Evidence

What the numbers mean.

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

11 public measures · updated July 30, 2026

Numbers

11 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.

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
Internal 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.

184 source records support 82 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.

Sources

198 sources.

198 sources145—168 shown
  1. 145
    Evaluation course #2Code · github.comSupports W&B AI curriculum: prompting, evaluation, and agents
    Open source ↗
  2. 146
    How to Save a Classifier to Disk in Scikit-learn — JapaneseWeb page · wandb.aiSupports Saving and reloading scikit-learn classifiers
    Open source ↗
  3. 147
    You Need Evals — a Primer and New TechniquesWeb page · stowers.co
    Open source ↗
  4. 148
    Automated PDF Summarization with Claude 3.5 Sonnet and W&B Weave — JapaneseWeb page · wandb.aiSupports arXiv paper summarization with Claude and Weave
    Open source ↗
  5. 149
    fugueCode · github.comSupports Fugue: governed AI-agent experiments
    Open source ↗
  6. 150
    GenAI Development: Building Production-Ready RAG SystemsWeb page · wandb.aiSupports RAG systems from prototype to production
    Open source ↗
  7. 151
    Kedro + MLFlow + WANDB?Web page · wandb.aiSupports Comparing Kedro, MLflow, and W&B
    Open source ↗
  8. 152
    Evaluating RAG ApplicationsWeb page · luma.comSupports RAG evaluation in practice
    Open source ↗
  9. 153
    Does the top_p Variable Exist in Chat Completions?Web page · learn.microsoft.com
    Open source ↗
  10. 154
    LangChain #9771Code · github.comSupports W&B integrations for LangChain
    Open source ↗
  11. 155
    Iterating On and Evaluating Production-Ready RAG Applications with Gemini and W&B WeaveWeb page · luma.comSupports Evaluating production RAG with Gemini and Weave
    Open source ↗
  12. 156
    Deep Learning Weekly Issue 340Web page · deeplearningweekly.com
    Open source ↗
  13. 157
    Meta Llama 3 HackathonWeb page · ai.meta.comSupports Meta Llama 3 Hackathon
    Open source ↗
  14. 158
    Mastering Model Customization: Fine-Tuning Azure OpenAI Service Models with W&BWeb page · wandb.aiSupports Fine-tuning Azure OpenAI models with W&B
    Open source ↗
  15. 159
    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 ↗
  16. 160
    Automated PDF Summarization of arXiv Papers with Claude 3.5 Sonnet and W&B WeaveWeb page · wandb.aiSupports arXiv paper summarization with Claude and Weave
    Open source ↗
  17. 161
    Fine-Tuning and Evaluating Multimodal LLMsWeb page · web.archive.orgSupports Multimodal LLM fine-tuning and evaluation
    Open source ↗
  18. 162
    Fine-Tuning and Evaluating Multimodal LLMsWeb page · linkedin.comSupports Multimodal LLM fine-tuning and evaluation
    Open source ↗
  19. 163
    Building and Evaluating AgentsWeb page · wandb.aiSupports Agent development with evaluation in the loop
    Open source ↗
  20. 164
    Let's Get Better Step By Step — LLMs in Your BusinessVideo · youtube.comSupports Adding LLMs to business workflows
    Open source ↗
  21. 165
    Large Language Models AgentsVideo · youtube.comSupports Large language model agents
    Open source ↗
  22. 166
    Evaluating and Integrating ML Models — MLOps Podcast #213Video · youtube.comSupports ML model evaluation and integration
    Open source ↗
  23. 167
    Introducing the W&B MCP Server: An Agent-Native Interface for Your Experiments and TracesWeb page · wandb.aiSupports W&B MCP Server for experiments and traces
    Open source ↗
  24. 168
    AI Customization: Fine-Tuning Azure OpenAI Service Models — BRK101Web page · web.archive.orgSupports Azure OpenAI model fine-tuning — BRK101
    Open source ↗