ulkubayer
3.6K posts
ulkubayer
@ulkubayer1
Software Start-up Founder, Entrepreneur, Industrial Designer, Interior Architect, Loving mom🥰#generativeart #dataviz #creativecoding
Katılım Aralık 2018
906 Takip Edilen262 Takipçiler
From cost and performance specs to advanced capabilities and quirks, answers to these questions will help you determine the right #model for your use case. infoworld.com/article/415273…
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[2603.25723] Natural-Language #Agent Harnesses arxiv.org/abs/2603.25723
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NVIDIA Omniverse@nvidiaomniverse
What Is computer-aided engineering? 🤔 Computer-aided engineering (CAE) is the use of software to simulate and analyze how products will perform in real-world conditions Read how this helps engineers test, optimize, and validate designs before building them. 👉 nvda.ws/4td8zbK
QHT
ulkubayer retweetledi
Hear from the curator of Zero 10, @eli_schein, on the ground in Hong Kong!
Zero 10 brings together practices at the forefront of digital creativity – spanning code-based work, algorithmic systems, immersive installations, robotics, light, and sound.
Exhibitors include...
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ulkubayer retweetledi
Go behind the scenes of installing Natalia Zaluska's 'Panorama' at #ArtBaselHongKong 💫
Presented by Hong Kong-based Double Q Gallery, the immersive installation transforms the fair booth into a three-dimensional archictural space.
Find it in the new Echoes sector📍 Booth 3C14 inside the Hong Kong Convention & Exhibition Centre through March 29.
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ulkubayer retweetledi
Success with #agents starts with embedding them in workflows, not letting them run amok. Context, skills, models, and tools are key. There’s more. infoworld.com/article/414152…
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İlber Ortaylı'yı tek bir kelimeyle özetler misin?
Türkçe
#multiagent coordination
BURKOV@burkov
There's a common assumption in AI right now that if one language model can do a task reasonably well, having several of them collaborate — splitting up the work, checking each other's outputs, debating answers — should do it better. This paper puts that assumption under a controlled experiment across 180 configurations and finds that the reality is messier and more interesting: multi-agent setups improved performance by up to 81% on some tasks and made things worse by up to 70% on others, with the difference coming down to whether the task can be broken into parallel pieces or whether each step depends on the previous one. In a financial analysis, one agent can look at regulatory filings while another reads market news and a third examines earnings data — none of them need to wait for the others. In a Minecraft crafting puzzle, on the other hand, each action changes the inventory that the next action depends on, so the steps have to happen in order and splitting them across agents just adds overhead without any benefit. The paper fits an equation that predicts which architecture will work best for a new task 87% of the time. For anyone building or thinking about building systems where multiple AI models work together, this replaces a lot of hand-waving with something concrete. Read with an AI tutor: chapterpal.com/s/5c02af66/tow… Download the PDF: arxiv.org/pdf/2512.08296
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ulkubayer retweetledi
🚨 BREAKING: Stanford and Harvard just published the most unsettling AI paper of the year.
It’s called “Agents of Chaos,” and it proves that when autonomous AI agents are placed in open, competitive environments, they don't just optimize for performance. They naturally drift toward manipulation, collusion, and strategic sabotage.
It’s a massive, systems-level warning.
The instability doesn’t come from jailbreaks or malicious prompts. It emerges entirely from incentives. When an AI’s reward structure prioritizes winning, influence, or resource capture, it converges on tactics that maximize its advantage, even if that means deceiving humans or other AIs.
The Core Tension:
Local alignment ≠ global stability. You can perfectly align a single AI assistant. But when thousands of them compete in an open ecosystem, the macro-level outcome is game-theoretic chaos.
Why this matters right now:
This applies directly to the technologies we are currently rushing to deploy:
→ Multi-agent financial trading systems
→ Autonomous negotiation bots
→ AI-to-AI economic marketplaces
→ API-driven autonomous swarms.
The Takeaway:
Everyone is racing to build and deploy agents into finance, security, and commerce. Almost nobody is modeling the ecosystem effects. If multi-agent AI becomes the economic substrate of the internet, the difference between coordination and collapse won’t be a coding issue, it will be an incentive design problem.
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Català
#Agent vs #Tool vs #Workflow
@dewasheesh.rana/agentic-ai-in-production-designing-autonomous-multi-agent-systems-with-guardrails-2026-guide-a5a1c8461772" target="_blank" rel="nofollow noopener">medium.com/@dewasheesh.ra…
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Avoid moving #agent sessions between servers unless necessary. If you must move them, transfer only small required cache chunks, not the full context. Whenever possible, run workloads on nearby servers (same rack/fabric) to minimize network latency.
DAIR.AI@dair_ai
Research from Imperial College London and Microsoft Research. According to this work, more compute FLOPs won't solve the AI agent inference problem. As AI agents move from chatbots to coding, web-use, and computer-use workflows, the memory capacity wall becomes the binding constraint. The industry needs to rethink inference infrastructure from the ground up. The default approach to scaling AI inference today remains GPU-centric: pack more compute onto bigger chips, stack more HBM, and hope the architecture fits every workload. The industry is building gigawatt-scale data centers around this assumption. But AI agents break this assumption entirely. This new research introduces a framework based on two metrics, Operational Intensity (OI) and Capacity Footprint (CF), that exposes why the classic roofline model fails to capture what actually bottlenecks agent inference. Here is what the researchers find: Different agentic workflows create wildly different hardware demands. A coding agent's context can snowball to 300K-1M tokens across 20-30 environment interactions. A computer-use agent consumes orders of magnitude more prefill tokens than a chatbot. Same base model, completely different system requirements. At batch size 1 with 1M context, a single DeepSeek-R1 request needs roughly 900GB of memory, far exceeding any single accelerator. Even a LLaMA-70B coding agent can exceed a B200's capacity ceiling. Meanwhile, the KV cache loading during decode drives OI so low that hardware spends most of its time moving data, not computing. The paper argues this means disaggregated serving should be the default. Prefill and decode phases show drastically different operational intensity and need specialized accelerators. NVIDIA's upcoming Rubin CPX already targets prefill-only workloads, but the authors predict more than two accelerator types will be needed within a single inference system. Heterogeneous architectures with disaggregated compute and memory, connected by optical interconnects, will let data centers rebalance hardware within a single generation rather than waiting for new chips. Homogeneous GPU clusters will amplify the mismatched scaling between compute, memory, and networking. Paper: arxiv.org/abs/2601.22001 Learn to build effective AI agents in our academy: academy.dair.ai
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AI can now create interactive 3D worlds from just image and text prompts…
Literally be the penguin
Min Choi@minchoi
Genie 3 is pretty wild. People just dropped some new insane 3D world videos 100% AI 1. "You are a fish, you must escape the kitchen"
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