I host figure drawing in NYC every Monday and got this an hour before tonight’s session. I understand not everybody cares about my family who were refugees in Israel 🇮🇱, but this is very unprofessional. Thankfully someone stepped in on short notice - come draw with us! IG: artdblockdotorg.
Like with all tech innovation it’s easier to adjust to new ways of working with flat or negative headcount because there’s less coordination overhead, then you can grow staff if you need to again when things are working well. The opposite is not true, if you fail to transform the way you work now, you are done for.
I used Copilot on my Copious Free Time™ to open 89 PRs (75 got merged) last month into a project at work. Coding with AI agents is now a baselines expectation for managers.
https://t.co/521oYNpnAd
Before AI coding assistants, a typical engineering team built expertise with the years. Now, exploring a codebase takes a day. So, at work, I asked my team to actively seek contributions from our internal customers with an active engagement for our proprietary code.
https://t.co/kxNptvXpTd
Are you returning to office? @Copilot did some remote work for me while I was having coffee this morning.
🎮 slack-gamebot now supports 4 rating algorithms: adaptive tau-decay (default), standard Elo (K-factor), Glicko-1 (rating deviation), and Glicko-2 (+ volatility). Switch per season, tune each algo's params. Serious rankings for your office games. Code is #opensource.
https://t.co/IQPaLVLu7e
https://t.co/yMp0vmqiHW
Adaptive is the default and most forgiving for casual play - new players experience big rating swings that gradually stabilize as they accumulate matches, thanks to a per-player tau value that dampens volatility over time.
Standard Elo is the classic textbook formula used in chess: a fixed K-factor scales every rating change equally regardless of experience, simple and predictable.
Glicko-1 improves on standard Elo by tracking a rating deviation (RD) per player - a measure of how confident the system is in your rating. New or inactive players have high RD (big swings), frequent players have low RD (small, precise adjustments), which means beating a well-established strong opponent is worth more than beating someone the system barely knows.
Glicko-2 goes further by also tracking per-player volatility (σ) - how erratically a player performs - so a consistent player and a streaky player at the same rating will experience different sized swings, with the streaky one remaining more sensitive to new results.