Discover microservices best practices to build resilient systems. Learn what the best patterns look like and how workflow automation fits into the process.
出品 | 虎嗅科技组作者 | 梁卡尔编辑 | 苗正卿头图 | 视觉中国Codex用户喊了快一年的“Tibo”，在中文互联网上有个更响亮的头衔“赛博义父”。他是OpenAI编程工具Codex的核心负责人，也被叫作“复位之神”（God of Reset），非正式头衔CRO（Chief Reset Officer，首席重置官）。额度一用完，用户就涌到他评论区刷“求义父重...
TL;DR We serve Meta’s Muse Spark Open, an 816B-parameter mixture-of-experts model, on a single TPU v7 host of four chips, with SGLang running on the TorchTPU PyTorch backend. At the... The post Optimizing Muse Spark Open on TPU v7 with SGLang-TorchTPU appeared first on PyTorch.
arXiv:2611.13309v1 Announce Type: new Abstract: AI coding agents can now construct scientific workflows, but operating them on shared cyberinfrastructure (CI) raises problems that code generation does not solve. Agents need current knowledge of the workflow system and its execution sites and access to the runtime state of distributed executions, while facilities need to attribute every agent action to a user and bound it by that user's privileges. We present PegasusAI Studio, an AI-assisted science gateway built on the Pegasus and HTCondor stack. Studio makes CI legible to agents by projecting one facility-curated knowledge base into the native configuration of several coding agents, by introspecting workflow interfaces and execution sites, and by grounding diagnosis in workflow and scheduler telemetry. Agent actions operate within a trust envelope that combines federated authentication, per-user execution environments and scheduler credentials, provider-neutral model access, observability, and approval controls for state-changing actions. The same application runs as a JupyterHub-based gateway on a research cloud and as an Open OnDemand application on HPC systems. We evaluate Studio through case studies of legacy CASA weather workflow modernization, a gravitational-wave reanalysis, an EEG analysis built with an open-weight model, and an Event Horizon Telescope (EHT) M87 image reconstruction that recovered from two failed runs to a zero-failure execution. The evaluation establishes that these mechanisms operate as designed.
arXiv:2611.12903v1 Announce Type: new Abstract: Context compression is now routine for language models with long inputs, but a compressed context is useful only if it keeps the evidence for the answer. Existing checks compare the reader model's answer or its most likely words, and thus miss a frequent failure, silent evidence loss: key evidence is removed while the answer stays the same, leaving the answer unsupported and risky for other models that reuse the context. To address this challenge, we propose SELD (Silent Evidence Loss Detector), which needs no training and no labels. Its principle is that when evidence is lost, a reader model's preferences among the answers it leaves unsaid shift even if its answer does not, so comparing these preferences before and after compression reveals losses that answer checks cannot see. We also build SEL-Pairs, a benchmark of length-matched evidence and control deletions from three question-answering datasets. Quantitative and qualitative experiments with five reader models on it show that SELD consistently outperforms strong and recent baselines across compressors, compression rates and models, identifying the evidence-removing edit in 83% of matched pairs with unchanged answers, against 70% for the strongest baseline. These results show that evidence loss in compressed contexts can be detected even when the answer is unchanged.
arXiv:2611.13176v1 Announce Type: new Abstract: Per-step model routers send each LLM call of an agent to the cost-optimal model dynamically within each session. However, every model switch discards prefix-cache reuse: KV caches are per model, so the new model must prefill the relevant context before it emits a token. Across representative agentic workloads, including a production coding-agent trace with a mean context of 63K tokens, the prefix hit rate falls from 80-89% on same model calls to 44-65% after a switch, and a switch call takes 1.5-3.2x as long. We present SHADOWPREFILL, which prefills the session's context on the model the router has not chosen, in capacity that instances serving normal traffic leave idle, so the next switch finds its prefix cached. SHADOWPREFILL harvests the per-iteration SLO slack of requests in flight, protects routed calls, spills shadow KV to host memory, and falls back to a cold switch whenever the shadow is incomplete. With LLMRouter routing 24 model pairs over five agentic benchmark suites, SHADOWPREFILL cuts switch-call latency by 8.5-21.4%, LLM-call latency by 3.7-17.7%, and routed token cost by 22.5-46.1%, while normal traffic keeps its SLO and energy per request rises by 0.4%.
Article URL: https://barterpilot.com/ Comments URL: https://news.ycombinator.com/item?id=50446649 Points: 2 # Comments: 0
Article URL: https://github.com/gmpetrov/brigade Comments URL: https://news.ycombinator.com/item?id=50442991 Points: 1 # Comments: 0
I grew up with Black Ops 2 and wanted to see if I could make it work on my 2020 MacBook Pro. So I used Claude Opus 5.5 to create a tool that converts your copy of the game (from Steam) to a native ARM64 Mac app, with almost 60fps in all areas. The whole game is statically recompilated ahead-of-time (AOT) and does not use any emulator whatsoever, be it Wine, Crossover or Rosetta. The best thing is the performance, although some heat issues are still to fix. You get to play the whole Campaign, local Multiplayer (with bots) and local Zombies (all DLCs maps; you need to have them in your Steam library obviously) at about 60fps. Split-screen was not in the PC version, but I've been working on it lately; it's coming soon! LAN mode is on the roadmap for later too :) Comments URL: https://news.ycombinator.com/item?id=50449891 Points: 1 # Comments: 0
I have spent countless years learning and reading about personal finance. This website felt like a good way to redirect people to one place for the repeated questions like "But tech stocks", "Waiting for a crash", "What about crypto, real estate, or gold?", etc.Would appreciate some thoughts and feedback. Comments URL: https://news.ycombinator.com/item?id=50450104 Points: 2 # Comments: 1
