CricketInSand

New research just exposed the biggest lie in AI coding benchmarks. LLMs score 84-89% on standard coding tests. On real production code? 25-34%. That's not a gap. That's a different reality. Here's what happened: Researchers built a benchmark from actual open-source repositories real classes with real dependencies, real type systems, real integration complexity. Then they tested the same models that dominate HumanEval leaderboards. The results were brutal. The models weren't failing because the code was "harder." They were failing because it was *real*. Synthetic benchmarks test whether a model can write a self-contained function with a clean docstring. Production code requires understanding inheritance hierarchies, framework integrations, and project-specific utilities. Different universe. Same leaderboard score. But it gets worse. A separate study ran 600,000 debugging experiments across 9 LLMs. They found a bug in a program. The LLM found it too. Then they renamed a variable. Added a comment. Shuffled function order. Changed nothing about the bug itself. The LLM couldn't find the same bug anymore. 78% of the time, cosmetic changes that don't affect program behavior completely broke the model's ability to debug. Function shuffling alone reduced debugging accuracy by 83%. The models aren't reading code. They're pattern-matching against what code *looks like* in their training data. A third study confirmed this from another angle: when researchers obfuscated real-world code changing symbols, structure, and semantics while keeping functionality identical LLM pass rates dropped by up to 62.5%. The researchers call this the "Specialist in Familiarity" problem. LLMs perform well on code they've memorized. The moment you show them something unfamiliar with the same logic, they collapse. Three papers. Three different methodologies. Same conclusion: The benchmarks we use to evaluate AI coding tools are measuring memorization, not understanding. If you're shipping code generated by LLMs into production without review, these numbers should concern you. If you're building developer tools, the question isn't "what's your HumanEval score." It's "what happens when the code doesn't look like the training data."

“Kalergi Plan? Don’t talk about that! That’s just a debunked conspiracy theory, that’s just low IQ antisemitism man” Meanwhile:

“The honeymoon is over for AI,” NVDA drops 3%. Goldman Sachs forecasts cloud capex growth will decelerate to just 19–26% in 2026 versus 54% in 2025, a 50-to-65% slowdown. Analysts warn 2026 AI capex growth may decelerate sharply. Link👇

NVIDIA CEO SAYS HE IS 100% COMMITTED TO ISRAEL AND WILL HAVE STAFF THERE FOR A VERY LONG TIME - PRESS CONF

Microsoft says it won’t auto-install the Microsoft 365 Copilot app on Windows 11, likely due to outrage over “Microslop.” The rollout was set for late 2025 but is now on hold.

Rabbi Mizrachi says Tucker Carlson shouldn't worry about the 3rd Temple being built bc when it is built "there will not be one anti-semite left in the world...they won't be around anyway" "When God will send the Messiah to purify the world...not one wicked gentile will be left"

AI moves so fast. Barely a month and he’s already been sidelined.

AI productivity bubble: Early adopters are already burning out. “There will be a wake up call and a reckoning for entire sectors who are adopting AI.” Natasha Bernal.

There is still no productivity gain from AI in the US data

The past year has seen an explosion in coding productivity, per FT: