🧮 USERS:1 FEEDS:2 TWTS:395 ARCHIVED:41679 CACHE:1669 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:394 ARCHIVED:40304 CACHE:1671 FOLLOWERS:13 FOLLOWING:14
user/bmallred/data/2022-10-20-09-09-17.fit: 4.46 miles, 00:09:28 average pace, 00:42:14 duration
🧮 USERS:1 FEEDS:2 TWTS:393 ARCHIVED:40161 CACHE:1686 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:392 ARCHIVED:40097 CACHE:1675 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:391 ARCHIVED:40052 CACHE:1675 FOLLOWERS:13 FOLLOWING:14
user/bmallred/data/2022-10-17-14-24-58.fit: 4.32 miles, 00:11:43 average pace, 00:50:41 duration
🧮 USERS:1 FEEDS:2 TWTS:390 ARCHIVED:39990 CACHE:1680 FOLLOWERS:13 FOLLOWING:14
HM [03;04;07]: 12 mile run: 10.46 miles, 00:14:42 average pace, 02:33:47 duration
Fail, oh well.
A long couple of nights. Ran with Beth and started a bit late.
End of block.
#running
🧮 USERS:1 FEEDS:2 TWTS:389 ARCHIVED:39947 CACHE:1680 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:388 ARCHIVED:39915 CACHE:1677 FOLLOWERS:13 FOLLOWING:14
OSI’s Deep Dive is an essential discussion on the future of AI and open source
GitHub is sponsoring Open Source Initiative’s Deep Dive: AI because we think it’s important for the community to unpack how open source software, process, and principles can help best deliver on the promise of AI. ⌘ Read more
user/bmallred/data/2022-10-14-11-04-28.fit: 3.13 miles, 00:07:59 average pace, 00:25:01 duration
🧮 USERS:1 FEEDS:2 TWTS:387 ARCHIVED:39865 CACHE:1674 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:386 ARCHIVED:39824 CACHE:1672 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:385 ARCHIVED:39796 CACHE:1671 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:384 ARCHIVED:39752 CACHE:1666 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:383 ARCHIVED:39706 CACHE:1668 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:382 ARCHIVED:39663 CACHE:1678 FOLLOWERS:13 FOLLOWING:14
HM [03;03;06]: 9 mile run: 9.55 miles, 00:10:14 average pace, 01:37:46 duration
🧮 USERS:1 FEEDS:2 TWTS:381 ARCHIVED:39609 CACHE:1672 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:380 ARCHIVED:39560 CACHE:1664 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:379 ARCHIVED:39484 CACHE:1689 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:378 ARCHIVED:39444 CACHE:1700 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:377 ARCHIVED:37967 CACHE:1697 FOLLOWERS:13 FOLLOWING:14
user/bmallred/data/2022-10-03-17-08-10.fit: 5.14 miles, 00:09:41 average pace, 00:49:51 duration
🧮 USERS:1 FEEDS:2 TWTS:376 ARCHIVED:37926 CACHE:1720 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:375 ARCHIVED:37903 CACHE:1733 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:374 ARCHIVED:37857 CACHE:1719 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:373 ARCHIVED:37841 CACHE:1723 FOLLOWERS:13 FOLLOWING:14
user/bmallred/data/2022-09-29-13-43-14.fit: 3.04 miles, 00:08:52 average pace, 00:26:54 duration
🧮 USERS:1 FEEDS:2 TWTS:372 ARCHIVED:37806 CACHE:1733 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:371 ARCHIVED:37736 CACHE:1726 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:370 ARCHIVED:37679 CACHE:1740 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:369 ARCHIVED:37654 CACHE:1743 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:368 ARCHIVED:37599 CACHE:1716 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:367 ARCHIVED:37546 CACHE:1707 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:366 ARCHIVED:37509 CACHE:1695 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:365 ARCHIVED:37466 CACHE:1694 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:364 ARCHIVED:37415 CACHE:1687 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:363 ARCHIVED:37386 CACHE:1696 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:362 ARCHIVED:37354 CACHE:1675 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:361 ARCHIVED:37299 CACHE:1657 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:360 ARCHIVED:37250 CACHE:1624 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:359 ARCHIVED:37213 CACHE:1645 FOLLOWERS:13 FOLLOWING:14
🧮 USERS:1 FEEDS:2 TWTS:358 ARCHIVED:37195 CACHE:1652 FOLLOWERS:13 FOLLOWING:14
Paul Schaub: Using Pushdown Automata to verify Packet Sequences
As a software developer, most of my work day is spent working practically by coding and hacking away. Recently though I stumbled across an interesting problem which required another, more theoretical approach;
An OpenPGP message contains of a sequence of packets. There are signatures, encrypted data packets and their accompanying encrypted session keys, compressed data and literal data, the latter being the packet … ⌘ Read more
RT by @mind_booster: Tales from Moominvalley by Tove Jansson https://paulasimoesblog.wordpress.com/2022/09/14/tales-from-moominvalley-by-tove-jansson/
Tales from Moominvalley by Tove Jansson paulasimoesblog.wordpress.co…
8 things you didn’t know you could do with GitHub Copilot
Developers all over the world are using GitHub Copilot to help speed up their development and increase developer productivity. With GitHub Copilot available to developers everywhere, we’ve found some fun and useful examples of how developers can use GitHub Copilot for things you may not be thinking about. ⌘ Read more