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 sources1—24 shown
  1. 001
    Internal analytics — rounded public summarymethodology · anishshah.devSupports 7K+ course enrollments across three AI courses · About half of course enrollments accompanied a W&B account created the same day · +7 more
    Open ↗
  2. 002
    Public footprint counting methodologymethodology · anishshah.devSupports 49 dated public event and session references · 19+ hours of public talks and interviews
    Open ↗
  3. 003
    Mastering Gen AI Applications with W&B — EMEA/APACweb page · cloudonair.withgoogle.comSupports Google Cloud Gen AI Startup School 2025
    Open ↗
  4. 004
    Observability and Reliability Credentialsweb page · grokify.github.io
    Open ↗
  5. 005
    Diffusers W&B integration #1209repository · github.comSupports Diffusers W&B integrations
    Open ↗
  6. 006
    Will Falcon / Lightning — Koreanweb page · wandb.aiSupports Gradient Dissent production
    Open ↗
  7. 007
    Architecting and Orchestrating AI Agents with Googleweb page · wandb.aiSupports Architecting and Orchestrating AI Agents
    Open ↗
  8. 008
    WandBot — GPT-4 Powered Chat Supportweb page · note.comSupports WandBot: GPT-4 Powered Chat Support
    Open ↗
  9. 009
    GPT Engineer #752repository · github.comSupports GPT Engineer contributions
    Open ↗
  10. 010
    Train, Fine-Tune, and Manage Models from Experimentation to Productionweb page · agorify.comSupports Train, Fine-Tune, and Manage Models from Experimentation to Production
    Open ↗
  11. 011
    Train, Fine-Tune, and Manage Models from Experimentation to Productionweb page · stacresearch.comSupports Train, Fine-Tune, and Manage Models from Experimentation to Production
    Open ↗
  12. 012
    Anish Shah Developer Profileweb page · getprog.ai
    Indexed · host unavailable
  13. 013
    Build and Deploy LLM-Based Appsweb page · linkedin.comSupports Build and Deploy LLM-Based Apps
    Open ↗
  14. 014
    Will Falcon / Lightning — Japaneseweb page · wandb.aiSupports Gradient Dissent production
    Open ↗
  15. 015
    Jehan Wickramasuriya: AI in High-Stress Scenariosweb page · wandb.aiSupports Gradient Dissent production
    Open ↗
  16. 016
    Fine Tuning Azure OpenAI Service Models with W&Bvideo · youtube.comSupports AI Customization: Fine-Tuning Azure OpenAI Service Models — BRK101
    Open ↗
  17. 017
    Google Cloud/W&B Multi-Agent Workshop Participant Recapweb page · linkedin.com
    Open ↗
  18. 018
    Cracking the Code of LLMOps: A Complete Guideweb page · toolify.ai
    Open ↗
  19. 019
    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 ↗
  20. 020
    Harnessing Langchain and Weights & Biases to Enhance Database Interactions on Snowparkweb page · wandb.aiSupports Harnessing Langchain and Weights & Biases to Enhance Database Interactions on Snowpark
    Open ↗
  21. 021
    Scaling out Autonomous Vehicle Use Cases: The Primerweb page · wandb.aiSupports Scaling out Autonomous Vehicle Use Cases: The Primer
    Open ↗
  22. 022
    Recapping the Latest Azure AI Announcements from Microsoft Igniteweb page · linkedin.com
    Open ↗
  23. 023
    Let's Get Better Step By Step — LLMs in Your Businessweb page · developer.microsoft.comSupports Let's Get Better Step By Step — LLMs in Your Business
    Open ↗
  24. 024
    PII safeguards #6repository · github.comSupports PII safeguards contribution
    Open ↗