www.trustyou.com/blog/technol...
This is how we have increased focus and flow in the engineering teams at TrustYou , specially when tackling the implementation of complex features.
www.trustyou.com/blog/technol...
www.trustyou.com/blog/technol...
In this post I give some tips for a smooth data migration!
www.linkedin.com/posts/jorgep...
In this post I give some tips for a smooth data migration!
www.linkedin.com/posts/jorgep...
www.trustyou.com/blog/technol...
This is how we have increased focus and flow in the engineering teams at TrustYou , specially when tackling the implementation of complex features.
www.trustyou.com/blog/technol...
This is how we have increased focus and flow in the engineering teams at TrustYou , specially when tackling the implementation of complex features.
Lesson learnt #1: idempotency is your ally. Data migration won’t be that job that you run once. Be ready to rerun it without a hassle.
No previous clean-up, no post-processing for what did not change. Idempotency.
Lesson learnt #1: idempotency is your ally. Data migration won’t be that job that you run once. Be ready to rerun it without a hassle.
No previous clean-up, no post-processing for what did not change. Idempotency.
In each temporary team there are a product lead and a technical lead, who may have or not a leadership role in their fixed product teams.
In each temporary team there are a product lead and a technical lead, who may have or not a leadership role in their fixed product teams.
These individuals leave their respective fixed product teams during the time the temporary team is active.
These individuals leave their respective fixed product teams during the time the temporary team is active.
LLMs struggle with large amounts of context. Bharani Subramaniam and I explain how to mitigate this common RAG problem with a Reranker which takes the document fragments from the retriever, and ranks them according to their usefulness.
martinfowler.com/articles/gen...
LLMs struggle with large amounts of context. Bharani Subramaniam and I explain how to mitigate this common RAG problem with a Reranker which takes the document fragments from the retriever, and ranks them according to their usefulness.
martinfowler.com/articles/gen...