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 sources145—168 shown
  1. 145
    Evaluation course #2repository · github.comSupports W&B AI course curriculum
    Open ↗
  2. 146
    How to Save a Classifier to Disk in Scikit-learn — Japaneseweb page · wandb.aiSupports How to Save a Classifier to Disk in Scikit-learn
    Open ↗
  3. 147
    You Need Evals — a Primer and New Techniquesweb page · stowers.co
    Open ↗
  4. 148
    Automated PDF Summarization with Claude 3.5 Sonnet and W&B Weave — Japaneseweb page · wandb.aiSupports Automated PDF Summarization with Claude and W&B Weave
    Open ↗
  5. 149
    fuguerepository · github.comSupports Fugue — governed experiments for AI agents
    Open ↗
  6. 150
    GenAI Development: Building Production-Ready RAG Systemsweb page · wandb.aiSupports GenAI Development: Building Production-Ready RAG Systems
    Open ↗
  7. 151
    Kedro + MLFlow + WANDB?web page · wandb.aiSupports Kedro + MLFlow + WANDB?
    Open ↗
  8. 152
    Evaluating RAG Applicationsweb page · luma.comSupports Evaluating RAG Applications
    Open ↗
  9. 153
    Does the top_p Variable Exist in Chat Completions?web page · learn.microsoft.com
    Open ↗
  10. 154
    LangChain #9771repository · github.comSupports LangChain W&B integrations
    Open ↗
  11. 155
    Iterating On and Evaluating Production-Ready RAG Applications with Gemini and W&B Weaveweb page · luma.comSupports Iterating On and Evaluating Production-Ready RAG Applications with Gemini and W&B Weave
    Open ↗
  12. 156
    Deep Learning Weekly Issue 340web page · deeplearningweekly.com
    Open ↗
  13. 157
    Meta Llama 3 Hackathonweb page · ai.meta.comSupports Meta Llama 3 Hackathon
    Open ↗
  14. 158
    Mastering Model Customization: Fine-Tuning Azure OpenAI Service Models with W&Bweb page · wandb.aiSupports Mastering Model Customization
    Open ↗
  15. 159
    How to Use Azure OpenAI and Azure AI Studio with Weights & Biases Weaveweb page · wandb.aiSupports How to Use Azure OpenAI and Azure AI Studio with Weights & Biases Weave
    Open ↗
  16. 160
    Automated PDF Summarization of arXiv Papers with Claude 3.5 Sonnet and W&B Weaveweb page · wandb.aiSupports Automated PDF Summarization with Claude and W&B Weave
    Open ↗
  17. 161
    Fine-Tuning and Evaluating Multimodal LLMsweb page · web.archive.orgSupports Fine-Tuning and Evaluating Multimodal LLMs
    Open ↗
  18. 162
    Fine-Tuning and Evaluating Multimodal LLMsweb page · linkedin.comSupports Fine-Tuning and Evaluating Multimodal LLMs
    Open ↗
  19. 163
    Building and Evaluating Agentsweb page · wandb.aiSupports Building and Evaluating Agents
    Open ↗
  20. 164
    Let's Get Better Step By Step — LLMs in Your Businessvideo · youtube.comSupports Let's Get Better Step By Step — LLMs in Your Business
    Open ↗
  21. 165
    Large Language Models Agentsvideo · youtube.comSupports Large Language Model Agents
    Open ↗
  22. 166
    Evaluating and Integrating ML Models — MLOps Podcast #213video · youtube.comSupports Evaluating and Integrating ML Models — MLOps Podcast #213
    Open ↗
  23. 167
    Introducing the W&B MCP Server: An Agent-Native Interface for Your Experiments and Tracesweb page · wandb.aiSupports W&B MCP Server
    Open ↗
  24. 168
    AI Customization: Fine-Tuning Azure OpenAI Service Models — BRK101web page · web.archive.orgSupports AI Customization: Fine-Tuning Azure OpenAI Service Models — BRK101
    Open ↗