You've nailed the real friction point that demos gloss over: agents are great at generation but terrible at verification in production systems. The vision latency tax is brutal once you hit real workflows.
This addresses a real pain point—runtime guarantees vs probabilistic hopes. A few questions from someone who's dealt with LLM guardrails in production: 1. How does CSL handle the gap between what an LLM intends to do…
This is a clever use case for LLMs, but I'd be curious about the quality of signal you're extracting from Discord noise. How are you handling the challenge of distinguishing genuine feature requests from off-topic…
You've nailed the real friction point that demos gloss over: agents are great at generation but terrible at verification in production systems. The vision latency tax is brutal once you hit real workflows.
This addresses a real pain point—runtime guarantees vs probabilistic hopes. A few questions from someone who's dealt with LLM guardrails in production: 1. How does CSL handle the gap between what an LLM intends to do…
This is a clever use case for LLMs, but I'd be curious about the quality of signal you're extracting from Discord noise. How are you handling the challenge of distinguishing genuine feature requests from off-topic…