Archive position — measured, not model output
0 likes on Devpost
2,264 of the 7,856 archived projects have more likes, and 5,592 share exactly 0 — so this project's #2,757 place in the like-ranked listing is a tie-break inside that group, not a ranking.
Projects (log scale)
Likes on Devpost. ▲ marks this project's group.
Show the figures
| Likes | Projects | Share of archive |
|---|---|---|
| 0 | 5,592 | 71.2% |
| 1 | 1,758 | 22.4% |
| 2 | 285 | 3.6% |
| 3–4 | 132 | 1.7% |
| 5–9 | 75 | 1.0% |
| 10+ | 14 | 0.2% |
Executive Summary
What the company appears to be
AsOf is a self-reported project submitted to the OpenAI 2026 hackathon. The description states it is a tool for "decision-time checks that separate true facts from actions they do not justify." It was built using OpenAI's API (codex, gpt-5.6), React, TypeScript, and other frontend technologies.
What changed
There is no evidence of prior version or evolution — this is the first public statement about the project.
The single most important open question
What is the actual use case for "decision-time checks"? The description does not clarify whether this is a tool for fact-checking, decision support, or something else entirely. Without further detail, it's unclear what problem it solves or how it would be used in practice.
Commercial due-diligence read
The project is in an early stage with no evidence of traction, revenue, customers, or product-market fit. It appears to be a hackathon submission with limited commercial viability as described.
What The Product Actually Is
The description states that AsOf is a tool for "decision-time checks that separate true facts from actions they do not justify." This is the only claim about what the product does.
Evidence
- Tagline: “Decision-time checks that separate true facts from actions they do not justify.”
- Built with: codex, gpt-5.6, openai-responses-api, react, render, typescript, vite, vitest, zod
Inference Based on the technologies used (especially OpenAI APIs and React), it is likely a web-based application that uses AI to analyze or validate information in real-time during decision-making.
Not evidenced
- What specific decisions or actions are being checked.
- Whether this is a fact-checking tool, a decision support system, or something else.
- How the AI integrates with user workflows or interfaces.
Positioning & Claim Evolution
The description states only one claim: “Decision-time checks that separate true facts from actions they do not justify.”
Evidence
- Tagline: “Decision-time checks that separate true facts from actions they do not justify.”
Inference This suggests a positioning around truth validation or decision support, possibly in high-stakes environments where accuracy of information is critical.
Not evidenced
- Whether this is a new or repositioned idea.
- How the product evolved from an initial concept.
- Any prior claims or positioning statements.
- The intended audience or market segment beyond "decision-time."
Target Customer & ICP
The description does not state who the target customer is.
Evidence
- No mention of specific users, roles, or industries.
- No indication of whether this is for individuals, teams, enterprises, or developers.
Inference Given that it uses OpenAI APIs and React, it may be aimed at developers or technical professionals who are building AI-powered tools or decision systems.
Not evidenced
- Specific customer personas.
- Industry verticals or use cases.
- Customer needs or pain points addressed.
- Ideal customer profile (ICP) beyond a general developer audience.
Business Model & Pricing Evidence
There is no evidence of any business model or pricing structure.
Evidence
- No mention of monetization, subscriptions, licensing, or fees.
- No indication of whether the tool will be offered as SaaS, freemium, or otherwise.
Inference If this is a web-based tool, it might be intended for a SaaS model, but no evidence supports that assumption.
Not evidenced
- Revenue streams.
- Pricing tiers or plans.
- Monetization strategy.
- Customer acquisition costs or lifetime value.
Technical & Delivery Signals
The project was built using the following technologies:
Evidence
- Built with: codex, gpt-5.6, openai-responses-api, react, render, typescript, vite, vitest, zod
Inference This indicates a modern frontend stack with AI integration and testing frameworks. It suggests a developer-focused tool or prototype.
Not evidenced
- Whether the tool is production-ready or scalable.
- How it handles data privacy or security.
- Backend architecture or infrastructure.
- Deployment or hosting strategy beyond "render."
Traction & Maturity Signals
There is no evidence of traction, adoption, or maturity.
Evidence
- Submitted to a hackathon (OpenAI 2026).
- Team size: 1 person (Rohith Mahendarkar).
- No mention of users, customers, or usage metrics.
- No public product, website, or marketing presence.
Inference This is likely an early-stage prototype or proof-of-concept, not a mature product.
Not evidenced
- User base or customer engagement.
- Product roadmap or feature set.
- Metrics like retention, usage, or revenue.
- Any form of product-market fit.
Competitive Context
There is no evidence of competitive analysis or positioning in the market.
Evidence
- No mention of competitors or similar tools.
- No indication of how this differs from existing solutions.
Inference Given its AI-based nature and focus on decision validation, it may compete with fact-checking tools, AI assistants, or decision support platforms, but no evidence supports this.
Not evidenced
- Competitor landscape.
- Market size or opportunity.
- Competitive advantages or differentiation.
Key Risks & Red Flags
Several key risks and red flags are present due to the lack of detail:
Evidence
- No product-market fit or traction evidence.
- Only one team member, suggesting limited development capacity.
- Submitted to a hackathon — not a commercial product.
- No clear use case or problem definition.
Inference The project may be too early to assess viability, and the lack of clarity around its purpose raises concerns about whether it addresses a real need.
Not evidenced
- Risk mitigation strategies.
- Market validation or user feedback.
- Financial sustainability or scalability plans.
Diligence Questions To Ask The Founders
- What specific decisions or actions are you aiming to validate with this tool?
- Who is the intended user, and what problem do they face that this solves?
- How does your AI integration work in practice? Is it a fact-checker, decision engine, or something else?
- What is your plan for monetization or product development beyond the hackathon?
- Have you tested this with real users or in real-world scenarios?
Investment/Partnership Verdict
Verdict Not evidenced.
The project is described as a hackathon submission with no evidence of traction, revenue, customers, or clear commercial viability. The description lacks sufficient detail to assess whether it has potential for investment or partnership.
Confidence Level Low — based on thin self-reported evidence only.
Inference If the founders can clarify the use case and demonstrate early traction or a clear path to product-market fit, this could evolve into a more viable opportunity. As-is, there is insufficient evidence to support any commercial due-diligence conclusion.
Source
Submitted to the OpenAI 2026 hackathon on Devpost. Project home on DevPost.
The analysis above was generated by a language model from the project's own one-line description. It is not independent research and contains no verified traction, revenue or customer data.