gomoboo

What are some pre-Jev classifier and ranker approaches? And how do you train them?

This is LLM-generated too.

It's not even close. vscode can approximate JB IDEs but that takes pulling in plugins on your own, managing their configuration, and adapting when the setup inevitably breaks. JB IDEs bake in everything you can get via vscode plugins with documentation, a cohesive experience, and regular maintenance to keep them working for you. Concrete examples that are better in JB land for me: git conflict resolution, refactoring (in numerous ways), jumping to definitions, finding usages (especially on Python projects).

I vouched for you here because this compiler is interesting to me and I'd like to see some discussion on it. All your comments are listed as 'dead' though. Likely because they seem to be LLM-generated. Maybe writing by hand or noting you're using an LLM for translation (if you are) would help.

It always bugs me to think about the Python community not rallying around PyPy. Faster Python is right there and has been there for years. Sure there can be C-extension issues but projects like HPy showed you can remedy that. Why doesn’t everyone just use and support PyPy?

How do you check style via PowerShell? You’ve pulled me in.

Mirror: https://xcancel.com/sidahuj/status/2086445625147793503

I grew up in another country where Jamaican patties were popular and ordering a 'beef patty' would never get you a burger patty unless you were in a burger joint. Same as in Toronto; asking for patty (or beef patty) will 99% of the time get you a Jamaican patty.

“While aggressive start cycles (>20 cycles per day) could lead to premature failure in the starter system of light- to medium-duty commercial fleet vehicles, modern fuel injection and engine control systems have eliminated any issues associated with drivers of typical light-duty vehicles turning the engine off while stationary for short periods and restarting the vehicles for <10 start events per day.” from https://publications.anl.gov/anlpubs/2015/05/115925.pdf

This paper seems to say that generally they aren’t a problem. I’ve only seen unsubstantiated claims that they are one.

After all of that why protect the company by not mentioning their name?

D uses a homegrown GC not Boehm: - https://dlang.org/spec/garbage.html - https://dlang.org/blog/2017/03/20/dont-fear-the-reaper/

Do these attributes actually help with search engine visibility or do they just make it easier for search engines to keep users from leaving the search page? Honest question here.

Refreshed gift link: https://www.theglobeandmail.com/gift/ac7e9825b17af566d8cdb52...

I put a firewall ahead of the Docker host so that they aren't running on the same system. Docker can do what it wants to on the host without stepping on my firewall rules.

Never used it but I’ll chime in that the naming choice is unfortunate. For me as an English speaker it collides with stool, a term for excrement.

I love reading battlefield notes like this for RAG/search systems. Anyone shooting for useful output is going to hit the same pain points but each article like this has a different set of solutions.

I’m leaning on OpenAI for my embedding needs but will be trying llama-server in the future. I stuck with Postgres because it was easy to run it on my Dokku installation. Great to know sqlite is an option there too. My corpus is too small for Postgres to elect to use an index so it’s running the full table scans that sqlite would. For seeding I use a msgpack file and ship that with the code when deploying.

This is my site: https://customelon.com (niche need of tariff and excise information for shipping to The Bahamas)

It’s built with ASP.NET, Postgres/pgvector, and OpenAI embedding/LLMs. Ingestion is via Textract with a lot of chunking helpers to preserve context layered on top.

Again, great article.

It’ll be interesting to see what went on here. Was the whistleblower all negative without bringing solutions forward? It doesn’t sound so with them lamenting the downgrading of their safety roadmap.

This seems like it would be great to use with Celery tasks. At some point you want distributed rate-limiting and at least with older versions of Celery that wasn’t baked in.

How I rein in YouTube on all my browsers:

- prevent autoplaying video

- redirect ‘ youtube.com/shorts/’ to ‘ youtube.com/watch?v=’

The second gets rid of the addictive Shorts UI. I don’t block them entirely because channels I subscribe to create interesting shorts.

That Workday description reads like the resumes one writes when desperate and the job search has expanded into totally unrelated professions.

PythonAnywhere: https://www.pythonanywhere.com/

Relevant post from a few years ago: https://news.ycombinator.com/item?id=25290112

“ NN-512 is an open-source Go program that generates fully AVX-512 vectorized, human-readable, stand-alone C implementations of convolutional neural nets”

With an iPhone you’d have AirDrop built in.

How does that work woth your friends? Always on access or just occasionally?

Congrats on the launch! If you make this a 'Show HN' you can add a description alongside your link. You might also get a different audience. Here are some examples: https://news.ycombinator.com/show

pg_repack gets rid of the need to lock tables for the duration of the vacuum: https://docs.aws.amazon.com/AmazonRDS/latest/UserGuide/Appen...

It is an extension though so downside there is it not being included in most Postgres installs. I’ve used it at work and it felt like a superpower getting the benefits of a vacuum full without all the usual drama.

They found out about the failing transistors via colleagues at a conference. Have any of you learned of something of this magnitude in the same way? It got me thinking that I need to interact with my fellow devs more often.

At the bottom of the page there is a credit for Shorthand (https://shorthand.com/). It seems to be the tool that produced such beautiful results.

The portion of the article the previous “17x” title referenced:

“In a presentation earlier this month, the venture-capital firm Sequoia estimated that the AI industry spent $50 billion on the Nvidia chips used to train advanced AI models last year, but brought in only $3 billion in revenue.”