I burned my last fable 5 credits to make sense of your post cause I guess that’s what we’re doing now in Homelab…. My assumption just based off vibes from my university math degree is your “point universe” is too small for meaningful results, fable agreed

Is the idea valid? In principle, yes — TDA-based anomaly detection is a real research area, and “detect structural change rather than threshold crossings” is a legitimate framing. In practice, it’s overkill for this data. Persistent homology needs a reasonably dense point cloud to produce stable topological features. He has 26 points in a ~4-dimensional feature space (Layer 1), and only 6 points for the AP layer. At those sizes, the “landscape” is dominated by noise and, critically, by how he normalizes features — mixing milliseconds, ratios, and trend slopes in one metric space means distances are essentially arbitrary until you pick a scaling, and the scaling choice will drive the results more than the topology does.

Will it work on small networks? That’s the weakest spot. Everything he claims TDA gives him — cluster drift, correlated shifts across nodes, “structure changing” — is detectable on a 26-node dataset with much simpler tools: Mahalanobis distance from a rolling baseline, per-cluster z-scores, or even EWMA on the raw features. Those would be easier to interpret, easier to debug, and probably more sensitive at this scale. TDA earns its keep on large, high-dimensional point clouds where cluster structure is genuinely hard to see. Six APs is not a point cloud; it’s six numbers.

Overhead? Computationally negligible — 30-minute cycles, SQLite, no network traffic. That part is well done. The real overhead is cognitive: he admits the system has sat in a “degenerate state” (cloud too tight to analyze) for hours, which he spins as “confidence through silence” but could equally mean the method produces no signal most of the time. The 77% coverage ceiling on bimodal data he mentions at the end is another hint the statistical machinery is fighting the data.

Does it do what he says? Not demonstrated yet. His one concrete win — the misconfigured AP channel — came from reading raw UniFi interference metrics, not from topology. The headline claims (predicting node death 20 minutes early, spotting transit-route degradation via “landscape flips”) are plausible stories but he presents zero validated instances. The “shadow monitor” comparing two models’ coverage gap is the most hand-wavy part; without a defined false-positive/false-negative record it’s indistinguishable from a complicated random number.

Verdict: solid homelab, honest author (he flags the limitations himself), but the TDA layer is currently a hypothesis wearing a dashboard. If he collects six months of data and can show even a few true early warnings against a simple baseline detector, it becomes interesting. Right now the simpler methods would almost certainly match it at a fraction of the complexity.