This source-evidence page is currently authored and reviewed in English.
商业摘要已提供本语言版本。产品详细流程和技术证据以英语维护。
产品与投资决策
LaunchpadOS
将产品判断转化为可重复使用的工作系统。
一项重要决策散落在多份文档中，其假设、负责人和重新审视的理由逐渐丢失。
产品负责人、创始人和跨学科团队
持续开发中的软件工具包
- Decision
- Advance, change or stop the proposed feature
- Evidence
- User need, technical constraints, economics
- Uncertainty
- Assumptions and dependencies to examine
- Reopen when
- A named assumption changes or evidence arrives
Explore the reasoning
Name the decision, accountable owner and credible alternatives—including keeping the current approach.
- Engage now
- A decision review connecting options, evidence, trade-offs and the next test to an accountable owner.
- Inspect
- Source toolkit under active development
- Versioned skill files and product workflows with examples, rubrics and orchestration paths.
- Still to validate
- Skill inventory and structural depth are source scope, not proof of equal factual authority across every method or model.
- Verify the applicable path-scoped licence and evidence rights, then expand independent method and output evaluation beyond the currently measured subset.
源码审查日期
The decision behind the system
What would make you change this product decision?
A product-management toolkit connecting skills, workflows, decision graphs and evidence. Make assumptions, dependencies and review triggers visible as a product moves from discovery to delivery.
LaunchpadOS structures reusable product judgement. TransformationOS explores organisational missions; E2A carries a consulting engagement from discovery to handover.
Reusable product workflows
Versioned skills and orchestration support discovery, strategy and delivery work through CLI, API and model-context interfaces.
Decision graphs
OPDG connects evidence, assumptions, dependencies, reversibility and review triggers so a decision can be revisited when conditions change.
Release-case reasoning
Configuration-bound Physical AI release cases expose missing evidence and human review gates. A structural verdict does not authorise deployment.
Read the complete system walkthrough
Frame
Name the decision, accountable owner and credible alternatives—including keeping the current approach.
Connect
Attach evidence, assumptions, dependencies and reversibility. Make missing information visible.
Challenge
Review the reasoning and relevant gates. Structural coherence does not establish factual correctness or permission to deploy.
Decide
Record the judgement, unresolved questions and the condition that should reopen the decision.
Inspect the source, test conditions and operating limits in the technical record.
查看技术记录与证据
业务 ↔ 技术桥梁 · Category operating system
Turn product judgement into an operating record without outsourcing authority to a model.
Makes full-lifecycle product judgement versioned, inspectable, and executable.
这是对项目组合与源代码的解读，并非客户、部署、认证或成果声明。
- 01
业务压力
Customer, portfolio, economic, delivery, and technical decisions are dispersed across documents and tools, so their original evidence and invalidation logic disappear.
- 02
产品决策
Define which product decisions matter, which evidence advances a gate, who owns the call, and what must cause the decision to reopen.
- 03
技术记录
Versioned skills, the Operated Product Decision Graph, Release Case boundaries, CLI and MCP surfaces, workflow state, evaluations, and audit records.
- 04
证据回流
Changed assumptions, technical limits, release evidence, and field signals return to scope, economics, sequencing, and portfolio commitment.
仓库／运行时边界
本页解读的是独立项目仓库中的源代码。源代码存在，并不表示该项目作为服务运行在 hyperion-consulting.io 内。
访客及客户数据的 AI 处理限于通过受治理的欧盟端点使用 Mistral。所有者授权的 Helio、搜索及 Product Council 流程已启用。独立的公开来源编辑流程使用 Anthropic，并以受治理的 Mistral 作为备用；该流程不接收网站咨询、客户文档或访客对话。远程 AI 不可用时，确定性工具仍可使用。
源码证据
源码实际能够证明什么
A versioned product-management toolkit combining skills, orchestration, decision graphs, evaluation, APIs and MCP surfaces. The current source adds evidence-linked product decisions and configuration-bound Physical AI release cases; generated judgement still needs accountable review.
详细技术快照以英文维护。
- 成熟度
- Source toolkit under active development
- 证据审查
- 源码快照截至
产品与架构决策
- Compile reusable product practices into explicit skills, rubrics, and workflow stages.
- Keep artefact generation, quality evaluation, memory, and trust records as separate concerns.
- Treat Physical AI release readiness as an assurance verdict with human gates, not an automated deployment permission.
可核验的证据
- Versioned skill files and product workflows with examples, rubrics and orchestration paths.
- Python CLI, API and MCP surfaces with workflow, evidence, audit and quality-test suites.
- OPDG decision graphs linking evidence, assumptions, dependencies, reversibility and review triggers, plus bounded release-case fixtures.
声明边界
- Skill inventory and structural depth are source scope, not proof of equal factual authority across every method or model.
- Platform and tool surfaces require path-specific configuration and verification; source presence does not establish identical behavior or external-system access.
- Software and method licences are path-scoped; evidence and trademark terms remain separate. Structural coherence does not establish factual correctness, deployment permission or legal conformity.
代码仓库证据不等同于客户、部署、认证或成果证据。
Tech Stack
Built With
所列技术仅标识仓库依赖项或适配器表面，并不证明存在活动集成或部署。
- Backend
- Python 3.12+, FastAPI, Click CLI, Redis caching, Pydantic v2, litellm multi-provider abstraction
- AI / LLM
- Provider-adapter and routing source for Anthropic, OpenAI, Mistral and local-model paths, plus retrieval components; model availability and behaviour depend on configuration and current provider terms
- Frontend
- React 18, Vite, Tailwind CSS, skill browser UI, brief input, output panel, history panel
- Integrations
- Notion (pages), Linear (issues), Jira (tickets), GitHub (releases), Figma (specs), Miro (boards), Slack (digests), Confluence (wikis)
- Infrastructure
- Docker Compose (5 services), Kubernetes Helm, AWS ECS Fargate, GCP Cloud Run, nginx reverse proxy, API key scoping
- Quality & Evaluation
- Pytest structural skill audit, selected rubric and evaluation fixtures, benchmark harnesses, and property-based tests; factual method authority requires separate source assurance
Evaluation & access
Evaluation
Get Started with LaunchpadOS
Contact me to evaluate where LaunchpadOS can support your product practice, what still requires human product leadership, and which integrations would need separate verification.
Request a Demo
Inspect the dated skill inventory, virtual-role and workflow source, and the innovation pipeline implementation. The demo separates what is present in source from what is configured, tested and ready for accountable use.
Physical AI Product Leadership Mission
Use the toolkit inside an accountable product mission.
Hyperion can use selected LaunchpadOS source patterns while leading one evidence-backed product milestone. The commercial mandate remains the Physical AI Product Leadership Mission; LaunchpadOS is supporting R&D, not a fourth offer or a promise of autonomous delivery.
Explore the Product Leadership MissionSource toolkit under active development
A product decision you can hand over.
For a technical founder
Structure a customer/problem brief, compare alternatives and record the assumptions behind an MVP or industrial pilot. Hyperion selects and reviews the relevant workflows; the founder owns business commitments.
For an existing product team
Organise approved field evidence, draft requirements and prepare a product-to-engineering handoff. Keep sources, open assumptions, decision owners and the next experiment attached to the record.
Prompt roles provide perspectives and workflow stages. They do not supply engineers or independently accountable employees. The current LaunchpadOS–TransformationOS handoff is local file exchange, not a unified production service.
Explore the Product Team Design Canvas