startup Scanr

@cruisemaniac2026-11-11

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Problem: Infrastructure teams need a way to stop coding agents from applying destructive Terraform changes to production before a human can review the plan.
Potential solution: Build a Terraform CI check that flags production deletes and replacements in an agent-generated plan and requires a human approval before apply.

@riyazmd7742026-11-11

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Problem: Agent builders need to catch failures caused by messy inputs and real tool behavior before releasing an agent that performed well in demos.
Potential solution: Build a regression test tool that replays anonymized failed support conversations against a sandboxed agent and compares tool calls and outcomes across releases.

@frederickcalj2026-11-11

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Problem: Early SaaS founders spread their time across acquisition channels without knowing which ones produce qualified conversations.
Potential solution: Build a two-week acquisition experiment tracker that attributes booked calls to each channel and ranks channels by qualified calls per founder hour.

@unusual_whales2026-11-11

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Problem: Startup founders may have difficulty translating a headline exit price into their personal proceeds after dilution and investor preferences.
Potential solution: Build a founder payout calculator that models liquidation preferences, dilution and exit values, then shows what each shareholder would receive.

@Polymarket2026-11-11

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Problem: Long-running research agents can spend days pursuing a hypothesis. Researchers need a way to see which leads were tested and where the evidence trail ends.
Potential solution: Build a research dashboard that records sources, rejected hypotheses and daily progress, with a budget limit and a checkpoint before another long run.

@rodrikdani2026-11-11

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Problem: Researchers and journal editors lack a consistent way to document where AI agents contributed to a paper.
Potential solution: Build a tool that turns research-agent activity into an editable disclosure of tasks, tools and human verification for manuscript submission.

@OptimaiNetwork2026-11-11

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Problem: Teams running multi-step AI agents need to see which model calls drive up the cost of completing a workflow.
Potential solution: Build a trace-based cost analyzer that measures cost per successful run and replays expensive steps against cheaper models to check for quality regressions.