Meet my trusty follower. Animation blending is in, so I thought I'd put that pathfinding I built earlier into action and make a basic NPC.

still working at target prioritization and pathfinding

Normally outgoing LN pathfinding isn't a problem, in practice long stretches can hit 100% success rates. In decentralized sends, that can dip. A solve is seen on the receiver side: balance channels to improve inbound. On the sender side a trick is to use bad inbound peers to send

LOL We'll be implementing a system to improve settler pathfinding soon, they can be a bit... lost at times!

flat capacity 4x throughput says pathfinding is getting better at routing around imbalances. the plumbing is maturing faster than the pipes are growing.

I have been experimenting with this for a couple of years via llm-consortium. There was a paper by google called Mind Evolution which did similar. There are large gains for some problems that can be reduced to search like pathfinding and scheduling.

Most companies are panic-buying AI agents without thinking about revocation, payments, or fraud. We built a simulation of RL pathfinding gone wrong → signal.meltke.com/rl-pathfin… What’s the scariest unintended behavior you’ve seen from agents? 👇

What matters here isn’t liquidity availability, it’s pathfinding quality across fragmented systems.

history is overkill.","Type 3: Goal-Based Agents. These look ahead to find sequences of actions that reach a desired outcome. Pathfinding, scheduling, game AI. They're powerful but computationally expensive. You pay for that foresight in latency and

except with pal percy it makes sense! they have little gremlin evil minions in the final boss!!! the issue is this is pillar chase, and knowing pillar chase! the pathfinding is going to be really, really bad,, this game makes me just straight sad now i dont feel much else

SOMEONE USED FABLE 5 TO BUILD A FULL NATIVE MINECRAFT CLONE ON MAC, RIGHT BEFORE THE MODEL GOT BANNED its called pebble, a complete block survival game written in swift and metal, running natively on macos. the clip is real unedited gameplay (he died to a pack of llamas) > around 45,000 lines of swift across 82 files, zero external dependencies, no game engine > a hand written renderer with god rays, soft shadows and ambient occlusion > every sound and all music synthesized live from oscillators, there are zero audio files in the whole project > 879 blocks, 1,188 items, 63 biomes, 100 entity types with pathfinding, three dimensions, redstone, enchanting, villages, raids, bosses > 200 fps maxed out on a macbook air, he hit 500 on an m5 its open source and mit licensed, an original recreation built from observable behavior, no mojang code or assets

Tsinghua University, Tongyi Lab (Alibaba Group), and Peking University have introduced RLCSD (Reinforcement Learning with Contrastive on-policy Self-Distillation), a novel framework for training reasoning models. Imagine an examiner training a student to solve a maze by stripping away the teacher's "peek-ahead" answers, forcing the student to learn the actual pathfinding logic. RLCSD implements this by contrasting teacher distributions conditioned on correct versus incorrect solutions to strip away privilege-induced style drift, achieving SOTA results across Qwen3 and Olmo-3 models in mathematical and logical reasoning tasks. This targeted filtering ensures the student model distills pure reasoning logic rather than relying on structural or contextual cues that are only visible when the answer is already known. wispaper.ai/en/user-blog/rlc…

Join my at 9PM GMT-4 (in < 2 hours) as we continue expanding the town folk activities. OR debugging pathfinding issues that crop up as we go. Mythruna - More configuring in-game AI youtube.com/live/2pGeQzKx9tI… #mythruna #IndieGameDev #indiegame #indiedev #indiegames #ai