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L

LangChain Cast

12/18/24

@ LangChain

By 11:00 a.m., we hypothesized that a bug in PostgreSQL, triggered by our query volume, was causing the issues we were experiencing.

Video

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How We Built It: Character.AI - Fireside Chat with Harrison Chase & James Groeneveld

@ LangChain

How We Built It: Character.AI - Fireside Chat with Harrison Chase & James Groeneveld

12/18/24

Related Takeaways

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LangChain Cast

12/18/24

@ LangChain

The bug in PostgreSQL led to a situation where transaction IDs were not being released properly, causing us to be unable to update records due to prior transaction IDs still being in use.

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LangChain Cast

12/18/24

@ LangChain

We discovered database latency issues and open connections that were different from what we had seen before, indicating a significant problem.

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LangChain Cast

12/18/24

@ LangChain

A bug in our caching system caused the model to respond as if it were talking to itself, leading to a significant drop in user satisfaction until we identified and fixed it.

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LangChain Cast

12/18/24

@ LangChain

The database layer has been a critical challenge in scaling our product, leading to catastrophic issues that required immediate attention from our team.

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LangChain Cast

12/18/24

@ LangChain

We faced a critical situation where we had only 16 hours left of transaction IDs before we would need to take the database offline for a week to perform a full vacuum procedure.

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LangChain Cast

12/18/24

@ LangChain

We faced unique challenges in scaling our platform, particularly with GPU local attention caching, which led to unexpected negative results during testing.

DG

Diane Greene

10/26/13

@ Y Combinator

When we launched our beta, we underestimated the demand and ran out of bandwidth, leading a Cornell student to host our site temporarily while we resolved the issue.

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OpenAI Cast

04/11/25

@ OpenAI

A bug in the torch sum function was discovered, which had been causing various symptoms, and fixing it resolved multiple issues at once, leading to a fun moment for the team.

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LangChain Cast

12/18/24

@ LangChain

Our architectural choices, such as multi-query attention, enabled us to control speed and latency more precisely without hurting user engagement.