Systems Under Strain
AI systems excel in benchmarks but fracture under real-world pressure. This site examines breaking points in perception, computation, and memory—where scaling hits fundamental limits.
Compiled Thoughts
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Optical Interconnects: Free or Bonded?
♞ FSO delivers 1.6 Tb/s at 2.3 pJ/bit but demands ±5 µm alignment♞ CPO's 100-150ns latency trades speed for proven fiber reliability♞ Precision requirements create fragility versus robust deployment -
Attention Thrashing: ADHD in Artificial Minds
♞ Quadratic O(N²) scaling creates bottleneck at million-token contexts♞ FlashAttention enables 32K tokens on A100s without approximation♞ Models lose middle context despite larger windows—more ≠ better -
Déjà Vu: Cog in the Machine or Cognizant Machines?
♞ Déjà vu reflects cache-memory coherency failure in biological hardware♞ Fast familiarity signals conflict with slow episodic retrieval♞ LLMs lack metacognitive monitoring that flags conflicting signals -
Whose Bits are Wiser, GPU | TPU?
♞ H100 flexibility versus TPU efficiency—near-parity at peak scale♞ GPUs parallelize via SIMT warps; TPUs pipeline via systolic arrays♞ Faster matrix math doesn't solve fundamental reasoning limits -
Who's Driving the Autonomous Vehicle Shift?
♞ Waymo's sensor fusion versus Tesla's vision-only bet♞ Zero injury collisions in a million autonomous miles proves redundancy♞ Edge case handling determines safety validation, not benchmarks