This source-evidence page is currently authored and reviewed in English.
Les descriptions produit actualisées et les dossiers techniques sont maintenus en anglais.
Produit et investissement
LaunchpadOS
Turn product judgement into a repeatable working system.
A consequential decision is spread across documents. Its assumptions, owner and reasons for reopening it are getting lost.
Product leaders, founders and multidisciplinary teams
Source toolkit under active development
- 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.
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 illustrative 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.
One inspectable artefact
Versioned skill files and product workflows with examples, rubrics and orchestration paths.
A current boundary
Skill inventory and structural depth are source scope, not proof of equal factual authority across every method or model.
How to start
Bring one product decision or workflow to a review. Generated artefacts support accountable leadership; they do not replace it.
These are source and research records. Illustrative examples do not establish deployment, integration, certification or customer outcomes.
Explorer le dossier technique et les preuves
Pont métier ↔ technologie · 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.
Il s'agit d'une interprétation du portefeuille et des sources, et non d'une affirmation de client, déploiement, certification ou résultat.
- 01
Pression métier
Customer, portfolio, economic, delivery, and technical decisions are dispersed across documents and tools, so their original evidence and invalidation logic disappear.
- 02
Décision produit
Define which product decisions matter, which evidence advances a gate, who owns the call, and what must cause the decision to reopen.
- 03
Dossier technique
Versioned skills, the Operated Product Decision Graph, Release Case boundaries, CLI and MCP surfaces, workflow state, evaluations, and audit records.
- 04
Retour des preuves
Changed assumptions, technical limits, release evidence, and field signals return to scope, economics, sequencing, and portfolio commitment.
Preuves issues du code source
Ce que le code source permet réellement d’établir
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.
Le relevé technique détaillé est maintenu en anglais.
- Maturité
- Source toolkit under active development
- Revue des preuves
- État du code au
Décisions produit et architecture
- 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.
Éléments vérifiables
- 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.
Limites des affirmations
- 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.
Les preuves du dépôt ne constituent pas des preuves client, de déploiement, de certification ou de résultats.
Tech Stack
Built With
Les technologies citées identifient des dépendances ou des adaptateurs du dépôt ; elles n'affirment ni intégration active ni déploiement.
- 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.
Work with Hyperion
Bring one product decision.
A decision review connecting options, evidence, trade-offs and the next test to an accountable owner.
Bring a short description of your situation, constraints and decision. We agree the scope, responsibilities and quote before work begins.
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 Mission