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JAXModels & infrastructure

JAX 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.

AI DEVELOPMENT PLATFORM分类推荐榜首Pricing unverifiedView details
EvoMapAgents & automation

EvoMap 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Pricing unverifiedView details
Open DesignDesign & 3D

OpenDesign 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.

AI design tools分类推荐榜首Free entry availableView details
MemoAIMusic & audio

MemoAI 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.

AI AUDIO TOOL分类推荐榜首Pricing unverifiedView details
KiroCoding & development

Kiro 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.

AI programming tool分类推荐榜首Free entry availableView details
AI ShortWriting & content

AIS ALERT MANAGEMENT AND SHARED PLATFORM, QUICK COMMAND FOR MULTIPLE SCENARIOS

AI HINT COMMAND分类推荐榜首Pricing unverifiedView details
AssemblyAIMusic & audio

AssemblyAI is a speech AI platform for developers, offering pre-recorded, real-time, and short-form synchronous transcription APIs. It enables the building of voice-based applications by integrating capabilities such as speaker identification, emotion analysis, chapter and summary extraction, as well as speech agent functionality, dictation, and sensitive information processing.

AI AUDIO TOOL待评分 · 分类列表推荐Pricing unverifiedView details
Zoom WorkplaceOffice & productivity

Zoom Workplace is Zoom’s team collaboration platform, integrating video conferencing, chat, calendars, and documents, and leveraging built-in AI to organize discussions, draft messages, and track follow-up tasks.

AI meeting tool待评分 · 分类列表推荐Pricing unverifiedView details
妙播Marketing & commerce

THE AI LIVE TELECOM SOLUTION FROM THE AD

待评分 · 分类列表推荐Pricing unverifiedView details
FinBusiness & support

CLIENT-ORIENTED AI SMART BODY。

Customer serviceSmartClient services待评分 · 分类列表推荐Pricing unverifiedView details

AI, as it happens

1679 published stories
2026-11-11
Follow the timeline
09:07

Show HN: Vaultbase – a self-hosted back end in a single binary

One binary to run and you have a full backend at your fingertips: a database with a REST API, sign-in, realtime, files, server-side hooks and jobs, on SQLite or Postgres. It's MIT licensed. You can try the demo at https://demo.vaultbase.sh (the login is filled in, and it resets every hour). I built it to ship fast. I always tend to write the same logic for any new project, whether it's a side project or a small to medium project for a client. I wanted something my friends and I could work with fast, try new things with, and that's easy to use. Then it became something I really wanted to work on and make as complete as I can for any type of backend. As of writing this post it has 80 releases since May this year and is backed by more than 2,800 tests. I also run security reviews with Claude and fix what they find. It hasn't had an outside audit yet. It was built with the help of Claude, from the codebase to the documentation and fact-checking it against the code. Claude helped speed up the pace, find bugs, and write tests. Vaultbase is pre-1.0, so expect some bugs and issues here and there. I'm working hard to fix each and every one of them. Thank you for taking the time to read this. I'd really appreciate any feedback, even if you didn't like Vaultbase. The demo is yours to try, and it's two commands to run locally. Comments URL: https://news.ycombinator.com/item?id=50448562 Points: 1 # Comments: 0

Startups & new venturesRead original
06:35

The agent evaluation playbook: Built-in, DeepEval, and AutoEval on Amazon Bedrock AgentCore Evaluations

When you deploy an agent, whether it is working is really several questions: did it finish the task, stay on topic, leak data, or skip steps? This post shows how to evaluate agents on Amazon Bedrock AgentCore with built-in, DeepEval, and AutoEval evaluators, and how to match each behavior that matters to the right evaluator at the right level.

Enterprise & work applicationsRead original
05:42

v0.45.0

ComfyUI v0.45.0 introduces multiple updates including new partner nodes for Pika, Tencent, Kling, OpenAI, Qwen, Gemini, Grok, Breeze, and ByteDance; performance enhancements for Qwen 3.x; bug fixes for crashes, database issues, and VAE selection errors; improved memory management with adaptive slice lengths; support for new models like Seedance 2.5, Seedream 5.5, and MiniMax-H3-image; added video input for Qwen 3.x, solid mode in mesh extraction, and texture rebaking; updated workflow templates to v0.11.105 and API documentation; default activation of local_web_sso and partner_node_token; and contributions from new developer GACLove.

Design & creative workflowsRead original
05:32

Show HN: Tinctile – hex grid color puzzle that you solve using counts

A coloring puzzle on a hex board with numbers telling you how many of each color go in a line. What's interesting is that were it a square grid you'd need additional clues on the board. The fact that three lines pass through each hex cell makes it possible to start with a completely empty board, which is pretty neat. Comments URL: https://news.ycombinator.com/item?id=50446597 Points: 1 # Comments: 0

Startups & new venturesRead original
2026-11-10
Follow the timeline
22:00

Adobe expands AI assistant into creative workflows

Adobe Inc. today announced an expansion of its CX Enterprise Coworker agentic artificial intelligence platform into its GenStudio content supply chain platform. The company said the integration move allows creative professionals to bring AI agents into production workflows to develop, manage and deliver personalized marketing content at large scale. The announcement, made at its Adobe […] The post Adobe expands AI assistant into creative workflows appeared first on SiliconANGLE.

MCP & agent protocolsRead original
21:09

b12246

On Qualcomm Adreno GPUs, scalar flash attention was limited to 8 rows per workgroup when head size exceeds 128 to prevent register spilling and performance degradation. This change improved performance significantly, reducing execution time from several seconds to milliseconds and enabling completion of the test-backend-ops case.

Inference systems & deploymentRead original
13:00

A Model Organism for Studying Unverbalized Evaluation Awareness

arXiv:2611.09945v1 Announce Type: new Abstract: Large language models can recognize when they are being evaluated and change their behavior accordingly, undermining the reliability of safety benchmarks. Evaluation awareness (EA) is usually measured by having an LLM judge scan reasoning traces for explicit statements of recognition, but this signal is increasingly unreliable. Recent frontier-model system cards report a drop in verbalized rates between releases, which is indistinguishable from awareness that has gone unverbalized. Interpretability methods could detect such unverbalized evaluation awareness (UEA) in individual interactions, but lack instance-level ground truth. Asking the model alters the input, and dataset labels do not reveal whether it recognized a particular prompt as an evaluation. We address this gap with a model organism. Using synthetic document fine-tuning, SFT, and DPO, we train Qwen3.5-27B to emit a marker (a designated token) in its final response when it recognizes an evaluation, while suppressing the marker and verbalized awareness in its reasoning. The marker largely tracks the evaluation concept as it can be strongly modulated by evaluation framing in the system prompt. When not told whether it is being evaluated, the organism recognizes evaluations from test-question cues, mirroring what its base model often verbalizes. Using the marker as a label, we benchmark linear probes, the Jacobian Lens, and Activation Oracles, built on the base model and blind to the marker. None recovers the label reliably. These results suggest that current interpretability methods are not yet reliable UEA detectors, even when recognition relies on simple cues. Our model organism offers a testbed for improving them.

Model evaluations & benchmarksRead original
13:00

Clinically Informed Colour Encoding Improves the Localisation and Stability of Explanations in Deep Learning ECG Models: External Validation in a UK Biobank Epilepsy Cohort

arXiv:2611.09971v1 Announce Type: new Abstract: Objective: It is not known whether the input representation governs the quality of explanations of deep learning models for electrocardiogram analysis. Risk of dangerous ventricular repolarisation is judged from the QT-interval and the shape of the T-wave, features that are hard to assess visually and harder still to verify in a model's reasoning. We evaluate a pseudo-colour encoding filling the area beneath the QT interval, its colour sequence designed from clinical electrophysiology knowledge and mapped to a validated risk prediction model, the QT-nomogram. Methods: Two identically configured residual networks were trained on single-heartbeat images from a clinical trial, differing only in whether pseudo-colour encoding was applied, so that any difference is attributable to the chromatic channel alone. To externally validate the effect, both models were applied without fine-tuning or threshold tuning to a previously unseen epilepsy cohort from the UK-Biobank at two visits, and eight explanation methods compared on faithfulness, localisation and stability. Results: The pseudo-colour model achieved areas under the receiver operating characteristic curve of 0.903 and 0.914 at the first and follow-up visits, against 0.719 and 0.720 for the monochrome model, recovering three to five times as many at-risk heartbeats at comparable specificity. Pseudo-colour encoding improved explanation localisation and stability for almost every method, and the improvement reproduced across both visits. Conclusion: The input representation is a controllable and previously overlooked determinant of both predictive performance and explanation quality. Significance: Treating interpretability as a representation-design problem offers a route towards explanations clinicians can check against known electrophysiology.

Training, tuning & alignmentRead original
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.

Datasets & synthetic dataRead original
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.

Model evaluations & benchmarksRead original
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.

Copyright, privacy & ethicsRead original
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.

Model evaluations & benchmarksRead original
04:00

Cisco research: Trust, not technology, is now the speed limit on enterprise AI

For several years, the conversation about enterprise artificial intelligence has focused on capability: which model is smartest and which GPU is fastest. That made sense when technology was the constraint. It isn’t anymore. AI can now do far more than most organizations will allow, and the gap is trust. That’s the core finding of “Permission […] The post Cisco research: Trust, not technology, is now the speed limit on enterprise AI appeared first on SiliconANGLE.

Enterprise & work applicationsRead original
00:41

b12229

A fix was implemented to ensure the OpenAI-compatible chat completions endpoint correctly processes JSON schema when provided at the top level in response_format.schema, rather than requiring it to be nested within response_format.json_schema.schema. The update also improves error messages, documents the correct usage in the README, and adds test cases to verify behavior, resolving issue #10732.

Inference systems & deploymentRead original
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