When AI is real automation, it disappears into the background. We’ve been calling that 'Boring AI' for a reason.
@ai21labs
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Boring AI: Agents for Operational Work and Business Outcomes
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@rohanpaul_ai We totally agree. The real value of AI is in quietly taking on the boring, operational work that keeps businesses running. That’s exactly what we’re building with Boring AI: agents designed for accuracy, reliability and outcomes, not hype. Check out our work:
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Jamba2: Open Source Model for February On-Device Applications
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Model to try out in February: Jamba2. Fully open source under Apache 2.0, includes 3B (dense) model for on-device apps, intended for grounded QA workflows that don't call for the heavy “thinking token” overhead of reasoning models. Further details:
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Jamba2: Open Source Model for On-Device QA Applications
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Model to try out in February: Jamba2. Fully open source under Apache 2.0, includes 3B (dense) model for on-device apps, intended for grounded QA workflows that don't call for the heavy “thinking token” overhead of reasoning models. Further details:
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Investigation: From Confident Gibberish to Scheduler Fix
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5/5 Wrote up the complete investigation-from confident gibberish to the scheduler fix. Includes the false starts (suspected CUDA bugs), the breakthrough (request tracking), and lessons for anyone running stateful models at scale. Read the blog:
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vLLM v0.14.0: Scheduler bug fix for Mamba token allocation
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4/5 The confusion: Scheduler saw "1 token allocated" and assumed decode. But 1 token can also mean "new request that hit token budget limits." Now fixed: check whether it's the request's first-ever token, not the allocation size. In vLLM v0.14.0 – update if running Mamba.
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SSM Recurrence Security: Mamba State Propagation vs Attention Safety
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3/5 SSMs are recurrent, each token depends on previous state. Mamba decode reads state → updates it. Misclassified request reads garbage → propagates recursively through all tokens. Attention writes K/V first, then attends. Safe even if misclassified.
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GPU Memory Utilization Bug: Token Classification Mismatch
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2/5 Used gpu_memory_utilization=0.2 to reproduce quickly, but this happens naturally when the scheduler runs out of token budget and GPU blocks get recycled. New request gets 1 token → misclassified as "decode" But num_computed_tokens=0 → should be "prefill".
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Debugging vLLM: Silent Corruption Bug in Jamba RL Training
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1/5 Debugging vLLM: The silent corruption bug
1/1000 Jamba generations collapsed into confident gibberish during RL training. No crashes, no errors, just wrong outputs with high logprobs. The culprit? A scheduler edge case that only triggers under memory pressure. -
Linda: Custom AI Agent for Banking Compliance Solutions
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This is Linda. 25,000 regulation docs. Before lunch. No smile.
She’s a custom AI agent for banking compliance. Consistent. Accurate. Reliable. Boring (the good kind).
Swipe to meet her. Build boring AI solutions with AI21 Labs: https://
ai21.com/boring-agents?
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