abstractor

🚨 Simulation Theory: The Double Slit Experiment proves particles act like waves until observed then they snap into particles. What if our reality only "renders" when we're looking, just like a video game optimizing resources? Check out this episode from The Why Files breaking it down, tying it to Simulation Theory. Are we in a sim? This could be the key to unlocking the true nature of existence! The Why Files video did a great job on explaining the Double Slit Experiment & Simulation Theory What do YOU think—real or rendered? Drop your thoughts below!

As part of our return, we’re excited to announce a new feature aimed at incentivizing higher quality deploys and improve conditions for traders on our platform: Balanced Mode Balanced Mode aligns traders and deployers For traders: 0.75% of all post-bonding volume is compounded into liquidity pools, making LPs ~4x deeper and improving token longevity. For deployers: 0.25% of bonding curve volume is allocated to a rewards pool every 2 hours, compounding throughout the day. Every 24 hours, the full amount is distributed to creators evenly across all successful bonds created within that 24 hour window.

This is insane. Scientists just taught living human brain cells to play DOOM. Cortical Labs in Australia grew about 800,000 neurons (human stem-cell derived plus mouse neurons) on a silicon chip and connected them to a computer using a high density microelectrode array. This system, called DishBrain, sends electrical signals representing the game environment and reads the neurons’ responses as control inputs. These cells don’t see graphics. They receive patterns of stimulation encoding movement and feedback, then reorganize their firing to improve performance. In earlier experiments, these neuron networks began learning tasks like Pong in about 5 minutes of gameplay. Because biological neurons adapt continuously and use extremely little energy, researchers are developing real bio-hybrid machines like the Cortical Labs CL1 biological computer, which runs living neural networks on silicon hardware. For perspective, the entire human brain operates on roughly ~20 watts of power. Modern AI systems require far more energy for comparable tasks. Researchers call this Synthetic Biological Intelligence. Future applications could include controlling robotic limbs, modeling neurological diseases, testing drugs, and building ultra-efficient computers that learn naturally instead of being trained from scratch. This isn’t consciousness or a “brain in a jar.” It’s proof that living tissue itself can function as computing hardware. Acceleration is everywhere.