标签：#AR

submitted by /u/ExtensionEcho3 [link] [留言]

According to federal prosecutors, Michele Spagnuolo made more than $1 million on the prediction market platform using confidential information about Google Search traffic.

I used to believe that the thing that we had to worry about with AI becoming more and more prevalent was like sentient robots that would take over like in the science fiction story I robot. But I don't think that's the case anymore because I think there is something far more sinister behind all the push for AI to become mainstream technology in the reason all these deep pockets are willing to pour so much of the cash into it. Because it spells control. If you control AI you can control the people because you can control the data they consume you can control how they consume it and what they will believe. Because as AI becomes more and more mainstream it leaves the door open for big corporations to feed us information they want us to have as well as the government. submitted by /u/crazyhomlesswerido [link] [留言]

[R] BEAM 100K memory benchmark: CSM vs Hindsight local artifact comparison I’m looking for feedback on a local agent-memory benchmark comparison, especially from people who care about evaluation methodology. I built an open-source R&D memory system called Context Swarm Memory (CSM). It uses bounded read-only memory shards, query routing, probe/recall/synthesis, cited packets, and explicit Committer-gated writes. The current comparison is against the accepted local Hindsight artifact on BEAM 100K: CSM: 0.757573 AMB score, 342 / 400 correct Hindsight: 0.733658 AMB score, 326 / 400 correct CSM uses 38.2% fewer answer-visible context tokens CSM is slower: 29.23s average retrieval vs 6.38s I want to be precise about the claim: This is not an official leaderboard claim. It is not a BEAM 10M claim. It is a committed local accepted-artifact comparison at 100K, and the next step should be independent replication or official chart acceptance. Repo: https://github.com/muhamadjawdatsalemalakoum/context-swarm-memory Evidence and reproducibility notes: https://muhamadjawdatsalemalakoum.github.io/context-swarm-memory/ The main question: what would make this comparison scientifically stronger before it is presented as a serious agent-memory result? submitted by /u/keonakoum [link] [留言]

New preprint. A Mixture-of-Experts inference kernel (TritonMoE) written entirely in OpenAI Triton, targeting portability across NVIDIA and AMD without vendor-specific code. Highlights: A fused gate+up GEMM computes both SwiGLU projections from shared tile loads, eliminating 35% of global memory traffic. 89-131% of Megablocks throughput at inference batch sizes (up to 512 tokens) on A100; the same kernel runs on MI300X unchanged. Limitations: falls behind at 2048+ tokens, and degrades with 64+ experts under extreme routing skew. Paper: https://arxiv.org/abs/2605.23911 Code: https://github.com/bassrehab/triton-kernels Writeup with benchmarks: https://subhadipmitra.com/blog/2026/fused-moe-dispatch-triton/ submitted by /u/bassrehab [link] [留言]

The Series B round was led by Battery Ventures.

Dataset for fine-tuning compliance assistants. Each pair includes: - A practical SME-facing question ("Can I use pre-ticked consent boxes?") - An answer with specific UK GDPR article references, ICO guidance by name, and actionable steps - Source metadata: which GDPR concepts were used, which generation strategy, timestamp Generation method: questions via local Qwen 14B from a curated term bank, answers via DeepSeek API for factual reliability. JSON + Parquet, MIT license for the 1K sample. This is a niche dataset — it's not a benchmark contender, it's for people building privacy tools for UK businesses. If you're doing legal NLP or compliance RAG, might be useful. Free sample: https://huggingface.co/datasets/Draeg82/uk-gdpr-small-business-qa submitted by /u/a_serial_hobbyist_ [link] [留言]

I am a junior in college, and have two accepted workshop papers at ICML 2026. Some background: I had an accepted workshop paper last year at ICLR, but couldn't attend due to a rejected visa, which led to all the more disappointment. So this year I was VERY eager to attend, and my supervisor really wants me to as well. However, the cost of attending (workshop pass, air tickets, etc.) is SO HIGH. Even if my university does offer to cover some of it, it's not gonna cover even half the cost. I'll have to fund it myself. I study for free at my current institution, so my parents wouldn't be mad about paying, but I'm also not someone comfortable asking my parents to pay (which is why I chose my current institution in the first place). So, as third year undergraduate student aiming for grad school, will presenting at ICML workshops/ attending the event have any particular benefits? There's still a part of me that really wants to experience this event, but the cost is going to be a burden. Is it worth it for a 2-day trip? Any insights, experiences, thoughts are welcome. What would you have done? submitted by /u/milasonder [link] [留言]

This is a prototype I’ve been working on, I’d be interested to know what others think, nothing that costs money, just interested in people’s opinion. https://darglark-s.github.io/darglarking-yellow-wiki/index.html submitted by /u/Darouck [link] [留言]

The update also introduces the app's take on film stock simulation.

Ferrari fans don’t like the design of the new Luce EV, an electric four-door sedan that just doesn’t look like the Ferraris of old. It was designed with help from Jony Ive’s LoveFrom, but what worked for Ive at Apple isn’t working for Ferrari. The Luce’s launch immediately preceded a stock drop that even an […]

Each brain is unique, and from the best generations that I save, a worm can pick random brain files to use, letting each worm be completely unique and feel alive. This is for Bonk Universe. submitted by /u/Lanse012 [link] [留言]

Nvidia will invest $150 billion a year to make Taiwan an AI “epicenter.”

. https://x.com/ethanCaballero/status/2059686905105563907 . submitted by /u/Glittering_Author_81 [link] [留言]

“I don't want recommendations! I know what I want to watch."

https://gemini.google.com/share/1b2ff803d882 I'm sorry earlier today i made a post comparing ChatGPT and Gemini . I asked Gemini to build a prompt and gave it to him in another chat and i got this... submitted by /u/ObjectiveOrchid5344 [link] [留言]

submitted by /u/andix3 [link] [留言]

Demis Hassabis has tightened his AGI timeline to 2029, making him the most aggressive sitting frontier-lab CEO on record with a public forecast. In an Axios interview, Hassabis named one or two remaining technical breakthroughs DeepMind needs to clear within three years. DeepMind's Co-Scientist multi-agent system is already live across all 17 DOE national labs, providing the kind of real-world deployment data that likely informed the revised estimate. Open questions Which specific technical breakthroughs Hassabis identified as remaining: the Axios interview did not name them publicly. Whether Co-Scientist's DOE deployment includes autonomous decision-making capabilities or operates under strict human oversight protocols. How other frontier lab CEOs (Sam Altman, Dario Amodei) will respond publicly to the 2029 anchor, given no comparable on-record forecast exists as of May 2026. source : https://aiweekly.co/alerts/deepmind-ceo-hassabis-moves-agi-deadline-to-2029 submitted by /u/Justgototheeffinmoon [link] [留言]

The Chinese company is adapting to the demise of Moore’s Law, which guides chip production. It could complicate US chip dominance.

AI tools are getting better quickly and many technical skills are becoming easier to automate. I often think about this: What AI-related skill will still be truly valuable in 5 years? Not using ChatGPT more effectively " but actual long-term skills that will still matter even as AI models get better. My thoughts are: * problem solving * really understanding systems * checking AI outputs * good communication and setting context I'm interested, in hearing your thoughts. What skill do you think will remain important despite AI advancements? submitted by /u/FollowingSuitable941 [link] [留言]