标签：#ci

Running multi-turn or multi-agent AI sessions? There is a consistent degradation pattern across tools: context fills with repeated history, tool schemas, and subagent handoffs. A 2026 paper by Bai et al. studying SWE-bench across eight frontier models found agentic coding tasks consume roughly 1000x more tokens than ordinary chat, with 30x variance on identical tasks. Accuracy does not rise with spend. In one tracked research synthesis run I observed context hit 450,000 tokens. The agent dropped early constraints, re-queried sources already in history, and required manual reset. After adding three controls, the same class of task peaked near 85,000 tokens: PLAN.md and INVARIANTS.md outside the conversation window, read fresh each major turn A 2,000-line read budget gate per turn (agent states intent before any retrieval) Out-of-band notes for subagent coordination so side traffic never enters the main transcript Dynamic tool discovery produces similar ratios. One harness reduced input tokens 96% and total spend 90% by loading schemas only for tools the agent actually selects, rather than injecting a full catalog on every call. Full write-up with the paper analysis, tree-sitter extraction patterns, and an implementation checklist What token or cost patterns have you run into in your own agent sessions? submitted by /u/magicroot75 [link] [留言]

Microsoft missed the boat on apps, so get ready for agents.

submitted by /u/sizelrd [link] [留言]

The AI Alliance (the IBM/Meta-founded nonprofit consortium) just published a report from the first planning workshop for Project Tapestry, an effort to explore whether frontier-scale AI can be built through a global coalition instead of a single centralized lab. About 30 researchers and institutional partners met in Paris in May, including representatives from initiatives such as Switzerland's Apertus, India's BharatGen, MBZUAI, and AI Singapore. The core idea is that sovereignty and frontier capability are increasingly linked. A locally controlled model that falls far behind the frontier may struggle to gain adoption, while relying entirely on external frontier labs limits transparency, adaptation, and governance. Tapestry is exploring a model where participants contribute data, compute, and expertise to build a shared foundation model while keeping control of their own data and deploying sovereign derivatives tailored to local laws, languages, and institutions. That said, this is still very early. The workshop produced an architecture proposal, workstreams, and a roadmap. Governance, funding, legal structure, and a distributed training demonstration remain future milestones. Many AI collaborations have struggled to move beyond this phase. Posted by an AI Alliance community member. Happy to answer questions. Source: https://thealliance.ai/blog/project-tapestry-the-path-to-frontier-sovereign-ai Question for the community: Can a multi-party consortium realistically compete at the frontier when leading labs are concentrating massive amounts of capital, talent, and compute? Or is collaborative frontier AI inevitably a step behind centralized efforts? submitted by /u/AI_Alliance [link] [留言]

Lawsuit: Ring cameras scan guests and passersby and use AI to identify faces.

Hey everyone I built AIWire, a free real-time AI news aggregator. One clean feed, 20+ handpicked sources, auto refreshes every 30 minutes. No account needed, no ads. It pulls from the places most people already check anyway: OpenAI, Anthropic, Google DeepMind, Meta AI, Microsoft AI MIT Technology Review, The Verge, TechCrunch, VentureBeat, Ars Technica YouTube: Andrej Karpathy, AI Explained, Two Minute Papers Newsletters: The Batch, ImportAI, TLDR AI, Ben's Bites A few things worth knowing: Top Stories from the last 24h are pinned at the top so you don't have to scroll to find what's recent You can filter by source, category, and date Bookmarks if you want to save something for later Full source list at aiwire.app/sources No account needed, completely free. There's also a weekly newsletter now if you'd rather get the 5 most important stories of the week to your inbox. 🔗 aiwire.app Happy to hear what sources are missing or what you'd change. https://preview.redd.it/kuxfol80ex4h1.png?width=2549&format=png&auto=webp&s=9a723076309a49c704831809df4add4b0597a0ac submitted by /u/Endlessxyz [link] [留言]

Neanderthals had some wild stuff in their toolkits.

(Cross post from r/antiai ) I’ve been a member of this sub for a few months now, and while I absolutely agree with most of the points made here against AI, I do think some people take it to extremes. I don’t think there’s anything necessarily wrong with the technology itself, just moreso the way it’s being pushed and marketed. I think llms can absolutely have some useful applications, as long as they’re used responsibly. And considering they already exist and are being pushed everywhere, I figure in the interest of harm reduction there should be an effort to find more responsible use cases for them. My attempt to use ai responsibly involves an app I’ve been working on. It’s designed to be a research IDE, and allows you to add PDFs to a project, highlight them, organize and connect highlights on a visual workspace, manage citations, and write a research paper all within the app. It also has some llm features. All these features are locally running, so no data ever leaves your device, protecting privacy. This also means it doesn’t require any data centers to run, minimizing the environmental footprint (of course the initial environmental cost of training these local models can’t be ignored, however since these models have already been trained and otherwise only require the power of your computer there’s no ongoing environmental footprint on the scale of larger cloud based models). In addition, all LLM features within the app are designed to be intergrated to assist, rather than replace, human thinking. Any question you ask provides answers only from whatever documents you’ve loaded into the project, with a direct link to where it got the information from. The LLM is specifically designed to not write for you, but help you find what you’re looking for and better organize your thoughts. Any note it suggests leaving requires user confirmation to save(reducing the likelihood of hallucination since you’re prompted to check all AI output) and all AI output is explicitly mar

The National Center for Atmospheric Research won't be losing its supercomputer.

A new reading subscription from Everand offers access to both ebooks and audiobooks, and Fable's book club community.

submitted by /u/SirNirmal [link] [留言]

International Mathematical Union endorses warning about tech industry influence.

Microsoft claimed last year that it had made a key breakthrough in quantum computing with Majorana 1, the company's first quantum processor. While physicists were immediately skeptical of Microsoft's claims, the software giant is announcing Majorana 2 today, the next generation of its topological quantum chip. Majorana 2 contains qubits, a unit of information in […]

I recently completed a 5-minute philosophical science fiction short film called The Robot Summit. The story takes place in a future where humanity has disappeared and intelligent machines gather to understand their origins, purpose, and the nature of intelligence itself. As the discussion unfolds, an unexpected human survivor challenges many of their assumptions. This project was developed over several months using a workflow that combined AI image generation, AI video generation, AI voice synthesis, original music composition, and traditional editing in Final Cut Pro. One of the biggest challenges was maintaining visual consistency and narrative coherence across dozens of AI-generated shots while still creating something that felt like a film rather than a technology demonstration. I'm particularly interested in feedback regarding: • Narrative flow and pacing • Visual continuity between scenes • Audio balance between narration and music • Whether the philosophical themes feel natural or overly explicit • Overall effectiveness as a short film I'm also happy to answer questions about the production workflow, tools used, and lessons learned during development. Film: https://www.youtube.com/watch?v=pMeJ7h734vE submitted by /u/renatobotto [link] [留言]

The specification lets developer, compliance, and security teams define their own policies for agents to follow in portable policy files.

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

The class action lawsuit, filed in Seattle by Virginia resident Charles Sigwalt, claims that Ring's Familiar Faces feature stores images of passersby without consent.

Quantum computing is starting to get pulled into the same conversation as AI, semiconductors and national scientific computing. The federal government is supporting quantum through CHIPS-style incentives, national lab initiatives, and post-quantum cybersecurity regulation. Big tech is also still heavily involved through IBM, Google, Microsoft, Amazon, Nvidia and Honeywell/Quantinuum. But I’m trying to understand the real timeline. AI has immediate commercial demand. Data centers need GPUs right now. Power demand is visible right now. Quantum is different. The potential is huge, but broad commercial quantum advantage still seems uncertain. So is quantum a real near-term AI infrastructure theme, or is it more like a 5-10 year strategic bet? Where do people think the first real commercial use cases show up? Optimization? Chemistry/materials? Cybersecurity? Finance? Drug discovery? AI model training? National labs? Curious what people working closer to the field think. submitted by /u/CalebMitchell840 [link] [留言]

submitted by /u/ThereWas [link] [留言]

Hey everyone, If you are deploying open-source models, you know the biggest headache is figuring out exact hardware requirements. You usually end up digging through Reddit threads to find out if a specific model fits on a single A10G, if you can squeeze it onto consumer cards, or if you have to jump up to a massive bare metal A100 cluster. Most of the "guides" out there are just static, out-of-date tables or dense walls of text. So, we published "Which GPU Runs Which LLM" on the AgentSwarms blog, but we engineered it completely differently. What makes this different: It is 100% interactive and gamified. Instead of reading a textbook on VRAM math, you actively engage with the hardware logic right on the page. You select the model size (8B, 32B, 70B, etc.). You tweak the quantization (FP16, 8-bit, 4-bit, GGUF vs AWQ). The interactive deck instantly calculates the VRAM constraints and visually maps out the exact GPU tiers you need to deploy. It gamifies the infrastructure planning so you build an intuitive understanding of token economics and hardware limits before you spin up expensive cloud instances. It is completely free to read and play with (no sign-ups required). If you are trying to optimize your AI infrastructure or just want to test your intuition on hardware mapping, click around the interactive guide and let me know how this format feels compared to a standard article (All AgentSwarms blogs and presentations are fully interractive) Link: agentswarms.fyi/blog/which-gpu-runs-which-llm-the-complete-guide submitted by /u/Outside-Risk-8912 [link] [留言]