👋 Hello Product Hunt! Now that anyone can ship code quickly, the bottleneck has moved upstream, to the question of what gets built and why . Rezonant sits above tools like Cursor, Claude Code and GitHub Copilot. It gives product managers a live, multiplayer workspace to turn product ideas into structured specs and tasks that AI coding agents can actually execute - grounded in the codebase, not floating around in Notion docs and Slack threads. Capture ideas with our Chrome extension, Rezonant Alter. Hit record, point to anything on your live product, prototypes or designs, and talk through your thinking out loud, just like you would with a dev or designer. Alter captures what you said and what you pointed to, maps it to your codebase, and surfaces it as a spec or PRD ready to edit, comment on, break into tasks, and ship. Try it for free: https://www.rezonant.app/ Can’t wait to see what you build!
创始人 / Maker 评论
优先展示 Product Hunt 上对理解条目有帮助的人类文本。
痛点为 AI 基于上游原始证据的初步提炼;未包含额外中国市场检索。
产品团队在将模糊的产品想法转化为可执行的工程任务时,面临严重的沟通和文档碎片化问题。原始想法通常散落在Notion文档、Slack线程和口头讨论中,缺乏与代码库的关联,导致PM、工程师、设计师和AI编码代理之间难以对齐。现有流程依赖人工整理和跨工具同步,容易造成信息遗漏、版本混乱和重复劳动,最终拖慢从想法到上线的速度。
精选 Product Hunt 讨论
保留原始讨论语境,用来交叉验证上游条目的真实反馈。
Is this for beginner teams that don’t have business analysts? What you described is usually done by a business analyst. Also, now everyone uses Claude Code, Claude Design, Figma, and other tools, so often you need not just a comment but specifically a comment in, for example, Figma, so that Claude Code can later understand it and apply the change. So at the current stage of AI development, I can’t imagine how to do this without a human.
Congrats on the launch! ChatGPT-vs-Rezonant comparison on the landing page is the best pitch I've seen this week, where Rezonant pulls the actual src/services/integrations/ path instead of giving a generic playbook. We're building in the SDLC execution space at Revolte (the agent runs from spec through deploy), so the spec-quality problem hits us directly downstream. Curious how you're handling the codebase index — persistent semantic index per repo, or retrieval at refinement time? We went persistent and the freshness problem is harder than I expected.
"Turn messy ideas into structured specs" is a deceptively hard problem because the messiness is where domain knowledge lives — strip too much of it out and the spec becomes generic; leave it in and the agent gets confused. I work on ModeLoop in financial modeling and the same pattern shows up there: the difference between a model an agent can build and one a human can defend is usually in the assumptions, not the cells. Curious how you've handled assumption capture in Rezonant.
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