chunhualiao

import openai
import time
import json
import logging
# read the key from environment variable stored in a file

# Initial Prompt
prompt = "Hello, Let's chat about some hot topic in the world. Could you please pick a topic on your mind?"

chunhualiao

For swe-bench, what are the differences among Lite, Verified, and Full? Which one should I look at if I care the overall quality of a system being benchmarked? why?

SWE-bench, a benchmark for evaluating AI models on software engineering tasks, comes in three main variants: Full, Lite, and Verified. Each variant has distinct characteristics and purposes:

SWE-bench Full

The original SWE-bench dataset contains 2,294 issue-commit pairs across 12 Python repositories[4]. It provides a comprehensive and diverse set of codebase problems verifiable using in-repo unit tests.

SWE-bench Lite

chunhualiao

extracted from my converstation with notebooklm:

Challenges

This paper presents AutoCodeRover, a system that combines Large Language Models (LLMs) with code search capabilities to automatically resolve GitHub issues and achieve autonomous program improvement. This addresses the challenge of moving beyond just automated coding to encompass software maintenance (e.g., bug fixing) and evolution (e.g., feature additions). Automating the resolution of real-life software issues is challenging for several reasons:

• Handling ambiguous natural language requirements in issue descriptions.
• The need to automatically repair generated code for trustworthiness.
• The large amount of time developers spend manually fixing bugs.

chunhualiao

There are several platforms where people can discuss arXiv papers and provide reviews or comments:

1. alphaXiv: Developed by Stanford AI Lab students, alphaXiv is an open discussion forum for arXiv papers. Users can post questions and comments directly on any arXiv paper by changing "arXiv" to "alphaXiv" in the URL[1][3].

2. Hugging Face: This platform allows users to comment on arXiv papers, providing a space for discussion and feedback[2].

3. SciRate: A site that enables users to rate and comment on papers from arXiv[5].

4. PubPeer: While not specifically for arXiv papers, PubPeer follows a similar model, allowing community-driven peer review and discussions on scientific publications[3].

chunhualiao

The OpenAI o1 model is an improved version of the o1-preview model, with several key differences and enhancements:

Capabilities

Image Analysis: The full o1 model introduces the ability to analyze and respond to uploaded images, a feature not present in o1-preview[4][7].

Enhanced Reasoning: The o1 model demonstrates a 34% reduction in major errors on difficult problems compared to o1-preview[7].

Expanded Context Window: O1 boasts a larger context window of 200K tokens and a maximum output of 100K tokens, providing more capacity for complex and detailed responses[4].

chunhualiao

Here’s a table comparing the Ring Protect Plans to help you decide which one suits your needs:

Feature	Free Plan	Protect Basic	Protect Plus	Protect Pro
Monthly Cost	$0	$4.99	$10	$20
Annual Cost	$0	$49.99	$100	$200
Video Recording & History	Live view only (no history)	180 days of video history	180 days of video history	180 days of video history
**Number o

chunhualiao

from openai import OpenAI
import os

client = OpenAI(
  base_url="https://openrouter.ai/api/v1",
  api_key=os.getenv("OPENROUTER_API_KEY")
)

try:

chunhualiao

DSPy is the framework for programming—rather than prompting—language models. It allows you to iterate fast on building modular AI systems and offers algorithms for optimizing their prompts and weights, whether you're building simple classifiers, sophisticated RAG pipelines, or Agent loops.

DSPy stands for Declarative Self-improving Python. Instead of brittle prompts, you write compositional Python code and use DSPy to teach your LM to deliver high-quality outputs.

This lecture excerpt by Omar Khattab introduces Compound AI Systems, a modular approach to building AI systems using Large Language Models (LLMs) as components. The presentation highlights the limitations of monolithic LLMs, emphasizing the advantages of Compound AI Systems in terms of improved reliability, controllability, transparency, and efficiency. Khattab then details DSPy, a framework for creating these modular systems by expressing them as programs with natural-language-typed modules, and discusses methods for optimizing th

chunhualiao

Several AI conferences and programs offer tracks or opportunities for high school students to submit papers or participate in research:

1. NeurIPS 2024: Introduced a new track for high school students to submit research on machine learning for social impact, with finalists presenting virtually and winners attending an award ceremony[5][9].

2. TAAI 2024: Features a "High School Student Session" for submitting projects on computational intelligence and AI applications[6].

3. EAAI-25: Encourages submissions related to AI education, though not exclusively for high school students[2].

These opportunities aim to engage younger audiences in AI research.

chunhualiao

How do AlphaFold 1, 2, and 3 work?

AlphaFold 1, 2, and 3 represent significant advancements in protein structure prediction using artificial intelligence. Here's an overview of how each version works:

AlphaFold 1

AlphaFold 1 introduced a deep learning approach to protein structure prediction:

• It uses a convolutional neural network trained on Protein Data Bank (PDB) structures[1].
• The network predicts distances between residues, creating distograms based on multiple sequence alignment (MSA) features[1].