in Vscode, bing, etc. Apple is integrating claude in Xcode, Jetbrains has their own AI.

Microsoft moved first with putting AI into their products then other companies put other AI into their products. Nothing about this seems ironic or in any way surprising.

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.

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.

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

"Taking Copilot out of the loop" if you ignore the massive ecosystems of Github, Visual Studio, and Visual Studio Code.

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.

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.

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.

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.

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.

> I also love the C++ security improvements on this release.

These are courtesy of LLVM/Clang (which Xcode ships with), rather than Xcode itself.

MSFT stock price deeply differs with your opinion.

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.

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.

Apple doesn't need telemetry to send emails about their favorite coding AI to the 2 Xcode users

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.

"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".

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.

> 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.

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.

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 am I doing wrong?

Providing a woefully inadequate descriptions to others (Claude & us) and still expecting useful responses?

> 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.

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

> 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

> 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.

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.

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.

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

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.

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).

LLMs struggle more with embedded software due to the relative lack of examples in the training data compared to javascript etc. They also struggle more with visual reasoning tasks like the character example you provided.

For your first task - give it smaller steps along the way that you can validate. Provide context where possible (like docs for st7789, examples of other zephyr projects). Use Opus instead of Sonnet for tasks that are on the edge of it's capabilities like this. It will still make mistakes, be prepared for iterating on the design and providing feedback.

For your font example, you always need to validate the output of the LLM, but did it still save you time? If so then I consider that a win. If not, give it the task that it's good at (ie generating the surrounding code and definitions) and then fill in the font data yourself.

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.

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 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.

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.

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.

LLMs are actually terrible at generating art unless they're specifically trained for that type of work. Its crazy how many times I've asked for some UI elements to be drawn using a graphics context and it comes out totally 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.

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 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.

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.

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.

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.

Claude is bad at embedded. Not sure why, it just is what it is for now.

If you ask more than a single function, its more trouble than worth

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.

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.

Write some hooks dawg

They are already forcing OpenAI to keep all logs. Go figure.

Anthropic has a strong stance on privacy. They won't rug pull.

/s

https://news.ycombinator.com/item?id=45062683 (Anthropic reverses privacy stance, will train on Claude chats)

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.

> "add their existing paid Claude account to Xcode and start using Claude Sonnet 4"

Wont work by default if I'm reading this correctly

Neovim and Emacs don’t have it built in. Use open source tools.

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.

Well that depends on whether you give it access or not, apple’s track record with privacy gives me some hope

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.

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.

Sublime Text doesn't by default.

If it's that confidential you should be on an airgapped network.

There's simply no way to properly secure network connected developer systems.

People working on highly confidential code will NOT have access to the public internet.

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.

Yeah I don't get why they don't support Opus given that you're bringing your own API key.

In a similar vein I can barely find an OS that refuses to connect to the internet

“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.

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.

Just don't use the features.

https://kate-editor.org/

How about Sublime Text (not really an IDE, just text editor)

Just disable the feature/plugin in your IDE of choice. Android Studio/IntelliJ: https://i.imgur.com/RvRMvvK.png

Gosh, it's almost like a proper IDE has synonymous features with LLMs

Neovim, emacs?

Ironically, you could probably vibe code your own.

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.

even nvim is getting native support for llms

Agreed. Claude Code is an amazing experience with Jetbrains IDEs, but for some reason Xcode just hates having claude directly edit the files.

I use VS Code with Claude Code, then I just use Xcode to build and launch

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)

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.

You can use VSCode and XCode will automatically update when the files change.

The Pro plan ($20/mo?) is not and never was unlimited.

Were there really such startups? It's so obviously a bad idea..

Xcode (26) already has that.

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.

This is true of all LLM agents. It’s a context window problem.

I'd encourage you to read TFA, but why bother? The submitter clearly didn't either.

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.

"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

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…)

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.

I’m sensing just a touch of anger that is making your comment very non-objective.

> Apple can’t even ship software that works.

Hey they're doing better than anyone else.

Ever notice Claude struggling to write Swift code? It might not be their fault!

Apple Developer docs are locked behind JavaScript, making them invisible to most LLMs. If they try to fetch it, all they see is This page requires JavaScript. Please turn on JavaScript in your browser and refresh the page to view its content.

This service translates Apple Developer documentation pages into AI-friendly Markdown.

https://sosumi.ai/

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.

Something you might find useful: https://askwwdc.com/

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

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.

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.

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

What’s the ‘30% take’?

Another decade, another claim Apple’s behind and struggling to catch up

Terms of service specifically prohibits this.

That is not how training works…