Setting the file. One moment.
Chapter 10 · Event Prospecting
Subchapter 10.2
references/example-research.mdMarkdown9 KBView on GitHub
Event-prospecting writes TWO kinds of markdown files:
seed_companies.txt, written to {OUTPUT_DIR}/companies/{slug}.md. Comes in two flavors: triage stubs (Step 5) and deep-research files (Step 7).{OUTPUT_DIR}/people/{slug}.md. Created in Step 8.The YAML frontmatter contains structured fields for report compilation. The body contains human-readable research.
{OUTPUT_DIR} is the per-run Desktop directory set up by the main agent in Step 0 (e.g., /Users/jay/Desktop/{event_slug}_prospects_2026-04-25-2030/).
Every company in seed_companies.txt gets one of these. It captures a 1-call, ICP-only assessment.
---
company_name: OpenAI
website: https://openai.com
product_description: AI lab building safe AGI; ChatGPT, GPT API, ChatGPT Agent
icp_fit_score: 9
icp_fit_reasoning: AI agents at scale need cloud browser infrastructure; ChatGPT Agent shipped Mar 2026
triage_only: true
event_context: Stripe Sessions 2026 — featured speaker on AI track
---
## Triage Notes
Homepage: "ChatGPT, GPT API, and ChatGPT Agent — AI tools and APIs for everyone."
Score 9 because ChatGPT Agent ships browser-using AI agents at consumer scale — the canonical fit for browser infrastructure.Required fields: company_name, website, icp_fit_score, icp_fit_reasoning, triage_only: true.
When a company’s icp_fit_score >= --icp-threshold, Step 7’s deep research overwrites the triage stub with this richer version. triage_only flips to false.
---
company_name: OpenAI
website: https://openai.com
product_description: Foundational AI lab; products span ChatGPT (consumer chat), GPT API (developer access), and ChatGPT Agent (browser-using autonomous agent)
industry: AI / Foundation Models
target_audience: Consumers, developers, enterprise — multi-segment
key_features: ChatGPT Agent | GPT-5 API | Sora video | enterprise data residency
icp_fit_score: 9
icp_fit_reasoning: ChatGPT Agent (Mar 2026) is a browser-using agent at consumer scale — directly addresses the "agents need a browser" wedge. Plus enterprise customers ship internal agents on top of GPT API.
employee_estimate: 3000+
funding_info: $11.3B raised; reported $300B valuation 2026
headquarters: San Francisco, CA
triage_only: false
event_context: Stripe Sessions 2026 — Greg Brockman featured speaker, Agents track
event_relevance: Three OpenAI speakers across the Agents and Infra tracks; ChatGPT Agent demo expected at the event
---
## Product
Foundational AI lab. Three product surfaces: ChatGPT (consumer/team chat), GPT API (developer platform), ChatGPT Agent (autonomous browsing agent that completes multi-step tasks). Recently shipped Sora 2 for video.
## Research Findings
- **[high]** ChatGPT Agent launched Mar 2026 — autonomous web-browsing agent that books, shops, and researches on user's behalf (source: openai.com/index/chatgpt-agent)
- **[high]** Stripe Sessions 2026 keynote includes Greg Brockman on the Agents track (source: stripesessions.com/speakers)
- **[medium]** Hiring across "Agent Reliability" team — 12 open roles for browser-automation engineers (source: openai.com/careers, search 2026-04)
- **[medium]** Reported partnership exploration with infrastructure providers for agent runtime (source: The Information, 2026-03)
## Event Relevance
Three speakers at Stripe Sessions across Agents and Infra tracks. ChatGPT Agent is the canonical use-case for browser-infrastructure-as-a-product. Pitch angle: durability + scale guarantees the in-house Browserbase-equivalent can't easily match.Additional fields vs the stub: industry, target_audience, key_features (pipe-separated), employee_estimate, funding_info, headquarters, event_relevance.
Body sections: ## Product, ## Research Findings, ## Event Relevance.
Only created for speakers at ICP-fit companies (those whose company file has triage_only: false after Step 7).
---
name: Greg Brockman
slug: greg-brockman
company: OpenAI
company_slug: openai
title: President & Co-founder
image: https://cdn.example.com/speakers/greg-brockman.jpg
links:
linkedin: https://www.linkedin.com/in/thegdb/
x: https://x.com/gdb
github: https://github.com/gdb
blog: null
podcast: https://lexfridman.com/greg-brockman/
hook: Recent Lex Fridman conversation on agent reliability — direct fit for the browser-infrastructure durability story
dm_opener: |
Hey Greg — caught your Lex conversation on agent reliability and the
"agents are bottlenecked on the browser" framing landed hard. We run
the cloud-browser layer that ChatGPT Agent's competitors are shipping
on. Worth a 15-min walkthrough before Sessions?
role_reason: Co-founder, sets infrastructure direction across product surfaces
event_name: Stripe Sessions 2026
event_context: Panelist, Agents track ("From demo to dependable: making agents reliable")
icp_fit_score: 9
icp_fit_reasoning: AI agents at scale need cloud browser infrastructure; ChatGPT Agent shipped Mar 2026
enriched_at: 2026-04-25T20:30:00Z
---
## Why reach out
- **Why the company**: ChatGPT Agent is the canonical browser-infra customer — see `companies/openai.md`
- **Why the person**: Co-founder; sets infra direction; specifically called out agent reliability on Lex (Mar 2026)
- **Hook**: Lex Fridman conversation on agent reliability (45 min, dropped 2026-03-12)
## Public links
- LinkedIn: https://www.linkedin.com/in/thegdb/
- X: https://x.com/gdb
- GitHub: https://github.com/gdb (OpenAI / personal)
- Podcast: https://lexfridman.com/greg-brockman/
## Recent activity
- **[high]** Lex Fridman podcast episode on agent reliability, Mar 2026 (source: lexfridman.com/greg-brockman)
- **[medium]** GitHub activity: contributed to openai/chatgpt-agent-evals (source: github.com/gdb)
- **[medium]** X thread on "the bottleneck for agents is the browser, not the model" — Apr 2026 (source: x.com/gdb)Required fields: name, slug, company, links (object), hook, dm_opener, role_reason, event_name, event_context, icp_fit_score.
Body sections: ## Why reach out (3 bullets that mirror the card), ## Public links, ## Recent activity (findings list with confidence levels).
key_features: pipe-separated (|) list, NOT a JSON arrayicp_fit_score: integer 1-10icp_fit_reasoning: one line, references specific findingstriage_only: boolean (true for stubs, false after deep research)event_context: how this company shows up at the event (sponsor tier, speaker count, track topics){OUTPUT_DIR}/companies/{slug}.md where slug is lowercase, hyphenatedimage: speaker headshot URL extracted from the event site (preserved verbatim from the people.jsonl input record). May be null on platforms that don’t expose it.links: YAML object with keys linkedin, x, github, blog, podcast. Use null when not found, not empty string.hook: one sentence, sourced from a specific finding (event-context, recent activity, or company-context). Never inferred from memory.dm_opener: 2-3 sentences, multi-line YAML string with | pipe. References the hook, names a wedge tie-in, ends with a soft CTA.icp_fit_score is INHERITED from the corresponding companies/{company_slug}.md — keeps cards rankable in the index.{OUTPUT_DIR}/people/{slug}.md where slug is the lowercased + hyphenated person name (e.g. greg-brockman.md).Subagents write these files using bash heredoc to avoid security prompts. Use the full literal {OUTPUT_DIR} path — no ~ or $HOME:
cat << 'PERSON_MD' > /Users/jay/Desktop/stripesessions_prospects_2026-04-25-2030/people/greg-brockman.md
---
name: Greg Brockman
slug: greg-brockman
...
---
## Why reach out
...
PERSON_MDUse 'PERSON_MD' (quoted) as the delimiter to prevent shell variable expansion. Use 'COMPANY_MD' for company files.
IMPORTANT: Write ALL files in a SINGLE Bash call using chained heredocs to minimize permission prompts. One subagent batch (~5 people) = one Bash invocation = one permission prompt.
cat << 'PERSON_MD' > {OUTPUT_DIR}/people/greg-brockman.md
---
...
---
PERSON_MD
cat << 'PERSON_MD' > {OUTPUT_DIR}/people/sam-altman.md
---
...
---
PERSON_MDChained heredocs in one bash call. The subagent reports back ONLY a count, never raw content.