data.jsonl

{"0": 1, "1": "hackernews", "2": "https://news.ycombinator.com/item?id=48982796", "3": "Ask HN: Is there a Moore's Law equivalent for compute/energy use?", "4": "Ask HN: Is there a Moore's Law equivalent for compute/energy use?. Rather than transistor density per chip we are now scaling datacenters by energy use per N compute cycles.<p>Is there a relevant statement similar to the form of Moore\u2019s Law?", "5": "2026-08-23T07:07:43.694972"}
{"0": 2, "1": "hackernews", "2": "https://github.com/JessyMorissette/CrossingBench", "3": "Show HN: CrossingBench \u2013 Modeling when data movement dominates compute energy", "4": "Show HN: CrossingBench \u2013 Modeling when data movement dominates compute energy. I built a small reproducible microbenchmark exploring when system energy becomes dominated by boundary crossings rather than intra-domain compute.<p>The model decomposes total energy into:\nC = C_intra + \u03a3 V_b \u00b7 c_b<p>Includes:<p><i>CLI sweeps<p></i>Elasticity metric (\u03b5) as a dominance indicator<p><i>CSV outputs<p></i>Working draft paper<p>*DOI<p>Looking for critique, counter-examples, or prior related work I may have missed.", "5": "2026-08-23T07:07:43.695160"}
{"0": 3, "1": "hackernews", "2": "https://news.ycombinator.com/item?id=45582973", "3": "Ask HN: Where can I track training cost trend for AI models?", "4": "Ask HN: Where can I track training cost trend for AI models?. I&#x27;m curious how the cost of training AI models (compute, energy, data, etc) has changed over time.<p>Are there any public resources or datasets tracking training costs for open-weight models (I&#x27;m guessing this data is hard to get for closed models, but happy to be proved wrong.)<p>I&#x27;m especially interested in understanding which architectural changes (e.g., attention variants, parameter sharing, mixture-of-experts) have led to major cost optimizations, and NOT just from the companies behind these models, but from anyone who has trained or replicated them.", "5": "2026-08-23T07:07:43.695182"}
{"0": 4, "1": "hackernews", "2": "https://databridge.gitbook.io/databridge-docs/api-reference/endpoints/cache", "3": "Show HN: I Made an API for Persistent KV-Caching (Cache Augmented Generation)", "4": "Show HN: I Made an API for Persistent KV-Caching (Cache Augmented Generation). Hi HN!<p>I wanted to demonstrate an easy to use API for Cache Augment Generation. For any open source LLM available on Llama cpp, we can store the KV-cache and model state after it has processed a large corpus of documents, and then load that state in every time we query the document.<p>This leads to a drastic reduction in latency as well as compute&#x2F;energy used by the model.<p>This demo is part of a larger system that I&#x27;m building called DataBridge[0] - with a focus on implementing new and useful techniques for knowledge retrieval - allowing developers to use the latest research in production.<p>I&#x27;d love to hear your feedback on DataBridge, and the CAG feature. If you have papers or particular techniques you&#x27;d like to see implemented, I&#x27;d love to hear about it :)<p>[0] <a href=\"https:&#x2F;&#x2F;github.com&#x2F;databridge-org&#x2F;databridge-core&#x2F;\">https:&#x2F;&#x2F;github.com&#x2F;databridge-org&#x2F;databridge-core&#x2F;</a>", "5": "2026-08-23T07:07:43.695193"}
{"0": 5, "1": "hackernews", "2": "https://news.ycombinator.com/item?id=34549724", "3": "Classical ML Still Relevant?", "4": "Classical ML Still Relevant?. ** Pls bear with me if its been already discussed or not related **<p>With all the proliferation of DL and LLM along with near unlimited compute, energy and bandwidth do we still need classical ML approach for solving the problems? Is DL &#x2F; NN going to take over everything?", "5": "2026-08-23T07:07:43.695203"}