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		<copyright>&#xA9; 2026 Triangle Technology Signals</copyright>
		<itunes:subtitle>Who is changing Triangle technology, what they did, and why it matters.</itunes:subtitle>
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	<title>AI Tech Signals: Weekly Summary for August 7</title>
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	<description><![CDATA[For long-running coding-agent work, this week's evidence points toward keeping an explicit, inspectable account of current state. Conversation history alone may not reliably show which facts remain current, which approaches failed, or whether the reported result passed its acceptance checks.]]></description>
	<itunes:subtitle><![CDATA[For long-running coding-agent work, this weeks evidence points toward keeping an explicit, inspectable account of current state. Conversation history alone may not reliably show which facts remain current, which approaches failed, or whether the reported]]></itunes:subtitle>
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	<itunes:title><![CDATA[AI Tech Signals: Weekly Summary for August 7]]></itunes:title>
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	<content:encoded><![CDATA[For long-running coding-agent work, this week's evidence points toward keeping an explicit, inspectable account of current state. Conversation history alone may not reliably show which facts remain current, which approaches failed, or whether the reported result passed its acceptance checks.]]></content:encoded>
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	<title>AI Tech Signals: Weekly Summary for August 14</title>
	<link>https://triangletechsignals.com/podcast/ai-tech-signals-week-ending-august-14-2026/</link>
	<pubDate>Sat, 29 Aug 2026 20:00:00 +0000</pubDate>
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	<description><![CDATA[This week's evidence points to a practical caution: a business builder should define outcomes and proof before copying a human engineering ritual wholesale into the agent loop. More reliable comparisons need repeated evidence and a frozen model configuration, especially when defaults and behavior are changing.]]></description>
	<itunes:subtitle><![CDATA[This weeks evidence points to a practical caution: a business builder should define outcomes and proof before copying a human engineering ritual wholesale into the agent loop. More reliable comparisons need repeated evidence and a frozen model configurat]]></itunes:subtitle>
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	<itunes:title><![CDATA[AI Tech Signals: Weekly Summary for August 14]]></itunes:title>
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	<itunes:summary><![CDATA[This week's evidence points to a practical caution: a business builder should define outcomes and proof before copying a human engineering ritual wholesale into the agent loop. More reliable comparisons need repeated evidence and a frozen model configuration, especially when defaults and behavior are changing.]]></itunes:summary>
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	<title>AI Tech Signals: Weekly Summary for August 21</title>
	<link>https://triangletechsignals.com/podcast/ai-tech-signals-business-builder-pilot/</link>
	<pubDate>Sat, 29 Aug 2026 20:00:00 +0000</pubDate>
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	<description><![CDATA[The sources suggest a practical operating rule: a business builder may improve the odds of useful AI-built software by defining success before construction and demanding visible evidence afterward. Specify the outcome, constraints, examples, approval boundaries, and acceptance checks, then use a coding agent for implementation and testing inside those permissions while you review the evidence. The evidence here comes from small benchmarks, exploratory experiments, practitioner examples, and vendor release notes. It supports practical operating guidance, not a universal rule.]]></description>
	<itunes:subtitle><![CDATA[The sources suggest a practical operating rule: a business builder may improve the odds of useful AI-built software by defining success before construction and demanding visible evidence afterward. Specify the outcome, constraints, examples, approval bou]]></itunes:subtitle>
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	<itunes:title><![CDATA[AI Tech Signals: Weekly Summary for August 21]]></itunes:title>
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	<content:encoded><![CDATA[The sources suggest a practical operating rule: a business builder may improve the odds of useful AI-built software by defining success before construction and demanding visible evidence afterward. Specify the outcome, constraints, examples, approval boundaries, and acceptance checks, then use a coding agent for implementation and testing inside those permissions while you review the evidence. The evidence here comes from small benchmarks, exploratory experiments, practitioner examples, and vendor release notes. It supports practical operating guidance, not a universal rule.]]></content:encoded>
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