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 sources73—96 shown
  1. 073
    Developer's Guide to LLM Promptingweb page · wandb.aiSupports W&B AI course curriculum
    Open ↗
  2. 074
    ash0ts — Hugging Faceprofile · huggingface.co
    Open ↗
  3. 075
    Architecting Multi-Agent Workflows with Google Cloud and W&Bvideo · brighttalk.comSupports Architecting Multi-Agent Workflows with Google Cloud and W&B
    Open ↗
  4. 076
    Understanding LLM Performance With W&B and Snowpark Container Servicesvideo · youtube.comSupports Understanding LLM Performance With W&B and Snowpark Container Services
    Open ↗
  5. 077
    Mastering Model Customization: Fine-Tuning Azure OpenAI Service Models with Weights & Biasesweb page · techcommunity.microsoft.comSupports Mastering Model Customization
    Open ↗
  6. 078
    OpenAI Cookbook #420repository · github.comSupports OpenAI Cookbook contributions
    Open ↗
  7. 079
    Evaluating and Integrating ML Models — Amazon Musicweb page · music.amazon.inSupports Evaluating and Integrating ML Models — MLOps Podcast #213
    Open ↗
  8. 080
    Evaluating and Integrating ML Models — Spotifyweb page · creators.spotify.comSupports Evaluating and Integrating ML Models — MLOps Podcast #213
    Open ↗
  9. 081
    Architecting and Orchestrating AI Agents — Class Centralweb page · classcentral.comSupports Architecting and Orchestrating AI Agents
    Open ↗
  10. 082
    Understanding the Landscape of the Latest Large Modelsvideo · youtube.comSupports Understanding the Landscape of the Latest Large Models
    Open ↗
  11. 083
    Judgment Day Hackathonweb page · luma.comSupports Judgment Day Hackathon
    Open ↗
  12. 084
    PyTorch Lightning #19297repository · github.comSupports PyTorch Lightning Fabric integration
    Open ↗
  13. 085
    NVIDIA data-flywheel #11repository · github.comSupports NVIDIA Data Flywheel Blueprint
    Open ↗
  14. 086
    How to Optimize GPU Cost with Weights and Biasesweb page · wandb.aiSupports How to Optimize GPU Cost with Weights and Biases
    Open ↗
  15. 087
    Judgment Day Hackathonweb page · judge.devpost.comSupports Judgment Day Hackathon
    Open ↗
  16. 088
    MLOps Sample Project: End to End Song Recommendervideo · youtube.comSupports MLOps Sample Project: End to End Song Recommender
    Open ↗
  17. 089
    How to Build AI Agents with MCP and Other Protocolsvideo · brighttalk.comSupports How to Build AI Agents with MCP and Other Protocols
    Open ↗
  18. 090
    LLM Apps: Evaluationweb page · wandb.aiSupports W&B AI course curriculum
    Open ↗
  19. 091
    AI Customization: Fine-Tuning Azure OpenAI Service Models — BRK101video · youtube.comSupports AI Customization: Fine-Tuning Azure OpenAI Service Models — BRK101
    Open ↗
  20. 092
    FourthBrain Graduate Testimonialweb page · fourthbrain.ai
    Open ↗
  21. 093
    LLM Agent Fine-Tuning: Enhancing Task Automation with W&Bvideo · youtube.comSupports LLM Agent Fine-Tuning
    Open ↗
  22. 094
    Deep Learning and MLOps for Health Care: A Look into MedSAMweb page · wandb.aiSupports Deep Learning and MLOps for Health Care: A Look into MedSAM
    Open ↗
  23. 095
    Simplify Building, Scaling, Tracking, and Monitoring Your AI/ML Modelsweb page · anyscale.comSupports Simplify Building, Scaling, Tracking, and Monitoring Your AI/ML Models
    Open ↗
  24. 096
    Streamlit August Monthly Roundupweb page · discuss.streamlit.io
    Open ↗