I hate the feeling of no longer being fully in the loop.
I tell agents to debug an issue. They explain what goes wrong. Propose a solution.
That's sure a lot of jargon. Do I fully understand what they're saying? Kind of. But certainly not like if my mind was solely focused on that bug fix, instead of juggling 5 other threads.
I tell them to do it. Make the fix, create a PR, the usual.
Is their solution really the right approach? Will they not include a dumb invariant that might later bite me in the ass?
Glancing at code, it *seems* fine. But I'm not sure. I'd have to go way deeper into the issue to be able to tell. But that would defeat the purpose of having agents do the thing instead of me. That'd be *inefficient*.
So I merge it in, and move on to the next thread.
Forever falling behind so I wouldn't fall behind.
$AMZN Fantastic Q2 earnings:
• Revenue $200.6B vs Est. $196.2B
• EPS $5.75 vs Est. $1.81
• AWS Revenue $42.2B vs Est. $40.6B
Q3 Guidance
• Revenue $200B vs Est. $204B
Barrel chest medically means a rounded expanded rib cage from chronic lung hyperinflation, mainly in emphysema or COPD where air gets trapped and pushes the chest wall outward. It can also tie to aging or arthritis.
The left drawing shows the classic version. Elons right-side photo shows a muscular torso in profile with forward chest projection. That shape comes from pectoral and upper back muscle growth through resistance training, his tall broad frame, and normal middle-age factors like posture or tissue.
No evidence of lung disease. Its a common athletic build exaggerated by the side angle and meme comparison.
On Agents' Last Exam, GPT‑5.6 Sol sets a new high of 53.6, eclipsing Claude Fable 5 (adaptive) by 13.1 points.
At medium reasoning, it beats Fable 5 by 11.4 points at roughly one-quarter the estimated cost. GPT‑5.6 Terra and Luna also outperforms Fable 5 at around one-sixteenth the cost.
Introducing SubQ - a major breakthrough in LLM intelligence.
It is the first model built on a fully sub-quadratic sparse-attention architecture (SSA),
And the first frontier model with a 12 million token context window which is:
- 52x faster than FlashAttention at 1MM tokens
- Less than 5% the cost of Opus
Transformer-based LLMs waste compute by processing every possible relationship between words (standard attention).
Only a small fraction actually matter.
@subquadratic finds and focuses only on the ones that do.
That's nearly 1,000x less compute and a new way for LLMs to scale.