---
title: "Slides: WF2026: Autoresearch & Keynotes ft. Anthropic, Google DeepMind, Amazon AGI, Sonar, Arena, Recursive"
category: "slides"
video_id: "4sX_He5c4sI"
sourceLabels: ["Public YouTube video frames", "Public YouTube metadata"]
---

# Slides: WF2026: Autoresearch & Keynotes ft. Anthropic, Google DeepMind, Amazon AGI, Sonar, Arena, Recursive

## Source Video
[WF2026: Autoresearch & Keynotes ft. Anthropic, Google DeepMind, Amazon AGI, Sonar, Arena, Recursive](https://www.youtube.com/watch?v=4sX_He5c4sI)

## Relationship To World's Fair 2026
These slides are extracted from a public AI Engineer YouTube video connected to World's Fair 2026. Speaker-matched clips are supporting context unless later confirmed as exact session recordings; official livestream recordings are day-level/event-level source material.

## Related Scheduled Sessions
- No individual scheduled session mapping has been assigned yet; treat this as an event livestream deck.

## Extracted Slides
![[assets/slides/4sX_He5c4sI/slide-001.jpg]]

OCR text:

> AI Engineer
> World's Fair
> STARTING SOON
> STREAM STARTS JULY 1, 2026 9:00AM PT

![[assets/slides/4sX_He5c4sI/slide-002.jpg]]

OCR text:

> AI Engineer
> World's Fair
> Livestream
> July 1, 2026
> EVENT STARTS IN 04:54
> DESIGN ENGINEERING
> View Full Schedule
> ai.engineer/worldsfair/2026/schedule

![[assets/slides/4sX_He5c4sI/slide-003.jpg]]

OCR text:

> AI Engineers

![[assets/slides/4sX_He5c4sI/slide-004.jpg]]

OCR text:

> | WAT dehe) nt | Worlds |
> World” a mid
> 
> _ - ; AlEngincer
> 
> ! VVfo) gto Koa
> el 4 my :

![[assets/slides/4sX_He5c4sI/slide-005.jpg]]

OCR text:

> PAO] a eG mele Basal
> Norld's Fair — OpenAl
> 7 | ALEngineer ————
> ole Rc Res irld's Fair
> ELLE Saree rexsas ——_—_

![[assets/slides/4sX_He5c4sI/slide-006.jpg]]

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![[assets/slides/4sX_He5c4sI/slide-007.jpg]]

OCR text:

> AI Engineer
> World's Fair
> PRESENTED BY
> Microsoft
> Models are grown,
> not designed.
> 7 / 40
> In the Land of AI Agents, the Verifiers Are King
> Tariq Shaukat / Chief Executive Officer
> Sonar

![[assets/slides/4sX_He5c4sI/slide-008.jpg]]

OCR text:

> PNT Sheena
> World's Fair :
> : CAPABILITY OVERHANG i 4.
> a Microsoft Claude gets smarter in ay
> spiky ways 4
> 
> asralr ke
> 
> roar sy AYA" algo a phage
> setae Akz Inthe Land of Al Agents, the Verifiers Are King
> 
> VADOG vane oe G sonar —-

![[assets/slides/4sX_He5c4sI/slide-009.jpg]]

OCR text:

> System Prompt Design
> Small system prompt,
> few tools, lots of
> examples
> Large system prompt,
> lots of examples, many
> tools
> Smaller system prompt,
> tool search, no examples
> 14 / 40

![[assets/slides/4sX_He5c4sI/slide-010.jpg]]

OCR text:

> Tern oe |
> a It's closer to a biology, than a physics.
> a Microsoft
> Eee hah ne eee ate enc
> Ope
> Woi ; . mS
> Engineering the future of Al
> nd ae

![[assets/slides/4sX_He5c4sI/slide-011.jpg]]

OCR text:

> World's Fair |
> Tradeoffs are not 5
> real a
> woras rdalr
> i= na! Bye
> Field Guide to Fable
> Thariq Shihipar ANTHROPAC 7

![[assets/slides/4sX_He5c4sI/slide-012.jpg]]

OCR text:

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![[assets/slides/4sX_He5c4sI/slide-013.jpg]]

OCR text:

> renigewer Coding agents are quickly getting a lot better
> World's Fair
> Time horizon of software tasks Al can complete
> at 50% accuracy
> ee
> : ele ee eS)
> Woric ir a La wh a #: 7 f a A
> f eereaten y wis . , ; 7 . 7 ok
> 
> Treks Ven Cm UCR ice UL Ca)
> ATC i Tariq Shaukat [ Real Renata

![[assets/slides/4sX_He5c4sI/slide-014.jpg]]

OCR text:

> Eye od .
> j ; The models are getting smarter, but
> World's Fair ; hae ;
> they are still producing problematic code
> Gemini 3.1 Claude Opus 4.7 GPT-5.5
> : cen alte la) Thinking Medium
> a Microsoft a a
> 158.4 Wane 151.4
> 614 803 504
> var! rach 68
> sere ar elute
> an TOE PCO UGA rant Gal Car)
> ok World Tariq Shaukat @ sonar ~--

![[assets/slides/4sX_He5c4sI/slide-015.jpg]]

OCR text:

> Se oa A temporary speed boost + a persistent quality decline
> World's Fair
> a Microsoft
> Temporary velocity Increase in security, Increase in code
> spike that disappears maintainability, and complexity
> within 3 months reliability issues
> OTe aN ean
> oe ctoralels
> Vey east ; ;
> in the Land of Al Agents, the Verifiers Are King
> rerice NOUR RS e @ sonar —-

![[assets/slides/4sX_He5c4sI/slide-016.jpg]]

OCR text:

> GUIDE
> Agents need enterprise context and constraints
> so that they are ‘verification aware’
> Repository-aware project specific context...
> • Architectural awareness
> • Semantic navigation
> ...and constraints...
> • Dependency guidance
> • Coding standards and guardrails
> • Intended architecture
> ...provided dynamically and/or embedded in models
> In the Land of AI Agents, the Verifiers Are King
> Tariq Shaukat / Chief Executive Officer
> Sonar

![[assets/slides/4sX_He5c4sI/slide-017.jpg]]

OCR text:

> IN tented Wadd oe A
> World's Fair |
> Verification should be
> and
> E r goo
> mo In the Land of Al Agents, the Verifiers Are King
> ‘ae Tariq Shaukat ; [ ME rat] eta - 7

![[assets/slides/4sX_He5c4sI/slide-018.jpg]]

OCR text:

> ai Verified agentic remediation in the Cl flow
> cee ees and in the background
> CODE MAINTENANCE LOOP
> Seconds / Dera aaa
> | PInT nea i aan a
> beta
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> 6 aaa Nee a Ee eae ea ee ere ee
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> ir @).4 Tariq Shaukat G sonar —-

![[assets/slides/4sX_He5c4sI/slide-019.jpg]]

OCR text:

> ae Master the critical verification loops to bring the Agent
> World's Fair Centric Development Cycle to life
> Ere wes en 7
> F soa aK» aay Raa eLRSTEC Ia %) aw
> a Microsoft aa ooo
> © giar ROD iaeneresrs
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> ate (
> In the Land of A! Agents, the Verifiers Are King
> 2.6) Wor Tariq Shaukat G sonar —--

![[assets/slides/4sX_He5c4sI/slide-020.jpg]]

OCR text:

> AI Engineer
> World's Fair
> Clicking was
> the easy part.
> Amazon AGI Lab
> Perception Agents
> Antje Barth / Member of Technical Staff Amazon AGI Lab

![[assets/slides/4sX_He5c4sI/slide-021.jpg]]

OCR text:

> Perceive
> Act
> Plan
> Perception Agents
> Antje Barth / Member of Technical Staff Amazon AGI Lab

![[assets/slides/4sX_He5c4sI/slide-022.jpg]]

OCR text:

> World's Fair |
> Why perception agents?
> a Willa cexxeyne They close the gap on computer use.
> They don’t need an API or back-end access.
> You show what you mean, don’t describe it.
> faves
> er | ME treo ae Xe. Uy
> tows act Met dec ra Amazon AGI Lab

![[assets/slides/4sX_He5c4sI/slide-023.jpg]]

OCR text:

> World'sFair AIEngineer Annotation
> SomePodcast
> etSoeLsy
> ehetheritec re aftar a contet sndoe setesh New episo fons abouf inteligencg = what'a is, how to Iukd a, and
> sFair aws Neural Network What Even Is Intelligence?(Asking for a Friend's
> ize spp AEn PerceptionAgents
> sFair ba AntjeBarth/MemberofTechnical StaffAmazonAGl Lab

![[assets/slides/4sX_He5c4sI/slide-024.jpg]]

OCR text:

> World'sFair AlEngineer VisualVerification
> L-1000-P-RACT Cicdds akbuon eet e
> pols/20260626-1933412 porLmd taskfliow-2:Complete a task
> taskflow-3:Deletea task Verificatin Rests fo chcp-
> eried tked mnt,oalountu
> taskfiow-4:Initial page state
> Vetedhead
> cormp
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> d'sFair av Artifacts l-verfcatIofos/-4foncaros vis/desi-human-re desigpecicon -veriicatin/specs/-5cmed ory fles82nles) sl-weririctim/sesso/arets-s
> orld AlEngi PerceptionAgents
> d'sFair AntjeBarth/MemberofTechnical StaffAmazonAGl Lab

![[assets/slides/4sX_He5c4sI/slide-025.jpg]]

OCR text:

> World'sFair AlEngineer PlatfConts|144|] 1eectes|17117] *ReswltI ALL PASS* kcesbiuty1u
> No visuel fallures detected.Allrwles match the design specification.
> tcap-1]fleeort/tcap-1.eert.Pdcastligpageilayalcntentectisste_clete
> est ALLPASS(/3）
> Aoditfininps
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> rld'sFair AJEn a --p--.- 7es7ea-es4c-4caf-855c-21394ac7fefa/
> ariz Worl AIE Perception Agents
> rld'sFa AlEnginee AntjeBarth/MemberofTechnicalStaffAmazonAGl Lab

![[assets/slides/4sX_He5c4sI/slide-026.jpg]]

OCR text:

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> rere Otis)
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> aretaT ae Antje Barth Amazon AGI Lab

![[assets/slides/4sX_He5c4sI/slide-027.jpg]]

OCR text:

> EET ead
> MW felale kd are
> From Assembly to Vibe Coding
> Benoit Schillings
> 
> occa i
> 
> Open&é re
> reg Research to Reality with Google DeepMind
> a. eee Google DeepMind

![[assets/slides/4sX_He5c4sI/slide-028.jpg]]

OCR text:

> END Sie eg
> ; The Software Eras
> World's Fair | |
> 
> Atira Cetra eren te) ee 7 ;
> The Modern/Cloud Era J . a
> The Frontier Al Era bs a A
> 
> Worle! 'T
> 
> OFelon Vu
> vs Research to Reality with Google DeepMind
> - Benoit Schillings Google DeepMind

![[assets/slides/4sX_He5c4sI/slide-029.jpg]]

OCR text:

> State of AI Software Engineering
> Super-human Syntax Generation 95%
> Local Problem Solving & Tasks 70%
> Multi-step Codebase Planning 45%
> Architectural System Decisions 25%
> Models generate localized code rapidly but struggle to form cohesive systems, design complex architectures, and predict overall security flaws.
> Research to Reality with Google DeepMind
> Benoit Schillings / Vice President of Research Google DeepMind

![[assets/slides/4sX_He5c4sI/slide-030.jpg]]

OCR text:

> EMI Spe saad
> fj 5 The AlphaZero Sandbox
> World's Fair |
> - 5 a & eetgreupayry ery oe yore : / vy - S Bs ; : 1~ 1, S a
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> Oper
> vf Research to Reality with Google DeepMind
> Benoit Schillings Google DeepMind

![[assets/slides/4sX_He5c4sI/slide-031.jpg]]

OCR text:

> The Economics of Code
> ~$0
> Marginal Cost of Code
> The Bottleneck Transformed
> When generating code becomes virtually free, software
> creation is no longer the key constraint. The system pressure
> shifts to validation and specification.
> The core disciplines of the future engineer will focus on
> defining system correctness, auditing security boundaries,
> and designing precise constraints.
> Write only code, fast code refresh. This is the same step we
> saw with compilers, who still reads the assembly output?
> Research to Reality with Google DeepMind
> Benoit Schillings / Vice President of Research Google DeepMind

![[assets/slides/4sX_He5c4sI/slide-032.jpg]]

OCR text:

> AI Engineer World's Fair
> Next-Gen Architecture Foundations
> Multimodal Design Reason
> Software architecture is visual and spatial, not just
> textual. Modern multimodal models like Gemini are
> bridging this gap, allowing direct evaluation of visual
> flowcharts and interface layouts against the
> synthesized code.
> LLM-Native Target Languages
> Human developers require languages that optimize for
> code readability. For AI models, do we still need
> standard languages, or do we pivot to mathematically
> precise, highly rigorous specification engines like Rust?
> Research to Reality with Google DeepMind
> Benoit Schillings / Vice President of Research Google DeepMind

![[assets/slides/4sX_He5c4sI/slide-033.jpg]]

OCR text:

> APARNA DHINAKARAN
> CO-FOUNDER & CPO
> arize

![[assets/slides/4sX_He5c4sI/slide-034.jpg]]

OCR text:

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> a Nera) cic arize

![[assets/slides/4sX_He5c4sI/slide-035.jpg]]

OCR text:

> Different Evals for Different Failures
> Deterministic code
> Prompt hallucinations
> Trajectory & harness failures
> Code Evaluators
> Unit tests, assertions, rules.
> Classic LLM-as-Judge
> Score a known, well-defined failure.
> input
> output
> criteria
> LLM
> label / score / explanation
> Agent-as-Judge
> Another agent reviews the whole trajectory.
> system
> status
> skills
> tools
> judgments
> insights
> failure modes
> evidence
> Agent
> arize
> Evals Track
> Aparna Dhinakaran / Co-founder & Chief Product Officer
> arize

![[assets/slides/4sX_He5c4sI/slide-036.jpg]]

OCR text:

> AI Engineer
> World's Fair

![[assets/slides/4sX_He5c4sI/slide-037.jpg]]

OCR text:

> eres eld
> 
> Agenda
> 
> | Meet OG Assist
> 
> 2. | The Origin Story
> 
> | Betting on Effect
> 
> a. | The Core Agent Loop
> 
> s. | A2A, Evals & Sandboxing ~
> 
> s. | Long Context Handling ga
> Monitoring & Observability 7 q
> 2. | Tools. Skills & Dev Workflows how we collect feedback, a ;

![[assets/slides/4sX_He5c4sI/slide-038.jpg]]

OCR text:

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> eee ne > ° asked me to join. OG Assist grew from there. We started to
> ‘ Baer er ee . . 4 integrate deeply with our products through frontend and
> backend tools.
> a little while back we we we saw

![[assets/slides/4sX_He5c4sI/slide-039.jpg]]

OCR text:

> AEngi
> World'sFair
> You are watching an AIE Online Talk
> Pre-Recorded for Worid's Fair 2026.
> OpenGov
> Effect
> throughoutthispresentation

![[assets/slides/4sX_He5c4sI/slide-040.jpg]]

OCR text:

> core
> World's Fair
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> pipe Stream runforfach part -» Effect sync s+ console. log part
> full control of our own agent

![[assets/slides/4sX_He5c4sI/slide-041.jpg]]

OCR text:

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![[assets/slides/4sX_He5c4sI/slide-042.jpg]]

OCR text:

> AI Engineer
> World's Fair

![[assets/slides/4sX_He5c4sI/slide-043.jpg]]

OCR text:

> World'sFair AIEngineer
> Open-Ended Evolution
> FromBiology toScience,TechnologysndAl
> World'sFair
> Fair raintrust
> rld'sFair Engineering the future of Al

![[assets/slides/4sX_He5c4sI/slide-044.jpg]]

OCR text:

> World'sFair AlEngineer Technologicalevolution Fig.1World Product,Data vs.Modets CES Combined Exp Model Hyperbolic Model Sum of ExpModel +WoridProductEstimates recursive
> 10,000,000.000.000
> 1000,000,000,000
> 100,000,000,000
> 10,000,000.000
> Revolution Industrial 1000,000,000
> Entightenment 10,000,000 100,000.000
> Hunting 1000,000
> 100,000
> W sFair 10,000,000 1000,000 100,000 10,000 1000 100 10 10,000
> Hanson,R.(2020).Long-Term Growth AsA Sequence of Exponential Modes.
> US1
> Engineering the future of Al

![[assets/slides/4sX_He5c4sI/slide-045.jpg]]

OCR text:

> es Technological evolution > Growth RCL
> World's Fair
> “We believe that there is no material problem — whether created by nature or by
> technology - that cannot be solved with more technology.
> ee eee ape ee hee ean a ae ec en ENC
> ar ce ee WO ee Oe ee
> We have a problem of poverty, so we invent technology to create abundance."
> oo Vv ee Felt
> a gs
> . , e ry
> bi Engineering the future of Al

![[assets/slides/4sX_He5c4sI/slide-046.jpg]]

OCR text:

> encase Technology <> Science and Theory RCS
> World's Fair |
> We choose the theory which best holds its own in
> ; competition with other theories ; the one which, by
> ; natural selection, proves itself the fittest to survive [...]
> Pm re cosrolns the one which is also testable in the most rigorous way.”
> a
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> owed
> , c. 23
> a mre! | eras :
> cL | Engineering the future of Al

![[assets/slides/4sX_He5c4sI/slide-047.jpg]]

OCR text:

> THE
> EUREKA
> MACHINE
> WHY AI IS THE KEY TO UNLOCKING A
> NEW ERA OF SCIENTIFIC DISCOVERIES
> Engineering the future of AI

![[assets/slides/4sX_He5c4sI/slide-048.jpg]]

OCR text:

> , f recursive
> World's Fair GPUs, The Internet, Browsers, Search will become infrastructure for
> scientific superintelligence and part of pillar 1 of the Eureka Machine.
> , ac Oa ames re Le DCT Tae ST Sl Men Ea OE Sh CD oD OTe ATRL Conran rears en ee
> = Microsoft erseeee al orbs Meare Or ge) mega os Mer olan cine Mn arcere tre
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> 
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> Engineering the future of Al

![[assets/slides/4sX_He5c4sI/slide-049.jpg]]

OCR text:

> AI Engineer World's Fair
> First simple proof points
> Better training, faster training, better kernels
> recursive
> 01 NanoChat Autoresearch
> Several dozens of humans and hundreds of their agents
> SOTA: 0.9372 BPB → 0.9109 BPB
> 1.3x speedup to reach the same loss
> 02 NanoGPT Speedrun
> 83 human record-setting contributions to the leaderboard
> SOTA: 79.7 s → 77.5 s
> Similar or larger improvement than recent human contributions
> 03 SOL-ExecBench
> 235 kernel-writing tasks derived from real workloads
> SOTA: 0.699 SOL → 0.754 SOL
> 18% reduction in gap to the optimal performance estimate of 1.0
> Engineering the future of AI

![[assets/slides/4sX_He5c4sI/slide-050.jpg]]

OCR text:

> AI Engineer
> World's Fair
> SOL-EXECBENCH · 03 / 03
> recursive
> SOL-ExecBench: SOL score per kernel
> SOL-ExecBench mean SOL score by kernel category
> 0.564
> Cursor
> 0.690
> doubleAI
> 0.699
> Leaderboard best
> 0.754
> Recursive
> 0.5 = optimized PyTorch baseline · 1.0 = analytical optimal performance estimate · 235 kernels
> Engineering the future of AI

![[assets/slides/4sX_He5c4sI/slide-051.jpg]]

OCR text:

> we rs Pa TT f recursive
> ; ; aces of Intelligence
> World's Fair | Pp |
> ewe em toma ose ae Wied OO Tani OU sent RommOu Mma Mac mose taca nari
> Visual Intelligence Physical Intelligence
> Natural Language Intelligence Social Intelligence & Morality
> Knowledge Creative Intelligence
> Reasoning Meta-Cognition
> Computational Speed Survival & Replication
> b WarkO° World
> voralakcam a . eetaeey
> ® ve 7 : ; e e
> 2 ee Engineering the future of Al

![[assets/slides/4sX_He5c4sI/slide-052.jpg]]

OCR text:

> rer nes Fair
> =} 7 oo
> 4 nl fa y, :
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> A GROUNDING QUESTION • WE RETURN TO IT AT THE VERY END
> You are Abraham Lincoln. Under what circumstances may a
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> AEng
> World'sFair
> You are watching an AJE Online Talk
> Pre-Recorded for Worle'sFair 2026
> Weallwantoutcomes.
> Agents that work on our behalf -reliable co-workers-while we're out on a hike.
> Thebottleneckisnotintelligence.lt'sreliability.lt'strust.
> Thenext day-they empty the entire contentsof my Solana wallel.

![[assets/slides/4sX_He5c4sI/slide-104.jpg]]

OCR text:

> AEngi
> World'sFair
> You are watching an AJE Online Talk
> Pre-Recorded for Worid'sFair 2026
> THESIS
> Today's agents are
> mismanaged geniuses
> The intelligence is there.
> Stop BabyalttingAgents,Start
> Authering Outcomes
> TURING POST-RAYMOND WEITEKAMP
> ALEXZHANG·ZEDLI·OMARKHATTAB
> Stop BabysittingAgents.StartAuthoring Outcomes
> The Mismanaged Genluses Hypothesis

![[assets/slides/4sX_He5c4sI/slide-105.jpg]]

OCR text:

> World'sFair AlEngineer AUTORESEARCH
> its justa whileloop
> PROPOSE CORRECT? BENCHMARK KEEP/REVERT
> d'sFair aZonAGI
> tintrust dd'sFa AEng autoresearch for kernels eteiasybhakta
> d'sFair ether Engineering the future of Al

![[assets/slides/4sX_He5c4sI/slide-106.jpg]]

OCR text:

> World'sFair AEngi Pre-Recorded for Worla'sFair2026. You are watching an AJE Online Tak
> EXAMPLE
> Count Unnecessary Reexplaining Explain theUI change
> Platform 02 Reexplain the published Ul change whle integratingitinto Module1
> Explain the Module1issue Utchange before publishing the updated
> Module1 integrating u change Module2 while lntgrating into Modue Reexplaln the fixedUl change
> Reexplain the fxedUl change whlle Integrating l into Module
> 2
> UI Explain everything white Integratir
> UNNECESSARY REEXPLAINING

![[assets/slides/4sX_He5c4sI/slide-107.jpg]]

OCR text:

> World'sFair AEng Pre-RecordedforWorid'sFair2026. You are watching an AiE Online Talk
> Session graph
> decisions about the work. All the conversations and
> Repograph
> Hardcode artifactsproduced by yourorg. auth-lib repe 1
> Soit leansontheonewhoactuallyunderstandswhatisgoingon:the developer.

![[assets/slides/4sX_He5c4sI/slide-108.jpg]]

OCR text:

> AEng
> World'sFair
> You are watching an AlE Online Talk
> Pre-Recorded for Worid's Fair 2026.

![[assets/slides/4sX_He5c4sI/slide-109.jpg]]

OCR text:

> World'sFair AEngi Pre-Recorded torWorld'sFar2026. You are watching an AJE Online Talk
> FIXINGAMNESIA
> The samemachineryfixesepisodicmemory.
> We captureyourwork,nomatter howmanyreposit touches.
> Intent Repos Pull requests Clstatuses Allagenttraces

![[assets/slides/4sX_He5c4sI/slide-110.jpg]]

OCR text:

> AEngi
> World'sFair
> You are watching an AJE Online Talk
> Pre-Recorded forWorid'sFair 2026.
> Create a plan to add a new API to the backend which returns the
> title of the frontendwebapp asa string and then plan the
> refactoring of the website title accordingly so it uses the one
> returned by the API.Don't inplement just yet,just create the
> plan
> OI'll investigate both repos to build an accurate plan.Let me
> explore the frontend directly and delegate a read-only
> investigation of the backend in parallel.
> Running 2agents（ctrl+o to expand)
> Explore （Explore frontend title handling)·18 tool uses·15.2k
> tokens
> LSearching for 4patterns,reading6 files
> polygraph:polygraph-delegate-subagent (Delegate backend
> investigation)
> （ctrl+b to run in background)
> Leavening-（3es↓2.θktokens)
> LTip:Use /voice to enable push-to-talk dictation
> poly-frontend-93df5cb8 git:(fetch-app-title-from-backend-c9be-
> auto mode on（shift+tab to cycle）·gh auth login
> main
> ↑/↓to select.Enterto view
> OExplore
> Explore front_14s↓16.8k tokens
> Opolygraph:poLygraph-delegat
> Delegateback7s·↓9.4k tokens

![[assets/slides/4sX_He5c4sI/slide-111.jpg]]

OCR text:

> AEngi
> World'sFair
> You are watching an AJE Online Talk
> Pre-Recorded forWorld'sFair 2026.
> branch,PR,or oter
> for instructions.
> sito
> untit
> explicitly
> Wait
> Addsanen
> hat
> GET/opi/app-info
> spoint that returns the frontend app title as JsoN:
> I'LL refresh froe the Polygraph guidance first since this is a resueed Polyoraph session, then
> json
> read the parent Log only.Iwon't make repository.branch,or PR cha
> 9es.
> Explored
> Read SKILL.nd (polygraph:polygraph skilL)
> B HOE
> service.tsrepository.tstypes.ts,each exporting a class plus a defautt-ired
> Nowsrc/app-info/doin airroring the existingproducts/pattern:routes.ts
> singteton.
> The Polygraph skill confiras thisisa resume/read-only checkpoint:restore context, then stop
> PolyShopning').
> only the relevant state.
> -Intine Json response schena,consistent with the Jso-onty conventions.
> Ranwc-1/Users/vsavkin/.polygraph/sessions/fetch-app-titte-fron-backend-c9be44e2/Logs/
> Tests
> parent.tog
> CoLocated vitest specs for routes/service/repository.
> test'all pass (18/18)
> npn run Lint'
> ipa runbuite"
> parent.log
> S
> Explored
> Related
> endpoint.
> Frontend consuser PR inpoly-fronteod-93efscb
> Read parent.Log
> Alt your peior Logs are avaflable here:
> The parent Log shoas the plan phase,the contract decision,and the user's Later request to
> 1nplenent and push PRs. I'a checking the final log Lines so I don't miss the handoff point.
> o-Snapshot-poLy-backend-93df5cb0-605e84f5s9ce.log
> Explored
> PLease read the log fite to become famstiar with what you were doing before.After you resume,do
> Read parent.log
> not nake code,branch,PR,or other repository changes until the user explicitly asks you to make
> changes.Wait for Instructions.
> Read parent.log and refreshed context. The Log ends at the earlier *ieptenent the plan and push
> I'll read the prior child-agent
> shettt
> the PRs request,hite your handoff says that work is already done and PRs exist for backend and
> frontend.
> The first chunk covers the read-o
> have the inplenentation and PR co
> shell（
> Resuned and read the available c
> branch,PR,or code changes sce
> the Fastify backend patterns, rou

![[assets/slides/4sX_He5c4sI/slide-112.jpg]]

OCR text:

> AEngi
> World'sFair
> You are watching an AIE Oniline Talk
> Pre-Recorded tor Worid'sFair 2026.
> readMcpResource(Read resource "polygraph://sessions/fetch-app-title-from-backend-c9be44e2" from server "plugin:polygraph:polygraph-mcp")
> "contents":[
> +33 tines （ctrl+o to expand）
> The previous work threaded the title into the Navbar and Footer.Let e look at how the title is rendered there.
> Listed 1directory(ctrl+oto expand)
> src)
> 36fac02 Initial commit
> --FILES--
> src/lib:
> +3lines （ctrL+o to expand)
> The merged code isn't in this snapshot's history (just the initial commit),but the title rendering lives in the Navbar.Let me look.
> Reading1file_（ctrl+otoexpand)
> Lsrc/components/Navbar.tsx
> Tempering-（29s·↓903 tokens·thinking with high effort)
> LTip:Run /install-github-app to tag @claude right fron your Github issues and PRs
> poly-frontend-93df5cb8 git:（fix-the-bug-with-title-1edeacf8)[0pus 4.8（1M context)]

![[assets/slides/4sX_He5c4sI/slide-113.jpg]]

OCR text:

> AEngin
> World'sFair
> You are watching an AJE Online Talk
> Pre-RecordedforWorld'sFair2026.
> Isearched across prior sessions.Two are directly relevant to what you're doing:
> vector-indexing mechanics you're about to apply.just on the repositories collection instead of PRs.
> 2.pr-description-search-opened by Hax Kless,~1month ago.Added full-text search to the PR collection specifically.Relevant for the
> collection shape,existing indexes,and any write-path considerations on PRs.
> Together they bracket your task well:one shows the vector-indexing pattern, the other shows the PR collection you're nodifying.
> The senantic search surfaced these by relevance but didn't return their session IDs,and neither appears in the recent-session List （both
> predate it),so I can't auto-load then yet.To pull the full detail-file paths,index definitions,migration approach,and any gotchas-
> I'dwant toopen then directly.
> Wantneto do oneofthese?
> 1.Load index-repositories （recommended)-the vector-indexing playbook to mirror.If you have its session ID or URL handy.paste it and I'Ll
> pull the full breakdown.
> 2.Loadpr-description-search-for how the PR collection'sindexes are currently set up.
> 3.Both,and I'll synthesize a combined “here's how to add vector indexing to the PR collectionplan grounded in what those sessions
> actually did.
> Ifyou don'thave theIDs,tell newhichone(s)you want and I'lldig them out.
> Workedfor41s
> ocean git:(add-pr-indexing-9c7ca610)[0pus4.8（1M context)]

![[assets/slides/4sX_He5c4sI/slide-114.jpg]]

OCR text:

> World'sFair The agent can see all the code and AEngine You are watching an AlE Online Talk Pre-Recorded tor World'sFar2026.
> remembereverydecisionmade.
> SesslonA suthrefactor SessionC APIV2
> SessionB addSsO
> sdk
> repo5
> auth-lib repo1 gateway reoo3 api-core repo4
> tokens repo2
> wet

![[assets/slides/4sX_He5c4sI/slide-115.jpg]]

OCR text:

> World'sFair
> You are watching an AJE Online Talk
> Pre-Recorded for Worla's Fair 2026.
> The agent is its data
> Specifically: the log
> Theagentisitsdata.

![[assets/slides/4sX_He5c4sI/slide-116.jpg]]

OCR text:

> ieee THE THESIS
> World's Fair A blueprint for 2026
> . The loop is the product.
> Build the operating muscle around any model.
> System distillation is the moat.
> Own the Al + data flywheel.
> 3 Valued work per watt is the score.
> , Maximize Al usefulness per unit of compute, latency, and risk
> ap Wortd’: “air
> ele I
> : Introspection:
> a
> z Engineering the future of Al

![[assets/slides/4sX_He5c4sI/slide-117.jpg]]

OCR text:

> PNT erg MRS
> World's Fair Takeaways
> 
> The loop is the product.
> System distillation is the moat.
> Valued work per watt is the score.
> 
> ir Amaze AGI Lab
> 
> ef ' ile
> 
> ie
> 
> 7 Engineering the future of Al

![[assets/slides/4sX_He5c4sI/slide-118.jpg]]

OCR text:

> World'sFair
> You are watching an AlEOnline Talk
> Pre-Recorded torWorio'sFair2026.
> WHATAFACTORYACTUALLYI!
> The company isn't the machines.It's the knowledge.

![[assets/slides/4sX_He5c4sI/slide-119.jpg]]

OCR text:

> World'sFair AEngine Pre-Recorded tor Worid'sFair 2026 You are watching an AIE Online Talk
> FEEDITEVERYTHING
> Years of quotes,drawings,schedules，emails.Our internet,not the internet.

![[assets/slides/4sX_He5c4sI/slide-120.jpg]]

OCR text:

> AEnpin
> World'sFair
> You are watching an AJE Online Talk
> Pre-RecordedforWorid'sFair2026.
> THIRTY-SIXAGENTS,ONEJOBEACH
> Athena

## Slide-Derived Subjects To Review
Subject extraction uses video title, related session titles/descriptions, transcript context, and OCR text when available. OCR is best-effort and should be reviewed against the embedded slide images.