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Slides: Recursive Coding Agents - Raymond Weitekamp, OpenProse

Source Video

Recursive Coding Agents - Raymond Weitekamp, OpenProse

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

• Recreated text/layout view: open HTML recreation
• AI slide classifier: title_card confidence 0.97
• Text source: agent_vision.

Slide text:

Recursive Coding Agents

Raymond Weitekamp

RAW.works | OpenProse

@raw_works

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.99
• Text source: agent_vision.

Slide text:

We all want outcomes.

Agents that work on our behalf - reliable co-workers - while we're out on a hike.

The bottleneck is not intelligence. It's reliability. It's trust.

One day - my agents build me a full SaaS app from a single prompt.

The next day - they empty the entire contents of my Solana wallet.

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.98
• Text source: agent_vision.
• OCR decision: ready — Dense slide with screenshot cards and small captions; OCR is better suited than manual transcription for the embedded text.

Slide text:

Today's agents are mismanaged geniuses

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.98
• Text source: agent_vision.
• OCR decision: ready — Contains a paper screenshot plus small diagram labels and bullet text; OCR should recover the embedded text more reliably.

Slide text:

Context itself is the object of computation

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.98
• Text source: agent_vision.
• OCR decision: ready — Dense comparison table with multiple columns and rows; OCR is the right extraction method.

Slide text:

Lots of things feel close.

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.99
• Text source: agent_vision.
• OCR decision: ready — Two-column slide with code-like examples and fine text; OCR is better than manual transcription here.

Slide text:

OpenProse explicitly declares subagent work

• Recreated text/layout view: open HTML recreation
• AI slide classifier: content_slide confidence 0.99
• Text source: agent_vision.

Slide text:

Recursive Coding Agents FTW

Trust is reliability

A new paradigm of inference-time compute

Coding agents can be RLMs

Classification audit: raw/sources/slide-ai-classification/slides/3hXJI2q0Jz8/audit.json

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.