Comments URL: https://news.ycombinator.com/item?id=48192330

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=48141264

Points: 1

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Comments URL: https://news.ycombinator.com/item?id=48129303

Points: 5

# Comments: 0

Feels a bit ironic... though this website is hosted on Cloudflare Workers so using an American company anyway?

Seems like your approach is a bit more "batteries-included" but I'd curious for your thoughts on the differences.

Been tinkering on this Olmsted-inspired map of all the trees in NYC for a long time - just added some more features (seasonal colors and better search/filtering) recently.

If you're in NYC, try finding some cherry blossoms near you: https://exploretrees.nyc/?species=cherry

Comments URL: https://news.ycombinator.com/item?id=46222384

Points: 3

# Comments: 0

As an engineer who's full-stack and has frequently ended up doing product management, I think the main value I provide organizations is the ability to think holistically, from a product's core abstractions (the literal database schema), to how those are surfaced and interacted with by users, to how those are talked about by sales or marketing.

Clear and consistent thinking across these dimensions is what makes some products "mysteriously" outperform others in the long run.

Not sure if you’re being figurative, but if what you wrote in your first comment is indicative of the tone with which you prompt the LLM, then I’m not surprised you get terrible results. Swearing at the model doesn’t help it produce better code. The model isn’t going to be intimidated by you or worried about losing their job—which I bet your junior engineers are.

Ultimately, prompting LLMs is simply a matter of writing well. Some people seem to write prompts like flippant Slack messages, expecting the LLM to somehow have a dialogue with you to clarify your poorly-framed, half-assed requirement statements. That’s just not how they work. Specify what you actually want and they can execute on that. Why do you expect the LLM to read your mind and know the shape of nginx logs vs nginx-ingress logs? Why not provide an example in the prompt?

It’s odd—I go out of my way to “treat” the LLMs with respect, and find myself feeling an emotional reaction when others write to them with lots of negativity. Not sure what to make of that.

This comment is like saying, “This diet didn’t work for me” without providing any details about your health circumstances. What’s your weight? Age? Level of activity?

In this context: What language are you working in? What frameworks are you using? What’s the nature of your project? How legacy is your codebase? How big is the codebase?

If we all outline these factors plus our experiences with these tools, then perhaps we can collectively learn about the circumstances when they work or don’t work. And then maybe we can make them better for the circumstances where they’re currently weak.

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I think the utility of generating vectors is far, far greater than all the raster generation that's been a big focus thus far (DALL-E, Midjourney, etc). Those efforts have been incredibly impressive, of course, but raster outputs are so much more difficult to work with. You're forced to "upscale" or "inpaint" the rasters using subsequent generative AI calls to actually iterate towards something useful.

By contrast, generated vectors are inherently scalable and easy to edit. These outputs in particular seem to be low-complexity, with each shape composed of as few points as possible. This is a boon for "human-in-the-loop" editing experiences.

When it comes to generative visuals, creating simplified representations is much harder (and, IMO, more valuable) than creating highly intricate, messy representations.

1. You can configure which LLMs you want to use, whereas Copilot just supports OpenAI models. I just use Claude 3.5 for everything.

2. Chatting with the LLM can produce file edits that you can directly apply to your files. Cursor's experimental "Composer" UI lets you prompt to make changes to multiple files, and then you can apply all the changes with one click. This is way more powerful than just tab-complete or a chat interface. For example, I can prompt something like "Factor out the selected code into a new file" and it does everything properly.

3. Cursor lets you tune what's in LLM context much more precisely. You can @-mention specific files or folders, attach images, etc.

Note I have no affiliation whatsoever with Cursor, I've just really enjoyed using it. If you're interested, I wrote a blog post about my switch to Cursor here: https://www.vipshek.com/blog/cursor. My specific setup tips are at the bottom of that post.

Comments URL: https://news.ycombinator.com/item?id=41312029

Points: 1

# Comments: 1

All that being said, it's very possible to prompt these generators to create images in a particular style. I usually include "flat vector art" in image generation prompts to get something less photorealistic that I've found is closer to the style I want when generating images.

If you really want to go down the rabbit hole, click through the styles on this Stable Diffusion model to see the range that's possible with finetuning (the tags like "Watercolor Anime" above the images): https://civitai.com/models/264290/styles-for-pony-diffusion-...