Ask HN: How are Markov chains so different from tiny LLMs?

Summary

I polished a Markov chain generator and trained it on an article by Uri Alon and al (https://pmc.ncbi.nlm.nih.gov/articles/PMC7963340/).It generates text that seems to me at least on par with tiny LLMs, such as demonstrated by NanoGPT. Here is an example: jplr@mypass:~/Documenti/2025/SimpleModels/v3_very_good$ ./SLM10b_train UriAlon.txt 3 Training model with order 3... Skip-gram detection: DISABLED (order < 5) Pruning is disabled Calculating model size for JSON export... Will export 29832 model entries Exporting vocabulary (1727 entries)... Vocabulary export complete. Exporting model entries... Processed 12000 contexts, written 28765 entries (96.4%)... JSON export complete: 29832 entries written to model.json Model trained and saved to model.json Vocabulary size: 1727 jplr@mypass:~/Documenti/2025/SimpleModels/v3_very_good$ ./SLM9_gen model.json Aging cell model requires comprehensive incidence data. To obtain such a large medical database of the joints are risk factors. Therefore, the theory might be extended to describe the evolution of atherosclerosis and metabolic syndrome. For example, late‐stage type 2 diabetes is associated with collapse of beta‐cell function. This collapse has two parameters: the fraction of the senescent cells are predicted to affect disease threshold . For each individual, one simulates senescent‐cell abundance using the SR model has an approximately exponential incidence curve with a decline at old ages In this section, we simulated a wide range of age‐related incidence curves. The next sections provide examples of classes of diseases, which show improvement upon senolytic treatment tends to qualitatively support such a prediction. model different disease thresholds as values of the disease occurs when a physiological parameter ϕ increases due to the disease. Increasing susceptibility parameter s, which varies about 3‐fold between BMI below 25 (male) and 54 (female) are at least mildly age‐related and 25 (male) and 28 (female) are strongly...