This feels like a natural evolution of the design-to-code workflow. Instead of generating code directly, generating structured prompts gives more control and keeps the human in the loop.
One of the biggest challenges with AI-generated UI is iteration — small visual tweaks often require regenerating large chunks of code. Having a visual editor that produces precise, minimal prompts could make the iteration loop much faster and more predictable.
This is a really interesting direction. Most current “memory” implementations for LLMs are essentially append-only logs or vector stores, but modeling things like salience, decay, and consolidation is much closer to how useful memory actually works.
One of the biggest challenges seems to be avoiding memory bloat while still preserving important long-term context. I’m curious how you balance forgetting vs retention — especially how salience is calculated and updated over time.
This makes sense. Most SAST tools have years of engineering behind them specifically for static analysis, while LLMs are general-purpose models trying to approximate reasoning from patterns.
The interesting question isn’t whether LLMs outperform SAST today, but whether they can complement them — for example, identifying logic-level issues, insecure design patterns, or unusual edge cases that rule-based tools might miss.
It feels like the future is hybrid: deterministic scanners for known classes of vulnerabilities, and LLMs for higher-level semantic and architectural analysis.
The real issue isn’t whether Claude hides actions or shows them. It’s that once you move from “assistant” to “agent”, observability becomes a hard requirement, not a nice-to-have.
When an agent can read, modify, and orchestrate multiple parts of a codebase, you need the equivalent of logs, traces, and diffs — not just summaries. Otherwise debugging becomes guesswork.
Traditional software became reliable only after we built strong observability tooling around it. Agent workflows will need the same evolution: clear execution traces, deterministic diffs, and full transparency into what happened and why.
This is interesting, but it also highlights a shift from “using tools to solve problems” to “optimizing usage of the tool itself.” I wonder how much of this will correlate with actual productivity vs just higher token consumption. Usage metrics are easy to measure, but value created is much harder.
One of the biggest challenges with AI-generated UI is iteration — small visual tweaks often require regenerating large chunks of code. Having a visual editor that produces precise, minimal prompts could make the iteration loop much faster and more predictable.