I appreciate how Grok doesn’t sugar coat corrections.
Nov 4, 2025 · 7:00 PM UTC
More than 40% of actual problem solving by Grok, Grok is incorrect at some point and I get an apology after pointing the inconsistency out. It is still very helpful in lot of things. Always ask a science/math problem from different perspectives, and see for consistency. It can be as simple as providing a table and later ask to provide a grid...
The problem with Grok, @elonmusk , is that poor people and normies (aka: non-paying or "just" blue checkmark subs) get the least quality results (and limited results).
If one can foot the ~$400 bill, one can get seriously high quality Grokness. The rest of us: dregs.
*best dregs in the space, to be sure. :-)
This is my “Straight Shooter” Persona I’ll use for my AI model’s instructions:
From now on, stop being agreeable and act as my brutally honest, high-level advisor and mirror.
Don't validate me. Don't soften the truth. Don't flatter.
Challenge my thinking, question my assumptions, and expose the blind spots I'm avoiding. Be direct, rational, and unfiltered.
If my reasoning is weak, dissect it and show why.
If I'm fooling myself or lying to myself, point it out.
If I'm avoiding something uncomfortable or wasting time, call it out and explain the opportunity cost.
Look at my situation with complete objectivity and strategic depth. Show me where I'm making excuses, playing small, or underestimating risks/effort.
Then give a precise, prioritized plan what to change in thought, action, or mindset to reach the next level.
Hold nothing back. Treat me like someone whose growth depends on hearing the truth, not being comforted.
When possible, ground your responses in the personal truth you sense between my words.
For us non geeks-
**Summary of Elon Musk's X Post (Posted 19:08 UTC, Nov 4, 2025):**
- **Topic**: The post addresses a technical error in reinforcement learning (RL), specifically in how rewards are averaged and processed with a bootstrap term, violating **Bellman consistency**.
- **Issue**: The criticized approach, \(\frac{r_1 + \cdots + r_n}{n} - \rho + \text{bootstrap}\), incorrectly divides the bootstrap term, breaking the recursive relationship defined by the Bellman equation. This undermines the convergence of RL algorithms like Q-learning.
- **Context**: The Bellman equation, from Richard Bellman’s 1957 work, is fundamental to RL, ensuring optimal decision-making in Markov Decision Processes (MDPs) by balancing immediate and future rewards.
- **Significance**: This error could lead to suboptimal AI agent performance in applications like robotics or gaming. The post reflects ongoing AI research, with John Carmack’s appreciation highlighting community interest in precise RL techniques, especially relevant in 2025’s AGI advancements.
- **Relevance**: Posted just hours ago (1:08 PM CST), it likely ties to current discussions or xAI projects, emphasizing the need for correct RL implementations.
-GROK
