are kids so easy to indoctrinate? i suppose tiktok self-indoctrination is fine ...
519 karma · joined June 25, 2025
``` { "question": "What is the distribution of Sepal Length?", "answer": "*Sepal Length*: mean=5.84, median=5.80, std=0.83, range=[4.30, 7.90]. N=150 [non-normal distribution].", "type": "distributional", "provenance": { "generated_at": "2025-11-19T19:21:28+00:00", "tool": "statqa", "tool_version": "0.2.0", "generation_method": "template", "analysis_type": "unknown", "variables": ["sepal_length"], "python_commands": ["valid_data.mean() # Result: 5.84", "valid_data.std() # Result: 0.83"] }, "visual": { "plot_type": "histogram", "caption": "Histogram showing sepal length distribution with mean=5.84 and std=0.83 (N=150). The data shows a approximately normal distribution.", "alt_text": "Histogram chart with sepal length values on x-axis and frequency density on y-axis, showing distribution shape with 150 observations.", "visual_elements": { "chart_type": "histogram", "x_axis": "Sepal Length", "y_axis": "Density", "key_features": ["distribution shape", "mean line"], "colors": ["blue bars", "red mean line"], "annotations": ["Mean: 5.84"] }, "primary_plot": "/path/to/univariate_sepal_length.png", "generation_code": "plot_factory.plot_univariate(data['sepal_length'], sepal_length_var, 'plot.png')" }, "vars": ["sepal_length"] } ```
"Many widely used machine-learning models rely on copyrighted data. For instance, Google finds the most relevant web pages for a search term by relying on a machine learning model trained on copyrighted web data. But the use of copyrighted data by machine learning models that generate content (or give answers to search queries than link to sites with the answers) poses new (reasonable) questions about fair use. By not sharing the proceeds, such systems also kill the incentives to produce original content on which they rely. For instance, if we don’t incentivize content producers, e.g., people who respond to Stack Overflow questions, the ability of these models to answer questions in new areas is likely to be lower. The concern about fair use can be addressed by training on data from content producers who have opted to share their data. The second problem is more challenging. How do you build a system that shares proceeds with content producers?"
https://www.gojiberries.io/generative-ai-and-the-market-for-...
"A single page on Doordash can make upward of 1000 gRPC calls (see the interview). For many engineers, upward of a thousand network calls nicely illustrate the chaos and inefficiency unleashed by microservices. Engineers implicitly diff 1000+ gRPC calls with the orders of magnitude fewer calls made by a system designed by an architect looking at the problem afresh today. A 1000+ gRPC calls also seem like a perfect recipe for blowing up latency. There are more items in the debit column. Microservices can also increase the costs of monitoring, debugging, and deployment (and hence cause greater downtime and worse performance)."
2. ben evans frequently makes fun of the business value. pretty clear a lot of the models are commodotized.
3. strategically, the winners are platforms where the data are. if you have data in azure, that's where you will use your models. exclusive licensing could pull people to your cloud from on prem. so some gains may go to those companies ...
and haha on “The “rebound” in future fertility for low-fertility countries is consistent with an expectation of continued progress toward gender equality and women’s empowerment and improving social and economic opportunities for young people and families.”