Anthropic posted an AMA style interview with Amanda Askell, the primary author of this document, recently on their YouTube channel.
It gives a bit of context about some of the decisions and reasoning behind the constitution: https://www.youtube.com/watch?v=I9aGC6Ui3eE
Not the author/contributor, but the app is built using Tauri for easy multi-platform support, so the backend logic is implemented in Rust and the frontend UI is implemented in TypeScript. I think it’s a valid choice. GitHub does not include any model _code_ in the stats; the models will be downloaded separately the first time you use them. Hope this helps.
I know many people hate sites like this, but I actually like them for these use cases. You can get a quick, LLM-generated overview of the architecture, e.g. here: https://codewiki.google/github.com/cjpais/handy
Most of my complex documents are, luckily, Markdown files.
I can recommend https://github.com/tobi/qmd/
. It’s a simple CLI tool for searching in these kinds of files. My previous workflow was based on fzf, but this tool gives better results and enables even more fuzzy queries. I don’t use it for code, though.
Given that preface, I was really expecting that link to be a grepping tool rewritten in golang or something, or perhaps customised for markdown to weigh matches in "# heading title"s heavier for example
I really enjoyed this relevant article about prompt caching where the author explained some of the same principles and used some additional visuals, though the main point there was why KV cache hits makes your LLM API usage much cheaper: https://ngrok.com/blog/prompt-caching/
Not all laws are hard science laws in the sense of the second law of thermodynamics; they are, however, good approximations based on experience and, in the right context, make a bit more sense.
Notes from the original author:
> I've been involved in spacecraft and space systems design and development for my entire career, including teaching the senior-level capstone spacecraft design course, for ten years at MIT and now at the University of Maryland for more than a decade. These are some bits of wisdom that I have gleaned during that time, some by picking up on the experience of others, but mostly by screwing up myself. I originally wrote these up and handed them out to my senior design class, as a strong hint on how best to survive my design experience. Months later, I get a phone call from a friend in California complimenting me on the Laws, which he saw on a "joke-of-the-day" listserve. Since then, I'm aware of half a dozen sites around the world that present various editions of the Laws, and even one site which has converted them to the Laws of Certified Public Accounting. (Don't ask...) Anyone is welcome to link to these, use them, post them, send me suggestions of additional laws, but I do maintain that this is the canonical set of Akin's Laws...
If you've never used Rust before, I couldn't find good documentation on how to run a existing Rust project nor could I find `cargo install` on the "Getting Started" page. I could read the Cargo Book, or check `--help` I guess, but this can be surprisingly time consuming as well, it might take 5-30 minutes of active searching to locate the information. If you can, try put yourself in a beginner's mindset and think though your argument again.
Regarding your second point, I think people actually underutilise LLMs for simple tasks. Delegating these tasks frees up your problem-solving skills for challenges that truly need human insight. In this case, asking an LLM is arguably the smart choice: it's a common task in training data, easy to verify, and low-risk to run and not a direct learning or benefit for your initial question.
You don’t need to cargo install anything. You just need cargo itself, which is linked on the main page. Once you have that, here’s an example google search that gives you all the info you need to run the project (hint: `cargo run`)
Thanks for the Google link, I was just asking GPT-5-Pro "How to Google: 'How do I run a rust project'", and am still waiting for the answer... the point was that searching for an answer (wherever/however) is not necessary in some cases, like this one, but asking the AI agent to find a solution can be sufficient and is totally ok. Engineers are allowed to delegate, there is not nothing wrong with this.
I love OpenRouter, since it is a simple way to get started and provides a wide range of available models.
You can buy credits and set usage limits for safe testing per API key to gain access from many AI models through one simple and unified API from all popular model providers (OpenAI, Anthropic, Google, xAI, DeepSeek, Z.AI, Qwen, ...)
Ten dollars is plenty to get started... experiments like in the post will cost you cents, not dollars.
Comparing this blog post to a 500-page book or a multi-hour course and calling it “weaksauce” misses the point. This post is meant as an introduction to the dot product, and it does that really well. The formal definition (6.1) and explanation in Axler’s book wouldn’t make a good starting point for most people, it isn't even a good next step in my opinion. It’s great that you’re passionate about the topic, really, but helping more people discover math means meeting them where they are and appreciating content like this for what it’s trying to do.
of course. dot products are a symmetric form on vector spaces. they let you compute the spheres of radius r.
given the sphere of radius r, for any pair of vectors v,w in the sphere
-r^2 <= dot(v,w)=dot(w,v) <= r^2
as w varies from v to -v the value moves from r^2 through 0 to -r^2
this is how we define parallel perpendicular and antiparallel.
the dot product is only meaningful in a geometric context. by definition it projects vectors down to scalars. fixing the scalar value finds the spheres, and for a sphere we can vary the vectors to compute cosines.
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