Readit Newsvipshek commented on OpenAI to buy AI startup from Jony Ive bloomberg.com/news/articl... · Posted by u/minimaxir
The only people I’ve ever known who actually thought Reddit ever really mattered was people in the HN sphere. Anecdata, but still. In terms of value per minute spent, it’s the same tier of slop as TikTok or Instagram, and I think most ordinary people hold that same view.
I find this perspective bizarre. Though I'm not happy about it all being centralized, the closest thing we have these days to the very niche phpBB forums of the 2000s is various subreddits focused on very specific topics. Scrolling through the front page is slop, sure, but whenever I'm looking for perspectives on a niche topic, searching for "<topic> reddit" is the first thing I do. And I know many people without any connection to the software industry who feel the same way.
I swear at the junior programmers less.
To be fair the guys I get are pretty good and actually learn. The model doesn't. I have to have the same arguments over and over again with the model. Then I have to retain what arguments I had last time. Then when they update the model it comes up with new stupid things I have to argue with it on.
Net loss for me. I have no idea how people are finding these things productive unless they really don't know or care what garbage comes out.
Perhaps swearing at the LLM actually produces worse results?
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.
vipshek commented on Microsoft cancels leases for AI data centers, analyst says bloomberg.com/news/articl... · Posted by u/suraci
All I hear is anecdotal statements from people claiming LLMs have made them some percent more productive. Yet few actually say how or demonstrate it.
For the last year, I've tried all sorts of models both as hosted services and running locally with llama.cpp or ollama. I've used both the continue.dev vscode extension and cursor more recently.
The results have been frustrating at best. The user interface of the tools is just awful. The output of any models from Deepseek to quen to Claude to whatever other model is mediocre to useless. I literally highlight some code that includes comments about what I need and I even include long explicit descriptions etc in the prompts and it's just unrelated garbage out every time.
The most useful thing has just been ChatGPT when there's something I need to learn about. Rubber ducking basically. It's alright at very simple coding questions or asking about obscure database questions I might have, but beyond that it's useless. Gotta keep the context window short, or it starts going off the rails every single time.
I would like to propose a moratorium on these sorts of “AI coding is good” or “AI coding sucks” comments without any further context.
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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vipshek commented on NeuralSVG: An Implicit Representation for Text-to-Vector Generation sagipolaczek.github.io/Ne... · Posted by u/lnyan
This is excellent!
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.
vipshek commented on Computer use, a new Claude 3.5 Sonnet, and Claude 3.5 Haiku anthropic.com/news/3-5-mo... · Posted by u/weirdcat
Is there anything out there yet that will let me issue the command:
> Refactor the api folder with any recommended readability improvements or improvements that would help DRY up code without adding additional complexity.
Then I can just `git status` to see the changes?
Install Cursor (https://cursor.com), go into Cursor Settings and disable everything but Claude, then open Composer (Ctrl/Cmd + I). Paste in your exact command above. I bet it’ll do something pretty close to what you’re looking for.
Wow, looks great. How is this different from VS Code‘s copilot?
I've completely switched over to Cursor from Copilot. Main benefits:
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.
> I live in a town where all the officials express politically popular laments about the affordability of housing, but every time a developer wants to build apartments or tear down some old run-down post-war cookie-cutter houses for modern duplexs or tri-levels these same officials run them through a gauntlet of demands and then often as not end up denying the permits.
Hah, in my town, the developers and officials are all best friends, posts all over Facebook, going to each other's kids soccer and football games, going on vacation together, going out fishing together...
Which state is your town located in, out of curiosity? I'm trying to build a mental rolodex of which states have towns that are development-friendly.
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.