Tootfinder

If Microsoft owns 27% of Open AI & Open AI declares an IPO... 🤔
✅ MSFT Stock: Why Microsoft's $13 Billion OpenAI Bet Now Pays Off
https://www.marketbeat.com/originals/why-openais-ipo-plans-could-be-a-massive-win-for-microsoft/

Why OpenAI’s IPO Plans Could Be a Massive Win for Microsoft
Microsoft’s OpenAI investment may now be worth over $228 billion after OpenAI’s restructuring and IPO plans. Here’s what it means for MSFT stock and AI growth.

Ever wondered what makes SFPs go? Wonder no longer.
I need to print this out and hang it up in my lab to go along with the rest of the series, just need to take some measurements and figure out how large.
(Art commissioned from Usanekorin from Collateral Damage Studios, not on fedi AFAIK)

If Microsoft owns 27% of Open AI & Open AI declares an IPO... 🤔
✅ MSFT Stock: Why Microsoft's $13 Billion OpenAI Bet Now Pays Off
https://www.marketbeat.com/originals/why-openais-ipo-plans-could-be-a-massive-win-for-microsoft/

Why OpenAI’s IPO Plans Could Be a Massive Win for Microsoft
Microsoft’s OpenAI investment may now be worth over $228 billion after OpenAI’s restructuring and IPO plans. Here’s what it means for MSFT stock and AI growth.

RE: https://infosec.exchange/@catsalad/114758254385767289
Aaaaaaaaaaaaaaaaaa!! :neocat_scream_stare:

Very sad to see that Monica Lennon did not get re-elected. She has been an excellent MSP, putting forward several bills of advantage to women.
To my mind, the only talented person in the Labour group. I've often thought her ideas had much more in common with the SNP/Green group than with her own.

📦 Structured concurrency — control coroutine lifetime with the Scope sandbox and manage groups of coroutines via TaskGroup.
🗄️ PDO Pool: connection pooling built right into PDO with automatic connection management for maximum performance.
🔄 Channels & ThreadPool for data exchange between coroutines. Buffered and unbuffered channels for producer/consumer patterns, cross-thread via ThreadChannel, and parallel CPU tasks via Thread and ThreadPool.

🔊 #NowPlaying on #BBCRadio3:
#ClassicalLive
- Prokofiev's 3rd Piano Concerto with Daniil Trifonov
Pianist Daniil Trifonov and today's featured conductor, Franz Welser-Möst, perform Prokofiev's fiendishly difficult and much-loved third piano concerto in a recording from Munich.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002wgb4

ANNS-AMP: Accelerating Approximate Nearest Neighbor Search via Adaptive Mixed-Precision Computing
Mingkai Chen, Cheng Liu, Shengwen Liang, Lei Zhang, Xiaowei Li, Huawei Li
https://arxiv.org/abs/2606.07156 https://arxiv.org/pdf/2606.07156 https://arxiv.org/html/2606.07156
arXiv:2606.07156v1 Announce Type: new
Abstract: Approximate nearest neighbor search(ANNS) is a critical kernel in modern applications such as LLM and recommendation systems.However,its efficiency is fundamentally limited by the need to compute distances between a query and a massive number of high-dimensional vectors,most of which are non-neighbors.Existing approaches reduce redundancy via index optimization or early termination,but remain constrained by fixed-precision computation,leading to unnecessary arithmetic and memory bandwidth overhead.This paper presents ANNS-AMP,an adaptive mixed-precision framework and accelerator that adapts the precision of distance computation to the characteristics of queries and data distribution.The key insight is that different regions of the vector space require different levels of precision to preserve top-k accuracy.ANNS-AMP leverages the clustered structure of PQ-based indices and introduces a lightweight predictor to determine cluster-level precision at runtime based on features such as scale,radius,and query distance.To efficiently realize variable-precision execution,we design a bit-serial accelerator with a bit-interleaved data layout,enabling throughput to scale with reduced precision while mitigating memory bandwidth bottlenecks and load imbalance through a greedy scheduling strategy.Moreover,the runtime predictor can also reuse the bit-serial computing array for efficient runtime prediction and can be fitted to the ANNS pipeline without performance penalty.According to our experiments on representative datasets,ANNS-AMP achieves 163.76x,10.57x,and 2.06x performance speedups on average,and reduces average energy consumption by 1100.00x,39.41x,and 6.66x compared to CPU,GPU,and customized ANNS accelerator baselines,respectively,while maintaining accuracy loss below 2.7%.These results demonstrate that adaptive mixed-precision computing is a promising direction for efficient large-scale ANNS.
toXiv_bot_toot

📋 #Kanban board moves tasks through BACKLOG − INBOX − ACTIVE − DONE with real-time agent feedback, plus Routines for cron-style recurring tasks and persistent per-project Memory

Got that dog in 'em? Cowboys look to build defensive roster of bullies https://cowboyswire.usatoday.com/story/sports/nfl/cowboys/2026/04/29/dallas-cowboys-defense-draft-picks-culture-shift-caleb-down…