Nicolay Gerold

@nicolaygerold

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We are excited about (i) NLP use cases (ii) built by industry experts that (iii) serve large, tech-laggard industries with intensely manual workflows for rich text data that (iv) often sit across multiple systems of record.

Shortwave — rajhesh.panchanadhan@gmail.com [Gmail alternative]

We had a CLI tool, written mostly as a bunch of shell scripts, with a ton of available commands that performed all kinds of utility functions related to observability and operations.

It was mostly written by an excellent coworker, and working with it is where I learned to write effective shell scripts. (Thanks to Nathan and shellcheck for this vital... See more

Bill Mill • notes.billmill.org

Command Line Mastery

A “market” is a set of buyers, with sufficiently similar needs, constraints, and goals, that the same product can be sold to all of them. The “AI” market consist of companies like OpenAI and Cohere, and indeed that is a large and growing market, but unless you’re competing directly with them, you’re not “in the AI market.”

A chatbot is in the chatbo... See more

Jason Cohen • AI startups require new strategies: This time it's actually different

I think he kinda beats up his own definition. A chabot also isn’t a market. A chatbot can solve many different problems. It can be helping your own employees get up to speed quicker during onboarding, serve as discovery, help out confused customers on a website. A chatbot is also in the solution space. And a chatbot is just an AI packaged up to talk with some information source.

I agree with the general statement. AI is in the solution space and we have to find the right problem to solve with it. But there are so many problems out there that you can find one that is solvable with AI. And I am yet to see one process, product, employee that I cannot improve with AI.

Data Integration. Integration is needed when your organization collects large amounts of data in various systems such as databases, CRM systems, application servers, and so on. Accessing and analyzing data that is spread across multiple systems can be a challenge. To address this challenge, data integration can be used to create a unified view of y... See more

Data Engineering • The Open Data Stack Distilled into Four Core Tools

Data Loading

First, AI agents are likely to lead to new go-to-market strategies for SaaS companies. With potentially fewer humans in the loop, there is likely to be a further decoupling of seats/users and contract value. To capture value, LLM applications will need a new scaling factor for pricing: variable value-based pricing is a plausible solution.

Moreover, ... See more

Chris Rainville • LLM agents: the next platform shift in B2B software

You always have to consider crashes after strong intakes. Play with dosages and timing.

Andrew Huberman • How Foods and Nutrients Control Our Moods

Although there are already many methods available for keyword generation (e.g., Rake, YAKE!, TF-IDF, etc.) I wanted to create a very basic, but powerful method for extracting keywords and keyphrases. This is where KeyBERT comes in! Which uses BERT-embeddings and simple cosine similarity to find the sub-phrases in a document that are the most simila... See more

MaartenGr • GitHub - MaartenGr/KeyBERT: Minimal keyword extraction with BERT

Overview

MaxText is a high performance , highly scalable , open-source LLM written in pure Python/Jax and targeting Google Cloud TPUs and GPUs for training and inference . MaxText achieves high MFUs and scales from single host to very large clusters while staying simple and "optimization-free" thanks to the power of Jax and the XLA compiler.

MaxText... See more

google • GitHub - google/maxtext: A simple, performant and scalable Jax LLM!

LLMs

When it comes to identifying where generative AI can make an impact, we dig into challenges that commonly:

Involve analysis, interpretation, or review of unstructured content (e.g. text) at scale

Require massive scaling that may be otherwise prohibitive due to limited resources

Would be challenging for rules-based or traditional ML approaches

Developing Rapidly with Generative AI

LLMs