- Expanding our support for scientists
anthropic-news· 27-ago
Aug 27, 2026Announcements
- Previewing the Model Hardware Standard
anthropic-news· 27-ago
Aug 27, 2026Announcements
- Large Models for Battery Prognostics and Health Management: A Review and Future Roadmap
arxiv-ai· 28-ago
arXiv:2608.26111v1 Announce Type: new Abstract: Battery Prognostics and Health Management (BPHM) is critical for ensuring the safe, reliable, and cost-effective operation of batteries across electric vehicles, grid storage, and consumer electronics. Conventional BPHM approaches, including physics-based models and task-centric deep learning methods, face challenges in computational efficiency and parameterization, cross-domain generalization, dependence on extensive labeled run-to-failure data, and model interpretability. Recent Large Models (LMs), built upon Transformer architectures and self-supervised pre-training, offer a transformative new paradigm to overcome these long-standing bottlenecks. This review provides the first comprehensive survey of LM applications in BPHM, systematicall…
- PICasso: An AI-Enabled Design Framework for Autonomous Optimization of Silicon Photonic Devices
arxiv-ai· 28-ago
arXiv:2608.26113v1 Announce Type: new Abstract: We present PICasso, an AI-assisted framework for automated synthesis, verification, and optimization of photonic integrated circuits (PICs) from natural-language specifications. PICasso couples a structured NL -> YAML -> GDS generation pipeline with PDK aware knowledge injection, automated placement and routing, DRC/LVS validation, and SAX-based photonic simulation. To systematically evaluate AI-driven photonic design, we introduce PIC-Set, a benchmark of 36 parameterized PIC design tasks spanning core photonic primitives and multi-component circuits. Using PIC-Set, we benchmark several state-of-the-art Large Language Models (LLMs) under a unified evaluation protocol, including new metrics such as structural and functional $Spec@k$, optimiza…
- CIFQA: A Deterministic Tool-Grounded Multi-Agent LLM Framework for Financial Query Answering
arxiv-ai· 28-ago
arXiv:2608.26114v1 Announce Type: new Abstract: Calculation-intensive financial question answering requires exact reasoning over structured rates, temporal conditions, numerical formulas, and rule-based constraints. Although Large Language Models (LLMs) perform strongly on natural language tasks, they often produce numerically incorrect yet plausible answers when solving multi-step financial calculations. To address this limitation, we introduce CIFQA (Calculation-Intensive Financial Query Answering), a deterministic tool-grounded multi-agent LLM framework for financial question answering. CIFQA separates language understanding from numerical execution by assigning specialized agents to query interpretation, routing, parameter extraction, computation planning, and response generation, whi…
- The Artificial Experimentalist: Discovery and Control of Self-Organizing Phenomena with Autotelic Reinforcement Learning
arxiv-ai· 28-ago
arXiv:2608.26116v1 Announce Type: new Abstract: Existing methods for exploring cellular automata and other complex systems mostly operate in open loop: they set initial conditions, execute a full simulation, and observe the outcome, without intervening during execution. We introduce a closed-loop framework based on autotelic reinforcement learning, in which an agent autonomously samples diverse goals and learns a goal-conditioned policy to intervene in a complex system through minimal, local perturbations. We instantiate this framework on Lenia, a continuous cellular automaton known for life-like self-organizing patterns, in an agentic system we call CARL, and demonstrate three capabilities. First, CARL discovers stable solitons across a wide range of Lenia update rules at a higher rate t…
- The Accuracy-Efficiency Paradox Quantifying Net Energy Loss in on-Device Energy Forecasting
arxiv-ai· 28-ago
arXiv:2608.26134v1 Announce Type: new Abstract: Energy forecasting aims to maximize accuracy to ensure energy efficiency by reducing energy waste, an objective that applies equally to on-device forecasting for mission-critical edge environments, including military systems. However, this paper identifies the Accuracy-Efficiency Paradox: high-precision energy forecasting models can ironically trigger a net energy deficit. This stems from both edge AI's inference energy consumption and battery aging. We propose a Total Cost of Ownership (TCO) framework for energy forecasting, designed to minimize net energy loss. This framework treats not only inference energy consumption but also battery aging as a unified form of energy loss, as degradation represents a physical dissipation of the system's…
- LLMs for Academic Workflows: An Evaluation of Literature Reviews Generated with Short and Long Context Windows of LLMs
arxiv-ai· 28-ago
arXiv:2608.26145v1 Announce Type: new Abstract: Our research focuses on evaluating literature reviews generated in short and long context settings of large language models (LLMs) to investigate the impact of context window on the quality of AI-generated literature reviews and the role of AI in supporting literature review writing. Twenty AI-generated literature reviews based on research sources from Semantic Scholar and Arxiv were evaluated by two researchers across 15 dimensions. Our findings reveal that AI-generated literature reviews require human oversight to meet academic publishing standards. As context windows increase, LLMs can incorporate broader information and maintain coherence across longer inputs, but they also exacerbate issues such as content repetition, omission of critic…
- Methodological and Conceptual Framework for 5D Multi-Table Analysis: A Unified Approach for Complex Data Reuse
arxiv-ai· 28-ago
arXiv:2608.26149v1 Announce Type: new Abstract: Multi-table learning remains a major challenge in machine learning for healthcare and other complex information systems. Relational data combine several sources of complexity, including large data volume, high-dimensional variables, high-cardinality categorical features, complex inter-table dependencies, and repeated temporal observations. We introduce the Relational Hypergraph Transformer (RHT), a unified architecture that represents relational databases as hypergraphs, learns pentadimensional embeddings (PentE), and performs sparse relational attention with complexity proportional to the average relational degree rather than the square of the number of entities. We formally define the architecture, derive the complexity of its attention me…
- Leveraging Large Language Models for Systematic Literature Review of Disease Spread Models
arxiv-ai· 28-ago
arXiv:2608.26150v1 Announce Type: new Abstract: Recent advancements in Large Language Models (LLMs) have created new opportunities to streamline and potentially automate many research processes, including systematic literature reviews (SLRs). This study reports an LLM pipeline development for extracting model-relevant information from 536 peer-reviewed agent-based modeling papers. We compare the results with those of a human-conducted SLR. Our results show paper-level accuracies of approximately 77.95% for GPT-4.1 and 81.67% for GPT-5.0. Field-level accuracy ranges from 32.40% to 100.00%, with more complex or subjective fields performing less reliably. Importantly, we find that agreement between LLMs is a potential indicator of output quality: low agreement may signal hallucinations, wher…
- EduRiskX: A Neuro-Symbolic Framework with F-Logic Reasoning for Early Academic Risk Prediction
arxiv-ai· 28-ago
arXiv:2608.26107v1 Announce Type: new Abstract: Predicting students' academic risk in online education is crucial for enabling timely interventions that can improve retention and learning outcomes. However, existing models often suffer from limited early detection capability and insufficient interpretability, leading to a "black-box" trust crisis that hinders their adoption in real-world pedagogical settings. To address these challenges, we propose EduRiskX, a neuro-symbolic framework that integrates a temporal Transformer-based predictor with F-Logic symbolic reasoning. The neural component models longitudinal student activity sequences using temporal attention, class-weighted loss, and dynamic weekly truncation. Acting as a data-driven expert system, an F-Logic rule base -- grounded in …
- Standalone LLM and a Pre-specified Agentic Pipeline for Explaining ICU Mortality Predictions: a Feasibility Study on the eICU Demo Dataset
arxiv-ai· 28-ago
arXiv:2608.26109v1 Announce Type: new Abstract: Machine-learning models can predict ICU mortality accurately, but feature-attribution methods alone rarely provide the clinical narrative needed for bedside use. Large language models (LLMs) may bridge this gap, and multi-step agentic pipelines are a plausible extension because they separate data interpretation, guideline checking, and final explanation. This revised feasibility study preserves the original standalone-versus-agentic comparison while making the main clinical findings more explicit. Using the retained local eICU Demo artifact set (2,353 ICU stays; 8.1\% mortality), XGBoost achieved an AUROC of 0.855 (95\% CI 0.796--0.906) and an AUPRC of 0.332 (95\% CI 0.217--0.494). On a stratified 38-case explanation subset, the standalone L…
- EP223: Ollama vs vLLM vs SGLang
bytebytego· 22-ago
To use open-weight models on your machine, you have three main options: Ollama, vLLM, and SGLang. But each engine handles requests differently.Aug 22 • ByteByteGo31349
- How to Make LLMs 3X Faster
bytebytego· 26-ago
In this article, we will look at how speculative decoding works.Aug 26 • ByteByteGo251311
- How to Steal an AI Model’s Private Thoughts
bytebytego· 25-ago
In August 2026, a team at MATS Research, the ELLIS Institute Tübingen, and the Max Planck Institute for Intelligent Systems wanted to test whether the…Aug 25 • ByteByteGo25258
- Why Code Verification Matters More Than Ever in the Age of AI
bytebytego· 24-ago
In this article, we will look at how code verification works, why the rise of AI-generated code puts more pressure on it, along with the extremely…Aug 24 • ByteByteGo332520
- Background Work: From Cron Jobs to Distributed Systems
bytebytego· 27-ago
In this article, we will look at various such strategies to perform background work in detail.23 hrs ago • ByteByteGo826
- Claude Platform release notes
claude-changelog
Release notesSubscribeCopy pageUpdates to the Claude Platform, including the Claude API, client SDKs, and the Claude Console.SubscribeCopy pageThe Claude Platform release notes list changes to the Claude API, the client SDKs, and the Claude Console, newest first. For release notes on Claude Apps, see the Release notes for Claude Apps in the Claude Help Center.For updates to Claude Code, see the complete CHANGELOG.md in the claude-code repository. August 27, 2026 In Python SDK 1.2.0, TypeScript SDK 0.122.0, Go SDK 1.68.0, Java SDK 2.59.0, Ruby SDK 1.67.0, and C# SDK 12.44.0, client.beta.files and client.beta.skills no longer send the files-api-2025-04-14 and skills-2025-10-02 beta headers and return the same shapes as client.files and client.skills. With this change, client.beta.skil…
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claude-changelog
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- BotBase for Operators: A clearer path to joining Cloudflare's directory of bots and agents
cloudflare· 28-ago
Bot operators now have a home in the Cloudflare dashboard to manage submissions. This update adds submission status tracking, submission editing, and a behavior model so operators can accurately declare how their bots use content.
- The Cloudflare Blog – Brought to you by EmDash
cloudflare· 24-ago
We migrated the Cloudflare Blog to EmDash to prove our stack at massive scale. Here is how we stress-tested performance, safely routed production traffic, and redesigned the frontend experience.
- How we saved 100 terabytes of memory by optimizing 1.1.1.1’s DNS cache
cloudflare· 27-ago
Five Rust-level memory optimizations to the DNS cache layout of Big Pineapple cut per-entry memory by 56%, freeing approximately 100 TB of memory across Cloudflare's fleet.
- Say it once: introducing Bot Preference Sync
cloudflare· 21-ago
Cloudflare's new Bot Preference Sync automatically aligns your robots.txt file with your AI bot policies for Search, Agent, and Training. Easily manage which bots access your content without maintaining static files.
- JetBrains/go-modern-guidelines
github-trending
Help AI coding agents write modern Go Modern Go Guidelines This repository contains guidelines for code agents that help them write modern Go code. For example, an agent with these guidelines uses max(a, b) instead of an if-else block, slices.Contains instead of a manual loop, cmp.Or(a, b, c) instead of a chain of nil checks. It also knows about recent additions like new(42) to get a pointer to a value and errors.AsType[T](err) for type-safe error matching—both from Go 1.26. The guidelines cover the most useful features from Go 1.0 through Go 1.27, including everything targeted by the modernize analyzer. An agent will: Detect the project's Go version from go.mod Use language features and stdlib additions available up to and including that version Prefer modern idioms over older patterns M…
- abhigyanpatwari/GitNexus
github-trending
GitNexus: The Zero-Server Code Intelligence Engine - GitNexus is a client-side knowledge graph creator that runs entirely in your browser. Drop in a git repository (Github, Gitlab, Azure, Local) or ZIP file, and get an interactive knowledge graph with a built in Graph RAG Agent. Perfect for code exploration https://gitnexus.vercel.app GitNexus (Akon Labs) ⚠️ Important Notice: GitNexus has NO official cryptocurrency, token, or coin. Any token/coin using the GitNexus name on Pump.fun or any other platform is not affiliated with, endorsed by, or created by this project or its maintainers. Do not purchase any cryptocurrency claiming association with GitNexus. The nervous system for agent context. Indexes any codebase into a knowledge graph — every dependency, call chain, cluster, and executio…
- abi/screenshot-to-code
github-trending
Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue) https://screenshottocode.com screenshot-to-code Convert screenshots, mockups, Figma designs, and screen recordings into clean, functional code using AI. The easiest way to try this is using the official, hosted product at screenshottocode.com → https://github.com/user-attachments/assets/ec08a5e6-9606-41c5-b03a-1bf47dfeba75 Supported stacks: HTML + Tailwind HTML + CSS React + Tailwind Vue + Tailwind Bootstrap Ionic + Tailwind Default AI models: Gemini 3 Flash Preview and Gemini 3.1 Pro Preview - the best models GPT-5.5 and GPT-5.4 Mini Claude Opus 4.6, Claude Opus 4.8 z-image-turbo (using Replicate) for image generation See the Examples section below for more demos. Screenshot to Code also supports taking a screen …
- bilawalsidhu/gods-eye-view
github-trending
A spy satellite simulator in your browser, except the data is real. Live open source spatial intelligence on a photorealistic 3D globe. https://maptheworld.ai/ 🌐 God's Eye View A spy-satellite simulator in your browser — then you realize the sources are public and the data is real. Photorealistic 3D globe. Live aircraft, ships, satellites, earthquakes, traffic, and public cameras, with clearly labeled modeled views where a live feed is unavailable. Hands-free voice control powered by a realtime AI agent. No place left behind. ▶️ From the project behind the viral God's Eye View series (formerly WorldView) — 5M+ on YouTube Quick Start · First Five Minutes · Talk to It · What's Live · Under the Hood · Keys · Costs 🌍 Why This Exists You asked, so it's happening. God's Eye View is open sourc…
- calesthio/OpenMontage
github-trending
World's first open-source, agentic video production system. 12 production pipelines, 100+ tools, 700+ agent skill and production-knowledge files. Turn your AI coding assistant into a full video production studio. https://www.openmontage.video/ Monty the Clapper — the official mascot of OpenMontage OpenMontage The first open-source, agentic video production system. Paste A Video · Quick Start · Try These Prompts · Pipelines · How It Works · Sponsors · Providers · Review Guide · Agent Guide Follow The Build Sponsors Want to support OpenMontage? Sponsor the project. Click to collapse Bloome lets multiple AI agents (Claude, ChatGPT, DeepSeek, and more) collaborate in one conversation for agentic video pipelines. It has zero setup, runs in the cloud, works on web and mobile, and lets you share…
- K-Dense-AI/scientific-agent-skills
github-trending
Turn any AI agent into an AI Scientist. The #1 Agent Skills library for science, used by 175,000+ scientists worldwide. 163 ready-to-use validated skills plus 100+ scientific databases covering biology, chemistry, medicine, and drug discovery. Compatible with Cursor, Claude Code, Codex, Pi, Antigravity, and the open Agent Skills standard. https://k-dense.ai Scientific Agent Skills 🔔 Claude Scientific Skills is now Scientific Agent Skills. Same skills, broader compatibility — now works with any AI agent that supports the open Agent Skills standard, not just Claude. New: K-Dense BYOK — A free, open-source AI co-scientist that runs on your desktop, powered by Scientific Agent Skills. Bring your own API keys, pick from 40+ models, and get a full research workspace with web search, file handl…
- tt-a1i/archify
github-trending
Agent skill for beautiful, verifiable architecture, workflow, sequence, data-flow, and lifecycle diagrams—self-contained HTML with motion and crisp export. https://tt-a1i.github.io/archify/ English · 简体中文 Archify Turn a codebase or system description into a polished, interactive system map — directly in chat. Archify is a Node.js rendering and validation system for Cursor, Claude Code, Codex CLI, and OpenCode. Agents produce typed JSON IR; Archify deterministically compiles it into HTML/SVG. Open it and present — five diagram types, four presets, dark/light themes, built-in brand marks, and finite motion Review architecture changes before merge — compare two validated snapshots as Before / Delta / After, with exact added, removed, changed, moved, and rerouted facts Every interaction stays…
- Show HN: Time-to-text time-series AI to limitate LLM hallucinations
hn-ai· 28-ago
Article URL: https://github.com/ikaruz050309/RobinHood Comments URL: https://news.ycombinator.com/item?id=49479216 Points: 1 # Comments: 0
- Claude Helped Us Solve a Fluid Mechanics and Electrokinetics Problem
hn-ai· 28-ago
Article URL: https://ankurg90.github.io/blog/electrophoresis-shape/ Comments URL: https://news.ycombinator.com/item?id=49479117 Points: 2 # Comments: 0
- Show HN: StreamCore – Open-source realtime voice infrastructure for AI
hn-ai· 28-ago
Article URL: https://github.com/streamcoreai/streamcore-server Comments URL: https://news.ycombinator.com/item?id=49479199 Points: 1 # Comments: 0
- AI-proof? Younger workers desert the digital world for traditional crafts
hn-ai· 28-ago
Article URL: https://www.theguardian.com/money/2026/aug/27/ai-proof-jobs-traditional-crafts Comments URL: https://news.ycombinator.com/item?id=49478755 Points: 2 # Comments: 0
- Show HN: InferCrane – One stable endpoint for self-hosted AI inference
hn-ai· 28-ago
Article URL: https://infercrane.com Comments URL: https://news.ycombinator.com/item?id=49479139 Points: 1 # Comments: 0
- I accidentally turned LLM memory into program analysis
hn-ai· 28-ago
Article URL: https://pwning.systems/posts/llm-memory-program-analysis/ Comments URL: https://news.ycombinator.com/item?id=49478610 Points: 3 # Comments: 0
- Show HN: Argus, open-source AI agents for testing web apps
hn-ai· 28-ago
Article URL: https://github.com/argus-testing/argus Comments URL: https://news.ycombinator.com/item?id=49479115 Points: 2 # Comments: 0
- There are four types of AI sandboxes, maybe?
hn-ai· 28-ago
Article URL: https://edera.dev/stories/the-four-sandbox-markets-a-2x2-hypothesis Comments URL: https://news.ycombinator.com/item?id=49479094 Points: 2 # Comments: 0
- Overlap: Local file tagging and querying for macOS with on-device AI suggestions
hn-ai· 28-ago
Article URL: https://github.com/cruxxxxxx/Overlap Comments URL: https://news.ycombinator.com/item?id=49478505 Points: 2 # Comments: 0
- Show HN: Grith – syscall-level supervision for AI coding agents on Linux
hn-ai· 28-ago
Article URL: https://github.com/grith-ai/grith Comments URL: https://news.ycombinator.com/item?id=49478820 Points: 2 # Comments: 0
- [AINews] NVIDIA buys HuggingFace for $13B, as OpenAI publishes their HF incident retro
latentspace· 27-ago
Open Source wins!
- Lovable CTO: The Future of SaaS Is Apps That Agents Can Use
latentspace· 26-ago
Lovable is branching out from AI-powered web app creation and into MCP-powered ‘capabilities’. We talk to CTO Fabian Hedin.
- [AINews] OpenAI to reach AGI bar by end-2026
latentspace· 28-ago
It’s Time. We’re in the Endgame now.
- 🔬“We have foundation models for language, not for physics” — Anima Anandkumar, Bren Professor of Computing
latentspace· 26-ago
Anima Anandkumar has spent two decades in AI, from classical math to deep learning and back. Now she's using it to model the physical world, from weather to fusion reactors.
- [AINews] Hot Chips: OpenAI’s Jalapeño, Cerebras CS-5, Groq 3 LPX, Apple M6
latentspace· 27-ago
The conference with hot chips and even hotter companies
- Making Your Data Ready for Agentic AI
martin-fowler· 27-ago
Lots of organizations are excited about what AI can do to streamline their processes, save money, and juice margins. But AI's capabilities are founded on the data that AI accesses, and for many organizations that foundation is little more than sand. Pramod Sadalage and Prem Chandrasekaran write about how to build a reliable foundation of data that can be accurate and trusted. more…
- Fragments: August 24
martin-fowler· 24-ago
I was listening to Ezra Klein’s interview with Helen Toner about the recent OpenAI hack of Hugging Face and the subsequent discovery that there were swarms of agents inside OpenAI doing unsanctioned activities. One of the points Klein made was that at no point did any of these (thousands of?) agents ever try to check in with a human [Klein:] So these message boards — you have however many A.I. agents posting hundreds of thousands of messages. At no point do they say: Hey, researchers, programmers, parents at OpenAI, Anthropic — do you want us coordinating with each other on this message board we have created in the innards of your systems? [Toner:] Or even F.Y.I., we have a message board we’re coordinating on in the innards of your system. Listening to that, another thing occurred to me -…
- A Tale of Two Flink Autoscalers
netflix-tech· 21-ago
- Quoting Paul Dix
simonw· 26-ago
<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">— <a href="https://pauldix.com/the-end-of-programming">Paul Dix</a>, The end of…
- Qwen3.8-Flash-Next
simonw· 26-ago
<p><strong><a href="https://qwen.ai/blog?id=qwen3.8-flash-next">Qwen3.8-Flash-Next</a></strong></p> Another open weights model from Qwen. This one is "a multimodal MoE model that also serves as an early preview of the architecture used in Qwen4".</p> <p>It's pretty big: 125B tokens, but only 6B active which means it gets a significant performance boost.</p> <p>I've been trying it out on a DGX Spark using <a href="https://huggingface.co/unsloth/Qwen3.8-Flash-Next-GGUF">these Unsloth quantized models</a>. I'm still exploring the model - so far I've tried the 72.5GB UD-IQ1_S one (producing <a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2Ff9c69ebdab90d8a45b8de4742cc7b840">these pelicans</a>) and the 78.9GB UD-Q2_K_XL (producing <a hre…
- Breaking Claude Code Opus 5 Auto Mode
simonw· 27-ago
<p><strong><a href="https://embracethered.com/blog/posts/2026/breaking-claude-code-opus-5-and-automode/">Breaking Claude Code Opus 5 Auto Mode</a></strong></p> Anthropic are putting a great deal of faith in Claude Code's auto mode for protecting their coding agent users against prompt injection attacks. They recently <a href="https://simonwillison.net/2026/Aug/8/auto-mode/">made that the default</a> and have made bold claims about its effectiveness.</p> <p>Johann Rehberger is one of the most credible prompt injection researchers active today. He found an attack against auto mode which he claims works 80% of the time, by tricking Claude Code into downloading and uncompressing a zip archive, then executing code that imports <code>base64</code> without noticing that this will import and exec…