Stop AI coding agents from over-engineering. Vibe-to-Spec is the essential bridge between vague ideas and precise execution for Claude, Cursor, and Windsurf. Keep your AI on-spec, drastically reduce token waste, and transition "vibe coding" into stable production code.
I've been asked how Vibe Coding Translator compares to just using "Custom Instructions" in Cursor. The key difference is Structural Consistency:
Rigid Schema: We enforce a Markdown schema that agents find much easier to follow without drifting.
Context Management: It prevents AI from losing the "Ground Truth" during long sessions.
Predictable Output: Unlike instructions, which can be ignored, trans act as a hard boundary.
Token Efficiency: By providing a fixed trans, you stop the agent from burning credits on unasked-for refactors and hallucination loops.
(By the way, we also have an open-source companion!) ๐ก๏ธ
For those interested in the underlying logic, we've shared our "Vibe Stack" coding rules on GitHub. These are the Markdown-based rules we use to maintain consistency and save tokens during AI development: ๐ https://github.com/solune-lab/the-vibe-stack
Iโd love to hear: whatโs your biggest pain point when prompt engineering for long coding sessions?
Gemini ่ชชไบ
Hi Product Hunt! I'm the builder of Vibe Coding Translator. ๐ถ
I built this to solve a personal pain point: Claude hallucinating and Cursor burning tokens during long sessions.
Why Vibe Coding Translator?
Stop Logic Loss: Prevents AI from rewriting entire files and losing intent.
Reduce Token Waste: Stops expensive AI "drifts" and unasked-for refactors.
Intent Locking: Provides a rigid "Ground Truth" for any LLM to follow.
๐ PH Special Offer (+10 Extra Trans):
Direct Link: https://soluneai.com/vibe-coding-translator/?ref=LWK086
Referral Code: LWK086
Quick update for everyone! ๐
I've been asked how Vibe Coding Translator compares to just using "Custom Instructions" in Cursor. The key difference is Structural Consistency:
Rigid Schema: We enforce a Markdown schema that agents find much easier to follow without drifting.
Context Management: It prevents AI from losing the "Ground Truth" during long sessions.
Predictable Output: Unlike instructions, which can be ignored, trans act as a hard boundary.
Token Efficiency: By providing a fixed trans, you stop the agent from burning credits on unasked-for refactors and hallucination loops.
(By the way, we also have an open-source companion!) ๐ก๏ธ
For those interested in the underlying logic, we've shared our "Vibe Stack" coding rules on GitHub. These are the Markdown-based rules we use to maintain consistency and save tokens during AI development: ๐ https://github.com/solune-lab/the-vibe-stack
Iโd love to hear: whatโs your biggest pain point when prompt engineering for long coding sessions?