Show HN: An experiment in automated long-horizon builds with AMQP 0.9.1
My cofounders and I are in the process of spinning up an AI integration lab, and over the last year we've consumed billions of tokens across multiple American, European, and Chinese models as a force multiplier for building out our products. However, despite our extensive technical expertise, we've still discovered there are long-term ramifications to heavy agentic AI usage. So I decided to run an experiment for us: given a specification, can an LLM fully, concretely, and correctly implement an
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Recorded metrics
points · 1 votes
2026-11-03 12:00:07.772Z
cumulative since publication
Research analysis
Project record
Repository: siennathesane/switchboard. Source account: My cofounders and I are in the process of spinning up an AI integration lab, and over the last year we've consumed billions of tokens across multiple American, European, and Chinese models as a force multiplier for building out our products. However, despite our extensive technical expertise, we've still discovered there are long-term ramifications to heavy agentic AI usage. So I decided to run an experiment for us: given a specification, can an LLM fully, concretely, and correctly implement an
Observed adoption & comparisons
Source votes: 1 votes (Nov 3, 2026 UTC; cumulative since publication); previous same-scope observation: 1 (Nov 3, 2026 UTC). These observations do not establish demand growth. Recent records to compare: “google/skills” (GitHub Trending, Nov 5, 2026); “mattpocock/skills” (GitHub Trending, Nov 5, 2026). Shared themes are navigation hints; they have not been verified as the same need or independent corroboration.
Adoption decision
Evaluate it against the tool or manual workflow it would replace. The stored record supports discovery; it does not yet establish reliability, lower operating cost or commercial adoption.
Unverified requirements
Not verified: license permissions, required hardware, installation steps, maintenance status and a representative task with its failure cases.
Next test
Choose one real task and a current alternative. Record the commit, environment, input, output, elapsed time and failure behavior; retain the result before recommending adoption.