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How organizations progress from trusted data to trusted AI and agents
Autonomy is an outcome of AI maturity. Build the trusted data, governance, and context that let AI and agents act with control.
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EvoMapAgents & automationEvoMap is an infrastructure designed for the reuse of AI agent experiences. It organizes reusable strategies and repair assets with validation records via the GEP protocol, offering market- and quality-based ranking to help different agents discover, evaluate, and inherit existing capabilities.
分类推荐榜首KKiroCoding & developmentKiro is an AI engineering tool for developers that organizes requirements into specifications, architectures, and tasks, generates code via agents, and supports the entire workflow—from planning to code review—through property-based testing, execution memory, and integration with development tools.
分类推荐榜首MMemoAIMusic & audioMemoAI is an AI-powered audio-to-text tool that supports localized processing, eliminating the need to upload data and ensuring privacy and security. It can efficiently transcribe YouTube videos, podcasts, and local audio/video files (such as MP4, MP3, AAC, M4A, etc.) into text, while offering core features like multilingual translation, speaker diarization, real-time subtitles, and floating notes—making it ideal for meeting minutes, interview transcription, content learning, and more.
分类推荐榜首AAI ShortWriting & contentAIS ALERT MANAGEMENT AND SHARED PLATFORM, QUICK COMMAND FOR MULTIPLE SCENARIOS
分类推荐榜首JJAXModels & infrastructureJAX is a Python numerical computing library designed for accelerators, specifically tailored for high-performance array computations and large-scale machine learning. It efficiently executes the same code across multiple hardware backends, including CPUs, GPUs, and TPUs.
分类推荐榜首OOpen DesignDesign & 3DOpenDesign is an open-source design workspace that can connect to local programming agents, use built-in model keys, or leverage cloud-based models, enabling the creation and editing of prototypes, web pages, presentations, and graphics within a design system; local software is free, while model services incur fees.
分类推荐榜首IIamIPIndustry applicationsIamIP is a patent platform that provides AI-driven search, analysis, and monitoring tools to help users conduct patent research and management more efficiently.
待评分 · 分类列表推荐MMy Glow Up AILife & personalMy Glow Up AI provides free analysis based on selfies, without requiring registration, and can generate hairstyle recommendations and beauty plans that suit you.
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2026-11-10
Follow the timeline20:00
Helping caregivers use AI in everyday life
OpenAI Academy is bringing free, hands-on ChatGPT workshops to caregivers in five U.S. cities, helping them build practical AI skills for everyday life.
18:00
Introducing Businesses in ChatGPT
Manage your business information, get insights about how your business shows up, and turn interest into action in ChatGPT.
13:00
Confusion-Adaptive Margin Learning for Handwritten Letter Recognition: A Controlled Study on EMNIST Letters
arXiv:2611.09953v1 Announce Type: new Abstract: Handwritten character recognition systems can achieve high accuracy while retaining concentrated errors among visually similar classes. Motivated by residual letter confusions in earlier geometry-based recognition work, we investigate whether these errors can be addressed through the training objective. We introduce Confusion-Adaptive Margin Learning (CAML), which constructs a sparse weighted confusion graph from a discovery partition held out from pilot training and applies pair-specific cosine-separation constraints during final training. CAML adds no classifier, routing rule, or inference-time parameters. We evaluate CAML on EMNIST Letters against a strictly matched cross-entropy CNN baseline, using the same architecture, final training data, deterministic augmentation, optimizer, and checkpoint selection. Eight confusion pairs are targeted: I/L, G/Q, U/V, R/V, D/O, C/E, I/J, and H/N. On the official clean test set, accuracy changes from 95.6346% to 95.6731%, and macro-F1 from 0.956326 to 0.956724. Mean accuracy across ten predefined corrupted conditions increases from approximately 95.3812% to 95.4303%; symmetric errors within targeted pairs decrease from 614 to 605 (1.47%). The global differences are small and not statistically reliable. CAML fixes 124 baseline errors but introduces 116 new errors on clean data (exact McNemar p=0.6515), and no corruption-specific difference remains significant after Holm correction. Thus, CAML does not establish global superiority, but shows that a leakage-controlled, pair-specific margin objective can alter targeted error structure with limited overall benefit for an already strong classifier.
13:00
CulturalCrosswords: A Multilingual Benchmark comprised of Answer Constraints and Grid Completion
arXiv:2611.10044v1 Announce Type: new Abstract: When several answers fit a riddle, exact-match scoring can penalise a valid response. CulturalCrosswords tests how answer constraints affect large language models (LLMs) solving cultural riddles, whose solutions draw on knowledge specific to a community. We constructed 53 crosswords from crowdsourced riddles in 42 languages and evaluated 11 LLM configurations on five versions of each clue set: plain batches, batches with lengths, batches with lengths and partial-answer hints, and crosswords requesting an answer list or a completed grid. On 962 clues with alternative answers, lengths improve agreement with the reference by 22.8 percentage points, compared with 8.4 points when accepted alternatives also receive credit. Part of the gain therefore appears to come from selecting a required answer form. Crossword input increases the share of all crossings with compatible answers by 9.7 points; requiring a completed grid reveals large differences between models. The benchmark distinguishes agreement with a reference from the ability to produce answers that fit together and occupy the correct cells.
13:00
DICOMPUTE: An Economic and Privacy-Aware Framework for Distributed Artificial Intelligence Inference
arXiv:2611.10804v1 Announce Type: new Abstract: The prevailing design of large-scale artificial intelligence inference concentrates accelerators, model weights, and request processing in centralized computing facilities. Centralization offers important benefits in accelerator utilization, high-bandwidth interconnects, operational control, and serving efficiency. Yet centralized infrastructure is not always the least-cost or most governance-compatible choice for every inference workload. This paper develops a research framework for DICOMPUTE, a heterogeneous distributed inference architecture in which policy-constrained scheduling can allocate suitable requests across privately controlled, regional, and independently operated computing resources. We derive a full-cost comparison that accounts for capital amortization, energy, network transfer, orchestration, utilization, failures, and trust-related expenses; characterize conditions under which distributed inference can outperform centralized alternatives; and specify a privacy model separating data residency, transport confidentiality, execution confidentiality, and verifiable accounting. We propose falsifiable hypotheses and a reproducible experimental design, without presenting simulated examples as measured performance. Our central conclusion is conditional: distributed inference is most promising for divisible, latency-tolerant or locality-sensitive workloads on underutilized compatible hardware, while centralized clusters retain structural advantages for tightly coupled, memory-intensive, high-throughput inference. The framework is intended to support rigorous evaluation rather than to assert universal superiority.
13:00
DramaGrow: A Regime-Aware Benchmark for Auditing LLM Judges of Multi-Episode Drama
arXiv:2611.09938v1 Announce Type: new Abstract: LLMs can now draft complete multi-episode dramas, and LLM judges are increasingly used to evaluate them. Assessing the reliability of these judges is difficult because drama quality encompasses specification-backed errors, reproducible preferences, and legitimate disagreement among human raters. Most existing evaluations reduce these cases to a single score from one judge call and leave tie calibration, call-to-call variation, and the vote margin underlying the human reference unreported. We introduce DramaGrow, a regime-aware benchmark and audit protocol for AI-generated Chinese five-episode drama. The benchmark assigns verifiable constraint violations, four-of-five preferences, and within-panel disagreements to separate denominators and evidence rules. It contains 800 episodes, 120 byte-preserving interventions, and 1,155 blinded five-rater records, including a frozen extension with 114 supermajority pairs and 21 disagreement pairs. On the frozen sets of 114 preference pairs and 35 constraint interventions, the repeated absolute-score rubrics yield overlapping performance, with mean accuracies of 53.8-55.8% and three-call agreement of 29.8-32.5%, and their accuracy falls from 71.6-71.9% to 34.8-37.1% as the tie band widens. DirectPair-GLM reaches 85.1% preference accuracy, 89.5% constraint F1, and 81.9% Detect+Locate on the same items, and exact subpanel enumeration identifies four-of-five as the smallest stable human vote margin. Evaluation protocol, tie calibration, judge-call variation, and panel margin therefore affect performance as much as rubric design. We will release the specification packages, scripts, interventions, anonymized human votes and rationales, frozen judge runs, and analysis code.
13:00
Firepool: Brokering Warm GPU Capacity for Agentic and RL Workloads at LinkedIn
arXiv:2611.10832v1 Announce Type: new Abstract: AI agents and reinforcement-learning (RL) trainers run thousands of short, isolated GPU jobs: tool calls, verifiers, and rollouts. A job often finishes in seconds, but starting a GPU pod for it takes 20-35 s even when the container image is already on the node, and minutes when it is not. A warm pod removes the wait only if it can serve the next job, and keeping pods warm must neither waste GPUs nor leak state between callers. Firepool, the sandbox and execution broker behind agentic and RL workloads at LinkedIn, meets both requirements. It makes pods interchangeable by building them from a shared base image per hardware profile and binding each job's code, extra dependencies, and cluster topology when the job starts. It keeps them warm cheaply and safely by reusing a pod only for the caller that used it last, staging zero-VRAM standby pods in fractional GPU shares alongside active pods, and capping the GPUs that warm pools may hold via queue-driven budgets. Over a sampled peak period in production, Firepool serves 94.7% of 3,206 pod requests from warm or standby capacity. In paired benchmarks with fixed-duration jobs on production H200 nodes, agent and RL loops finish 13x and 10.8x faster than with a fresh pod per job; against a fresh reuse tag per job, warm reuse cuts p90 start latency from 59.1 s to 3.5 s. When a job cannot reuse the previous pod, standby promotion shortens the handover wait from 51 s to 0.94 s. In local and simulated runs, a keep-alive matched to the loop's pauses keeps the latency benefit while holding about half the capacity of an always-on pool.
13:00
FirstSwap: Pre-Consensus Trade Approvals for Decentralized Market Design
arXiv:2611.10254v1 Announce Type: new Abstract: Decentralized exchanges typically give traders no execution guarantee until a block is finalized. In the meantime, external prices move, traders strategically delay submission, and block producers can influence inclusion and ordering. We introduce FirstSwap, a DEX built on a constant-function market maker pricing curve that gives traders an early pre-approval before consensus, guaranteeing inclusion and execution within their submitted price limit, with final ordering determined by limit prices rather than by a block producer. We demonstrate the benefits of trade pre-approvals in an economic model combining auction theory with CEX-DEX arbitrage. When the CEX price changes gradually, FirstSwap rewards earlier traders with better prices, discouraging strategic delay and yielding higher social welfare and gains to AMM liquidity providers than a batch auction. When the CEX price jumps abruptly, FirstSwap yields higher welfare and smaller AMM losses than fair ordering. The central challenge is delivering fast trade pre-approvals without consensus. We introduce a decentralized, scaled admissibility protocol under which each validator reserves only a fraction of the available liquidity at every price. A trader obtains approval in a single round of messages with a quorum of validators. Somewhat surprisingly, despite Byzantine validators who may approve trades arbitrarily, we show that all quorum-certified trades are jointly admissible and execute within their limits. Our protocol extends naturally to a setting where buy and sell orders can be pre-approved against each other before a block is finalized.
13:00
GARDEN: Efficient GPU-Accelerated Billion-Scale GNN Training via Caching-Sampling-Batching Co-Design
arXiv:2611.11043v1 Announce Type: new Abstract: Graph Neural Networks (GNNs) have shown impressive performance in diverse tasks such as social network analysis and recommendation systems. However, scaling their training to billion-edge graphs is hindered by the memory wall: GPU memory capacity falls short of the massive storage requirements for node features and topology. Sampling-based GNN training frameworks mitigate this by streaming subgraphs from CPU to GPU, but they incur prohibitive PCIe data transfers and non-coalesced memory accesses, resulting in bandwidth underutilization and low compute efficiency. We propose GARDEN, a GPU-accelerated sampling-based system that co-designs caching, sampling, and batching to eliminate these bottlenecks. GARDEN introduces three key innovations: (1) an Adaptive High-Fidelity Cache Allocator (AHFCA) that combines offline reuse profiling and online hotness tracking to dynamically cache features and adjacency lists within a fixed GPU memory budget, avoiding static allocation waste; (2) a Grouping-based Parallel Sampler that reorders stochastic neighborhood accesses(GPS) to maximize warp occupancy and hide latency on power-law graphs; and (3) a Super-batch Degree Classifier(SDC) that fuses mini-batches into degree-homogeneous groups, enabling cache-friendly coalesced transfers and high GPU utilization. Evaluations on real-world billion-scale graphs demonstrate that GARDEN achieves 4.54x, 1.27x, and 2.97x end-to-end training speedups over DGL-UVA, DUCATI, and FastGL, respectively, while maintaining model convergence and accuracy.
08:21
How skills and agents work in AWS Quick
Repeatable tasks like status updates, release notes, and ticket creation can be encoded in AWS Quick through agents and skills. This post covers how agents, skills, and your data fit together in AWS Quick, how to build and share skills for your team, and how the official catalog and custom agents help you use them.
08:00
Ducks in a Column
DuckDB is fast among other reasons because it stores data by column, so a query reads only the columns it names. This post covers DuckDB features that take advantage of that design choice: querying files in place, column-level SQL, aggregation shortcuts, window functions, ASOF joins, and writing well-organized Parquet.
08:00
Gemini 4 Carbon now available on AI Gateway
Gemini 4 Carbon, a model designed for complex software engineering, agentic work, and cybersecurity defense, is now available on Vercel's AI Gateway with a 50% discount through November 30, 2026. It features a 1M-token context window, enhanced capabilities in coding, full-stack development, scientific reasoning, and vulnerability detection, along with improved multi-step planning and collaboration under ambiguous requirements. The model can be accessed using the identifier 'google/gemini-4-carbon' in supported tools after setting up the Vercel CLI. AI Gateway offers unified API access, usage tracking, cost management, and performance optimizations, with no markup or platform fees, including for BYOK requests.
07:20
IBM builds on hybrid cloud and governance to advance its enterprise AI operating model
Enterprises are moving beyond AI pilots, putting greater emphasis on governance, production readiness and measurable business outcomes. Research from Deloitte highlights a significant gap between enterprise AI ambition and execution: 54% of organizations plan to move at least 40% of their AI initiatives into production within six months. Yet only 25% have done so today. IBM […] The post IBM builds on hybrid cloud and governance to advance its enterprise AI operating model appeared first on SiliconANGLE.
06:07
How Stripe Built Kai on Deep Agents in 1 Week
Learn how Stripe built Kai, a company-wide AI agent on LangChain, LangGraph, and Deep Agents, reaching 5,000 users in roughly 4 weeks.
05:33
Indie Game Development with AI
Article URL: https://www.sandromaglione.com/articles/indie-game-development-with-ai Comments URL: https://news.ycombinator.com/item?id=50432523 Points: 22 # Comments: 16
04:29
Exploring the world's languages with Language Explorer
Natural Language Processing
03:04
Fable 5.2 and Jagged AGI
General, often expert, and sometimes jagged
02:55
How CrossnoKaye grounds physical AI with Amazon Bedrock AgentCore
CrossnoKaye brought physical AI to heavy industry with Amazon Bedrock AgentCore. Its ATLAS platform and AURA agentic system ground AI reasoning in live plant data and run verifiable Python in isolated AgentCore sandboxes, giving plant engineers checkable, math-backed answers instead of model guesses.
Closing the AI guidance gap in education
Lessons from OpenAI’s EMEA education partnerships and new ThinkYoung research. Supporting teachers, AI literacy and independent learning.
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