LLMs
One interesting thing about LLMs is that they can actually recover (and without error loops). You can have a step that doesn't work right, and a later step can use its common-sense knowledge to ignore some of the missing results, conflicting information, etc. One of the problems with developing with LLMs is that the machine will often cover up... See more
Ask HN: What are some actual use cases of AI Agents right now? | Hacker News
In the simplest form, we can use the model’s detection confidence to determine a score. But even here there are quite a few options to choose from:
- Lowest confidence - the score is the lowest confidence of all detected objects
- Average confidence - average of all confidences of detected objects
- Minimizing confidence delta - difference between
Active Learning with Domain Experts, a Case Study in Machine Learning
What data to label?
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
Jason Liu • Tips for probabilistic software - jxnl.co
🤖 Cutting-edge framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
- Why CrewAI
- Getting Started
- Key Features
- Examples
- Local Open Source Models
- CrewAI x AutoGen x ChatDev
- Contribution
- 💬 CrewAI Discord Community
- Hire Consulting
- License
joaomdmoura • GitHub - joaomdmoura/crewAI: Framework for orchestrating role-playing, autonomous AI agents. By fostering collaborative intelligence, CrewAI empowers agents to work together seamlessly, tackling complex tasks.
Humans are bad at coming up with search queries. Humans are good at incrementally narrowing down options with a series of filters, and pointing where they want to go next. This seems obvious, but we keep building interfaces for finding information that look more like Google Search and less like a map.
All information tools have to give users some... See more
All information tools have to give users some... See more
thesephist.com • Navigate, don't search
ANY
LLM of your choice, statistical methods, or NLP models that runs
locally on your machine
:
- G-Eval
- Summarization
- Answer Relevancy
- Faithfulness
- Contextual Recall
- Contextual Precision
- RAGAS
- Hallucination
- Toxicity
- Bias
- etc.
GitHub - confident-ai/deepeval: The LLM Evaluation Framework
Overview
Loki is our open-source solution designed to automate the process of verifying factuality. It provides a comprehensive pipeline for dissecting long texts into individual claims, assessing their worthiness for verification, generating queries for evidence search, crawling for evidence, and ultimately verifying the claims. This tool is... See more
Loki is our open-source solution designed to automate the process of verifying factuality. It provides a comprehensive pipeline for dissecting long texts into individual claims, assessing their worthiness for verification, generating queries for evidence search, crawling for evidence, and ultimately verifying the claims. This tool is... See more
Libr-AI • GitHub - Libr-AI/OpenFactVerification: Open-source solution designed to automate the process of verifying factuality
The exact metrics we use depend on the application — our main goal is to understand how users use the feature and quickly make improvements to better meet their needs. For internal applications, this might mean measuring efficiency and sentiment. For consumer-facing applications, we similarly focus on measures of user satisfaction - direct user... See more
