moozilla
Joined 18 years ago
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180
Submissions
52
Member since
Dec 25
Recent submissions
Comment · 2 months ago
Comment · 4 months ago
This is only true for poor quality coffee. The hot temperature masks defects (bitterness). Good coffee tastes better as it cools. https://www.baristamagazine.com/the-temperature-paradox-why-...
Comment · 4 months ago
Midjourney is not VC backed. They have a bit about this in the article:
> As a reminder, Midjourney has no investors. We are a totally new kind of research lab. We've seen academic, corporate, and government labs - but we are a distinct (and curious) new thing: we are a community-backed research lab.
Comment · 8 months ago
Last time I tried home row mods I could not get over how bad it felt having the letter not appear until I lifted the key rather than immediately on the keypress. Am I just overly sensative or is this just something you get used to over time?
Comment · 11 months ago
I also found this bit funny:
> We also suspect that the geographical and cultural factors may have influenced interaction patterns, given that all our participants were residing in Türkiye.
Comment · 1 year ago
You could go with the title from the associated YouTube video (https://www.youtube.com/watch?v=lXUZvyajciY)?
Andrej Karpathy — “We’re summoning ghosts, not building animals”
Comment · 1 year ago
I like to think of models leaving "useless comments" as a way to externalize their reasoning process - maybe they are useless at the end, but leaving them in on a feature branch seems to marginally improve future work (even across conversations). I currently leave them in and either manually clean them up myself before putting up a PR for my team to review or run a final step with some instructions like "review the diff, remove any useless comments". Funnily enough Claude seems pretty competent at identifying and cleaning up useless comments after the fact, which I feel like sort of proves my hypothesis.
I've considered just leaving the comments in, considering maybe they provide some value to future LLMs working in the codebase, but the extra human overhead in dealing with them doesn't seem worth it.
Comment · 1 year ago
If anyone is curious on the context:
https://x.com/thdxr/status/1933561254481666466 https://x.com/meowgorithm/status/1933593074820891062 https://www.youtube.com/watch?v=qCJBbVJ_wP0
Gemini summary of the above:
- Kujtim Hoxha creates a project named TermAI using open-source libraries from the company Charm.
- Two other developers, Dax (a well-known internet personality and developer) and Adam (a developer and co-founder of Chef, known for his work on open-source and developer tools), join the project.
- They rebrand it to OpenCode, with Dax buying the domain and both heavily promoting it and improving the UI/UX.
- The project rapidly gains popularity and GitHub stars, largely due to Dax and Adam's influence and contributions.
- Charm, the company behind the original libraries, offers Kujtim a full-time role to continue working on the project, effectively acqui-hiring him.
- Kujtim accepts the offer. As the original owner of the GitHub repository, he moves the project and its stars to Charm's organization. Dax and Adam object, not wanting the community project to be owned by a VC-backed company.
- Allegations surface that Charm rewrote git history to remove Dax's commits, banned Adam from the repo, and deleted comments that were critical of the move.
- Dax and Adam, who own the opencode.ai domain and claim ownership of the brand they created, fork the original repo and launch their own version under the OpenCode name.
- For a time, two competing projects named OpenCode exist, causing significant community confusion.
- Following the public backlash, Charm eventually renames its version to Crush, ceding the OpenCode name to the project now maintained by Dax and Adam.
Comment · 1 year ago
I'm surprised this doesn't get brought up more often, but I think the main explanation for the divide is simple: current LLMs are only good at programming in the most popular programming languages. Every time I see this brought up in the HN comments section and people are asked what they are actually working on that the LLM is not able to help with, inevitably it's using a (relatively) less popular language like Rust or Clojure. The article is good example of this, before clicking I guessed correctly it would be complaining about how LLMs can't program in Rust. (Granted, the point that Cursor uses this as an example on their webpage despite all of this is funny.)
I struggled to find benchmark data to support this hunch, best I could find was [1] which shows a performance of 81% with Python/Typescript vs 62% with Rust, but this fits with my intuition. I primarily code in Python for work and despite trying I didn't get that much use out of LLMs until the Claude 3.6 release, where it suddenly crossed over that invisible threshold and became dramatically more useful. I suspect for devs that are not using Python or JS, LLMs have just not yet crossed this threshold.
[1] https://terhech.de/posts/2025-01-31-llms-vs-programming-lang...
Comment · 1 year ago
Here's an attempt at cleaning it up with Gemini 2.5 Pro: https://rentry.org/nyznvoy5
I just pasted the YouTube link into AI Studio and gave it this prompt if you want to replicate:
reformat this talk as an article. remove ums/ahs, but do not summarize, the context should be substantively the same. include content from the slides as well if possible.
Comment · 1 year ago
Which plugins are you using? I've been looking to upgrade my zsh experience so some suggestions would be helpful.
Comment · 1 year ago
OP is likely referring to people who call LLMs "stochastic parrots" (https://en.wikipedia.org/wiki/Stochastic_parrot), and by "doomers" (not boomers) they likely mean AI safetyists like Eliezer Yudkowsky or Pause AI (https://pauseai.info/).
Comment · 1 year ago
Highly recommend Three-Body, the Chinese version of the Three-Body Problem. I enjoyed it much more than the Netflix adaptation, much closer to the source material, and more of a slow burn. Episodes are available on YouTube with subs (https://www.youtube.com/watch?v=3-UO8jbrIoM).
Comment · 1 year ago
The link the article uses to source the 60 GWh claim (1) appears to be broken, but all of the other sources I found give similar numbers, for example (2) which gives 50 GWh. This is specifically to train GPT-4, GPT-3 was estimated to have taken 1,287 MWh in (3), so the 50 GWh number seems reasonable.
I couldn't find any great sources for the 200 plane flights number (and as you point out the article doesn't source this either), but I asked o1 to crunch the numbers (4) and it came up with a similar figure (50-300 flights depending on the size of the plane). I was curious if the numbers would be different if you considered emissions instead of directly converting jet fuel energy to watt hours, but the end result was basically the same.
[1] https://www.numenta.com/blog/2023/08/10/ai-is-harming-our-pl...
[2] https://www.ri.se/en/news/blog/generative-ai-does-not-run-on...
[3] https://knowledge.wharton.upenn.edu/article/the-hidden-cost-...
[4] https://chatgpt.com/share/678b6178-d0e4-800d-a12b-c319e324d2...
Curious what method you like for doing this? I've tried a few options and I haven't found one I'm happy with yet.