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Vibe Coding2026-08-12 · source-backed
AGPL-3.0, ~570 stars, replacing the usual pile of small tools with a single command_run macro so the model builds a multi-step execution tree in one LLM turn (GitHub). Self-reported across 270 sessions on 20 DeepSWE v1.1 tasks: balanced config at 35.8% fewer turns and 31.1% fewer tokens with 80% success versus 63.3%; direct config at 69.1% fewer turns and 77.5% fewer tokens at 65% success. Vendor-authored numbers on 20 tasks, no replication. The architectural bet, fewer and fatter tools, is testable in an afternoon and worth an afternoon.
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LLM uses OpenAI / Shared entities / Same source domain / Shared topic / Earlier coverage
Linked by a graph relationship (LLM uses OpenAI); both cover Codex CLI, GitHub; reported by the same outlet (github.com).
Linked by a graph relationship (LLM uses OpenAI); both cover GitHub, LLM; reported by the same outlet (github.com).
LLM uses OpenAI / Shared entities / Same source domain / Earlier coverage / Tension
Linked by a graph relationship (LLM uses OpenAI); both cover GitHub, LLM; reported by the same outlet (github.com).
LLM uses OpenAI / Shared entity: GitHub / Same source domain / Shared topic / Earlier coverage
Linked by a graph relationship (LLM uses OpenAI); both cover GitHub; reported by the same outlet (github.com).
headroom uses LLM / Shared entity: GitHub / Same source domain / Shared topic / Earlier coverage
Linked by a graph relationship (headroom uses LLM); both cover GitHub; reported by the same outlet (github.com).
LLM uses OpenAI / Shared entities / Shared topic / Earlier coverage
Linked by a graph relationship (LLM uses OpenAI); both cover GitHub, LLM; overlapping topics (task, token).
Codex CLI uses MCP / Shared entities / Same source domain / Shared topic / Earlier coverage / Tension
Linked by a graph relationship (Codex CLI uses MCP); both cover Codex CLI, GitHub, LLM; reported by the same outlet (github.com).
LLM uses OpenAI / Shared entity: LLM / Shared topic / Earlier coverage / Tension
Linked by a graph relationship (LLM uses OpenAI); both cover LLM; overlapping topics (agent, codex, tool).