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 sources169—192 shown
  1. 169
    Will Falcon: Making Lightning the Apple of MLweb page · wandb.aiSupports Gradient Dissent production
    Open ↗
  2. 170
    Emad Mostaque: Stable Diffusion, Stability AI, and What's Nextvideo · youtube.comSupports Gradient Dissent production
    Open ↗
  3. 171
    OpenAI Cookbook #419repository · github.comSupports OpenAI Cookbook contributions
    Open ↗
  4. 172
    Optimizing AI Evaluationsweb page · wandb.aiSupports Optimizing AI Evaluations
    Open ↗
  5. 173
    Will Falcon: Making Lightning the Apple of MLvideo · youtube.comSupports Gradient Dissent production
    Open ↗
  6. 174
    How to Adapt Your LLM for Question Answering with Prompt-Tuningweb page · home.mlops.community
    Open ↗
  7. 175
    Open Interpreter #390repository · github.comSupports Open Interpreter integration
    Open ↗
  8. 176
    Microsoft Ignite BRK101 Japanese Recap — Part 1web page · zenn.dev
    Open ↗
  9. 177
    Hugging Face Community Roundupweb page · reddit.com
    Open ↗
  10. 178
    Investigating the Evolution of Evaluation from Model Training to GenAI Inferenceweb page · web.archive.orgSupports Investigating the Evolution of Evaluation from Model Training to GenAI Inference · HEMM: Holistic Evaluation of Multi-modal Generative Models
    Open ↗
  11. 179
    Prompt Engineering LLMs with LangChain and W&Bweb page · wandb.aiSupports Prompt Engineering LLMs with LangChain and W&B
    Open ↗
  12. 180
    Prompting course #142repository · github.comSupports W&B AI course curriculum
    Open ↗
  13. 181
    Emad Mostaque / Stable Diffusion — Japaneseweb page · wandb.aiSupports Gradient Dissent production
    Open ↗
  14. 182
    AI Engineering: Agentsweb page · linkedin.comSupports W&B AI course curriculum
    Open ↗
  15. 183
    Fine Tuning and Evaluating LLMs for Agentic Use Casesweb page · wandb.aiSupports Fine-Tuning and Evaluating LLMs for Agentic Use Cases
    Open ↗
  16. 184
    PyTorch Lightning #19077repository · github.comSupports PyTorch Lightning Fabric integration
    Open ↗
  17. 185
    Building an LLM Judge with Weights & Biasesvideo · youtube.comSupports Building an LLM Judge with Weights & Biases
    Open ↗
  18. 186
    GenAI Development: Building Production-Ready RAG Systemsvideo · brighttalk.comSupports GenAI Development: Building Production-Ready RAG Systems
    Open ↗
  19. 187
    7 Top Machine Learning Operations Startups and the Problem They Solveweb page · odsc.medium.com
    Open ↗
  20. 188
    Architecting and Orchestrating AI Agentsweb page · linkedin.comSupports Optimizing and Evaluating Agentic AI Applications · Architecting and Orchestrating AI Agents
    Open ↗
  21. 189
    GPT Engineer #701repository · github.comSupports GPT Engineer contributions
    Open ↗
  22. 190
    Meta Llama 3 Hackathonweb page · metallama3.devpost.comSupports Meta Llama 3 Hackathon
    Open ↗
  23. 191
    Architecting and Orchestrating AI Agentsweb page · wandb.aiSupports Architecting and Orchestrating AI Agents
    Open ↗
  24. 192
    Introduction to PyTorch Geometric and Weights & Biasesweb page · wandb.aiSupports Introduction to PyTorch Geometric and Weights & Biases
    Open ↗