Markov

Markov (Markov Studios) is a San Francisco-based Y Combinator (Summer 2026) startup building data and environments for training computer-use AI. The company captures how people actually use computers - screen recordings with synchronized mouse and keyboard inputs, annotations, and open datasets across real workflows in tools like Salesforce, Blender, and Photoshop, plus gaming data from Valorant, Minecraft, and GTA. Markov sources high-quality tasks and data to train the next generation of computer-use and reinforcement-learning models, releasing much of its work in public on Hugging Face (150k+ dataset downloads). Founded in 2026 by Dev Mandal (CEO) and Harish Ashok, Markov distributes its datasets through Hugging Face rather than a first-party API; this profile currently tracks no public API surface.

Markov is profiled on the APIs.io network. Tagged areas include Company, Artificial Intelligence, Machine Learning, Data, and Computer Use.

🌐 Visit website 📡 Profile data on GitHub

3.4/100 minimal ▬ flat Agent 0/100 human only Front door AI 3/6 light Full breakdown ↓
scored 2026-11-06 · rubric v0.27.0 · Show this score on your site →

AccessNo public API

0 APIs

Company Artificial Intelligence Machine Learning Data Computer Use Reinforcement Learning Datasets Training Data

Kin Score

Kin Score How this is scored →
scored 2026-11-06 · rubric v0.27.0

Composite quality — 3.4/100 · minimal

Contract Quality 0.0 / 21

Developer Ergonomics 0.0 / 17

Access Clarity 0.0 / 17

Operational Transparency 0.0 / 11

Contract Governance 0.0 / 10

Discoverability 4.1 / 9

Regulatory Posture 0.8 / 15

Agent readiness — 0/100 · human only

Machine-Readable Contract 0 / 18

Agentic Access Contract 0 / 10

Documented Reversibility 0 / 6

MCP Server 0 / 12

Machine-Readable Auth 0 / 10

Idempotency 0 / 9

Stable Error Semantics 0 / 8

Request/Response Examples 0 / 7

Rate-Limit Signaling 0 / 7

Typed Event Surface 0 / 6

Agent Skills 0 / 5

Well-Known Catalog 0 / 4

Consent & Bot Identity 0 / 3

A2A Agent Card 0 / 8

Dry-Run / Simulate Mode 0 / 4

Delegated User Identity 0 / 6

Protected Resource Metadata 0 / 5

Registration Without a Human 0 / 6

Agentic Commerce Well-Known Document 0 / 5

Pagination 5 / 5

Distinct Operations 3 / 3

MCP Tool Quality 6 / 6

Regulatory Posture applies to this provider. Its tags matched the Horizontal (data, software, accessibility, platform) regime, so Regulatory Posture carries 15 points of the composite. If this regime is wrong for your business, say so on your provider repo — the applicability map is public and we will correct it.

Create-or-Update Ergonomics could not be measured. We hold no machine-readable contract for this provider to read, so there is nothing to measure a write surface against. Excluded rather than scored zero: never-measured and measured-empty are different facts. Publishing an OpenAPI is what makes this facet — and several others — scorable at all.

The six quality facets above are damped to 85 points between them, because the conditional facet above carries the other 15. That is why each facet's contribution is shown against a damped maximum: raising a quality facet moves the composite by 85% of its nominal weight, not 100%. The full arithmetic is at apis.io/rating/.

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Agent readiness — checks not met, largest first (exact, from this profile):

Machine-Readable Contract +18 pts — api_specs includes an OpenAPI document
MCP Server +12 pts — deployment.mode + probe verdict on the mcp/ manifest, not the pointer; an unprobed slug falls back to authorship
Agentic Access Contract +10 pts — agentic_access artifact present (x-agentic-access), graded by author
Machine-Readable Auth +10 pts — a served openid-configuration/oauth-authorization-server carrying `issuer` (served), else the securitySchemes class in the provider OpenAPIs — bound (mutualTLS, DPoP, Signature) > negotiable (oauth2 authorizationCode/clientCredentials, openIdConnect) > bearer (http bearer, apiKey)
Idempotency +9 pts — OpenAPI mutating operations declare an idempotency key — a header, a request-body property, or a named de-duplication token whose own description says so (verified); else common[].type matches /idempoten/i (documented)
Stable Error Semantics +8 pts — OpenAPI $refs a shared error schema across 4xx/5xx responses (verified), else common[].type includes ErrorCatalog / ErrorCodes / DeclineCodes

13 more unmet checks in the Agent Readiness table above.

Kin Score — facets with the most composite points still available (weight × headroom):

Contract Quality scores 0.0 — up to +25.0 composite points
Developer Ergonomics scores 0.0 — up to +20.0 composite points
Access Clarity scores 0.0 — up to +20.0 composite points

Each check links to what satisfies it. Publish the artifact, or tell us where it already lives, and the score moves on the next scoring run.

Improve this rating by publishing the missing artifacts — every area above can be raised, and the full rubric is at apis.io/rating/. Every facet and dimension name above is a link: it opens that measurement's own page — what it means, the exact checks that feed it, how the whole catalog distributes on it, and the providers at the top of it. This rating is computed from the provider profile at artifacts.apis.io/markov/apis.yml: open an issue on github.com/api-evangelist/markov to ask a question or point us at an artifact you publish. Submit an artifact on GitHub — free → Manage your own listing — the Influence plan, $499/mo →

Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Markov Domain Security

TLSv1.3 · HSTS · DMARC

SECURITY

Resources

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity

DomainSecurity

Company 1

The organization behind the API

Website

Website

Other 1

Properties that don't map to a standard resource type

HuggingFace

HuggingFace

Source (apis.yml)

aid: markov
name: Markov
description: Markov (Markov Studios) is a San Francisco-based Y Combinator (Summer 2026) startup building data and environments
  for training computer-use AI. The company captures how people actually use computers - screen recordings with synchronized
  mouse and keyboard inputs, annotations, and open datasets across real workflows in tools like Salesforce, Blender, and Photoshop,
  plus gaming data from Valorant, Minecraft, and GTA. Markov sources high-quality tasks and data to train the next generation
  of computer-use and reinforcement-learning models, releasing much of its work in public on Hugging Face (150k+ dataset downloads).
  Founded in 2026 by Dev Mandal (CEO) and Harish Ashok, Markov distributes its datasets through Hugging Face rather than a
  first-party API; this profile currently tracks no public API surface.
url: https://raw.githubusercontent.com/api-evangelist/markov/refs/heads/main/apis.yml
x-api-posture: no-product-api
x-api-posture-basis: observed
x-api-posture-note: Founded in 2026 by Dev Mandal (CEO) and Harish Ashok, Markov distributes its datasets through Hugging
  Face rather than a first-party API; this profile currently tracks no public API surface.
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
x-tier: stub
x-tier-reason: portfolio-lead
x-yc-batch: Summer 2026
x-founded: '2026'
x-hq: San Francisco, CA
deliveryModel:
  model: unknown
  open_source: false
  commercial: false
  callable_host: false
  label: Delivery model not determined — needs a product licence on record
  confidence: low
  source:
  - none
  generated: '2026-08-28'
  method: derived
accessModel:
  pricing: unknown
  onboarding: unknown
  trial: false
  try_now: false
  public: false
  gated: private
  gated_source:
  - x-api-posture
  label: Unknown
  confidence: low
  source:
  - security
  generated: '2026-11-06'
  method: derived
image: https://markovstudios.com/og.png
specificationVersion: '0.23'
created: '2026-07-17'
modified: '2026-07-20'
tags:
- Company
- Artificial Intelligence
- Machine Learning
- Data
- Computer Use
- Reinforcement Learning
- Datasets
- Training Data
tags_raw:
- Company
- Artificial Intelligence
- Machine Learning
- Data
- Computer Use
- Reinforcement Learning
- Datasets
- Training Data
- Machine-Learning
apis: []
common:
- type: DomainSecurity
  url: security/markov-domain-security.yml
- type: Website
  url: https://markovstudios.com
- type: HuggingFace
  url: https://huggingface.co/markov-ai
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-20'
  status: minimal
  artifacts_added: 1
  pass: local-v1

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