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@Techmeme@techhub.social
2025-06-12 20:30:49

Shopify partners with Coinbase to enable USDC payments for its merchants through Coinbase's Base layer-2 network, starting with a limited group of merchants (CoinDesk)
coindesk.com/business/2025/06/

Coinbase (COIN), Shopify (SHOP) Partner to Enable USDC Payments on Base for Merchants Worldwide
The integration is set to roll out on June 12 to a limited group of early access merchants, with wider availability expected later in the year.

@kurtsh@mastodon.social
2025-06-13 02:07:20

Dear God... Kristi Noem is a certified PoS.
Republican dog lovers: Look yourself in the mirror & say outloud that you won't stand by this level of cruelty.
☑️ Kristi Noem Dog-Killing Story Is Actually Worse in Context
nymag.com/intelligencer/articl…

Kristi Noem Dog-Killing Story Is Actually Worse in Context
The Kristi Noem dog-killing story is actually worse in context. The governor is encouraging people to read her new book, ‘No Going Back,’ and judge the anecdote about fatally shooting her dog, Cricket, for themselves. She probably shouldn’t.

@karlauerbach@sfba.social
2025-06-12 19:41:04

I just tried to use the US Senates system to contact my Senator (Padilla) to thank him.
But the system is overloaded.
padilla.senate.gov/contact/con

@netzschleuder@social.skewed.de
2025-06-12 19:00:04

contact: Haggle human proximity network (2006)
A network of human proximities, as measured by carried wireless devices. Each node represents a person, and an edge denotes when two people were within a certain proximity of each other, as measured by a wireless protocol (Bluetooth or Wifi). The edges are timestamped.
This network has 274 nodes and 28244 edges.
Tags: Social, Offline, Unweighted, Timestamps

contact: Haggle human proximity network (2006). 274 nodes, 28244 edges. https://networks.skewed.de/net/contact

contact — Haggle human proximity network (2006)
A network of human proximities, as measured by carried wireless devices. Each node represents a person, and an edge denotes when two people were within a certain proximity of each other, as measured by a wireless protocol (Bluetooth or Wifi). The edges are timestamped.

@memeorandum@universeodon.com
2025-06-13 22:06:08

Feds from IRS agents to refugee officers are deploying to assist ICE conduct raids (Eric Katz/GovExec.com)
govexec.com/management/2025/06
memeorandum.com/250613/p130#a2

@usul@piaille.fr
2025-06-11 11:31:32

Focus and Context and LLMs | Taras' Blog on AI, Perf, Hacks
#AI

Focus and Context and LLMs
I decided to write down some thoughts on agentic coding and why it’s a very hyped wrong turn. Let me start with some background on my LLM experience. I adopted LLMs into my work in Aug 2020. I was sold when I saw that GPT-3 could generate usable SQL statements. Something that used to take 4-8 hours of RTFMing, now took 15min. I have since worked on chatcraft.org, various RAG frameworks, etc. I use aider heavily for work, frequently switch models, have been struggling with tool calling during …

@arXiv_csCR_bot@mastoxiv.page
2025-06-12 07:21:51

ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
Ronal Singh, Mohan Baruwal Chhetri, Surya Nepal, Cecile Paris
arxiv.org/abs/2506.09365

ContextBuddy: AI-Enhanced Contextual Insights for Security Alert Investigation (Applied to Intrusion Detection)
Modern Security Operations Centres (SOCs) integrate diverse tools, such as SIEM, IDS, and XDR systems, offering rich contextual data, including alert enrichments, flow features, and similar case histories. Yet, analysts must still manually determine which of these contextual cues are most relevant when validating specific alerts. We introduce ContextBuddy, an AI assistant that learns from analysts' prior investigations to help them identify the most relevant context for new alerts. Rather than …

@arXiv_csSE_bot@mastoxiv.page
2025-06-13 08:33:20

From Tea Leaves to System Maps: Context-awareness in Monitoring Operational Machine Learning Models
Joran Leest, Claudia Raibulet, Patricia Lago, Ilias Gerostathopoulos
arxiv.org/abs/2506.10770

From Tea Leaves to System Maps: Context-awareness in Monitoring Operational Machine Learning Models
Machine learning (ML) models in production do not fail due to statistical anomalies in their input data; they fail due to contextual misalignment -- when their environment deviates from training assumptions, leading to unreliable predictions. Effective ML monitoring requires rich contextual information to move beyond detecting statistical shifts toward meaningful alerts and systematic root-cause analysis. Yet, surprisingly, despite extensive research in ML monitoring and related disciplines (dr…

@jorgecandeias@mastodon.social
2025-06-12 00:14:21

Foram estas contas que me levaram para o twitter.
Foi o facto de ter de as deixar para tršs uma das coisas que mais me fizeram demorar a largar o twitter.
Se soubesse que iam acabar assim, provavelmente tinha batido com a porta bastante mais cedo.

Adeus, Curiosity? NASA silencia contas icónicas em grande reorganização das redes sociais
 Numa medida que está a deixar muitos entusiastas da exploração espacial nostálgicos, a NASA anunciou que vai encerrar várias das suas contas de redes sociais,

@netzschleuder@social.skewed.de
2025-06-13 23:00:04

sp_hypertext: Hypertext 2009 dynamic contact network
The temporal network of contacts among attendees of the ACM Hypertext 2009 conference, which spanned 2.5 days of time.
This network has 113 nodes and 2196 edges.
Tags: Social, Offline, Unweighted, Temporal
networks.skewed.de/net/sp_hype…

sp_hypertext: Hypertext 2009 dynamic contact network. 113 nodes, 2196 edges. https://networks.skewed.de/net/sp_hypertext#intervals

sp_hypertext — Hypertext 2009 dynamic contact network
The temporal network of contacts among attendees of the ACM Hypertext 2009 conference, which spanned 2.5 days of time.