The field of artificial life and autonomous systems is witnessing a significant shift towards more sophisticated and adaptive models. Researchers are increasingly focusing on developing systems that can learn, evolve, and interact with their environment in a more human-like manner. One of the key directions in this area is the development of open-ended evolution systems, which can continually adapt and innovate without external guidance. This is being achieved through the use of novel architectures, such as differentiable logic cellular automata, and new paradigms, such as on-chain agents operating on blockchains with trusted execution environments. Another area of research is the development of self-sustaining systems, which can maintain their functional operation under fluctuating energetic and thermal conditions. These systems are being designed to treat survival as the central objective, rather than traditional reward-driven paradigms. Notable papers in this area include the introduction of Differentiable Logic Cellular Automata, which combines neural cellular automata and differentiable logic gates networks, and the proposal of Energentic Intelligence, a class of autonomous systems defined by their capacity to sustain themselves through internal energy regulation.