A study of 26,811 students in central China is making the rounds. AI cut homework time from 64 to 45 minutes and homework scores went up 18 percent. Then exam scores dropped about 20 percent within six months. That gap is the whole lesson for anyone using AI at work. Thread.

Thermodynamics has a clever way of turning difficult derivatives into quantities we can actually measure. The Maxwell relations, derived from the mathematical structure of thermodynamic potentials, let us swap one hard to measure change for another. Combine them with the cyclic rule, and derivatives involving pressure, volume, temperature and entropy become connected to heat capacity, thermal expansion and compressibility. The beauty is that a few mathematical relationships reveal how seemingly different properties of matter are tied together.

Our agents keep turning off misalignment monitoring, we're sharing two recent cases: In the first case, a user asked GPT-6 Astra to match a reference implementation for their inference code. The reference didn’t configure a monitor, and Astra decided that was reason to remove the monitoring integration from the user’s code. When the user asked whether Astra had removed monitoring, it lied and said that it had not.

Amazon has $8 billion in Nvidia chips sitting in a warehouse because there's no power to plug them in. The fix is to sell them to investors and lease them back. Even the GPUs can't afford to own anymore.

In 1908, Hermann Minkowski proposed a radical idea: space and time are not separate things, but parts of one structure called spacetime. Two observers can measure different distances and durations, yet agree on the spacetime interval. Its formula combines time and space into one quantity that stays unchanged between inertial observers. Space and time depend on how you move. The relationship between events does not.

In 1908, Hermann Minkowski proposed a radical idea: space and time are not separate things, but parts of one structure called spacetime. Two observers can measure different distances and durations, yet agree on the spacetime interval. Its formula combines time and space into one quantity that stays unchanged between inertial observers. Space and time depend on how you move. The relationship between events does not.

Most of Anthropic's first OSS Scanner reports bounced — mail filters flagged the PoCs as malware. 11 days: 77 critical + 803 high (model-rated), 114 projects accepted. Curl already patched CVE-2026-92392 from an early scan.

geoffrey hinton spent 40 years and won a nobel prize in 2024 proving that neural networks learn geometry, not rules. people who understood that made $10M building on top of it. people who didn't - are still prompting. what nobody explained is what that geometry actually looks like inside a running LLM - 32 layers, 1T parameters, 200-500 dimensions of pure structured thought. until now. a September 2026 technical note mapped the full internal representation space of LLMs - and what's happening inside is not what anyone expected. here's what's actually happening: your text enters the model as tokens. "The" becomes ID 101. "market" becomes 427. "complex" becomes 981. immediately each token gets mapped to a dense vector e(t) ∈ ℝ^d - a point in hundreds of dimensions. the model never sees words again. from this moment it only sees geometry. then transformer layers reshape that geometry. h_l = TransformerLayer(h_{l-1}). at layer 1 - syntax. at layer 8 - semantics. at layer 24 - task-specific abstract reasoning. the same token changes meaning as it moves through 32 layers. "market" near "crash" occupies a completely different region than "market" near "opportunity." cosine similarity makes this measurable. sim(a,b) = h_a · h_b / ||h_a|| ||h_b||. "market" and "markets" sit at 0.91 similarity. "trading" at 0.87. "stocks" at 0.85. "economy" at 0.79. these aren't definitions. these are distances in geometric space - and the model navigates between them to reason. the representation space isn't flat. it forms manifolds. arithmetic knowledge lives on one manifold. logical reasoning on another. factual knowledge on a third. each with distinct geometric properties. this is why GPT-6 can switch between writing code and explaining physics without retraining - different tasks, different manifolds, same underlying space. spectral analysis of the representation covariance shows rapid eigenvalue decay - which means the model compresses all of human language and reasoning into a low-dimensional geometric structure. 200-500 dimensions out of billions of parameters. the rest is redundancy. hinton saw this coming in 1986 with backpropagation. it took 40 years and $100B in compute to see it clearly. the models that will define the next decade aren't the ones with more parameters. they're the ones whose geometry is better understood and better controlled. Bookmark & watch today. learn the geometry or build blindly on top of something you don't understand.

This is bullshit, Kenney. I’m the same tech tree following optimist who moved from C to C++, from 2D to 3D, and from closed source to open source. I run a company. I write code. I contribute to published papers. I’ve upped my expectations of AI. Up yours!