Reading Room
This site is built out of other people’s work. The reading room is where that work is credited and examined rather than summarized — what a report claimed, what the people discussing it said back, and which of the two the evidence supports. Nothing here republishes a source; every entry quotes it, cites where the quotation came from, and links to the original.
Examined — entries with a sheet of their own
- Kimi K3: Open Frontier IntelligenceA 2.8-trillion-parameter open-weights model is a model story. What earns this report a sheet is how much of it is about the machine around the model — nine specialist policies distilled back into one, a reward model that writes its own rubric, and a reinforcement-learning loop trained against a swappable harness interface so the policy never overfits to a single one. Read against the four talks this site is built on, it says the harness is no longer only a runtime concern.Moonshot AI, 2026-07-28 · discussed by The Hugging Face research team · read into The HarnessCorr2Cont1Exte3Unre1
- AI-DLC: Redesigning the Lifecycle Around the ModelTwo vendors, six months apart, with no evident contact between them, open on the same controlled study: developers using AI tools believed they were 20% faster and were measured 20% slower. Both reach the same diagnosis — the coding gains are real, and every one of them is absorbed by the waiting around them — and both conclude that the fix is to redesign the lifecycle rather than bolt AI onto it. The second talk is not a room convened on the first. It is an independent arrival at the same terrain, which is why the examination below has no contested band, and why that absence is the finding.Anupam Mishra and Raja SP, AWS, 2025-12-04 · discussed by Cedric Clyburn, IBM · read into Agentic SDLCCorr5Cont0Exte3Unre1
- FDE: The Motion, and the Method UnderneathThe pillar this reading feeds stands on one talk, and that talk is about a market — which quadrant needs forward deployed engineers, what they cost, what they earn. It never says what the engineer does on the Tuesday after the contract is signed. The discussion here answers exactly that, and it is the weakest-provenance source this site has read: no named author, a channel compiling four talks it names but does not link, numbers relayed rather than cited. That is why it sits in this room instead of on a sheet. A record whose attribution is its worst property can still be examined — and six of the nine bands below are extended rather than corroborated, because the two sources barely touch. Where they do touch, they disagree once, and the disagreement is the useful part.Kevin Bai, Anthropic, 2026-08-07 · discussed by AI LABS · read into Forward DeployedCorr1Cont1Exte6Unre1
Read into a pillar — the talks this site was built from
These 19 talks are the source material for the 4 pillars. Each is credited on the pillar it produced — or, where a talk goes deep enough, on the sheets it raised — and in the title block of every sheet inside it. One of them is also examined above, in the register: a talk can be both the material a pillar is built from and a source worth reading against another.
- Harness Engineering MasterclassThe Carbon LayerThe Harness
- Context Management MasterclassThe Carbon LayerCredited on sheet 03 Context ManagementThe Harness
- Self-Improving AI Agents: Evolving the Harness, Not the ModelThe Carbon LayerCredited on sheet 11 EvolutionThe Harness
- Why Agentic Systems Need OntologiesFrank Coyle, UC BerkeleyCredited on sheet 10 Verification & ObservabilityThe Harness
- Why This Is the Most Exciting Time to Be HumanKen Ono, Axiom MathCredited on sheet 10 Verification & ObservabilityThe Harness
- AI Will Create New Wealth, But Not Where You ThinkPo-Shen Loh, Carnegie Mellon UniversityCredited on sheet 10 Verification & ObservabilityThe Harness
- How to Build a Software Factory for AI Coding AgentsDex Horthy, HumanLayer, and Vaibhav Gupta, BoundaryCredited on sheet 05 Execution EnvironmentThe Harness
- Practical Loop EngineeringAddy OsmaniCredited on sheet 07 OrchestrationThe Harness
- AI Agent Memory MasterclassThe Carbon LayerMemory
- Memory Harnesses for Long-Running Research AgentsStefania Druga, Sakana AICredited on sheet 05 Context AssemblyMemory
- Agentic SDLC ExplainedPavan BelagattiAgentic SDLC
- AI Can Code. That's Why You Should LearnChris Piech, StanfordCredited on sheet 04 Who Owns WhatAgentic SDLC
- How to Build a Software Factory for AI Coding AgentsDex Horthy, HumanLayer, and Vaibhav Gupta, BoundaryCredited on sheet 05 The Agent Factory FloorAgentic SDLC
- Introducing AI Driven Development Lifecycle (AI-DLC)Anupam Mishra and Raja SP, AWSCredited on 6 sheets 01 From Waterfall to Agents, 02 Beyond Code Generation, 03 The ADLC Loop, 04 Who Owns What, 05 The Agent Factory Floor, 06 The Unified Context LayerAgentic SDLC
- It Ain't Broke: Why Software Fundamentals Matter More Than EverMatt Pocock, AI HeroCredited on sheet 07 Code Is Not CheapAgentic SDLC
- Forward Deployed Engineering 101Kevin Bai, AnthropicForward Deployed
- Trust. Product. Impact.Colin Jarvis, OpenAI, with Apoorv Agrawal, AltimeterCredited on 3 sheets 02 Selling the Outcome, 03 The Half-Million Gap, 04 The Platform of PrimitivesForward Deployed
- Forward Deployed Engineering at CursorPauline Brunet, CursorCredited on 4 sheets 01 The 2×2, 02 Selling the Outcome, 04 The Platform of Primitives, 05 The Two QuestionsForward Deployed
- AI tools for Forward Deployed EngineeringVasuman Moza, Varick AgentsCredited on 3 sheets 02 Selling the Outcome, 05 The Two Questions, 06 When Every Platform Went AgenticForward Deployed