Nicolay Gerold

The best vertical AI solutions will be like a functional worker who is also a team player: expert in their core task (summarization, for instance), but also diligent in updating the horizontal systems of record that the broader organization relies on

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A single field tells us in plain language what the status of the object is by using enums instead of booleans. Another upside is the extensibility and future-proofing that this technique gives us. If we go back to our previous example of adding a “pause” mechanic,... See more

You can think your way into solving a deterministic system, but you cannot think your way into solving a probabilistic system.

The first thing that I want to call out is that deterministic software has edge cases, while probabilistic software has long tails.

I find that a lot of junior folks try to really think hard about edge cases around... See more

It turns out AutoGPT recently went through “vector memory revamp” (pull request) that removed all vector db implementations and left out only a couple of classes responsible for memory management, with a JSON file becoming the default way to store memories/embeddings.

Utilize both full-text search and vector search (embeddings) for retrieving relevant documents. Ideally, you should use a single database system to avoid synchronization issues.

• Implement both full-text search and vector search


• Test the performance of each method on your specific use case


• Consider using a single database system to store both types of

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Here are example ways to increase the success rate of these custom agents:

Provide a more focused, use-case specific system prompt to kick things off. Since you know what kinds of goals the agent supports, you can improve the odds of coming up with the right set of tasks initially. In fact, there’s nothing wrong with actually defining the set of... See more

Kids:- Secure attached.- Avoidant.- Ambivalent: reactions are inconsistent, wanted to bond but annoyed.- Disorganized: avoided interaction and fearful upon return of caregiver.

2-5x faster 50% less memory local LLM finetuning

• Manual autograd engine - hand derived backprop steps.


• 2x to 5x faster than QLoRA. 50% less memory usage.


• All kernels written in OpenAI's Triton language.


• 0% loss in accuracy - no approximation methods - all exact.


• No change of hardware necessary. Supports NVIDIA GPUs since 2018+. Minimum CUDA Compute

... See more