AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Give your old monitor a second chance at life as a dedicated streaming screen or over-the-air TV with just a few simple steps.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Quality scores 100 = best · Line = minimum Three documentation quality scores out of 100. Each bar includes its minimum passing score. Codebase coverage Important systems and workflows are documented. Minimum passing sc…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The “CrysVCD” tool developed at MIT could cut the huge amounts of time and money spent on screening out chemically unstable designs.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Uber and Wayve had said they would start trips ‘later this summer’ but launch now looks unlikely this year The rollout of robotaxis on the streets of London is unlikely to happen this year, as regulatory and technical hurdles push back the ambitious schedule set out by the UK government. Only weeks ago Uber and London-based Wayve were granted the first minicab licences in the capital to allow them to offer self-driving taxi rides to paying customers – but with a human safety driver in place, for now – and said they would start trips “later this summer” before a full public launch. Continue reading...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：My Anti-AI manifesto As a software developer, the recent escalation of Artificial Intelligence (AI) has aroused strong emotions and several concerns in me, either of technical and philosophical nature. The human-machine…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：25 Aug 2026 · Essay Should we let AI govern us? What if AI were in charge of public policy and politics? Perhaps it would shift food subsidies, electrify transport, build cheap power, sign the plastics treaty, restore t…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Shift comes after Chris Bowen said Labor would use constitutional powers to override states resisting renewable rules Get our breaking news email, free app or daily news podcast Anthony Albanese has backed down on demands that states must power new AI datacentres entirely using sustainable energy, with Wednesday’s meeting of national cabinet flagging carve outs for jurisdictions including Queensland and the Northern Territory. Anthony Albanese welcomed the “positive and construction” discussions in Sydney, but opened the door to a flexible approach under new nationally consistent standards to deal with AI development, including for governments with state-owned power systems. Continue reading...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：‘This is crazy. This is insane’: Bill Gates has changed his mind about AI and jobs Aug 26, 2026, 3:00am EDT Technology PostEmailWhatsapp The News Bill Gates says it’s time to hit the AI panic button. The technology has…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Drive-By Agent Hijacking: One Website Visit, Persistent Model Poisoning CustomersPricing Back Back Back Back Get a demo Elad Luz Ofek Itach Nemoclaw CVE-2026-65105: One Website Visit to Hijack Your AI Agent A vulnerabil…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Uh oh! There was an error while loading. Please reload this page. Notifications You must be signed in to change notification settings Fork 0 Star 12 BranchesTags Open more actions menu Latest commit History 6,169 Commit…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：<blockquote cite="https://pauldix.com/the-end-of-programming"><p>The fact that AI wrote 1M LOC and then refined it over the course of the next couple of months to produce a reliable piece of software that is currently running on millions of developer machines is absolutely mind blowing. And you can say, “well it’s not that impressive because they had an oracle to compare against, so it was simple to go from one language to another”, but I think that’s selling this entire thing short. If you can build a verification system and give proper direction, AI can produce a highly complex, highly sophisticated piece of software and it can continue to refine it until it just works.</p></blockquote> <p class="cite">&mdash; <a href="https://pauldix.com/the-end-of-programming">Paul Dix</a>, The end of programming</p> <p>Tags: <a href="https://simonwillison.net/tags/coding-agents">coding-agents</a>, <a href="https://simonwillison.net/tags/ai-assisted-programming">ai-assisted-programming</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/bun">bun</a>, <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a></p>

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：NewPower reliable AI agents with accurate, relevant data Read the blog > NewBuild software faster with AI agents—without losing control Read the blog > Blog home How We Used AI to Bring MongoDB to DynamoDB August 24, 20…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Limits of robot autonomy The fact that many events still permitted humans to directly control robotic motions shows that autonomous robot systems still have a long way to go, Patel said. Whereas humans can quickly learn…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Live visual sketches on any video call Sketch, share, and collaborate live Expressed turns your tablet into a live canvas. Draw with your hand and watch it appear instantly on the desktop you're screen-sharing — the way…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Prompt First-person perspective, plummeting at high speed through a cosmic waterfall of stars, then lightning-fast reaching out to touch a planet, triggering an explosive white light. The camera plunges vertically at ex…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：5 Join the conversation Follow us Add us as a preferred source on Google A Russian Molniya drone carrying an Nvidia Jetson Orin module crashed and killed three civilians at a gas station in Zaporizhzhia last month after…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Make every site work for you. Describe the outcome. Retriever AI works across the open web and the sites you’re signed into, then brings back the finished result. Add to Chrome⭐⭐⭐⭐4⭐Run in cloud 7M+ tasks automated#1 on…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：← Back to blog Can AI Music Tools Really Replace Epidemic Sound? An Honest Look MuseGen Team 7/30/2026 #Epidemic Sound alternative#AI music vs stock music#royalty-free AI music#AI music for creators If you make videos,…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：IBM has released Granite 4.2, a family of open reasoning language models in 3B, 8B, and 30B sizes, all under Apache 2.0. Every model exposes a thinking / low-effort / non-thinking switch and native tool calling. The 8B and 30B additionally go through an agentic RL block that trains them to edit code, drive a terminal, and run web searches inside real sandboxed environments. The 30B reports 57.00 on SWE-Bench Verified and 29.24 on Terminal-Bench 2.1. The post IBM Releases Granite 4.2: Bringing Native Reasoning and Agentic RL to Open Enterprise Models appeared first on MarkTechPost.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Now Perplexity is trying to get into the local AI action Amid talk of an Nvidia deal, the AI search biz is looking beyond the cloud Thomas Claburn Thomas Claburn AI AND SOFTWARE REPORTER Published wed 26 Aug 2026 // 00:…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：Hey all, The goal is to earn on token margins for LLM calls when you build an AI-powered webapp. I proxy OpenAI and Anthropic calls so that when you deploy a site to a subdomain, your users token usage will be tracked.…

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：The vast majority of the global public wants international cooperation on human rights, climate and AI. Like-minded countries must stand together to deliver Britain’s new prime minister, Andy Burnham, is already having to make one of his gravest decisions. He has to issue the instructions that he alone gives to the military, setting out the UK response in a doomsday scenario of a nuclear weapons attack on us. He will, as I did two decades ago, sign a piece of paper telling commanders whether or not to retaliate and, if so, whether through targeting civilian conurbations or military sites. These instructions are written down in the aptly named “letter of last resort”. Now, more than at any time since the 1960s Cuban missile crisis, the European public fears a third world war. With the nuclear Doomsday Clock developed by atomic scientists moving ever closer to midnight, and Japan, South Korea, Saudi Arabia, the UAE, Egypt, Poland and Germany contemplating either acquiring nuclear weapons or siting them on their soil, our world is descending from a rules-based order to a power-based one, where might is deemed right and brute force dominates. Gordon Brown is the UN’s special envoy for global education and was UK prime minister from 2007 to 2010 The future starts with us: Gordon Brown in conversation On Thursday 10 September, join Hugh Muir and Gordon Brown to discuss the intricate connections between global instability and civic decline, as explored in Brown’s new book, The Future Starts With Us. Book tickets here Continue reading...

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23994v1 Announce Type: new Abstract: Human-robot teaching focuses on enabling nontechnical experts to customize robots according to their needs after deployment. With recent advances in machine learning, human-robot teaching is no longer confined to offline learning where the data gathering step from a human teacher is separated from when the robot learns. Instead, more recent approaches for human-robot teaching focus on coupling human teaching with robot learning. This coupling impacts the structure, timing, and content of the teaching and learning interaction. However, it is currently unclear how such coupling dynamics affect humanrobot teaching effectiveness and human perceptions towards the teaching process. Informed by human learning theories, in this paper we propose a new scale for classifying human-robot teaching interactions according to coupling dynamics present between the human teacher and robot learner. We apply this scale to a subset of the human-robot teaching literature to identify how coupling dynamics and human teacher mental model mismatches with the ground truth robot learning system affect teaching effectiveness and human perceptions towards the teaching process

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23983v1 Announce Type: new Abstract: Robotic fruit harvesting must hold produce securely without bruising it, yet compression stiffness varies several-fold with ripeness within a single species, so no fixed grip force spans the range. Rather than tune force, we bound deformation: a controller closes the gripper until the object's estimated compression strain reaches a user-specified limit $\varepsilon$, using only the encoder position and motor-effort signal on every servo gripper---no tactile or force-torque sensor. Dividing an effort-based contact force by a lower bound on object stiffness makes the stop provably conservative---true compression stays at or below $\varepsilon$---for any $\varepsilon$ above a contact-detection strain floor we identify and quantify: robust detection itself spends compression, linearly in closing speed, making speed an explicit throughput--gentleness knob. Unlike a hand-tuned force threshold, $\varepsilon$ is a certified, size-scaling, operator-interpretable damage limit, and a ready safe-action parameter for learned grasping policies. In MuJoCo simulation over a realistic fruit-stiffness range, under a sensor-noise model calibrated to the real servo, the controller holds $\ge 98\,\%$ grasp at $0\,\%$ damage across all medium-to-firm stiffnesses for the entire certified $\varepsilon$ range, which neither fixed-force baseline attains; on stiffness-graded 3D-printed TPU cubes it matches baseline grasp success at roughly half the grip force and cuts soft-object damage from $100\,\%$ to $40\,\%$.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23972v1 Announce Type: new Abstract: Safety-aware motion planning remains a challenge in robotics, especially when missions are time-critical and are under complex specifications. In this paper, we propose safety-aware-stl-mppi, a computationally efficient sampling-based receding-horizon planning framework designed to promote satisfaction of constraints expressed in Signal Temporal Logic (STL). Our approach encodes discrete-time STL formulas into candidate time-varying control barrier functions (CBF), which are integrated into a model predictive path integral (MPPI) controller. Our method inherits the benefits of low computational cost from an efficiently parallelizable sampling based planner and utilizes CBF for constraints expressed in STL. We compare against several MPPI baselines using four artificial Mars Rover planning case studies with a diverse environment and cost setups, where we show our method consistently achieving high safety and efficiency. We show a quadcopter planning experiment with NVIDIA Isaac Lab.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23887v1 Announce Type: new Abstract: Latent-conditioned adaptive policies can control robots across changing dynamics, but their learned latents remain internal representations of the policy rather than physical models that can be inspected, rolled out, or used by other control modules. This limits closed-loop analysis, diagnosis, and further improvement of a fixed policy. A direct mapping from latent to physical parameters is also under-specified, because multiple systems can induce similar closed-loop behavior. We therefore decode each operational latent into a distribution of quadrotor models using conditional flow matching. The decoded distribution enables two downstream uses without modifying the policy: online predictive tuning of a high-level controller around the fixed low-level policy, and robustness analysis under specified disturbances. Under perturbed actuator dynamics, decoded-model predictive tuning reduces position tracking RMSE by $23\%$ and heading RMSE by $45\%$ relative to fixed gains. Under Gaussian force disturbances, decoded-model ensembles closely predict the lateral tracking-error evolution. Together, these results show that control latents can be converted into physical model ensembles for tuning, robustness analysis, and diagnosis of frozen adaptive policies.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23863v1 Announce Type: new Abstract: Robots are beginning to act on world-model predictions, yet reliability is still expressed through instantaneous, model-internal signals. DreamLedger instead treats reliability as a persistent deployment object: an execution-settled credit file recording how often consumed predictions are borne out, indexed by operating condition, region, and prediction horizon, and consulted before each use. Each consumed prediction is registered as a claim; attributable outcomes are settled against arriving reality at zero labeling cost, an attribution stage excludes measurement-contaminated outcomes, and a settlement-supervised head complements sparse bins. The resulting credit gates consumption: low-credit predictions shorten the dependent horizon or trigger additional observation; every reliance event remains auditable via dependency tickets and replayable logs. We evaluate DreamLedger in three simulated domains (indoor flight, tabletop manipulation, 2D navigation), via mounts on unmodified DreamerV3, TD-MPC2, and V-JEPA 2-AC, and on a real Franka manipulator. Claim failure is dose-monotone in all 12 held-out condition-horizon cells. Credit-gated planning reduces burned imagination (consumed claims that later fail to redeem) by 62% (95% CI 43-81%) versus blind consumption, with equal success and comparable collision rates. At matched risk targets, persistent books cut verification probes from 1.00 to 0.36/episode in manipulation, at success 0.94 versus 0.98; settlement-grounded calibration retains moderate, seed-consistent operating points unlike raw instantaneous gates. The same trust layer operates across decoder-, latent-, and token-space interfaces, including V-JEPA 2-AC settled on real robot frames. On hardware, settlement remains operational under real sensing and contact noise, a deployment failure loop is re-priced online, and all 1,062 registered spends replay from the audit logs.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23839v1 Announce Type: new Abstract: Embodied Agents System (EAS) are increasingly deployed in open-world physical domains, where reliability directly dictates deployment quality and human-agent trust. However, existing evaluations rely on outcome-centric metrics as success rate or safety scores that collapse diverse execution trajectories into coarse scores, obscuring the dynamic processes underlying agent behavior. Therefore, they ignore a critical property of EAS -- which we define as the Resilience -- that reflects how EASs recover, stabilize, and extend under perturbations and across iterative updates. The lack of resilience is particularly critical in open-world environments due to continuous unexpected disruptions, thus directly affecting the quality of EAS deployment. To address this problem, we gain insight from the resilience-engineering concepts to EAS groundings and propose a novel resilience evaluation framework that can be flexibly applied to any EAS. Specifically, we define the first comprehensive resilience metrics suite for EASs system that exposes Rebound, Stability, and Graceful Extensibility across embodied tasks execution, providing a practical grounding for EAS resilience analysis. We further implement the resilience evaluation layer that transforms execution process into assessments for diagnosis and optimization. Across 400 household tasks with 10 EAS, we reveal the process-level distinction hidden by outcome metrics, including recovery cost differences among successful episodes ($\Delta C_{rec}=25.2$), increased instability and task-family degradation. Metrics-guided optimizations reduce recovery cost and increase stability, graceful extensibility completion, showing the diagnostic effect of resilience evaluation. Our results reveal a trade-off among resilience characteristics, suggesting that a resilient EAS construction should be configured according to deployment-specific requirements.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23831v1 Announce Type: new Abstract: While reinforcement learning (RL) allows generalist robot policies to continually improve during deployment, the large model size of modern generalist policies, such as VLAs, poses a fundamental obstacle to effective RL improvement. In particular, their severe inference latency---which can lead to pauses or jerky movements---can alter the effective environment dynamics and, if not correctly accounted for, break the Markov assumption that RL relies on, causing standard RL algorithms to fail completely. In this work, we introduce a latency-aware framework, Asynchronous RL with Intermediate Information (ARLI), that enables RL-based improvement of generalist policies under inference delays. Our framework builds on asynchronous inference approaches, which interleave action generation with execution to hide latency, and addresses its incompatibility with RL by providing a low-latency RL policy design that maximizes reactivity within the inference window through two contributions: state augmentations that restore near-Markovian structure by incorporating committed actions and a mid-inference observation. We evaluate our approach across simulated and real-world manipulation tasks, and find that it enables effective finetuning under inference delays where standard RL fails entirely, even matching or exceeding the performance of standard RL in idealized no-latency settings.

AI 服務暫時不可用，以下為來源摘要，待恢復後補全翻譯：arXiv:2608.23650v1 Announce Type: new Abstract: The perception of 3D space by mobile robots is rapidly moving from flat metric grid representations to hybrid metric-semantic graphs built from human-interpretable concepts. While most approaches first build metric maps and then add semantic layers, we explore an alternative, concept-first architecture in which spatial understanding emerges from asynchronous concept agents that directly instantiate and manage semantic entities. Our robot employs two spatial concepts (room and door), implemented as autonomous processes within a cognitive distributed architecture. These concept agents cooperatively build a shared scene graph representation of indoor layouts through active exploration and incremental validation. The key architectural principle is hierarchical constraint propagation: Room instantiation provides geometric and semantic priors to guide and support door detection within wall boundaries. The resulting structure is maintained by a complementary functional principle based on prediction-matching loops. This approach is designed to yield an actionable, human-interpretable spatial representation without relying on any pre-existing global metric map, supporting scalable operation and persistent, task-relevant understanding in structured indoor environments.