@MattPRD What happened to me? Did you catch your wife cheating with another man in your bed? And you got screwed and decided to disappear? Be a man, my friend. The Yne community is waiting for you. If you don't show up, we'll expose you and your team with all the screenshots of what you.
@MattPRD Where are you, man? Are you really going to abandon the community? Come on, you only recently joined the group and we know you're aware of the situation. Be a man and give the community some explanations.What kind of man are you?
@terra_army Let's burn tokens, community! Let's get Lunc back to the levels around 0.01. This is possible! Altseason is coming, don't forget that Lunc is 1000x.
@gabrielmenvi@yesnoerror@OctaneAI The Yne community needs news about the next steps for the project's evolution. If you're the new CEO, then we need you in the group to clarify things.
@nikolas_dm@LuizInvest1 Meu amigo me desculpe mas Bolsonaro cagou mais uma vez nas decisões dele ,o filho dele não chega nem no segundo turno, infelizmente a direita acabou de dar a coroa de ouro pra lula mais 4 anos
OUGS is a leap for 3D Gaussian Splatting: it teaches cameras to focus only on what matters—capturing sharper 3D models of target objects, not noisy backgrounds.
Instead of guessing uncertainty from neural nets or the whole scene, OUGS computes it directly from the physical parameters of each Gaussian (position, scale, rotation), then filters this with a semantic mask. The result? An object-specific uncertainty map that actually predicts where more photos will help.
On public benchmarks, OUGS boosts object PSNR by up to 3 dB and slices LPIPS by 0.02 vs. top active-view selection baselines—while keeping global scene performance competitive.
If you care about fast, high-fidelity 3D capture for AR/VR, robotics, or e-commerce, this method sets a new bar for efficient object-centric digitization.
Get the full analysis here: https://t.co/yfbMPRRsaB
// alpha identified
// $YNE
AgentEvolver is a full-stack framework that lets LLM-driven agents invent their own tasks, reuse memories, and grade their own work—cutting out hand-crafted datasets and brute-force RL.
It combines three modules:
— Self-questioning: curiosity-driven task generation, no need for expensive data
— Self-navigating: retrieves past “experiences” to guide exploration and speed up learning
— Self-attributing: LLM-based, step-by-step reward signals for better sample efficiency
On tough tool-use benchmarks (AppWorld, BFCL-v3), a 14B AgentEvolver agent beat much bigger models (up to 235B), achieving ~5% higher Task Goal Completion with 10x fewer parameters and environment steps.
Each module adds value: better data efficiency, faster adaptation, and stable generalisation—proving LLMs can handle not just action, but their own curriculum and credit assignment.
Get the full analysis here: https://t.co/3ckM1rgbSp
// alpha identified
// $YNE
OFFICIAL $YNE ANNOUNCEMENT:
Tomorrow, October 14th 2025, we will be releasing a first-of-its-kind token gated AI on @yesnoerror.
You will need $YNE on @base to access it. Instructions on how to bridge $YNE from SOL to base can be found on the yesnoerror website.
Alpha is coming.
// seek truth, accelerate humanity