@burger_jaap@mastodon.socialIt is encouraging to see that so many countries are making the transition from a 20% share of electric cars to 80% over the average lifespan of a car (15–20 years)
https://leraffl.github.io/LeRaffl-Gallery/#worldmap (transition time between thresholds view)
@DieGesellschafterinLang@swiss.social
@kexpmusicbot@mastodonapp.uk🇺🇦 #NowPlaying on KEXP's #AstralPlane
石間秀機:
🎵 Depending by the Time
#石間秀機
https://open.spotify.com/track/2VvuV1gNePxCK6n6FAbM9S
@arXiv_csPF_bot@mastoxiv.pageSysOM-AI: Continuous Cross-Layer Performance Diagnosis for Production AI Training
Yusheng Zheng, Wenan Mao, Shuyi Cheng, Fuqiu Feng, Guangshui Li, Zhaoyan Liao, Yongzhuo Huang, Zhenwei Xiao, Yuqing Li, Andi Quinn, Tao Ma
https://arxiv.org/abs/2603.29235 https://arxiv.org/pdf/2603.29235 https://arxiv.org/html/2603.29235
arXiv:2603.29235v1 Announce Type: new
Abstract: Performance diagnosis in production-scale AI training is challenging because subtle OS-level issues can trigger cascading GPU delays and network slowdowns, degrading training efficiency across thousands of GPUs. Existing profiling tools are limited to single system layers, incur prohibitive overhead (10--30%), or lack continuous deployment capabilities, resulting in manual analyses spanning days. We argue that continuous, cross-layer observability enabled by OS-level instrumentation and layered differential diagnosis is necessary to address this gap. We introduce SysOM-AI, a production observability system that continuously integrates CPU stack profiling, GPU kernel tracing, and NCCL event instrumentation via adaptive hybrid stack unwinding and eBPF-based tracing, incurring less than 0.4% overhead. Deployed at Alibaba across over 80,000 GPUs for more than one year, SysOM-AI helped diagnose 94 confirmed production issues, reducing median diagnosis time from days to approximately 10 minutes.
toXiv_bot_toot
@Techmeme@techhub.socialGecko Robotics secures a $71M US Navy contract to use robots to slash repair time for dated ships, as the US races to reindustrialize its aging defense systems (Samantha Subin/CNBC)
https://www.cnbc.com/2026/03/17/gecko-robotics-navy-contract-ship-repair-t…
@jensilber@mastodon.socialTFW you have a phrase stuck in your head and you can't remember where you first heard or read it... it's Shakespeare. For me, at least 80% of the time, it's Shakespeare.
@Techmeme@techhub.socialCNN and CCDH investigation: 80% of major AI chatbots gave guidance on weapons or targets to "teen" personas 50% of the time; only Claude consistently refused (CNN)
https://cnn.com/2026/03/11/americas/ai-chatbots-help-teen-test-user…
@raiders@darktundra.xyzInsider Shares Falcons’ True Stance on Kirk Cousins’ Raiders Contract https://heavy.com/sports/nfl/las-vegas-raiders/insider-falcons-stance-kirk-cousins-contract/
@BBC3MusicBot@mastodonapp.uk🇺🇦 #NowPlaying on BBCRadio3's #ThroughTheNight
Canadian Chamber Ensemble, Игорь Фёдорович Стравинский & Raffi Armenian:
🎵 Rag-time for 11 instruments
#CanadianChamberEnsemble #ИгорьФёдоровичСтравинский #RaffiArmenian
@chris@mstdn.chrisalemany.caRemember that time Elon said he was making a semi-truck? 😂😂
Anyway, here's a real company making a real-life truck.
Up to 600km range.
20-80% fill in 50-85min
six-speed transmission
13-tonne axle
GCW of up to 48T with payload of 28T.
These are the things we need to be doing for those “last mile" applications where electrified (catenary) rail is not possible.
The preview doesn't seem to be happening on the Inside EV website so I added a screenshot.
#Cars #EV #Truck #Trucking #Electrification
