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thoughts, updates, and insights from the superagent team.
AI Is Getting Better at Everything—Including Being Exploited
As AI models become more capable and obedient, safety improvements struggle to keep pace. The GPT-5.1 safety score drop reveals a structural problem: capability and attack surface scale faster than safety.
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Are AI Models Getting Safer? A Data-Driven Look at GPT vs Claude Over Time
Are frontier models actually getting safer to deploy—or just smarter at getting around guardrails? We analyze 18 months of Lamb-Bench safety scores for GPT and Claude models.
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Introducing Lamb-Bench: How Safe Are the Models Powering Your Product?
We built Lamb-Bench to solve a problem every founder faces when selling to enterprise: proving AI safety without a standard way to measure it. An adversarial testing framework that gives both buyers and sellers a common measurement standard.
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VibeSec: The Current State of AI-Agent Security and Compliance
Over the past weeks, we've spoken with dozens of developers who are building AI agents and LLM-powered products. The notes below come directly from those conversations and transcripts.
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The March of Nines
The gap between a working demo and a reliable product is vast. Andrej Karpathy calls this the 'march of nines' — when every increase in reliability takes as much work as all the previous ones combined. This is the hidden engineering challenge behind every production AI system.
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The case for small language models
Most agents today rely on large, general-purpose models built to do everything. If your agent has a single, well-defined job, it should also have a model designed for that job. This is the case for small language models: models that handle one task, run locally, and can be retrained as your data evolves.
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