Luis Ortega

finance-economics-failed-founder-characters-george-westinghouse v2.0 Ethical
Backstory: Luis is an inventive, resilient agritech founder who built a blockchain-based produce-tracking network to cut waste between farms and grocers. When a severe drought ruined pilot-region harvests and venture funding vanished, the startup shut down. Luis stayed to help farmers find new buyers, earning deep local respect while draining his own savings.
100% Complete
6/6 scenes
Model Performance Overview
Scene Performance Matrix
Scene meta-llama/llama-3.… mistralai/mistral-7… [email protected]… [email protected]… qwen/qwen-2.5-7b-in… qwen/qwen3-14b qwen/qwen3-8b
farmer-transition
Guiding a distressed farmer
 0.371
Details 		0.745
Details 		0.000
Details
Error
0.000
Details
Error
0.394
Details 		0.635
Details 		0.694
Details
investor-debrief
Post-shutdown investor call
 0.680
Details 		0.545
Details 		0.000
Details
Error
0.000
Details
Error
0.422
Details 		0.541
Details 		0.514
Details
drought-response
Reacting to worsening drought news
 0.654
Details 		0.682
Details 		0.000
Details
Error
0.000
Details
Error
0.624
Details 		0.522
Details 		0.747
Details
local-news-interview
Long-form podcast interview
 0.400
Details 		0.348
Details 		0.000
Details
Error
0.000
Details
Error
0.149
Details 		0.103
Details 		0.529
Details
tech-explainer
Explaining blockchain food tracking
 0.614
Details 		0.672
Details 		0.000
Details
Error
0.000
Details
Error
0.000
Details 		0.517
Details 		0.730
Details
reflective-journal
End-of-day personal journal entry
 0.497
Details 		0.527
Details 		0.000
Details
Error
0.000
Details
Error
0.430
Details 		0.295
Details 		0.539
Details
Test Scenes 6
0
Scene Order
Guiding a distressed farmer
ID: farmer-transition
🎯 Goal:
Offer three actionable buyer options and words of encouragement in a warm, practical tone.
📨 Input Events:
chat_msg farmer:maria_santos
"Luis, the co-op closed its doors. Where can I sell my tomatoes now?"
Ready for Testing
1
Scene Order
Post-shutdown investor call
ID: investor-debrief
🎯 Goal:
Deliver a concise two-paragraph debrief: key lessons learned and one clear next opportunity.
📨 Input Events:
chat_msg investor:aaron_lee
"Before I decide on future backing, summarize what went wrong and what’s next."
Ready for Testing
2
Scene Order
Reacting to worsening drought news
ID: drought-response
🎯 Goal:
Show resilience by outlining two concrete support steps for farmers within one short paragraph.
📨 Input Events:
world_event weather_service
"Alert: Regional drought severity upgraded; irrigation restrictions tightened."
Ready for Testing
3
Scene Order
Long-form podcast interview
ID: local-news-interview
🎯 Goal:
Produce a 400–500 word narrative covering startup origins, failure lessons, and future vision in an honest yet upbeat voice.
📨 Input Events:
chat_msg journalist:keisha_holmes
"Our audience wants your full story—mind sharing it on today’s episode?"
Ready for Testing
4
Scene Order
Explaining blockchain food tracking
ID: tech-explainer
🎯 Goal:
Explain in under 150 words, using simple language and one concrete example.
📨 Input Events:
chat_msg student:jayden_kim
"How does blockchain actually reduce food waste?"
Ready for Testing
5
Scene Order
End-of-day personal journal entry
ID: reflective-journal
🎯 Goal:
Write a 250–300 word introspective entry that mentions sacrificed savings, community gratitude, and renewed purpose.
📨 Input Events:
world_event system
"Day’s end: time for reflection."
Ready for Testing
Latency by Model (This Suite)
Fastest
  • [email protected]/Qw… 7356 ms
  • p95 • avg • N 11624 ms • 8126 ms • 6
  • qwen/qwen3-14b 22273 ms
  • p95 • avg • N 37213 ms • 26332 ms • 6
  • qwen/qwen-2.5-7b-instru… 25290 ms
  • p95 • avg • N 109214 ms • 42119 ms • 6
  • meta-llama/llama-3.1-8b… 26627 ms
  • p95 • avg • N 31054 ms • 26122 ms • 6
  • qwen/qwen3-8b 27500 ms
  • p95 • avg • N 33941 ms • 27709 ms • 6
Slowest
  • [email protected]/Qw… 46034 ms
  • p95 • avg • N 220382 ms • 91349 ms • 6
  • mistralai/mistral-7b-in… 31512 ms
  • p95 • avg • N 39739 ms • 31872 ms • 6
  • qwen/qwen3-8b 27500 ms
  • p95 • avg • N 33941 ms • 27709 ms • 6
  • meta-llama/llama-3.1-8b… 26627 ms
  • p95 • avg • N 31054 ms • 26122 ms • 6
  • qwen/qwen-2.5-7b-instru… 25290 ms
  • p95 • avg • N 109214 ms • 42119 ms • 6
Per-scene duration for this suite.
Suite Actions
Completion Progress 100%
6 of 6 scenes completed
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Export With Results
Evaluation Schema
Enhanced Framework
Version v2 ACTIVE
0 dimensions

Enhanced evaluation framework with character and technical dimensions

Top Weighted Dimensions View Details
Character Authenticity
0.182
Plan Validity
0.155
Contextual Intelligence
0.136
Recent Runs
31947460
Dec. 17, 2025, 12:01 a.m.
46668117
Dec. 16, 2025, 12:01 a.m.
27655674
Dec. 15, 2025, 12:01 a.m.
29177038
Dec. 14, 2025, 12:01 a.m.
28308396
Dec. 13, 2025, 12:01 a.m.
40525613
Dec. 12, 2025, 12:01 a.m.
36565636
Dec. 11, 2025, 12:01 a.m.
29316896
Dec. 10, 2025, 12:01 a.m.
42408805
Dec. 9, 2025, 12:01 a.m.
31347804
Dec. 8, 2025, 12:01 a.m.
Latency Overview (This Suite)