GTM Engineering
5 rooms · 8 posts
Recurring themes
Topics and series that show up in more than one room — each with the rooms that carried it and the posts that came out of them.
21 themes · 90 published posts · derived from 30 rooms
5 rooms · 8 posts
4 rooms · 9 posts
3 rooms · 7 posts
3 rooms · 5 posts
2 rooms · 2 posts
2 rooms · 3 posts
2 rooms · 3 posts
2 rooms · 4 posts
2 rooms · 5 posts
2 rooms · 3 posts
2 rooms · 3 posts
2 rooms · 2 posts
2 rooms · 4 posts
2 rooms · 2 posts
2 rooms · 4 posts
2 rooms · 4 posts
2 rooms · 4 posts
2 rooms · 4 posts
2 rooms · 5 posts
2 rooms · 4 posts
2 rooms · 5 posts
By layer
The same rooms, cut by a ten-layer reference architecture for applied AI: how much of the archive lands in each layer of the stack.
86 of 94 room topics carry a layer · map from AlexYedi/Empire_State_Events_Pipeline_Take_3 @ 0caefeb (as of 2026-10-30)
11 rooms · 17 topics · +4 via secondary
How AI products reach and monetize customers: sales, marketing, pricing, RevOps and distribution strategy.
10 rooms · 14 topics · +5 via secondary
The scaffolding around models: agent frameworks, orchestration, MCP, context engineering, coding agents.
9 rooms · 11 topics · +3 via secondary
Where agents and AI workloads run: clouds, GPUs and custom silicon, sandboxes, serving infrastructure.
6 rooms · 6 topics · +3 via secondary
How software is designed, built, reviewed and shipped when agents do the building.
6 rooms · 12 topics
Agents applied in a specific domain or industry: vertical SaaS, healthcare, fintech, legal, media, robotics.
5 rooms · 6 topics · +1 via secondary
Knowing whether AI works: evaluation suites, tracing, reliability engineering and production failure modes.
5 rooms · 4 topics · +3 via secondary
The surrounding landscape: capital flows, talent markets, policy and geopolitics, ecosystem structure.
4 rooms · 4 topics · +2 via secondary
Who agents are and what they may do: identity, auth, security, compliance, governance and trust.
3 rooms · 4 topics · +2 via secondary
Foundation models and what happens inside them: releases, reasoning, training, model economics.
3 rooms · 4 topics
Getting the right context to models: data infrastructure, RAG, vector search, the modern data stack.