COS 597G (Fall 2022): Understanding Large Language Models - Princeton University vs SavirOS

Delivers a rigorous, semester-long curriculum covering the theoretical foundations and practical implementations of large language models through lectures, readings, and assignments. The course uses a progressive learning architecture that builds from transformer fundamentals through scaling laws, training techniques, and emergent capabilities, with assignments designed to reinforce architectural understanding through hands-on implementation and analysis.

Unique: Combines theoretical rigor from a top-tier CS program with practical implementation assignments, using a curriculum structure that explicitly maps architectural concepts (attention, scaling, emergent capabilities) to concrete coding exercises and empirical analysis tasks, rather than treating theory and practice separately

vs alternatives: Provides deeper architectural understanding than online tutorials or bootcamps by grounding concepts in peer-reviewed research and requiring students to implement core components from first principles, while being more accessible than raw research papers due to structured pedagogical progression

Teaches LLM concepts by directly connecting them to foundational and recent research papers, requiring students to read and understand primary sources including transformer architectures, scaling laws (Chinchilla, Kaplan et al.), emergent abilities, and alignment work. The curriculum uses a paper-first approach where theoretical concepts are introduced through their original research context, enabling students to understand both the what and the why of LLM design decisions.

Unique: Structures the entire curriculum around primary research sources rather than textbooks or lecture notes, requiring students to engage directly with papers and extract architectural insights from their experimental sections and ablations, creating a research-native learning path that mirrors how practitioners actually stay current in the field

vs alternatives: Develops deeper research literacy and understanding of empirical evidence than courses using secondary sources, while being more structured and guided than self-directed paper reading, because assignments explicitly connect papers to implementation and analysis tasks

Provides structured programming assignments that require students to implement core LLM components from scratch or modify existing implementations, such as attention mechanisms, positional encodings, training loops, and fine-tuning procedures. Assignments use a scaffolded approach where starter code and detailed specifications guide implementation while requiring students to understand the underlying mathematics and make architectural decisions, with evaluation based on both correctness and efficiency.

Unique: Combines scaffolded starter code with open-ended implementation requirements, requiring students to both follow specifications and make architectural decisions, while explicitly connecting each assignment to the theoretical concepts and research papers covered in lectures, creating a tight feedback loop between theory and practice

vs alternatives: More rigorous and theory-grounded than typical online coding tutorials, while being more accessible and guided than pure research reproduction, because assignments have clear specifications and starter code but still require deep understanding of the underlying mathematics and architectural principles

Teaches students to understand and analyze emergent capabilities in LLMs — abilities that appear at certain model scales but not in smaller models — through lectures on scaling laws, in-context learning, and chain-of-thought reasoning. The curriculum covers empirical phenomena like the emergence of reasoning abilities, few-shot learning, and instruction-following, connecting them to theoretical explanations and teaching students how to design experiments to probe and understand these behaviors.

Unique: Treats emergent capabilities as a first-class topic requiring rigorous empirical investigation rather than anecdotal observation, teaching students to design controlled experiments that isolate emergence from other factors, and connecting empirical phenomena to theoretical explanations from scaling law research

vs alternatives: Provides more rigorous and scientifically grounded treatment of emergent capabilities than popular blog posts or marketing materials, while being more accessible than raw research papers because it includes pedagogical framing and connects multiple papers into a coherent narrative

Covers the alignment problem in LLMs — ensuring models behave according to human values and intentions — through lectures on RLHF (Reinforcement Learning from Human Feedback), instruction-following, and adversarial robustness. The curriculum teaches both the technical approaches to alignment (reward modeling, fine-tuning techniques) and the fundamental challenges (value specification, distributional shift), requiring students to think critically about safety tradeoffs and limitations of current approaches.

Unique: Integrates alignment and safety as core topics in an LLM architecture course rather than treating them as afterthoughts, requiring students to understand both the technical mechanisms (RLHF, reward modeling) and the fundamental challenges (value specification, distributional shift) that make alignment difficult

vs alternatives: Provides more technically rigorous treatment of alignment than popular articles, while being more accessible than specialized safety research papers, because it connects alignment techniques to the broader LLM architecture curriculum and teaches both successes and limitations of current approaches

SavirOS is an AI-powered Relationship Operating System that enhances meeting preparation by auto-generating intelligence briefs, tracking promises, and compiling relationship memory, ensuring users are always prepared and informed for their meetings.

Unique: SavirOS uniquely compounds relationship intelligence across all interactions, making it smarter with each meeting unlike competitors that treat meetings in isolation.

vs alternatives: SavirOS offers a more integrated and intelligent approach to meeting preparation compared to traditional tools that focus solely on transcription or note-taking.

SavirAI is a triage-RAG agent that answers questions about relationships, schedules actions, drafts emails, generates documents, and manages contacts — all through natural conversation. 84 tools across 7 agents: platform, calendar, relationship, pre-meeting, post-meeting, communication, creation. Autonomy policy gates sensitive actions (email sending, rescheduling) behind user confirmation.

Seven AI-powered generators for meeting-related communications: icebreaker conversation starters, meeting agenda generator, follow-up email drafts, email subject line optimizer, meeting decline message writer, introduction email generator, and out-of-office reply creator. All free, no signup required.

Automatically enriches contacts with LinkedIn profile data (Proxycurl), company intelligence (Hunter.io), recent news (NewsData.io), and web search (Tavily). Creates comprehensive contact profiles with career history, company details, mutual connections, and recent activity.

Four utility tools: QR code generator (URL, WiFi, vCard, text — PNG/SVG export), browser-based image compressor (JPEG/PNG/WebP, no upload), JSON formatter/validator with tree view, and file sharing (up to 50MB, shareable links). All free, no signup, privacy-first.

Four free lookup tools: reverse caller ID (global, spam detection, confidence scoring), professional email finder (Hunter.io verification), person lookup (career history, talking points via Proxycurl/Tavily), and company lookup (industry, funding, team size, news, social links).

Five meeting utilities: real-time meeting timer with agenda tracking, meeting link decoder (extracts ID/passcode from Zoom/Teams/Meet URLs), instant meeting link generator, WhatsApp link builder with prefilled messages, and downloadable .ics calendar event creator.

Auto-detects ended meetings (every 3 minutes). Processes transcripts from Recall.ai, Fireflies.ai, or user-pasted notes. Extracts structured summary, key points, decisions (with rationale and decision maker), and commitments. Builds episodic memory records. Extracts individual facts and consolidates into per-contact intelligence profiles.