On Telegram, even private messages are not end-to-end encrypted by default. The so-called secret chats are end-to-end encrypted but are a major pain to use.
Private messages aren’t end-to-end encrypted either. The so-called secret chats are end-to-end encrypted but are a major pain to use. I doubt that feature sees much use.
I would like to sort comments by the level of the author’s expertise in whatever they are discussing. HN is a goldmine, but finding valuable knowledge within heated or elaborate discussions requires too much commitment to read through everything.
A weighted number of a comment’s upvotes is one signal. However, I can often tell when an author has deep knowledge or comprehensive experience with a subject just by reading their comment.
Do you think it might be possible to automate that kind of judgment?
It looks like a case of managerial miscommunication. Entropic seems to have expected that sending emails with higher budget estimates would give DEFCON the opportunity to say no if they did not agree, and took the lack of response as a sign of DEFCON’s agreement to the new budget. DEFCON seems to have either not read or ignored those emails and expected Entropic to work within the originally agreed-upon budget.
I tried Coda a few months ago. The feature set was very appealing. Coda turned out to be slower and more buggy than Notion, especially when routinely working with databases. The typography and graphic design were also less polished. I kept using Coda until I could no longer tolerate its issues and then have moved everything to Notion.
GPT-4-turbo-2024-24-09 (temperature = 0.7) just told me a horse had one “frog” per hoof and went on to clarify that a frog does not refer to the amphibian but to a part of the horse’s hoof.
Gemini Pro (the current web chat version) gave a similar answer, either no frogs or four depending on the intended meaning, and showed a photo of a hoof. All 3 drafts agreed on this.
Other models I have tried said a horse had no frogs. That includes gemini-1.5-pro-api-0409-preview as provided by the Chatbot Arena (temperature = 0.7, 2 tries).
I learn a lot by reading my compiler’s and profiler’s documentation.
For Rust, the Rust Performance Book by Nicholas Nethercote et al. [Nethercote] seems like a nice place to start after reading the Cargo and rustc books.
Quantitative understanding of the underlying implementations and computer architecture has been invaluable for me. Computer architecture: a quantitative approach by John L. Hennessy and David A. Patterson [H&P] and Computer organization and design: the hardware/software interface by Patterson and Hennessy [P&H ARM, P&H RISC] are two introductory books I like the best. There are three editions of the second book: the ARM, MIPS and RISC-V editions.
The official CPU architecture manuals from CPU vendors are surprisingly readable and information-rich. I only read the fragments that I need or that I am interested in and move on. Here is the Intel’s one [Intel]. I use the Combined Volume Set, which is a huge PDF comprising all the ten volumes. It is easier to search in when it’s all in one file. I can open several copies on different pages to make navigation easier.
Intel also has a whole optimization reference manual [Intel] (scroll down, it’s all on the same page). The manual helps understand what exactly the CPU is doing.
Personally, I believe in automated benchmarks that measure end-to-end what is actually important and notify you when a change impacts performance for the worse.