How Well Does Agent Development Reflect Real-World Work? https://
buff.ly/wQAIPZy
#AI #MachineLearning #DeepLearning #LLMs #DataScience AI agents are increasingly developed and evaluated on benchmarks relevant to human work
RESEARCH
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How AI Agents Reflect Real-World Work Benchmarks
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Deep Learning Medicine Founder Discusses AI Research Expertise
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(I created the first deep learning for medicine company, and my wife is an AI researcher who has a masters in immunology, so we're involved enough in this stuff to not need to rely on instinct.)
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Expert Consensus in Cancer Biology Outweighs Instinctive Skepticism
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Folks deeply involved in cancer biology and treatment have worked on these kinds of things for many years. "Instinctive skepticism" is no substitute for closely listening to them.
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Tested 5 AI models across performance spectrum
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I tested 5 models across the lineup: – Flash (fastest, ultra-low latency)
– 27B (balanced performance)
– 35B-A3B (multimodal powerhouse)
– 122B-A10B (complex reasoning)
– 397B-A17B (frontier-level outputs) Every single one outperformed my expectations for efficiency. Try them -
Comparison of multimodal AI models’ costs and capabilities
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Here’s where it gets interesting:
— God of Prompt (@godofprompt) 14 mars 2026
– GPT-4o: Multimodal, but expensive to deploy at scale
– Claude Sonnet: Great quality, high compute cost
– Gemini 1.5: Multimodal, but resource-heavy
– Qwen 3.5: Natively multimodal + designed for real-world agents WITHOUT scaling your compute… pic.twitter.com/IVHBFXXewYHere’s where it gets interesting: – GPT-4o: Multimodal, but expensive to deploy at scale
– Claude Sonnet: Great quality, high compute cost
– Gemini 1.5: Multimodal, but resource-heavy
– Qwen 3.5: Natively multimodal + designed for real-world agents WITHOUT scaling your compute -
Qwen 3.5-Flash: Linear Attention + Sparse MoE Breakthrough
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Most companies are scaling models UP to get better performance.
— God of Prompt (@godofprompt) 14 mars 2026
Qwen went the opposite direction.
Their 3.5-Flash model uses linear attention + sparse MoE architecture.
Translation: You get near-frontier performance without needing a data center to run it. pic.twitter.com/rN6cXx8Ox0Most companies are scaling models UP to get better performance.
Qwen went the opposite direction. Their 3.5-Flash model uses linear attention + sparse MoE architecture. Translation: You get near-frontier performance without needing a data center to run it. -
Qwen 3.5 models outperform in AI benchmark tests
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I just spent the morning testing Alibaba's new Qwen 3.5 models against GPT-4o, Claude Sonnet, and Gemini.
— God of Prompt (@godofprompt) 14 mars 2026
The results? Qwen 3.5 is punching way above its weight class especially the small models.
Here's what shocked me about this release:@AlibabaGroup pic.twitter.com/ut3KjAtMK7I just spent the morning testing Alibaba's new Qwen 3.5 models against GPT-4o, Claude Sonnet, and Gemini. The results? Qwen 3.5 is punching way above its weight class especially the small models. Here's what shocked me about this release: @AlibabaGroup
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Alibaba Introduces Qwen 3.5 Small Model Series
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Alibaba just introduced the Qwen 3.5 Small Model Series. Four models. 0.8B to 9B parameters. Natively multimodal. Built for edge devices, mobile, and real-world deployment. More intelligence, less compute. Here's what this release actually means:
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MedOS deployed at Stanford, presented at NVIDIA GTC as AI-native milestone
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The big deal: this isn’t a lab demo. MedOS just deployed inside Stanford Blood Center and Stanford Pathology, and it’s being presented at NVIDIA GTC: the same conference where most of the modern AI stack gets unveiled. If it works, this is the first real glimpse of AI-native
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Continual Learning Experience Skills Multimodal Agents
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Continual Learning from Experience and Skills for Multimodal Agents