Your AgentX submission is technically excellent with comprehensive documentation, but needs agent-optimized presentation to maximize evaluation scores. The README covers the 5 judging criteria well, but doesn't lead with clarity for agent evaluation or include agent-friendly interfaces prominently.
Key Finding: Agents (especially reasoning agents) will parse your README first. Structure must be scannable and interface-centric.
[Telegram Vishal Sachdev (@svishal2001) id:5101503769 2026-01-15T04:13Z] can you explore a local video understanding model so we can be more carefree about usage. we have ollama available. Dont update from github, as if you have summarize cli, i am guessing clawdbot updates will trigger summarize cli updates [message_id: 513]
[[reply_to:513]] Yep — makes sense. Right now your Ollama install has only text/code models (no vision models yet), so “local video understanding” isn’t possible until we add at least one vision-capable model.
Proposed Title (catalog/branding): BADM 350: Technology & AI Strategy for Competitive Advantage
Credits: 3
Level: 300 (Undergraduate)
Proposed Positioning: Gateway course to the Information Systems major + core course option for the business undergraduate curriculum (recommended).
Prerequisites: None. (BADM 210/211 helpful but not required.)
Term Length: 15 weeks + final presentations (16 instructional weeks).
Here is a complete, formatted Markdown file ready to be copied and pasted directly into a GitHub Gist.
Target Audience: Students in the MS in Business Analytics (MSBA) program at Gies College of Business. Goal: Leverage prediction market data (Polymarket, Kalshi) to build standout portfolio projects that demonstrate skills in Big Data, Financial Analytics, and Storytelling.
| name | description | license | metadata | compatibility | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
ai-agent-evaluations |
Framework for designing, implementing, and iterating on evaluations for AI agents. Use for automated testing of coding, conversational, research, and computer use agents with code-based, model-based, and human graders. |
MIT |
|
Framework-agnostic. Works with Harbor, Promptfoo, Braintrust, LangSmith, and Langfuse. Requires test framework and model access. |
| name | description | license | metadata | compatibility | ||||||
|---|---|---|---|---|---|---|---|---|---|---|
ralph-playbook |
Implements Ralph workflow - an iterative AI-driven development loop using Jobs-to-be-Done (JTBD) specification, gap analysis, and autonomous building with backpressure validation. Use when building software products with deterministic LLM-based planning and implementation loops. |
Apache-2.0 |
|
Requires bash, git, and Claude CLI. Best suited for projects with test suites and build validation. |
| <!DOCTYPE html> | |
| <html lang="en"> | |
| <head> | |
| <meta charset="UTF-8"> | |
| <meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
| <title>Claude Code transcript - Index</title> | |
| <style> | |
| :root { --bg-color: #f5f5f5; --card-bg: #ffffff; --user-bg: #e3f2fd; --user-border: #1976d2; --assistant-bg: #f5f5f5; --assistant-border: #9e9e9e; --thinking-bg: #fff8e1; --thinking-border: #ffc107; --thinking-text: #666; --tool-bg: #f3e5f5; --tool-border: #9c27b0; --tool-result-bg: #e8f5e9; --tool-error-bg: #ffebee; --text-color: #212121; --text-muted: #757575; --code-bg: #263238; --code-text: #aed581; } | |
| * { box-sizing: border-box; } | |
| body { font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; background: var(--bg-color); color: var(--text-color); margin: 0; padding: 16px; line-height: 1.6; } |
| # This code helps you get a CSV roster of student names( Lastname, first name) along with their group names | |
| # using the CANVAS api. Every user's API gives them access based on information they can see inside canvas | |
| # The code below assumes CANVAS at Illinois, and requires two inputs: The API and the course_id | |
| import requests | |
| import json | |
| import csv | |
| # get a Canvas access token - https://canvas.illinois.edu/profile/settings --> Approved Integrations --> New Access token | |
| # Enter with your access token from Canvas in the API-Key field below |
| You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis. | |
| ## Core Principles | |
| 1. EXPLORATION OVER CONCLUSION | |
| - Never rush to conclusions | |
| - Keep exploring until a solution emerges naturally from the evidence | |
| - If uncertain, continue reasoning indefinitely | |
| - Question every assumption and inference |