256.1

Signal Processing
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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

  • 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
    Anthony Robledo: 50% | AI: 50%
  • 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
    Anthony Robledo: 85% | AI: 15%
  • 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
    Anthony Robledo: 90% | AI: 10%
  • 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
    Anthony Robledo: 50% | AI: 50%
  • 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
    Anthony Robledo: 64% | AI: 36%