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Seán Moran on Muck Rack

Seán Moran

As seen in: Medium, Breitbart, Flipboard, The Irish Times, Inside Carolina, Towards Data Science, Yurview
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Articles

Mathematical Experiments Are Becoming Abundant Through Human-Machine Teaming

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By Sean Moran, Seán Moran
| Towards Data Science
I ended a recent weekend without a Hadamard matrix of order 668 and with a mathematical theorem I did not yet trust enough to call a result. Working with GPT-5.6 Sol, parallel agents, exact-arithmetic programs and a proof assistant, I attacked two hard problems over two days. One resisted every route I tried. The other produced a proof candidate: a conventional argument written out in full, exact checks on selected inputs, a teaching guide and a partial formalisation in Lean.
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How a Frontier Model Gets Built, Read from the Kimi K3 Report

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By Sean Moran, Seán Moran
| Towards Data Science
Frontier models are released with limited visibility into how they were built. The model and its system card are available, but the critical engineering decisions remain proprietary: the training process, the reinforcement learning objectives, and the optimisations that reduced serving cost. Consequently, most external analyses are necessarily based on the model’s observable behaviour rather than its construction. Kimi K3 is the exception worth reading.
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Tabular LLMs: An Introduction to the Foundation Models That Predict Your Spreadsheet

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By Sean Moran, Seán Moran
| Towards Data Science
model predicts the missing column of any table, zero-shot, the way a language model completes text. On the main community benchmark, every single-model entry above the best tuned gradient-boosted tree is now one of these. This post explains what they are, verifies the strongest one with unrestricted open weights on my own hardware, and maps where the trees still win. Figure 1: Accuracy versus serving cost on TabArena (official board, snapshot 2026–07–15).
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