Readit Newszerop commented on Modular monolith and microservices: Modularity is what matters binaryigor.com/modular-mo... · Posted by u/BinaryIgor
IMO, 99.999% of apps should just use monoliths with basic 1+1 redundancy. Unless you’re working at a FANG and require just insane scale, you really don’t need microservices or even lots of modularity. Just keep it simple. If you need more resources, buy a bigger system and scale up, not out. Only consider scaling out when you have exhausted scaling up.
Microservices created the need for more teams, devs, coordination, meetings, scrum teams, scrum master, Jira, CI CD tools, cloud market and so on.. It is very well planned :)
zerop commented on Tracing the thoughts of a large language model anthropic.com/research/tr... · Posted by u/Philpax
The explanation of "hallucination" is quite simplified, I am sure there is more there.
If there is one problem I have to pick to to trace in LLMs, I would pick hallucination. More tracing of "how much" or "why" model hallucinated can lead to correct this problem. Given the explanation in this post about hallucination, I think degree of hallucination can be given as part of response to the user?
I am facing this in RAG use case quite - How do I know model is giving right answer or Hallucinating from my RAG sources?
zerop commented on Building AI agents to query your databases blog.dust.tt/spreadsheets... · Posted by u/vortex_ape
yes, that shouldn't be a problem.
with that many tables, you might want to use Views: https://cube.dev/docs/reference/data-model/view
Thanks. sorry, asking more question - Do we need human in the loop with Cube to define the views for all kinds of queries.
In my use case, it's going to be exposed to various kind of stakeholders and there will be versatility of user queries. I can't pre-create views/aggregations for all scenarios.
zerop commented on Building AI agents to query your databases blog.dust.tt/spreadsheets... · Posted by u/vortex_ape
Using a semantic layer is the cleanest way to have a human in the loop. A human can validate and create all important metrics (e.g. what does "monthly active users" really mean) then an LLM can use that metric definition whenever asked for MAU.
With a semantic layer, you get the added benefit of writing queries in JSON instead of raw SQL. LLM's are much more consistent at writing a small JSON vs. hundreds of lines of SQL.
We[0] use cube[1] for this. It's the best open source semantic layer, but there's a couple closed source options too.
Currently exploring cube for a "natural language to SQL" solution.
My schema is - 90+ Tables, 2500+ Columns, well documented
From your experience, does Cube look a fit? My use cases will definitely have JOINS.
zerop commented on India's food shortage filled American libraries bbc.com/news/articles/cev... · Posted by u/yannis
How people are unearthing knowledge from ancient libraries using Claude
zerop commented on Show HN: I made a website to semantically search ArXiv papers papermatch.mitanshu.tech/... · Posted by u/Quizzical4230
This looks great, thanks for building this.
Something on similar lines which many may link, Research Rabbit - https://www.researchrabbit.ai/
Can we give reference of these articles to LLMs and get them to write articles like this for educational contents and produce similar WebGL graphics code to render images. I mean, just use this style and produce educational content using AI. that might make the studies more interesting.
Ciechanowski is likely the best content producer of our time, absolutely fascinating reads. Imagine having such a person as a teacher - he could probably excite students about any scientific topic.
I'd love to spend my time working on such articles when I'm retired :)
Can we give reference of these articles to LLMs and get them to write articles like this for educational contents and produce similar WebGL graphics code to render images. I mean, just use this style and produce educational content using AI. that might make the studies more interesting.