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 sources49—72 shown
  1. 049
    Emad Mostaque / Stable Diffusion — Koreanweb page · wandb.aiSupports Gradient Dissent production
    Open ↗
  2. 050
    AI in Production: Evals & Observabilityweb page · toronto.aitinkerers.orgSupports AI in Production: Evals & Observability
    Open ↗
  3. 051
    NYC AI Innovators Meetupweb page · wandb.aiSupports NYC AI Innovators Meetup
    Open ↗
  4. 052
    Mastering GenAI Applicationsweb page · wandb.aiSupports Mastering GenAI Applications
    Open ↗
  5. 053
    NYC CoreWeave/W&B Workshop Attendee Recapweb page · linkedin.com
    Open ↗
  6. 054
    Fine-Tuning the GPT Model Series for Agent Applicationsvideo · youtube.comSupports Fine-Tuning the GPT Model Series for Agent Applications
    Open ↗
  7. 055
    Evaluating and Integrating ML Models — Apple Podcastsweb page · podcasts.apple.comSupports Evaluating and Integrating ML Models — MLOps Podcast #213
    Open ↗
  8. 056
    Enhancing LLM Agent Performance through Dynamic Plugin Selection and W&B Promptsweb page · wandb.aiSupports Enhancing LLM Agent Performance through Dynamic Plugin Selection and W&B Prompts
    Open ↗
  9. 057
    WandBot: GPT-4 Powered Chat Supportweb page · wandb.aiSupports WandBot: GPT-4 Powered Chat Support
    Open ↗
  10. 058
    Build and Deploy LLM-Based Appsweb page · luma.comSupports Build and Deploy LLM-Based Apps
    Open ↗
  11. 059
    Best AI Courses for LLM Evaluationweb page · research.com
    Open ↗
  12. 060
    Scaling AI with Precisionweb page · wandb.aiSupports Scaling AI with Precision
    Open ↗
  13. 061
    Deploying Models to Azure ML with Weights & Biasesweb page · wandb.aiSupports Deploying Models to Azure ML with Weights & Biases
    Open ↗
  14. 062
    Microsoft Ignite BRK101 Japanese Recap — Part 2web page · zenn.dev
    Open ↗
  15. 063
    How to Create a Biomedical RAG Application Using Snowflake Arctic for PubMed Paper Understandingweb page · wandb.aiSupports How to Create a Biomedical RAG Application Using Snowflake Arctic for PubMed Paper Understanding
    Open ↗
  16. 064
    l5kit-ray-wandb-demorepository · github.comSupports Scaling Out Motion Prediction for Autonomous Vehicles with L5Kit, Ray, and W&B
    Open ↗
  17. 065
    How Do We Actually Evaluate LLM Apps?web page · nyc.aitinkerers.orgSupports How Do We Actually Evaluate LLM Apps?
    Open ↗
  18. 066
    Developer's Guide to LLM Promptingweb page · wandb.aiSupports W&B AI course curriculum
    Open ↗
  19. 067
    Bay Area AI + Ray Summit Meetupweb page · linkedin.comSupports Bay Area AI + Ray Summit Meetup
    Open ↗
  20. 068
    Orchestrating Reproducible AutoML Experiments using PyCaret, W&B, and Prefectweb page · app.qwoted.comSupports Orchestrating Reproducible AutoML Experiments using PyCaret, W&B, and Prefect
    Open ↗
  21. 069
    Agent Observabilityweb page · wandb.aiSupports Agent Observability
    Open ↗
  22. 070
    Iterating On and Evaluating Production-Ready RAG Applications with Gemini and W&B Weavevideo · youtube.comSupports Iterating On and Evaluating Production-Ready RAG Applications with Gemini and W&B Weave
    Open ↗
  23. 071
    Evaluation course #1repository · github.comSupports W&B AI course curriculum
    Open ↗
  24. 072
    FourthBrain Graduate Testimonialweb page · fourthbrain.ai
    Open ↗