vishalsachdev

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

vishalsachdev

# 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

vishalsachdev

name	ralph-playbook
description	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.
license	Apache-2.0
metadata	author version source Clayton Farr 1.0 https://github.com/ClaytonFarr/ralph-playbook
compatibility	Requires bash, git, and Claude CLI. Best suited for projects with test suites and build validation.

vishalsachdev

name	ai-agent-evaluations
description	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.
license	MIT
metadata	author version source Anthropic 1.0 https://www.anthropic.com/engineering/demystifying-evals-for-ai-agents
compatibility	Framework-agnostic. Works with Harbor, Promptfoo, Braintrust, LangSmith, and Langfuse. Requires test framework and model access.

vishalsachdev

Here is a complete, formatted Markdown file ready to be copied and pasted directly into a GitHub Gist.

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Prediction Markets & Analytics: A Portfolio Guide for Gies MSBA Students

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.

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vishalsachdev

title	Multipass on macOS: My Agentic Flywheel Ubuntu VM Setup
date	2026-01-11
publication	The Hybrid Builder
notes	AI-written; collaboration recap

Multipass on macOS: My Agentic Flywheel Ubuntu VM Setup

I didn’t start last night by saying “I need a VM.”

vishalsachdev

BADM 350 — Technology & AI Strategy (Proposed Core Course Syllabus)

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).

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vishalsachdev

Conversation excerpt (from: "can you explore a local video understanding model...")

USER — 2026-01-15T04:13:13.017Z

[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]

ASSISTANT — 2026-01-15T04:14:33.509Z

[[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.

vishalsachdev

AgentX README Review & Improvement Suggestions

AgentBeats Competition - Phase 1 (Green Agent)

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Executive Summary

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.

vishalsachdev

Week 2 Instructor Guide: Competitive Advantage

In-class reference for Session 1 (Tue) + Session 2 (Thu)

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Session 1: Strategy Frameworks + Zara Case

Time: 75 min | Date: Tue, Jan 27, 2:00 PM