“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.
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.
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.
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.
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.
For a given photo of a person, it will provide you with lip cosmetics that match those the person is wearing. My wife gave me the idea saying it would be cool to match what celebrities are wearing.
It definitely helped landing a job. My interviewer said it impressed him and that he had shared it around the company. Everyone called me the lipstick guy for a bit after joining. During the interview it helped to have a non-trivial software project to discuss.
Its only relation to my job is that it’s a Python web app based on Django. I don’t touch any of the computer vision aspects in my day to day.
Now that I’m in a position to hire I put a lot of weight on deployed and working projects. There’s no better way (outside verified career experience) to show you can back up your skill claims.
This paper seems to say that generally they aren’t a problem. I’ve only seen unsubstantiated claims that they are one.