David Ryan weytani

SFDMU Test Data Quickstart

Seed Accounts, Contacts, Cases, Contracts, and Products into lower Salesforce environments using SFDMU (Salesforce Data Move Utility).

How It Works

SFDMU uses a single export.json config file to define which objects and fields to migrate. You can pull from a source org or from local CSV files. Objects are processed in dependency order automatically — parent records (Account) before children (Contact, Case, etc.).

Case Escalation Email — Scheduled Apex Design

Architecture Overview

Case Created → Trigger sets Next_Escalation_Email__c = NOW() + 5min
                          ↓
Scheduled Apex (runs every 15min) → Queries cases where Next_Escalation_Email__c <= NOW()
                          ↓
 Sends email → Updates Next_Escalation_Email__c to next interval

Apophis 2029 Market-Reaction Analysis

Methodology: HCP-MAD (Heterogeneous Consensus-Progressive Multi-Agent Debate) — Liu et al., arXiv:2604.09679 Protocol stage resolved at: Stage 1 — HCV (Heterogeneous Consensus Verification), round 0 Consensus mode: semantic-judge (Haiku 4.5) Agent pair: Opus 4.7 ∥ Sonnet 4.6 (heterogeneous) Consensus token: mild-sector-transient Date: 2026-04-18 | ~3 years to flyby (April 13, 2029)

Apophis 2029 Market Reaction Analysis — Agent 1 (HCP-MAD)

Model: claude-opus-4-7 Role: Agent 1 (HCV / HPAD pair member) Round: 0 (HCV) Date: 2026-04-18

David, one preface: I did real web research via WebSearch/WebFetch. The consistent finding across ~15 queries is that measurable, clean market reactions to widely-publicized "non-event" catastrophic predictions are surprisingly thin in the public record — most reactions are muddied by concurrent macro events (dot-com, GFC aftermath) or are sector-specific anecdotes rather than index moves. I'll be honest about that limitation rather than fabricate precision.

Market Reactions to Catastrophic Non-Events: Historical Precedents and the Apophis 2029 Scenario

Model: claude-sonnet-4-6 Role: Agent 2 (HCV / HPAD pair member) Round: 0 (HCV) Date: 2026-04-18 | T-minus ~3 years to Apophis flyby

Section 1: Historical Precedents — Market Reactions to Catastrophic Predictions That Did Not Materialize

Apophis 2029 Market-Reaction Analysis — HCP-MAD Full-Pipeline Run

Methodology: HCP-MAD (Heterogeneous Consensus-Progressive Multi-Agent Debate) — Liu et al., arXiv:2604.09679 Run type: Full-pipeline ablation — forced execution of all three stages Date: 2026-04-18 | ~3 years to flyby (April 13, 2029) Pair: Opus 4.7 (bear-primed) ∥ Sonnet 4.6 (bull-primed) Consensus mode: semantic-judge (Haiku 4.5)

HCP-MAD Methodology Comparison — Same Task, Two Runs

Task (identical in both runs): Historical analysis of market reactions to catastrophic predictions that became non-events, applied to predict market reactions to 99942 Apophis 2029 close approach. Date: 2026-04-18

Two runs of HCP-MAD (Liu et al., arXiv:2604.09679) on the SAME prompt, with different seeding and execution rules, to measure:

Answer stability across independent runs
Effect of adversarial seeding on stage progression
Token-cost profile per stage
ECV vs HPAD answer divergence when forced

HCP-MAD Full Pipeline — Agent 1 (Opus 4.7, Bear-Primed)

Role: HCV / HPAD pair member Prior: Bear-leaning tail-risk strategist (retail dominance, 0DTE gamma, viral misinfo make 2026 regime different from prior non-events) Round: HCV round 0 Tokens: 50,112 | Tool uses: 9 | Duration: 112s

	Feedback II Reflection

	Date of feedback conversation: Around the time of the session instructing us to complete this deliverable

	How did you prepare for the conversation?

	No prep really, I think we are both pretty well aware of our shortcomings and there was much that was going to be said that we weren't already somewhat aware of. We also had a number of personal discussions during the course of the project that made us more comfortable with eachother to begin with.

	How did the conversation go for you? What was easy about the conversation? What was more difficult?

	How have you changed since you started at Turing? How have your strengths helped you in that progress?

	More accutely aware of my shortcomings and what I need to work on. Knowing that Adaptability can be a double edged sword when I can't say no to people helped bring that aspect of my issues into focus.

	How do you feel like you have used your strengths when you've paired with others?

	When others might get bogged down in the minutia of trying to step through a problem I usually have been able to push us through it stratigically someway. I also have tended to lead every pairing, build out waffle and set expectations for milestone completion and general workflow. Also, refactoring comes easier to me as well as discovering and fixing bugs.
	How could you utilize these strengths more?

	I think I should continue to push to take more of a leadership role and continue to get comfortable directing the management of a project.