Scaling Laws: The Law Behind Every LLM
Scaling Laws: The Law Behind Every LLM — Kaplan vs. Inference-Optimal (2020-2026)Dose #10 — Production Agentic AI Under PressureDr. Ryan RadJul 29, 20261ShareFor decades, this curve was blueprint of deep learning.
- ▪Scaling Laws: The Law Behind Every LLM — Kaplan vs.
- ▪Inference-Optimal (2020-2026)Dose #10 — Production Agentic AI Under PressureDr.
- ▪Ryan RadJul 29, 20261ShareFor decades, this curve was blueprint of deep learning.
2 outlets in our directory ran this story, first to last over 6 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
- ▪ LLM-style scaling laws hold for sensor data — Empirical Health
Hacker News (AI / LLM) files mainly under ai. We currently carry 3,105 of its stories.
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Story provenance
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Record
| Original publisher | Hacker News (AI / LLM) |
| Canonical URL | https://aidoses.substack.com/p/scaling-laws-the-law-behind-every |
| Publication time | Thu, 30 Jul 2026 15:32:26 +0000 |
| Retrieval time | 2026-07-30T15:37:05.185Z |
| Last seen | 2026-07-30T15:37:05.185Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | BJLhaJNATJWs · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
Scaling Laws: The Law Behind Every LLM — Kaplan vs. Chinchilla vs. Inference-Optimal (2020-2026)Dose #10 — Production Agentic AI Under PressureDr. Ryan RadJul 29, 20261ShareFor decades, this curve was blueprint of deep learning. If your model is too simple (relative to the complexity of the task), it underfits, it can’t capture the pattern. Increasing the complexity of the model usually led to better results, but if you keep pushing the complexity up on a fixed dataset, then something worse happens: error stops falling and starts climbing again. The model isn’t learning anymore. It’s memorizing noise (overfitting).That second half of the curve is the one that mattered. It’s why “just make the model bigger” used to be bad advice, not good advice. And it wasn’t really a modeling problem.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Hacker News (AI / LLM).
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