There is no doubt a debate around Client-side and Server-side connection pooler.
But, utilizing the Client-side connection pooling using such advanced drivers should never be taken lightly.
Understood.
It is no doubt the maintenance and the time to build.
However, it does take some efforts to build Oracle forms that requires a good amount of development time in PL/SQL and for customization of forms screens.
However, the extension to that is the Copyrights now. According to me, as long as the Idea creator is a Human, it should not matter if its AI and any LLM creating the art.
The human who gives the prompt may be the Author. This can be concerning but challenging for Legal.
PostgreSQL is definitely going to be the de-facto database for all things AI. It is a database for the future with extensive integrations, advanced extensions for semantic and hybrid search involving vectors and keywords.
Some of the upgrades techniques presented in this talk are :
1. Using pg_dumpall
2. Using pg_dump and pg_restore
3. Using pg_upgrade without hard links
4. Using pg_upgrade with hard links
5. Logical Replication between major versions
Any other Upgrade methods you would like to discuss ?
That's a great point! The idea of robots with living skin raises so many questions. Do you think this technology could actually lead to robots that are indistinguishable from humans in our lifetime? And what ethical implications should we consider as we get closer to this reality? How should laws and regulations evolve to address these advancements?
Will this make people who make indirect money through their content, less motivated from publishing their content on the Web ?
This might be arguable.
May be, there should be a similar amount of openness in publishing the content used for training commercial models.
The copyright owner should have a privilege to ask for that content to be removed from training. This may also allow individual authors to gain their share with their Advanced RAG applications, that are specially focussed on the content they own and also published on the web.
While I cannot argue about a Specific approach, I could say that hallucinations can only be minimized but with a various level of measures while working with large language models.
As an example, while we were building our AI Chatbot for Ora2Pg, the main challenge was that we used OpenAI and several other models to begin with. To avoid hallucinations to the most possible extent, we went through various levels including PDR and then Knowledge Graphs and added FAQs and then used an Agentic approach to support it with as much as information as possible from all possible contexts.
As it is very challenging for anybody and everybody to build their own models trained with their data set, it is not something possible to avoid hallucination with generic purpose LLM's unless they are trained with our data sets.
The chatbot that we built to avoid hallucination as much as we can.