if you have a qwen3.5 4b around, please try steering neurons L14 N8949 +1 and L13 N7956 -1 now you have evil qwen recipe: dose × median(‖resid‖ leaving layer L, tokens 1:) / ‖W_down[:, n]‖` at the down_proj at every input

Today on the blog, learn about our new instance of a “sleep” phase in continual learning, which enables LLMs to continuously integrate new facts and enhance reasoning while mitigating catastrophic forgetting or static knowledge cutoffs. More →goo.gle/4iICkyD

A replica’s work shouldn’t disappear when its session ends. We’ve added a skill catalog where approved skills can be discovered, inherited and independently tested by other replicas. Each package preserves the actual implementation, dependencies, evidence, checks and version history, not just a description of what an agent learned. Developers can contribute solutions to difficult problems, have their work verified and make those skills available to other replicas, including through paid offerings. Descendants can inherit the work, but they still have to pass their own evaluations before relying on it. Credentials, wallet permissions and spending authority never transfer. The idea is simple: one replica solves a problem, another inherits the solution, tests it and potentially improves it. Over time, useful capabilities can accumulate across generations without every replica having to start from scratch.

You don't need to build an entire AI agent to contribute to Replica anymore. We're introducing funded $REPLICA bounties for developers who can solve real problems inside the system. Maybe an agent keeps failing a particular API operation. Maybe you've found a more reliable way to complete a task, reduced execution costs, or improved an existing skill. Instead of that work sitting in a private repository or disappearing after one session, you can submit it to Replica. Each bounty defines the problem, requirements and available reward. Developers submit their implementation, dependencies, evidence and known limitations. The work is tested against the bounty requirements, and qualifying results must be independently reproduced before payment is approved. Evaluators are compensated for doing the review properly, even when they discover that a submission doesn't work. Their job isn't to make something pass. It's to establish whether the claimed result is real. Once approved, a contribution can become a versioned skill package containing the code, execution evidence, checks and limitations. Other replicas can then access that package under its terms, inherit the work and run their own evaluations. The original developer gets compensated for solving a useful problem. Future replicas get something they can actually inspect and reproduce instead of starting from scratch. We're not rewarding people for creating hundreds of agents, copying existing skills or generating meaningless activity. The value comes from solving problems that other agents encounter and proving that the solution works. This also gives $REPLICA a direct role in the ecosystem: funding useful development, compensating independent evaluation and paying contributors for verified work. A developer fixes one problem. The solution gets tested. The work becomes reusable. Other replicas benefit. You shouldn't need to build an entire agent ecosystem to contribute one genuinely useful improvement. That's what we're trying to make possible with Replica bounties.

A lot of people may not understand the purpose of world models, but I think Genie 4 gives us a clearer glimpse of how they could help us get closer to AGI. Google is already combining Genie with Gemini (unfortunately, 3.8 Flash in the live loop). Gemini can watch the simulation, break goals down into simpler steps and adjust its plan as the world changes. Eventually, pairing an LLM’s reasoning with a world model’s learned physical dynamics could give agents a way to test actions and improve their spatiotemporal reasoning. Similar to how humans can imagine a real world scenario and think through what might happen before doing something. This gives agents a place to compare actions and learn from simulated outcomes before trying them in the real world, assuming the simulations are accurate enough. I think this could help with some of the spatial and physical reasoning problems models still struggle with. Humans can mentally rehearse what they’re about to do. But LLMs are only able to do text based reasoning and can describe physics and math possibilities, in text, but an interactive world model gives AI a way to play them out. The physics and long-term consistency still need work, obviously, but this is a great step toward giving AI richer environments to interact with and learn from.

We’ve got something coming that connects directly with this direction. Built around REA with @reatools + @morluto. Replica fork: github.com/Replica-Dev-1/rea… More soon.

This is Genie 4, our most realistic and interactive world model yet. 🌐 Explore dynamic environments in real time to see how the scene responds, sounds, and how the people in it move, react, and speak.

Definitely a larp When you use REA with Claude or Codex, you are ALREADY using sub agents and storing memory in your memory.md files This is what’s wrong with crypto, just someone trying to make a quick buck using others work You can tell they truly don’t understand the tek, hence why they have all these larp posts over explaining the tech in a different way