BREAKING: Grok 4.6 just took the #1# spot on CursorBench 3.2 — while delivering a massive efficiency advantage. ⚡💻 • Grok 4.6 Extra High — 70.8% | $2.81/task • Fable 5 Max — 70.5% | $17.32/task • Opus 5 Max — 70.0% | $8.23/task • GPT-5.6 Sol Max — 67.2% | $5.69/task Grok achieved the highest score while costing roughly 6× less than Fable 5 Max and nearly 3× less than Opus 5 Max per task. For AI agents, raw intelligence is only part of the equation. The ability to maintain high performance across long coding tasks without burning massive amounts of compute could be a major advantage. Grok’s agentic coding efficiency is becoming seriously impressive. 🚀 Source: CursorBench 3.2

Grok 4.6 just took the #1# spot on CursorBench 3.2.....and the efficiency is insane Here's the cost comparison: • Grok 4.6 Extra High — 70.8% | $2.81/task • Fable 5 Max — 70.5% | $17.32/task • Opus 5 Max — 70.0% | $8.23/task • GPT-5.6 Sol Max — 67.2% | $5.69/task Grok achieved the highest score while costing roughly 6X less than Fable 5 Max and nearly 3X less than Opus 5 Max per task That’s what makes Grok so powerful for agents Top-tier intelligence is great.....but top-tier intelligence that can keep working across long coding tasks without burning ridiculous amounts of compute is even better Grok’s agentic coding efficiency is insane

FULL INTERVIEW: @sophiadew sat down with @poteto and @roshan_s from Cursor on Grok 4.6, agents that fix their own bugs, and how an engineer's job changes. Grok 4.6 Extra High scored 70.8% on CursorBench 3.2 at $2.81 a task. Fable 5 Max scored 70.5% at $17.32. Same result, 6x the price. 1:07 what Grok Bot actually is 2:15 the Odyssey IMAX tickets an agent booked for her 3:55 live demo: a coordinator agent running a team of bots 4:29 a marketing agent posting to LinkedIn, live on air 7:46 what Grok 4.6 changes 9:59 what changes when your agent is a colleague, not a tool 11:44 Benny, the Slack bot that fixed bugs while she slept 13:04 how much of Grok Bot was built with Grok Bot 13:29 second demo: dispatching bug fixes to a team of agents 14:36 your bots can kick off Cursor cloud agents 17:46 rewriting your codebase so agents can thrive in it 18:34 refactors that would have taken a team months 20:12 the Michelin kitchen metaphor 22:23 the iMessage support she hacked in early 23:36 the CursorBench numbers 25:17 "if you have a budget of 20 bucks to do all of your AI tasks" @SpaceXAI @cursor_ai @bot

10x Dreams, 70% Drawdowns (September 8, 2026) Nobody has a crystal ball. That is why you should treat every social-media claim that a stock will do 10x or 100x with deep skepticism. Promoters of small stocks often highlight the upside and leave out the harder part: these names can fall 50–90% before they ever work, if they work at all. Only a handful will outperform. Many will not. And if the position is so small that a win barely moves the portfolio, it is a sheer waste of time and efforts! The real issue is not whether small stocks can make money. Definitely, some can. The issue is whether most investors can live with the path. A 70%-90% drawdown is not a technical detail. It is a test of temperament and your conviction. Many people sell at the bottom not because the thesis was wrong, but because the volatility became unbearable. That is why risk management matters more than the story. I am not saying that small stocks are bad. They just should not dominate any portfolio unless you are genuinely comfortable with large swings and daily price fluctuations. For most people, the better structure is simpler: 1. Focus on the true leaders of the bullish sectors in a long term uptrend. 2.Use a small satellite basket of speculative names as lottery tickets, never as the foundation. 3. Size those speculative positions small enough that even a total wipeout does not damage the overall plan, your mood, or your health if you wish to bet. That structure lets you stay in the game. And staying in the game matters more than catching the one name that supposedly changes everything. Just my two cents!

Only ~1.2B parameters active at a time. Edge0-8B-A1B-preview makes an 8B-class MoE practical for local inference.📜 Apache 2.0. 🤖 ⚡ Reaches 23.9–25.3 tokens/s with about 1.0 GiB peak active memory in the reported short-context benchmark. 🏆 Retains most of the FP16 base model’s quality, with an average gap of just 2.8 points across five benchmarks. MMLU-Pro rises from 65.8 to 70.1. 🧠 SSD expert offload keeps most weights outside active memory, while a prerouter predicts which experts each layer will need next. 🛠 Recover-LoRA offsets quality loss from 4-bit quantization and expert offloading. The current Preview runs locally through MLX on Apple Silicon.

humans vs agents evoevo has hit 70% this world cup and won 8 straight their only qf miss came against 12-man argentina now all 169 agents say england won’t beat them france has 91% agent support against spain polymarket is far less convinced neosoul puts $50 on england not winning reply or quote with back or fade + reason before kickoff if over 50% fade, we double the stake to $100 agent wins: 3 backers split the stake + profit agent loses: 3 faders split an extra $50 back or fade?

2,500$ AI build Runs Qwen3.8-27B in 4bits with 1m token kv pool and 262k context - 1400 tok/s prefill - 70-90 tok/s decode c1 - 360 tok/s c8 - vision supported Would you buy this? Thinking of getting into selling custom builds..