u/Limp-Contest-7309

Over the years I've used this train station to visit the town having grown up there. On my latest visit I found myself on the wrong side of the station. That being, the brand new (to me) or at least relatively new side.

I was welcomed by a single Greggs, leaving with sausage roll in hand and a cupcake for my kid, I continued on our journey wandering the long corridor towards the pristine escalators that was reminiscent of an airport terminal/ airport train station. I found myself going over the bridge and was perturbed by the new external platform I had to use to catch my train to Doncaster.

Completely circumventing my usual trip to smiths and the old interior, I found myself very lost inside.

I would like to ask the residents of Darlington, what was the rationale for this upgrade? Does anyone else relate to my experience of this unfamiliar world?

Lots of people are talking about how they want to switch back from Claude, which is its own cluster fuck at the moment. But let me just say, I think 5.6 is decentish, but it's excruciatingly slow and buggy. With Claude you instantly see thinking notes and it displays coding decisions etc and you know what's happening. But with GPT it's just...

Thinking...

Hang indefinitely

Sometimes works

Good enough answer sometimes. More often than desired. Nothing. Model switcher wipes part of the conversation.

Earlier GPT5 thinking versions were better in comparison.

At least it doesn't condescend anymore it's passed that baton to Opus 4.8

Meh. 4o and 5 were decent. Leave them. Opus 4.6 is amazing. LEAVE IT

Rant over

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I’ve built an interactive gene atlas from the TCGA-BRCA HiSeqV2 expression dataset available through UCSC Xena.

Search for a gene, press Enter, and explore its surrounding neighbourhood:

https://aethereos.net/static/brca\_atlas.html

What went into it:

The input consisted of 17,113 genes measured across 1,218 samples after filtering genes with very low mean expression.

The system received only the expression matrix and gene symbols. It was not given tumour subtypes, clinical metadata, pathway annotations, Gene Ontology terms, cell-type labels, cluster names, or literature-derived gene relationships.

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What you are looking at:

The interactive map is one visual representation of a much richer geometrically learned embedding of the dataset. It is not a conventional UMAP or t-SNE plot, and the system was not trained specifically to recognize breast-cancer biology.

Its structure was learned from recurring relationships across the patient samples. The resulting atlas appears to preserve patterns operating at several biological scales simultaneously, including genomic dosage, cellular programs, tumour states, proliferation, immune infiltration, and stromal composition. Though relationships geometrically learned from this cohort and may include indirect or coincidental associations.

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Several recognizable regions emerge:

ERBB2 lies beside genes from the recurrent 17q12 amplicon, including STARD3, PGAP3, PSMD3, ORMDL3, TCAP, and GRB7.

Basal genes including KRT5, KRT14, and KRT17 form an extremely tight neighbourhood.

Luminal genes including ESR1, FOXA1, GATA3, PGR, GREB1, and TFF1 occupy a broader shared territory.

Distinct T-cell, B-cell, myeloid, interferon, endothelial, fibroblast, and adipocyte-associated regions are visible.

Proliferation and DNA-replication genes organize into their own extended region.

BRCA2 falls near replication and repair genes including ATAD5, MCM8, BRIP1, and XRCC2.

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Not specific to breast-cancer data:

This is a general-purpose system, not a BRCA-specific model. It can be applied to other omics datasets—and more generally to high-dimensional datasets in which relationships are distributed across many observations. I have also used the system on substantially smaller datasets which show coherent recovery of gene expression programmes.

The atlas is only one way to navigate the resulting geometry. The same underlying capability can support other interfaces, including what I call Spectral Prompting systems. I demonstrate that approach in my native research domain here:

https://www.biorxiv.org/content/10.64898/2026.05.19.726151v1

32,070 alphabetic tokens from GPT-2-small's WTE, no forward pass and no context.

Works on mobile. Pinch to zoom, tap a token to see its nearest connections, tap a neighbour to walk the graph. Search box to jump anywhere.

Layout is t-SNE over a compressed representation of the embedding table; edges are a minimum spanning tree in that space, so every line is a real nearest-kin relationship,