Evidence

What the numbers mean.

Each claim keeps its source, date, attribution, and limit.

15 public measures · updated October 22, 2026

Numbers

15 claims, each with its limit.

Learning reach

Enrollment and launch measures for three AI courses.

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
Internal analytics
SourcesInternal 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
Internal analytics
SourcesInternal analytics — rounded public summary

Product adoption

Rounded usage measures for the W&B MCP Server.

745K+

cumulative MCP tool calls

Owner-validated cumulative tool calls, not HTTP requests or sessions. Direct project usage, not revenue; review date is not a measurement cutoff.

Scope
work
Attribution
direct
Period
Owner-reviewed October 22, 2026 · as of 2026-10-22
Source
Owner-reviewed analytics
SourcesInternal analytics — rounded public summary
76%

growth in average weekly active identities in Multi Tenant Cloud

Average weekly active identities rose from 368.5 (April 19–May 16) to 649 (September 13–October 10), using consistent identity handling. Rounded growth; not unique people or growth attributable solely to one contributor.

Scope
work
Attribution
direct
Period
April 19–May 16 vs. September 13–October 10, 2026 · as of 2026-10-22
Source
Owner-reviewed analytics
SourcesInternal analytics — rounded public summary
70%

active identities previously observed within the dashboard window

Of active identities in October 4–10, 70% appeared in an earlier observed week within the same dashboard window. Returning use, not cohort retention or verified repeat users.

Scope
work
Attribution
direct
Period
October 4–10, 2026 · as of 2026-10-22
Source
Owner-reviewed analytics
SourcesInternal analytics — rounded public summary
≈4,800

cumulative recorded MCP identities

Approximately 4,800 identities ever recorded interacting with MCP. Identities are not verified distinct people; review date is not a measurement cutoff.

Scope
work
Attribution
direct
Period
Owner-reviewed October 22, 2026 · as of 2026-10-22
Source
Owner-reviewed analytics
SourcesInternal analytics — rounded public summary
30

companies at the observed usage peak

Owner-validated company-level aggregate at an observed peak, not a current count, customer count, endorsement, or verified key-workflow adoption. Review date is not the peak date.

Scope
work
Attribution
direct
Period
Owner-reviewed October 22, 2026 · as of 2026-10-22
Source
Owner-reviewed analytics
SourcesInternal 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
Internal analytics
SourcesInternal 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
Internal analytics
SourcesInternal analytics — rounded public summary

Program impact

Reach and supported-pipeline measures for 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
Internal analytics
SourcesInternal analytics — rounded public summary

Archive reach

Counts derived from published project records.

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
Public footprint inventory
SourcesPublic 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
Public footprint inventory
SourcesPublic footprint counting methodology

Method

Three rules.

202 source records support 88 projects and series. Related records remain searchable without being counted twice.

  1. 01

    Count each project once

    Recordings, translations, mirrors, profiles, excerpts, and source pages do not inflate the total.

  2. 02

    Separate contribution from causality

    Individual contribution, program reach, supported pipeline, and organization outcomes remain separate.

  3. 03

    Keep the limit beside the claim

    Each derived measure keeps its period, source, attribution, rounding, and caveat.

Owner-reviewed contributions

Owner-reviewed entries describe curricula or internal tooling whose materials are not public; they are not independently inspectable source code. Status distinguishes developed material, merged implementation, and delivered work. Private records and repository links stay private.

Sources

209 sources.

209 sources1—24 shown
  1. 001
    Internal analytics — rounded public summaryMethod note · anishshah.devSupports 7K+ course enrollments across three AI courses · About half of course enrollments accompanied a W&B account created the same day · +11 more
    Open source ↗
  2. 002
    Owner-reviewed contributions — materials not publicMethod note · Anish ShahSupports AutoResearch labs · Demo-generation tooling · +4 more
    Open source ↗
  3. 003
    Public footprint counting methodologyMethod note · anishshah.devSupports 49 dated public event and session references · 19+ hours of public talks and interviews
    Open source ↗
  4. 004
    Mastering Gen AI Applications with W&B — EMEA/APACWeb page · cloudonair.withgoogle.comSupports GenAI application development at Google Cloud Startup School
    Open source ↗
  5. 005
    Observability and Reliability CredentialsWeb page · grokify.github.io
    Open source ↗
  6. 006
    Diffusers W&B integration #1209Code · github.comSupports W&B experiment tracking in Hugging Face Diffusers
    Open source ↗
  7. 007
    Will Falcon / Lightning — KoreanWeb page · wandb.aiSupports Gradient Dissent transcripts and production
    Open source ↗
  8. 008
    Architecting and Orchestrating AI Agents with GoogleWeb page · wandb.aiSupports Agent architecture and orchestration series
    Open source ↗
  9. 009
    WandBot — GPT-4 Powered Chat SupportWeb page · note.comSupports WandBot documentation assistant
    Open source ↗
  10. 010
    GPT Engineer #752Code · github.comSupports GPT Engineer upstream contributions
    Open source ↗
  11. 011
    Train, Fine-Tune, and Manage Models from Experimentation to ProductionWeb page · agorify.comSupports Training and managing models from experimentation to production
    Open source ↗
  12. 012
    Train, Fine-Tune, and Manage Models from Experimentation to ProductionWeb page · stacresearch.comSupports Training and managing models from experimentation to production
    Open source ↗
  13. 013
    Anish Shah Developer ProfileWeb page · getprog.ai
    Source unavailable
  14. 014
    We Benchmarked Jev Against Gemma Inside Agent LensWeb page · forge.coreweave.comSupports Agent Lens: failure analysis and judge evaluation
    Open source ↗
  15. 015
    Build and Deploy LLM-Based AppsWeb page · linkedin.comSupports LLM applications from build to deployment
    Open source ↗
  16. 016
    How We Benchmark ARIA—and Why It Runs on WeaveWeb page · coreweave.comSupports ARIA: durable conversations and evaluation
    Open source ↗
  17. 017
    Will Falcon / Lightning — JapaneseWeb page · wandb.aiSupports Gradient Dissent transcripts and production
    Open source ↗
  18. 018
    Jehan Wickramasuriya: AI in High-Stress ScenariosWeb page · wandb.aiSupports Gradient Dissent transcripts and production
    Open source ↗
  19. 019
    Fine Tuning Azure OpenAI Service Models with W&BVideo · youtube.comSupports Azure OpenAI model fine-tuning — BRK101
    Open source ↗
  20. 020
    Google Cloud/W&B Multi-Agent Workshop Participant RecapWeb page · linkedin.com
    Open source ↗
  21. 021
    Cracking the Code of LLMOps: A Complete GuideWeb page · toolify.ai
    Open source ↗
  22. 022
    How to Use Azure OpenAI and Azure AI Studio with Weights & Biases WeaveWeb page · wandb.aiSupports Tracing Azure OpenAI with W&B Weave
    Open source ↗
  23. 023
    Harnessing Langchain and Weights & Biases to Enhance Database Interactions on SnowparkWeb page · wandb.aiSupports Database interactions with LangChain on Snowpark
    Open source ↗
  24. 024
    Scaling out Autonomous Vehicle Use Cases: The PrimerWeb page · wandb.aiSupports Autonomous vehicle scaling with L5Kit and Ray
    Open source ↗