I've mostly enjoyed having WSL to leverage Linux dev tools, but it seems like it's still adding overhead that prevents me from taking advantage of the GPU in full, so I'll likely get another drive and install Linux.

I tried Qwen, Llama, Mistral and Gemma. Gemma 4 was pretty impressive.

One thing I would really love to see, however, is a way to better attach code review comments to the specific version of code they were left on. I find it quite difficult to do with git and github, considering that commit hashes change every time one is forced to rebase (say, for example, to handle a merge conflict).

Do you expect DeltaDB to help address this problem?

I've worked in large organizations where a small number of staff+ engineers were approaching programming as factory, while many more engineers were obsessing about the details of their own narrowly-focused work and liked it that way.

It feels like the scale is tipping more towards the factory model and, understandably, not everyone is excited about that.

Comments URL: https://news.ycombinator.com/item?id=48370132

Points: 2

# Comments: 0

I wrote up the full 'war story' of how I had to profile the heap and optimize the node representation (shaving bytes off the edge storage) just to get it to boot without an OOM error: https://www.abahgat.com/blog/the-programming-puzzle-that-got...

It’s the most tangible example I've run into of where theoretical O(n) space complexity meets the reality of object pointer overhead.

Using Gemini's canvas for chord progressions is a great example of this. When I was building this suffix tree visualizer, I kept thinking about how much "spatial" intuition is required to understand the algorithm; having these live, interactive environments available to students is a massive step forward.

https://openai.com/index/new-ways-to-learn-math-and-science-... https://claude.com/blog/claude-builds-visuals

But it still took weeks to test and merge.

The takeaway for me was that for teams operating with legacy debt, or teams where verification requires physical interaction (you can't throw prompt engineering at a hardware test bench), AI doesn't solve the bottleneck; it just shifts it. We are making code generation incredibly cheap, but the cost of verification and code review isn't shrinking, and the burden is falling on our most senior engineers.

I’d be curious to hear how others managing complex or non-standard codebases are adapting their CI/CD and review processes to keep up with the volume of AI-generated code.

Comments URL: https://news.ycombinator.com/item?id=47137447

Points: 1

# Comments: 1

Comments URL: https://news.ycombinator.com/item?id=24271562

Points: 1

# Comments: 1

"Every pitch received improves our personalized matching algorithm, making pitches get even better over time."

how would you distinguish a "good" pitch from a "bad" one?

Comments URL: https://news.ycombinator.com/item?id=3797750

Points: 3

# Comments: 0

Asking candidates their github usernames is starting to seem a common trend, in my experience.

But maybe it is just because I write code for a living...

Comments URL: https://news.ycombinator.com/item?id=3515845

Points: 512

# Comments: 98

Being able to guess the user intentions from the context would be a great advantage, but the challenge would be balancing context-dependent commands from the most general ones, in my opinion.

Comments URL: https://news.ycombinator.com/item?id=3456247

Points: 1

# Comments: 0