Don't Build Cascaded Pipelines: Skilling Up Coding Agents for System Observability

🚀 We are hitting the "language-only ceiling" in AI

🚀 We are hitting the "language-only ceiling" in AI. To build true physical agents, models must transition from text translation to sensory fluency. The era of Native Multimodal Intelligence is here: Universal Tokens, Transfusion, and Mixture of Transformers! 👇 All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] #AI #DeepLearning #MultimodalAI #MachineLearning #Robotics

Are we hitting the "language-only ceiling" in AI? 🌐

Are we hitting the "language-only ceiling" in AI? 🌐 In a fascinating Stanford CS25 lecture, Victoria Lynn of Thinking Machines Lab highlighted that our world isn't just text—it's a dense tapestry of visual, auditory, and spatial information. To evolve into real-world physical agents, AI must transition from symbolic text translation to true sensory fluency. Welcome to the era of Native Multimodal Intelligence. Here are the key breakthroughs driving this shift: 🔹 Universal Tokenization: Treating images, video, and audio as sequences of tokens, allowing the same autoregressive logic from LLMs to process the entire sensory world. 🔹 Transfusion Architectures: Solving the "discretization dilemma" by combining discrete text prediction with continuous image representations via diffusion. 🔹 Mixture of Transformers (MoT): Using deterministic routing to process different modalities without capacity competition or "catastrophic forgetting." The physical world is the next great AI frontier. Moving toward true robotics requires bridging vision, language, and action. Check out the full breakdown below! 👇 All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] #learnbydoingwithsteven #AI #DeepLearning #MachineLearning #MultimodalAI #Stanford #Robotics #Innovation

🚀 The AI Agent "evaluation gap" is real. To deploy agents in high-stakes environments, our benchmarks must evolve beyond static datasets.

🚀 The AI Agent "evaluation gap" is real. To deploy agents in high-stakes environments, our benchmarks must evolve beyond static datasets. We need to measure 3 things: 1️⃣ Environment Complexity 2️⃣ Autonomy Horizon 3️⃣ Output Complexity Are your agents ready? 👇 All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] #AI #AIAgents #MachineLearning #Tech

The AI agent era is here, but our benchmarks are lagging behind. We are facing a critical "evaluation gap." 📊

The AI agent era is here, but our benchmarks are lagging behind. We are facing a critical "evaluation gap." 📊 While coding agents are advancing rapidly, deploying them in high-stakes environments (healthcare, finance) requires rigorous measurement. We need to evolve from static datasets to dynamic environments that reflect real-world messiness: org policies, flaky toolchains, and Slack context. Future benchmarks must focus on: 🔹 Environment Complexity: Realistic, dynamic operating environments 🔹 Autonomy Horizon: Measuring reliability over weeks or months, not just minutes 🔹 Output Complexity: Verifiable standards for nuanced artifacts, not just text The ultimate goal? "Trustworthy outputs"—agents that know when they are uncertain and pause to ask for help. Check out my full deep dive into the Art and Science of Benchmarking AI Agents below! 👇 All my links: https://linktr.ee/learnbydoingwithsteven [https://linktr.ee/learnbydoingwithsteven] #learnbydoingwithsteven #AI #MachineLearning #AIAgents #Benchmarking #Evaluation #TechTrends #FutureOfWork

Don't Build Slop: The 4 Levels of AI Agent Maturity

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