Set git.conflictStyle to zdiff3 and ask Claude to resolve the conflict, or even better, complete the entire rebase for you. A quick diff sanity check against the merge base of the result takes just a few seconds.

Comments URL: https://news.ycombinator.com/item?id=45697033

Points: 2

# Comments: 0

Microsoft provides the editor base, foundation models provide the smarts, and Cursor provides some, in my experience, extremely buggy context management features. There is no moat.

[0] https://knowyourmeme.com/memes/unfinished-horse-drawing-flam...

For users who've already switched to the forks, the cost of switching back is essentially zero, especially if Microsoft begins introducing changes that break fork compatibility. In that case, the migration direction would reverse almost overnight.

Microsoft has clearly taken notice. They're already starting to lock down the upstream VSCode codebase, as seen with recent changes to the C/C++ extension [0]. It's not hard to imagine that future features like TypeScript 7.0 might be limited or even withheld from forks entirely. At the same time, Microsoft will likely replicate Windsurf and Cursor's features within a year. And deliver them with far greater stability and polish.

Both Windsurf and Cursor are riddled with bugs that don't exist upstream, _especially_ in their AI assistant features beyond the VSCode core. Context management which is supposed to be the core featured added is itself incredibly poorly implemented [1].

Ultimately, the future isn't about a smarter editor, it's about a smarter teammate. Tools like GitHub Copilot or future agents will handle entire engineering tickets: generating PRs with tests, taking feedback, and iterating like a real collaborator.

[0] https://www.theregister.com/2025/04/24/microsoft_vs_code_sub...

[1] https://www.reddit.com/r/cursor/comments/1kbt790/rules_in_49...

So, to avoid depressed AIs ending the world randomly, have a stable of multiple AIs with different provenance (one from Anthropic, one from OpenAI, one from Google...) require a majority agreement to reduce the error rate. Adjust thresholds depending on criticality of the task at hand.

Yeah they keep pushing higher-level services, but the uptake of these is extremely limited. If you used something like SageMaker, which has an extremely high lock-in factor, it's probably because you're an old school company that don't know what you're doing and AWS held your developer's hand to get the Hello World-level app working, but at least you got your name printed in their case study materials of the project at the end.

I think OpenAI looks at AWS and thinks they can do better. And for their investors, they must do better. But in the end I think the commoditization of LLMs is already almost complete, and this is just a futile attempt to fight it.

If you built on the Assistant API, maybe take the hint and don't just rewrite to the Responses API? Own your product, black box the LLM-of-the-day.

Comments URL: https://news.ycombinator.com/item?id=42165054

Points: 2

# Comments: 0

Comments URL: https://news.ycombinator.com/item?id=41479985

Points: 1

# Comments: 0

The problem is, for all it's talk over the last few years, AWS remains a complete non-player in the GenAI space, much less so than Azure. In my opinion the problem is exactly the same as for every other high-level service they've tried to launch. QuickSight, Lex, Polly, Cognito, CodeGuru, SageMaker, etc: they're not good. Nobody ever said "I really like QuickSight, I sure wish it had GenAI capabilities". So when the hastily-expanded QuickSight team(s) then goes on to release 42 different Q enabled SKUs, nobody cares. For various reasons, AWS is organizationally incapable of launching a non-infrastructure product that is simply great, as doing so would take attention to detail and deeply caring about things like UX which are anathema to Amazon.

On the positive side, GenAI model access will be commoditized and part of the basic undifferentiated cloud infra, and AWS will do fine there.

Comments URL: https://news.ycombinator.com/item?id=40801642

Points: 1

# Comments: 0

If we're talking about 6 TB of data:

- You can upgrade to 8 TB of storage on a 16-inch MacBook Pro for $2,200, and the lowest spec has 12 CPU cores. With up to 400 GB/s of memory bandwidth, it's truly a case of "your big data problem easily fits on my laptop".

- Contemporary motherboards have 4 to 5 M.2 slots, so you could today build a 12 TB RAID 5 setup of 4 TB Samsung 990 PRO NVMe drives for ~ 4 x $326 = $1,304. Probably in a year or two there will be 8 TB NVMe's readily available.

Flash memory is cheap in 2024!

This might be a way to encode personality traits into models, by emphasizing some aspects and damping others.

Comments URL: https://news.ycombinator.com/item?id=39352893

Points: 3

# Comments: 0

But if a human is allowed time and internal reasoning iterations, so should the LLM when determining if it has deep insight. Right now we're simply observing input -> output of LLMs, the equivalent of snap answers from a human. But nothing says it couldn't instead be an input -> extensive internal dialogue, maybe even between multiple expert models for seconds, minutes or hours, that are not at all visible to the prompter -> final insightful answer. Maybe future LLMs will say, "let me get back to you on that".