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How Large Language Models Work: Three Tailored Explanations For an Experienced Software Engineer What makes LLMs fascinating from a systems perspective is that they're essentially massive distributed computing graphs that learn to compress and reconstitute information through backpropagation.

For the Experienced Software Engineer Think of an LLM as a lossy compression function trained on a massive corpus. During training, you're essentially building a statistical model of "given this sequence of tokens, what's the probability distribution over the next token?"

Collaborative Document Editor: Architecture Review Failure Modes Analysis WebSocket Connection Failures Issue: Client connections can fail due to network issues or server restarts, causing lost updates.

Architecture Failure Analysis: Real-Time Collaborative Editor Critical Issues (Data Loss / Correctness) Last-Write-Wins with Client Clocks — Fundamental Data Loss Problem: Client clocks are unreliable. They drift, can be manually set, and differ across timezones.

Analysis: The 3 Weakest Claims in MindMeld AI's Pitch Deck "MindMeld AI reads your brainwaves to predict what you want to type before you think it" Why it's weak: This claim makes a scientifically impossible promise.

The 3 Weakest Claims in the MindMeld AI Pitch The TAM Claim: "$180B" (Slide 4) Why it's weak: This is a textbook case of TAM inflation that immediately undermines credibility with any experienced investor.