Anish Shah

The public record

Sources, counts, and claims.

Public counts come from the public inventory. Internal outcomes are deliberately rounded, dated, qualified, and separated from revenue or individual-causality claims.

11 public measures · updated July 30, 2026

Archive accounting

Two inputs, two different outputs.

Footprint records describe the archive. Impact records support the claim ledger. They are reconciled separately.

Public archive

  1. 184footprint observations
  2. 82bodies of work
  3. 186public forms
  4. 198evidence records

Impact ledger

  1. 85impact observations reviewed
  2. 11rounded public measures

Attribution boundary

What this site does not claim.

  • Pipeline supported is not booked revenue or ARR.
  • Program reach and organization outcomes are not personal causality.
  • Course enrollments are not unique learners or completion counts.
  • Same-day account creation is an association, not a conversion rate.
  • MCP tool calls describe usage, not users, organizations, or revenue.

Reading rules

Four rules stay visible.

  1. 01

    Count one underlying work once

    All 186 public forms remain discoverable. Recordings, translations, mirrors, profiles, excerpts, and evidence pages do not inflate unique-work totals.

  2. 02

    Keep forms attached to their source

    A talk, regional appearance, replay, repository, and translation can describe the same body of work without becoming five unrelated accomplishments.

  3. 03

    Separate contribution from causality

    Individual roles, program reach, supported pipeline, and organization-wide outcomes are named separately.

  4. 04

    Keep the qualifier beside the number

    Every internally derived measure keeps its time window, source class, attribution, rounding, and caveat in the same record.

Claim ledger

11 measures in four contexts.

Learning reach

Enrollment and launch measures from three practical AI courses.

7K+

course enrollments across three AI courses

Enrollments, not unique learners.

Scope
work
Attribution
direct
Period
2024–2026 · as of 2026-07-30
Source class
Internal analytics
Public evidenceInternal analytics — rounded public summary
About half

of course enrollments accompanied a W&B account created the same day

An observed same-day association, not a causal conversion claim.

Scope
work
Attribution
direct
Period
2024–2026 · as of 2026-07-30
Source class
Internal analytics
Public evidenceInternal analytics — rounded public summary
1K+

Evals-course signups in roughly 36 hours

Rounded down from the internal launch checkpoint.

Scope
work
Attribution
direct
Period
~36 hours · as of 2025-01-10
Source class
Internal analytics
Public evidenceInternal analytics — rounded public summary

Product adoption

Rounded usage and adoption signals for the W&B MCP Server.

200K+

MCP tool calls through July 29, 2026

Rounded down from the July 29 internal analytics checkpoint; direct project usage, not revenue.

Scope
work
Attribution
direct
Period
Through July 29, 2026 · as of 2026-07-29
Source class
Internal analytics
Public evidenceInternal analytics — rounded public summary
1K+

MCP users across 1K+ organizations

Rounded launch-period adoption snapshot.

Scope
work
Attribution
direct
Period
Launch period · as of 2026-06-11
Source class
Internal analytics
Public evidenceInternal analytics — rounded public summary
More than two-thirds

of weekly active MCP users were returning users

Weekly active-user composition at the cited checkpoint.

Scope
work
Attribution
direct
Period
Week of 2026-06-07 · as of 2026-06-11
Source class
Internal analytics
Public evidenceInternal analytics — rounded public summary
Hundreds

of MCP users re-engaged in a week

Rounded weekly re-engagement checkpoint.

Scope
work
Attribution
direct
Period
Week of 2026-07-20 · as of 2026-07-28
Source class
Internal analytics
Public evidenceInternal analytics — rounded public summary

Program impact

Bounded reach and supported-pipeline measures from public programs.

$4M+

in supported event pipeline across tracked 2024–2025 programs

Supported pipeline across tracked event programs; not revenue, closed ARR, or personal attribution.

Scope
portfolio
Attribution
associated
Period
2024–2025 · as of 2025-10-01
Source class
Internal analytics
Public evidenceInternal analytics — rounded public summary
500+

leads from NVIDIA GTC 2024

Program reach, not individual attribution.

Scope
program
Attribution
contextual
Period
NVIDIA GTC 2024 · as of 2024-04-01
Source class
Internal analytics
Public evidenceInternal analytics — rounded public summary

Archive reach

Counts derived only from the normalized public archive.

49

dated public event and session references

Unique inventory rows; recordings and mirrors are counted separately.

Scope
collection
Attribution
contextual
Period
2021–2026 · as of 2026-07-30
Source class
Public footprint inventory
Public evidencePublic footprint counting methodology
19+

hours of public talks and interviews

Derived from 23 unique recording durations; mirrors and excerpts are excluded.

Scope
collection
Attribution
contextual
Period
2021–2026 · as of 2026-07-30
Source class
Public footprint inventory
Public evidencePublic footprint counting methodology

Public bibliography

198 sources, open to inspect.

198 public sources121—144 shown
  1. 121
    Building and Evaluating Agents — Class Centralweb page · classcentral.comSupports Building and Evaluating Agents
    Open ↗
  2. 122
    Automated Medical Note Generation: Fine-Tuning GPT Models for Clinical Documentation Using Azure OpenAI and Weights & Biasesweb page · wandb.aiSupports Automated Medical Note Generation: Fine-Tuning GPT Models for Clinical Documentation Using Azure OpenAI and Weights & Biases
    Open ↗
  3. 123
    Google Cloud generative-ai #1807repository · github.comSupports Google Cloud Generative AI contribution
    Open ↗
  4. 124
    Agents course #2repository · github.comSupports W&B AI course curriculum
    Open ↗
  5. 125
    EvalForgerepository · github.comSupports EvalForge
    Open ↗
  6. 126
    Jehan Wickramasuriya: AI in High-Stress Scenariosvideo · youtube.comSupports Gradient Dissent production
    Open ↗
  7. 127
    Understanding LLM Performance With W&B and Snowpark Container Servicesvideo · youtube.comSupports Understanding LLM Performance With W&B and Snowpark Container Services
    Open ↗
  8. 128
    wandb/skillsrepository · github.comSupports W&B Skills
    Open ↗
  9. 129
    How to Build AI Agents with MCP and Other Protocolsweb page · wandb.aiSupports How to Build AI Agents with MCP and Other Protocols
    Open ↗
  10. 130
    Accelerate GenAI Application Development with W&B Weave and NVIDIA — EXS74222web page · wandb.aiSupports Accelerate GenAI Application Development with W&B Weave and NVIDIA — EXS74222
    Open ↗
  11. 131
    Automated PDF Summarization with Claude 3.5 Sonnet and W&B Weave — Koreanweb page · wandb.aiSupports Automated PDF Summarization with Claude and W&B Weave
    Open ↗
  12. 132
    W&B Weave arXiv cookbook #2089repository · github.comSupports Automated PDF Summarization with Claude and W&B Weave
    Open ↗
  13. 133
    LangChain v0.0.274 Release Notesrepository · github.comSupports LangChain W&B integrations
    Open ↗
  14. 134
    Large Language Models Agentsweb page · luma.comSupports Large Language Model Agents
    Open ↗
  15. 135
    Scaling Out Motion Prediction for Autonomous Vehicles with L5Kit, Ray, and W&Bweb page · wandb.aiSupports Scaling Out Motion Prediction for Autonomous Vehicles with L5Kit, Ray, and W&B
    Open ↗
  16. 136
    Diffusers W&B integration #1245repository · github.comSupports Diffusers W&B integrations
    Open ↗
  17. 137
    OpenAI Python v1 Migration Discussionrepository · github.com
    Open ↗
  18. 138
    How Do We Actually Evaluate LLM Apps?web page · nyc.aitinkerers.orgSupports How Do We Actually Evaluate LLM Apps?
    Open ↗
  19. 139
    Seattle AI Innovators Meetup Attendee Recapweb page · linkedin.com
    Open ↗
  20. 140
    Compare Methods for Converting and Optimizing HuggingFace Models for Deploymentweb page · wandb.aiSupports Compare Methods for Converting and Optimizing HuggingFace Models for Deployment
    Open ↗
  21. 141Open ↗
  22. 142
    How to Adapt your LLM for Question Answering with Prompt-Tuning using NVIDIA NeMo and Weights & Biasesweb page · wandb.aiSupports How to Adapt your LLM for Question Answering with Prompt-Tuning using NVIDIA NeMo and Weights & Biases
    Open ↗
  23. 143
    LLM Agent Fine-Tuning — Bilibili Subtitle Mirrorweb page · bilibili.comSupports LLM Agent Fine-Tuning
    Open ↗
  24. 144Open ↗