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	<title>Tech News &#8211; Firethering</title>
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		<title>MiniMax M2.7: The Agentic Model That Helped Build Itself</title>
		<link>https://firethering.com/minimax-m2-7-agentic-model/</link>
					<comments>https://firethering.com/minimax-m2-7-agentic-model/#respond</comments>
		
		<dc:creator><![CDATA[Mohit Geryani]]></dc:creator>
		<pubDate>Sun, 12 Apr 2026 08:29:30 +0000</pubDate>
				<category><![CDATA[Tech]]></category>
		<category><![CDATA[AI Models]]></category>
		<category><![CDATA[Trends]]></category>
		<category><![CDATA[AI]]></category>
		<category><![CDATA[Tech News]]></category>
		<guid isPermaLink="false">https://firethering.com/?p=6218</guid>

					<description><![CDATA[MiniMax handed an internal version of M2.7 a programming scaffold and let it run unsupervised. Over 100 rounds it analyzed its own failures, modified its own code, ran evaluations, and decided what to keep and what to revert. The result was a 30% performance improvement with nobody directing each step. That is not a benchmark result. That is a different way of thinking about how AI models get built.

M2.7 is now available on HuggingFace with weights you can download and deploy. NVIDIA is offering free API access if you want to try it without the hardware overhead. The license has a commercial limitation worth knowing about, we will get to that.]]></description>
		
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