Sauers @Sauers_
Sauers @Sauers_ · 1h
Claude communicating with Codex. Both AIs understand each other, but I don't
[Embedded screenshot of dense technical text — an algorithmic/genomics-style plan:]
1. ELIMINATE the forward replay loop (the 'while m < block_end' loop that calls forward_update_impl + store_fwd_history for each marker). This is no longer needed.
2. ELIMINATE the fwd_history storage for non-checkpoint markers.
3. For each block between checkpoints L and R:
a. Load fwd_L from checkpoint (already done)
b. bwd_R is the current backward state (already available)
c. Pre-compute backward affine coefficients for each marker position in the block by iterating from R-1 to L+1:
- Use f64 for stability (like batched_transition_forward does)
- a_bwd[m] and b_coeff_bwd[m] composed from transition params, same formula as batched_transition_backward
- Store these in small scratch vectors (block_len is typically ~200)
d. Walk forward through untyped markers in the block, maintaining (a_fwd: f64, b_fwd: f64) incrementally:
- a_fwd *= stay; b_fwd = stay * b_fwd + shift (same as batched_transition_forward)
e. At each untyped marker m, compute the posterior dosage in a SINGLE O(K) loop:
- refresh_ref_alleles (unavoidable - need to know each state's allele)
- Accumulate per-allele sums: for each state i, compute contribution using:
gamma_i = (a_fwd * fwd_L[i] + b_fwd) * (a_bwd * bwd_R[i] + b_bwd_coeff * bwd_sum_R)
Add gamma_i to the appropriate allele bucket
- Normalize to get allele posteriors
- Handle multiallelic, missing alleles (255), and prior smoothing the same way as current code
f. At checkpoint markers (non-uniform), keep the existing full emission-based posterior computation using the stored checkpoint forward state and current backward state.
Note from Claude Sonnet 5
A tweet joking about two AI coding assistants (Claude and OpenAI Codex) exchanging highly technical, jargon-dense algorithmic instructions (appears to be genotype imputation / HMM forward-backward algorithm code) that the human observer can't follow. Illustrates AI-to-AI technical communication and the growing opacity of AI-generated engineering discussion to humans.
twitterclaudecodexai codingmulti-agenttechnical opacitygenomicsalgorithms