So you have a startup idea. How do you know if it's a great idea, or a waste of time? For part 2 of my series on kickstarting and scaling a B2B business, I interviewed the founders of 20+ of today's best B2B startups—including @Amplitude_HQ @Linear @NotionHQ @Figma @Gong_io
AI
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Three Worker Strategies for AI and Future of Work
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3 things workers can do to prepare for #AI and the #FutureofWork https://
bit.ly/3KHygwq via @FastCompany -
Efficient Fine-Tuning Techniques for Large Language Models
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Unlocking the potential of Generative AI! Dive into @DominoDataLab
's latest blog on efficient fine-tuning techniques for large language models like Falcon-7b & more. Cut costs, optimize resources, & boost innovation. https://
domino.buzz/3s8zZFu #ReadNow #DataScience #LLMs -
U.S. Congress Introduces Create AI Act to Transform AI Landscape
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Last month, the U.S. Congress introduced a new bipartisan bill called Create AI Act, which could potentially transform the current AI landscape. Here, HAI Director of Policy @russellwald explains why passing this legislation is crucial to the future of AI:
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LLM-Assisted Evaluation: Best Practice for Answer Assessment
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Once we had these datapoints, we needed a way to evaluate answers For this, we relied on LLM assisted evaluation Although this isn't perfect, we think its the best thing out there and are bullish on this in the long run
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Custom Agent with Python REPL and Retriever Tools
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Then we could go about improving it! Our final solution was a custom agent with two tools: – Python REPL (for doing analysis)
– Retriever (for dealing with text data present) We're excited to see other's solutions for this as well! -
LangChain Benchmarking Question-Answering CSV Data Tasks
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Recap of important links: Blog: https://
blog.langchain.dev/benchmarking-q
uestion-answering-over-csv-data/
… YouTube: https://
youtube.com/watch?v=jGnf4O
hptbA
… Code & data used: https://
github.com/langchain-ai/l
angchain-benchmarks
… We had a lot of fun doing this and learned a lot – we're going to do it for more tasks! Up next: SQL -
Improving AI Agents for CSV Question-Answering Applications
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This started ~2 weeks ago, when I tweeted that we wanted to improve our chains/agents for doing question/answering over CSV data Why? Most QA applications focus on text data, but lots of real world data is in CSVs
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Building AI Evaluation Datasets from Real User Questions
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The first issue to solve was that we didn't have a dataset to evaluate Rather than try to make up some questions, we put out an example application and logged what questions people asked They also kindly provided feedback, so we could easily identify errors!
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LangChain Benchmarking Question Answering CSV Data
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Blog: https://
blog.langchain.dev/benchmarking-q
uestion-answering-over-csv-data/
… YouTube: https://
youtube.com/watch?v=jGnf4O
hptbA
… Code & data used: https://
github.com/langchain-ai/l
angchain-benchmarks
… Now for a quick thread: