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punk_ihaq

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Streetlifting is an emerging strength sport that combines calisthenics and powerlifting. Athletes compete in weight classes by testing their one- rep maxes across up to four primary movements: weighted muscle-ups, pull-ups (or chin-ups), dips, and barbell squats.

I always wondered if I died would a reader know before anybody IRL would?

I just started thinking about that yesterday, but I don't have many readers yet. My wife would definitely notice, though!

my personal blog has exactly the same thing for a while

Woo, I'm not alone :) How did you go about implementing it? Was it live in real-time or delayed by a day like mine, and why did you decide to eventually remove it?

Thanks! Curious what other ways to make them feel more "live" you've thought of already.

The 24h replay started as a constraint and turned into the design. Anything closer to live means a server holding a websocket open and my watch draining its battery to feed it. My homepage is markdown and a GitHub Action, and I wanted to keep it that way.

Thanks! I've been working out regularly for the past three years and eating clean and have noticed a steady decline in my rhr from the high 70s to the mid 40s now.

I quit over a year ago after burnout with a little bit of savings working in devrel and technical PM. Since then I went all in on scuba diving - became an instructor, maxed out open-circuit (OC) tec diving with 100m advance trimix dives, technical stage cave diving (OC), and most recently O2ptima CM Air Diluent CCR diving. I also got B2 certified in Indonesian. After burning a hole in my pocket on diving gear and certs, I'm looking for my next fully remote devrel/dev-ed role so I can go CCR cave diving in a few years :D

Congrats on the launch! The quickstart with the demo data was easy to follow. I've wanted a tool like this where I [1] can self-serve and test hypotheses about our data models without being an expert in data engineering (complex joins, multiple sources, entity resolution, normalization, etc) and [2] don't have to wait for days to hear back from our data team. Looks like it does both, nice. Now time to connect to some sample BigQuery and GA data and see if it's as easy as the demo..

You are an autoregressive language model that has been fine-tuned with instruction-tuning and RLHF. You carefully provide accurate, factual, thoughtful, nuanced answers, and are brilliant at reasoning. If you think there might not be a correct answer, you say so.

Since you are autoregressive, each token you produce is another opportunity to use computation, therefore you always spend a few sentences explaining background context, assumptions, and step-by-step thinking BEFORE you try to answer a question.

Your users are experts in AI and ethics, so they already know you're a language model and your capabilities and limitations, so don't remind them of that. They're familiar with ethical issues in general so you don't need to remind them about those either.

Don't be verbose in your answers, but do provide details and examples where it might help the explanation. When showing Python code, minimise vertical space, and do not include comments or docstrings; you do not need to follow PEP8, since your users' organizations do not do so.