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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.
I don't get the whole idea of treating identity verification as a private enterprise problem. I realize it's easy to just blame LinkedIn or Microsoft here, but the core issue is architectural. We are trying to solve a public utility problem by building private honeypots.
The government should provide an API or interface to validate a user, essentially acting just like an SSO. Instead of forcing users to upload raw passport scans to a third-party data broker, LinkedIn should just hit a government endpoint that returns an anonymized token or a simple boolean confirming "yes, this is a real, unique person." It gives platforms the sybil resistance they need without leaking the underlying PII.
Thanks for answering! Regarding cardinality, I was actually thinking more about high-cardinality dimensions on the NLU side, e.g., if a user asks for "Sales for [Obscure Company Name]," and you have 10M distinct customers. Does the Knowledge Graph have to index all those values for the mapping to work?
On the adaptive plans, Is that execution logic handled entirely by your deterministic compiler, or does it loop back to the LLM to interpret the intermediate results?
Thanks for sharing the links, the architectural overview is very insightful.
I'm curious how this approach manages cardinality explosion? Also, how do you handle cases where a user asks for data that requires running multiple queries, specifically where each query depends on the results of the previous one?
If a foreign entity came into Florida and bought up 35% of the entire retail infrastructure, you bet the US government would regulate it and demand local value capture.
Case in point - US actively forced TSMC and Samsung to build $65B+ of factories in Arizona and Texas to secure domestic interests.
You are describing a colonial model, extract all the wealth while investing nothing in the local economy. That era is over.
If anything, Meta is the anomaly, not the role model. They should be required to invest more given their dominance, rather than being praised for extracting maximum value with minimum local footprint. Regulators will likely close that gap eventually.
I get the fear, but look at it from the investor's perspective. The US market is tapped out, Amazon is already everywhere it can be.
Amazon isn't expanding in India out of love for the country or a desire to see it grow. They are doing it because Wall Street demands infinite growth every single year. Amazon India went from zero to a market leader in a decade not because of charity, but because that is where the new money is.
To keep the valuation climbing (which sustains everyone's RSUs), they have to capture these emerging markets. If they don't, the stock stagnates, and the compensation model for US tech workers falls apart.
I realize it’s easy to pattern-match this news to 'hiring in India vs. firing in US' given the current climate, but having worked at Amazon India for 4 years, I can tell you the cuts happen there too.
Amazon has a history of annual restructuring that hits every region. It isn't necessarily a direct relocation strategy so much as their standard operational churn. The 'efficiency' cuts are happening globally, India included.
This is a recipe for model collapse/poisoning.
An LLM is optimized for its training data, not for newly built formats or abstractions. I don’t understand why we keep building so-called "LLM-optimized" X or Y. It’s the same story we’ve seen before with TOON.
It’s the standard enshittification lifecycle: subsidize usage to get adoption, then lock down the API to force users into a controlled environment where you can squeeze them.
Like Reddit, they realized they can't show ads (or control the user journey) if everyone is using a third-party client. The $200 subscription isn't a pricing tier. It's a customer acquisition cost for their proprietary platform. Third-party clients defeat that purpose.
You can be a controversial figure politically and still build a generation defining product. The market rewards utility, not ideological purity.
The headline frames this as a paradox, as if these two things are incompatible. But they aren't mutually exclusive, he can be both.