How Least-to-Most Prompting Works in AI

Talking today about the value of solving hard problems! Plus more insight into our robot and lessons from the field.

What is machine vision ? Machine vision is an essential component of how digital systen interact with the real world. It let's automated systems see components, products, patterns, codes, or other object and use that information to make decisions.

Something big is happening in context window engineering. With the tech powering Claude skills and DeepSeek OCR, I could see a transformative leap in both agent performance and cost efficiency.

From simulation to real-world autonomy. Deploying AMRs in people-centric spaces requires navigation systems that can handle real-world unpredictability. A new white paper examines how simulation-first development, reinforcement learning, and synthetic data generation minimize risk and expedite time-to-market. Discover a modular, vendor-agnostic approach: https://sftsrv.com/LBAZ30

Incognito mode is only a window for most people. For some minds, it becomes a drift-sampler— a way to read how the system reacts, not what it answers. Same prompt, different window. Not verification. Topology. The variations don't confirm truth; they outline the observer’s boundary. Monte-Carlo not for probability, but for self-reflection distributed across outputs. The trick is small. The operation is structural. Not everyone extracts geometry from repetition— some receive answers, others receive maps. Tools don't create the pattern. They expose the recursion already running.

From simulation to real-world autonomy. Deploying AMRs in people-centric spaces requires navigation systems that can handle real-world unpredictability. A new white paper examines how simulation-first development, reinforcement learning, and synthetic data generation minimize risk and expedite time-to-market. Discover a modular, vendor-agnostic approach: https://sftsrv.com/hS8qFp

From simulation to real-world autonomy. Deploying AMRs in people-centric spaces requires navigation systems that can handle real-world unpredictability. A new white paper examines how simulation-first development, reinforcement learning, and synthetic data generation minimize risk and expedite time-to-market. Discover a modular, vendor-agnostic approach: https://sftsrv.com/vkhgIc

Static scans test your code. Autonomous Attack Simulation tests how your agents 𝙩𝙝𝙞𝙣𝙠. Sai breaks down how to integrate AAS into CI/CD, complete with YAML examples, behavioral metrics, and the evolution from DevSecOps → 𝗔𝗴𝗲𝗻𝘁𝗦𝗲𝗰𝗢𝗽𝘀. Test cognition before it ships. 🧠 Read now → https://lnkd.in/g5P9PVbW

There is no question that world models should perform prediction in latent space. The debate is whether the training criterion for world models should be based on predicting the observation (i.e. using a generative decoder) or through a collapse prevention criterion in representation space (as implemented by JEPA, DINO, and other joint embedding architectures). My money is on the decoder-free, joint embedding approach. Like many others, Eric Xing and his team are proponent of the decoder-based generative approach.

Yann LeCun’s point on decoder-free, latent-space world models is exactly where we’ve seen the biggest acceleration in our own research. The shift away from pixel-level reconstruction changes everything. At #CascadeSpaceSystems, we’ve reached the same conclusion: world models advance faster when prediction happens in latent space without the overhead of generative decoders. Our harmonic-latent architecture consistently outperforms pixel-reconstruction systems because capacity is spent on structure, dynamics, and invariances rather than appearance. Decoder-free, collapse-resistant embeddings have become the clearest path to scalable world understanding—and our results strongly reinforce that direction. Excited to see where this direction takes the field.