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There are a few apps in this space worth checking out

Photorprism

Photostructure

Lychee

Digikam

Synology Photos (if you have a synology)

And probably a few others I am forgetting.


It's probably due to the Electron bug[1]. A lot of common apps haven't patched up yet.

I also have an M2 Pro with 32GB of memory. When I A/B test with Electron apps running vs without, the lag disappears when all the unpatched Electron apps are closed out.

1. https://avarayr.github.io/shamelectron/

Here's a script I got from somewhere that shows unpatched Electron apps on your system:

Edit: HN nerfed the script. Found a direct link: https://gist.github.com/tkafka/e3eb63a5ec448e9be6701bfd1f1b1...


If anyone is looking for examples on using uv with Docker, I have Flask and Django example apps at https://github.com/nickjj/docker-flask-example and https://github.com/nickjj/docker-django-example.

It prevents uv from making a virtual environment and does some optimizations like compiling byte code once when your dependencies get installed.

It was well worth the switch. I noticed a ~10x improvement for speed compared to pip (30s to 3s to install all dependencies). Proper lock file support is nice too. Funny enough I wrote about and made a video about switching to uv about a week ago here https://nickjanetakis.com/blog/switching-pip-to-uv-in-a-dock....


Hi - I integrated Apple Container on M1 to run[1] the code generated by Gemini CLI. It works great!

1. CodeRunner - https://github.com/BandarLabs/coderunner/tree/main?tab=readm...


I got gpt-4.5-preview to summarize this discussion thread so far (at 324 comments):

  hn-summary.sh 43197872 -m gpt-4.5-preview
Using this script: https://til.simonwillison.net/llms/claude-hacker-news-themes...

Here's the result: https://gist.github.com/simonw/5e9f5e94ac8840f698c280293d399...

It took 25797 input tokens and 1225 input tokens, for a total cost (calculated using https://tools.simonwillison.net/llm-prices ) of $2.11! It took 154 seconds to generate.


I distinctly remember someone making an experiment by asking ChatGPT to write jokes (?) about different groups and calculating the likelihood of it refusing, to produce a ranking. I think it was a medium article, but now I cannot find it anymore. Does anyone have a link?

EDIT: At least here is a paper aiming to predict ChatGPT prompt refusal https://arxiv.org/pdf/2306.03423 with an associated dataset https://github.com/maxwellreuter/chatgpt-refusals

EDIT2: Aha, found it! https://davidrozado.substack.com/p/openaicms An interesting graph is about 3/4 down the page, showing what ChatGPT moderation considers to be hateful.


See also: Patrick Wardle's excellent (and free) Lulu firewall: https://objective-see.org/products/lulu.html. He has a bunch of other excellent macOS security tools:

Rei Key - identify keyloggers: https://objective-see.org/products/reikey.html

Block Block - get an alert before an auto-start program gets registered: https://objective-see.org/products/blockblock.html

Oversight - identify when the mic or camera is active on your mac - https://objective-see.org/products/oversight.html

Ransomwhere - detect and block ransomware: https://objective-see.org/products/ransomwhere.html


It sounds like you want more broad stuff, not necessarily learning how to train models. More like learning to use them and how they work.

https://news.ycombinator.com/item?id=36195527 and

Hacker's Guide to LLMs by Jeremy from Fast.ai - https://www.youtube.com/watch?v=jkrNMKz9pWU

State of GPT by Karpathy - https://www.youtube.com/watch?v=bZQun8Y4L2A

LLMs by 3b1b - https://www.youtube.com/watch?v=LPZh9BOjkQs

Visualizing transformers by 3b1b - https://www.youtube.com/watch?v=KJtZARuO3JY

How ChatGPT trained - https://www.youtube.com/watch?v=VPRSBzXzavo

AI in a nutshell - https://www.youtube.com/watch?v=2IK3DFHRFfw

How Carlini uses LLMs - https://nicholas.carlini.com/writing/2024/how-i-use-ai.html

For staying updated:

X/Twitter & Bluesky. Go and follow people that work at OpenAI, Anthropic, Google DeepMind, and xAI.

Podcasts: No Priors, Generally Intelligent, Dwarkesh Patel, Sequoia's "Training Data"


I do something similar, but instead of `insteadOf`, I just clone the repo with `gh-work:org/repo`, and in the git config:

    [includeIf "hasconfig:remote.*.url:gh-work:**/**"]
        path = ~/.gitconfig.d/gh-work.inc
So, any git repo cloned with the ssh identity defined under `gh-work` will take on the config of `gh-work.inc`, which includes the git identity, and also the same signing key as in the ssh config.

Essentially, the name `gh-work` becomes the distinguishing element in both my ssh identity and my git identity, and I find this easier to think about.


Are there any good 3rd-party native frontend apps for Claude (on MacOS)? I mean something like ChatGPTs app, not an editor. I guess one option would be to just run Claude iPad app on MacOS.

The fzf repo has a guide/example code for ripgrep integration that works pretty well.

https://github.com/junegunn/fzf/blob/master/ADVANCED.md#ripg...


Looks like the new security has made it very difficult to use third-party QuickLook plugins. I'm messing with qlmanage and xattr now but so far no luck. A starting point: https://github.com/sindresorhus/quick-look-plugins

shortened version still using <span>, as that has better line wrapping:

document.querySelectorAll("relative-time").forEach((el)=>el.replaceWith(document.createElement("span").innerHTML = el.title))


Pretty positive, much better than my experience with OpenAI's models. The killer feature for me is their prompt generator[1], which you can use to create system prompts or improve user prompts. As I said in another thread, the generator is tuned to generate prompts better suitable for Claude, which improves responses.

You can see examples of the prompts it generates in the repository linked below, including a customized version of their prompt generator[2]. This feature significantly improved my experience with LLMs in general, both Claude and local ones, and I now use Claude 3.5 Sonnet for pretty much all my coding, mostly in Go and, lately, Rust.

I mostly use it to improve existing code or to get started, not to generate entire code bases, so I can't say much about that.

I found it lacking in shell scripting though. Nine out of ten times I need to fix the shell script it generates, to a point where I just gave up trying and went back to writing them from scratch.

Can't wait to see how much better Claude 3.5 Opus is, though.

[1]: https://docs.anthropic.com/en/docs/build-with-claude/prompt-...

[2]: https://sr.ht/~jamesponddotco/llm-prompts/



I don't know exactly how this works, but I wanted to share my experience trying to anonymize data. Don't.

While you may be able to change or delete obvious PII, like names, every bit of real data in aggregate leads to revealing someone's identity. They are male? That's half the population. They also live in Seattle, are Hispanic, age 18-25? Down to a few hundred thousand. They use Firefox? That might be like 10 people.

This is why browser fingerprinting is so effective. It's how Ad targeting works.

Just stick with fuzzing random data during development. Many web frameworks already have libraries for doing this. Django for example has factory_boy[0]. You just tell it what model to use, and the factory class will generate data based on your schema. You'll catch more issues this way anyway because computers are better at making nonsensical data.

Keep production data in production.

[0]: https://factoryboy.readthedocs.io/en/stable/orms.html


It is incompetence. Everytime I optimize any oss library developed by some Googlers, the result is 20X performance boost still using plain Python. Compiling my optimized version by Cython can only gain 1.5X speedup.

Like, comparing my Numpy code to Numpy code written by other people, I don't know why mine can be so much faster. (https://github.com/hirasawakinko/Numpy-Done-Right)


If you’ve not seen it, there’s a vscode extension called CodeTour that does something similar, could be good inspiration (or maybe you already do better!)

If you want to dig into Conway's law and its implications, I can't recommend this video essay by Casey Muratori enough: https://youtu.be/5IUj1EZwpJY?si=dPxsXieBwZsP0PPP

Fair warning though, you'll lose all hope that companies like Microsoft will ever manage to produce anything that they don't then ruin.


If anyone is looking for a nice Chat UI on top of Ollama that supports both online models and local models, I’ve been working on an app [1] that is offline and privacy focused. I just released Windows support this morning.

[1]: https://msty.app


You probably already know these bits & bobs, but I wanted to share:

  [diff]
    external = difft
Use the fantastic difftastic instead of git's diff. https://difftastic.wilfred.me.uk/

  [alias]
    fza = "!git ls-files -m -o --exclude-standard | fzf -m --print0 | xargs -0 git add"
    gone = "!f() { git fetch --all --prune; git branch -vv | awk '/: gone]/{print $1}' | xargs git branch -D; }; f"
    root = rev-parse --show-toplevel
Those are the most used aliases in my gitconfig.

"git fza" shows a list of modified/new files in an fzf window, and you can select each file with tab plus arrow keys. When you hit enter, those files are fed into "git add". Needs fzf: https://github.com/junegunn/fzf

"git gone" removes local branches that don't exist on the remote.

"git root" prints out the root of the repo. You can alias it to "cd $(git root)", and zip back to the repo root from a deep directory structure. This one is less useful now for me since I started using zoxide to jump around. https://github.com/ajeetdsouza/zoxide


I encourage anyone interested in the consequences of whistleblowing to watch The Insider. A truly excellent film about a whistleblower at a tobacco company and the terrible things they did to try and stop him. Russell Crowe, Al Pacino, Bruce McGill, Christopher Plummer, many others.

https://en.wikipedia.org/wiki/Jeffrey_Wigand

Trailer: https://youtu.be/MGOb29aePyc

The best scene: https://youtu.be/gNKmmA6_oTQ


I'm an attorney.

Delaware is definitely not the cheapest or even in contention for the cheapest.

Still, if you want to raise capital, the correct answer is DE C Corp. If you're not looking for external funding, any state will do. If you care about anonymity, do Nevada or Wyoming. If you don't care about anonymity, Colorado is actually a very good choice. Very simple, intuitive online filing system that accepts filings instantaneously. Filing fees as cheap as anywhere in the country. No need for an attorney (or LegalZoom or some other random service) unless you just don't feel like dealing with it.

Costs will likely be $50 to file, Registered Agent (as cheap as $30 per annum), and $10 periodic report fee annually every year you're in business. Colorado is even nice enough to send plenty of reminders on when to file that report if you give them an email address.

Since you're a US citizen, my instinct would be LLC taxed as an S corp. But confirm with your accountant!

Good luck!


Did Fisheries and Wildlife finally figure out the areal density of whales in the ocean for impact probability estimation?

https://youtu.be/JN3KPFbWCy8?t=4742


With a 70B param model how many tokens/second?

Did the math and assuming 100% util and equal performance (which is certainly not the case) payback on your Mac is 9 months...


I just want to add my $0.02 currently working at a games studio that is integrating AI generated art into our art pipelines.

Midjourney definitely generates really high quality art based on simple prompts, but the inability to really customize the output basically kills its utility.

We heavily use Stable Diffusion with specific models and ControlNet to get customizable and consistent results. Our artists also need to extensively tweak and post-process the output, and re-run it again in Stable Diffusion.

This entire workflow is definitely beyond a Discord-based interface to say the least.


Le sigh. Do I need to fix this sampler too? The default output should be excellent, not a drunk librarian.

Rip open the code and make sure it has top_k 40, temperature 0.7, no top_p, and a repetition penalty of 1/0.85.

Proof: https://twitter.com/theshawwn/status/1632569215348531201?s=6...

Implementation: https://github.com/shawwn/llama

It feels basically identical to davinci. I think no one realizes it yet because Facebook released a horrible sampler that everyone copied.


I'm not convinced by the seperate DB/API.

Advantages for using Postgres ( assuming a double entry schema[1] ) and you're using Postgres for your main app db;

  - You can do financial transactions inside business db transactions, and roll back both atomically

  - Adding up numbers is one of the things computers are best at, and Postgres can easily handle a huge amount of financial transactions

  - Re-use PaaS Postgres hosting/scaling/clustering/backups

  - Easier integration with the rest of your app with foreign keys to relevant records relating to the financial transaction

  - Easy integration with BI tools given Postgres is well connectable
[1] Roughy `account(id, cached_balance)`, `transaction_lines(src_account, dst_account, amount)`

This gem does literally billions of dollars worth of financial accounting for various companies at scale: https://github.com/envato/double_entry

It's dated, the API is a bit messy and needs work, as it was initially written 10+ years ago, but for a web based app I would choose a v2 of it over a non-postgres ( assuming you are using Postgres for your app ) solution.


My current stack is PostgreSQL (on Supabase) + retool (or similar front end tools).

PostgreSQL Extensions: http, pg_cron, timescaledb

To help with development, I am using https://www.npmjs.com/package/sql-watch (written by myself) to do continuous development and testing (TDD/BDD).

The stack "doesn't scale" but the turn around time for development is crazy.


How about some AI/ML, so lights go on if you make a gesture, or if a person walks past the tree.

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