What's really fun is when you give your AI commands and then it forgets the rules that you told it. Then it gaslights you after you yell at it and says, "Oh, I'm sorry, I made a mistake; I'll never make that mistake again." And then a week later, it makes the same freaking
SYSTEMS
-
Agent and Harness Traceability for Cross-Session Verification
By
–
Agent / harness traceability Allows you to ask an agent to basically remind you of what happened in a previous session and cross-check across different agents / harnesses as well Much needed in this space Repo: eric-tramel/moraine
-
Infrastructure gaps for autonomous energy system optimization
By
–
What's the biggest infrastructure gap preventing your energy systems from true autonomous optimization? @IIoT_World @CRudinschi @agentic_factory @vishalpanchal85 @OneLinders @APGuha
-
Infrastructure foundations critical before advancing energy AI models
By
–
The models will follow, but only if the ground they stand on is solid. Infrastructure problems come before model problems in energy AI.
-
Prefill-as-Service Enables Cross-Datacenter LLM Serving
By
–
new paper from Kimi! "Prefill-as-a-Service makes long-context LLM serving cross-datacenter" Main idea: smaller KV Cache turns prefill into a scalable remote service. Prefill is compute-bound, decode is memory-bandwidth-bound, but KV cache transfer keeps them trapped in the
-
Data Foundation and Orchestration: Keys to Real AI Progress
By
–
Organizations making real progress started with the foundation: normalized data, semantic context, edge compute, and orchestration to manage it all over time.
-
Distributed Energy Fleets Self-Optimize With AI Edge Orchestration
By
–
The future everyone's working toward – distributed energy fleets that self-optimize with minimal human intervention – depends on getting data normalization, semantic context, and edge orchestration right first. Partner content with @IOTechSystems. #iotechsys_iiot pic.twitter.com/vV5TgDHzxJ
— Lucian Fogoros (@fogoros) 18 avril 2026The future everyone's working toward – distributed energy fleets that self-optimize with minimal human intervention – depends on getting data normalization, semantic context, and edge orchestration right first. Partner content with @IOTechSystems
. #iotechsys_iiot -
Competing Neural Networks Evolve in Digital Petri Dish Ecosystem
By
–
What happens when you put competing neural networks in a Petri Dish and start changing the rules while they adapt?
— Sakana AI (@SakanaAILabs) 18 avril 2026
Last year we released Petri Dish NCA, where neural nets are the organisms that learn during simulation. Today we're releasing Digital Ecosystems: a browser-based… pic.twitter.com/9UeAQ2mX1dWhat happens when you put competing neural networks in a Petri Dish and start changing the rules while they adapt? Last year we released Petri Dish NCA, where neural nets are the organisms that learn during simulation. Today we're releasing Digital Ecosystems: a browser-based
-
Harnessed LLM Agent Architecture: Intelligence Through Modular Composition
By
–
A harnessed LLM agent.
— Akshay 🚀 (@akshay_pachaar) 18 avril 2026
Most people picture this as a model with tools bolted on. The real architecture inverts that relationship.
The model itself is deliberately thin. Intelligence gets pushed outward, and the harness composes it at runtime.
Three dimensions orbit the harness… https://t.co/MiA6mrH64m pic.twitter.com/tBUHQn4e3NA harnessed LLM agent. Most people picture this as a model with tools bolted on. The real architecture inverts that relationship. The model itself is deliberately thin. Intelligence gets pushed outward, and the harness composes it at runtime. Three dimensions orbit the harness
-
Prefill-as-a-Service reduces expensive local connection requirements
By
–
Researchers from Moonshot AI and Tsinghua University just introduced Prefill-as-a-Service (PrfaaS). The system breaks the requirement for expensive, high-speed local connections by offloading heavy initial memory setup to remote, specialized clusters. It uses smart scheduling