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@andycarolan@social.lol
2026-01-09 12:50:19

I’m in my second/third place again… No coffee today as I already had two, so I’m having a Plum and Cherry Posh Pop instead 🍒
#ThirdPlace

@catsalad@infosec.exchange
2026-01-08 09:59:04

Tradwife? Oh, I though you meant Trade⁠wife.
*puts away my Pokémon themed DS*

@kexpmusicbot@mastodonapp.uk
2025-12-08 10:38:27

🇺🇦 #NowPlaying on KEXP's #JazzTheatre
Trudy Pitts:
🎵 Never My Love
#TrudyPitts

@andycarolan@social.lol
2025-12-09 10:26:45

I’m in the coffee shop again…
As it’s a community coffee shop attached to a larger venue, so every time I buy a coffee, I also buy a Pay It Forward.
It’s a really busy place, so is a bit more vibrant than sitting on my own at home. I’m thankful that I found it!
#Coffee #ThirdPlace

@Mediagazer@mstdn.social
2026-01-07 22:15:48

The US House Judiciary Subcommittee scrutinizes the WBD-Netflix deal, and Rep. Jamie Raskin expresses concern over Trump's influence on Paramount and Netflix (Katie Arcieri/Bloomberg Law)
news.bloomberglaw.com/business

@padraig@mastodon.ie
2026-01-08 01:38:39

If you are using #DuckDuckGo and hate the AI bollocks, there is noai.duckduckgo.com (AI Images are not always filtered out, but you can report it as AI)

@arXiv_csGT_bot@mastoxiv.page
2025-12-08 08:18:30

Robust forecast aggregation via additional queries
Rafael Frongillo, Mary Monroe, Eric Neyman, Bo Waggoner
arxiv.org/abs/2512.05271 arxiv.org/pdf/2512.05271 arxiv.org/html/2512.05271
arXiv:2512.05271v1 Announce Type: new
Abstract: We study the problem of robust forecast aggregation: combining expert forecasts with provable accuracy guarantees compared to the best possible aggregation of the underlying information. Prior work shows strong impossibility results, e.g. that even under natural assumptions, no aggregation of the experts' individual forecasts can outperform simply following a random expert (Neyman and Roughgarden, 2022).
In this paper, we introduce a more general framework that allows the principal to elicit richer information from experts through structured queries. Our framework ensures that experts will truthfully report their underlying beliefs, and also enables us to define notions of complexity over the difficulty of asking these queries. Under a general model of independent but overlapping expert signals, we show that optimal aggregation is achievable in the worst case with each complexity measure bounded above by the number of agents $n$. We further establish tight tradeoffs between accuracy and query complexity: aggregation error decreases linearly with the number of queries, and vanishes when the "order of reasoning" and number of agents relevant to a query is $\omega(\sqrt{n})$. These results demonstrate that modest extensions to the space of expert queries dramatically strengthen the power of robust forecast aggregation. We therefore expect that our new query framework will open up a fruitful line of research in this area.
toXiv_bot_toot

@crell@phpc.social
2026-01-02 02:00:02

Programming language tradeoffs.
garfieldtech.com/blog/language

@UP8@mastodon.social
2026-01-31 02:01:26

💩 Understanding ammonia energy's tradeoffs around the world
#energy

@fanf@mendeddrum.org
2025-12-28 09:42:01

from my link log —
How Rust views tradeoffs.
infoq.com/presentations/rust-t
saved 2019-07-02 do…