kburman

Maybe they trained it to do.

Not to downplay Anthropic's engineering work here, but I suspect we'll see a lot more reinvention of the wheel, even at smaller startups, as AI makes writing code significantly cheaper.

The tradeoff between building something custom and adapting an existing solution is changing. Whether maintaining all that custom software turns out to be equally cheap is another question.

Loved the idea, but I don’t think it would be able to handle real-world data effectively. There are a lot of nuances that actually require a reasoning model to think through, connect the dots, and make sense of the broader context.

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You can probably blame Chinese distillation efforts for some of this paranoia.

offtopic: Is there any chance we could see competing models from other countries in the next 5 years?

Just add a queue. Now the outage is eventually consistent. /s

This isn't a GoPro. The whole point is that it doesn't look like you're recording.

With a GoPro, everyone knows there's a camera. With these, most people won't.

I wonder what JetBrains is doing. They had such a strong lead, but with the rise of AI, they seem to be heading down the same path as Stack Overflow.

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They want to achieve AGI first because, once it is achieved, no one knows what the world will look like.

They could've simply cut the usage limits, removed K3 from the plan, added a weekly cap, and called it "fair usage." Anthropic has been running that playbook for a while now, and they're a trillion-dollar company.

Honestly, I initially thought rewriting an entire codebase with AI would be a huge mistake. After reading this, I'm starting to think I was wrong.

If projects like Bun can be substantially rewritten and shipped to millions of users, it suggests we're entering a very different phase of software development.

Today's AI-generated rewrites may not produce code that humans would consider high quality or maintainable. But I'm beginning to wonder whether that will even matter in a few months. If AI is the primary consumer, maintainer, and refactorer of code, human readability becomes far less important than correctness, performance, and the ability to iterate.

This feels like a shift where software may no longer exist as a long-lived artifact in its current form. Instead of writing and maintaining applications for years, we may generate, adapt, and discard them continuously for each use case.

`seams` is the new emdash

I wonder if Elon would now reconsider IPO.

All new accounts created within few min. Nothing to see here.

> If it stays at arm's length, and if it can "read only", then I am OK with it and actually somewhat pleased with it.

This isn't actually about AI. it's just classic human psychology.

You’ve had a rock-solid workflow for 25 years, so it makes total sense to be cautious and reject features you don't need.

Right now, keeping it at "arm's length" and "read-only" feels safe. But that's usually just phase one. Once that initial trust is established, those boundaries naturally start to melt away. Give it a couple of tax seasons, and you’ll probably find yourself wanting it to take on more of the heavy lifting.

Simultaneous speech is best.

What a time. I am back here genuinely wishing for OpenAI to release a great model, because without stiff competition, it feels like Anthropic has completely lost its mind.

Has anyone here used Kimi for actual work?

I tried it once, although it looks amazing on benchmarks, my experience was just okay-ish.

On the other hand, Qwen 3.6 is really good. It’s still not close to Opus, but it’s easily on par with Sonnet.

Anthropic must be loving it. It's free money.

Recently, Anthropic has been making bad decisions after bad decisions.

looks like opus have been nerfed from day1

Interactive tutorial - https://diffusion.fyi/tutorials/fundamentals?ch=0

I’m struggling to understand the recent wave of backlash against MCP. As a standard, it elegantly solves a very real set of integration problems without forcing you to buy into a massive framework.

It provides a unified way to connect tools (whether local via stdio or remote via HTTP), handles bidirectional JSON-RPC communication natively, and forces tools to be explicit about their capabilities, which is exactly what you want for managing LLM context and agentic workflows.

This current anti-MCP hype train feels highly reminiscent of the recent phase where people started badmouthing JSON in favor of the latest niche markup language. It’s just hype driven contrarianism trying to reinvent the wheel.

The "Junior Trap" is real: if you offload your thinking to Claude or GPT-4, you’re hitting "Done" for the day, but you’re accruing massive Learning Debt. You aren't building the failure-pattern recognition that actually makes an engineer valuable.

In a world where "Code is no longer a skill," the only way to survive is to stop being a "Prompt Operator" and start being a "System Auditor." If you can’t explain the trade-offs of the architectural pattern the AI just gave you, you aren't an engineer, you're just the person holding the screwdriver while the machine builds the house.

Amazing work! I'd love to hear more details about your workflow with Claude Code.

As a side note and this isn't a knock on your project specifically. I think the community needs to normalize disclaimers for "vibe-coded" packages. Consumers really need to understand the potential risks of relying on agent-generated code upfront.

Honestly, with recent models, these types of tasks are very much possible. Now it mostly depends on whether you are using the model correctly or not.

Since Opus 4.5, things have changed quite a lot. I find LLMs very useful for discussing new features or ideas, and Sonnet is great for executing your plan while you grab a coffee.