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MemoAIMusic & 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EEvoMapAgents & 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AAssemblyAI 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.
待评分 · 分类列表推荐闪FlashCut is an AI-powered creation platform for voice-over and short-video marketing. It can generate finished videos with voiceovers, subtitles, and visuals from text, offering digital human customization, photo-to-digital-human conversion, live-stream clipping, and intelligent editing. It also provides APIs and branded mini-program solutions for businesses.
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.
待评分 · 分类列表推荐UUdacity AI学院Education & languagesUdacity AI Academy is the gateway to AI courses and learning paths, covering machine learning, deep learning, computer vision, natural language processing, and AI product management, with courses and project-based training available at both beginner and advanced levels.
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2026-11-10
Follow the timeline21:14
Show HN: Natel, a self-hosted contacts editor for CardDAV servers in 20MB of RAM
Hi HN, I decided to move away from Google Contacts and self-host my contacts two months ago. After installing a Radicale server, I was really missing a way to edit my contacts on the desktop. Nextcloud is overkilled for my needs, and InfCloud is the only other web contacts editor but it hasn't had a release in more than 10 years.So I built what was the missing piece in my setup : Natel, a lightweight self-hosted contacts editor for a CardDAV server. The goal is to edit your contacts on the web, be robust enough to not make you lose anything when there's an edit conflict with the server, and be super light in resources on the server. I came up with building a docker image of just a proxy caddy server and a go process that stores your credentials only in-memory, for a compressed image of less than 30MB and it uses less than 20MB of RAM when idle.Tested against four CardDAV servers : Nextcloud, Radicale, Baïkal and Xandikos. I already use it myself and it syncs with my server and DAVx5 back-and-forth, but I consider it still in beta and want to add more testing to prove compatibility for features I don't (yet ?) use. Note that any field that isn't displayed is never lost, it's just written back as-is.Demo: https://demo.natel.app/contacts Website: https://natel.appI wouldn't have done this side-project if I couldn't delegate writing the code to agents. We iterate on architectural decisions together, and once I'm satisfied with the direction, they write the result in a file (find it in docs/DECISIONS.md in the repo), then write the code to meet the expectations and independently review each other's iterations. I drive the project's direction and decisions, but I don't review the code myself. I keep an eye on the tests to know what's covered, and will read and review code when an agent draws my attention to it. The e2e tests were able to find a bug in SoGO, a CardDAV server : https://bugs.sogo.nu/view.php?id=6269 (and I didn't add that server in the compatilibity list yet because that bug breaks one the promises of the app, which is to be warned if we're trying to delete a contact that is modified elsewhere ; the server doesn't correctly enfore the If-Match instruction)As I don't have that many users yet, I welcome feedback on any part of the project but especially the parts that I have not yet tested like Contacts on a Mac ! Comments URL: https://news.ycombinator.com/item?id=50439845 Points: 1 # Comments: 0
20:00
Startup Founders Are Using AI To File Patents. Here’s Where DIY Becomes A Liability.
AI can help startups reduce patent preparation costs, but DIY applications may overlook critical issues. In this guest commentary, attorneys Michael Drapkin and Nathan Mutter of AI-Native law firm Landfall IP, argue that combining AI with legal expertise can produce more efficient, commercially valuable patent protection that withstands investor scrutiny and is harder for competitors to circumvent.
19:00
The AI Safety Debate Has Lost the Plot. We Asked 10 Experts What the Real Nightmares Are
The risk of nuclear catastrophe is real, as is the fallout of the China–US domination game (hi there, spies). But no need to panic about Skynet.
17:36
Show HN: Show HN: Sowbot – Open-hardware agricultural robot 8months progress
8 Months ago I made this post https://news.ycombinator.com/item?id=47123894Since then we've done stuff, the software is much more mature, we've done some basic field testing on small dev platforms.We've moved from ESP32 for the microcontroller to a STM32.Volunteers have come, gone again, new ones arrived.We're getting ready for full scale field trials now.Been putting some videos of sim up on https://www.youtube.com/@rosmo-robot/videosPlease critique again, and come join us! Comments URL: https://news.ycombinator.com/item?id=50437978 Points: 1 # Comments: 1
17:12
Show HN: Video Analysis with Homomorphic Encryption
Article URL: https://github.com/belfortlabs/blindsight-demo Comments URL: https://news.ycombinator.com/item?id=50437839 Points: 3 # Comments: 1
16:58
Show HN: Run multiple Claude Desktop instances side by side on macOS
Article URL: https://github.com/Yuix-Networks/claude-orchestrator Comments URL: https://news.ycombinator.com/item?id=50437743 Points: 1 # Comments: 0
16:55
Show HN: Pg-interleave (Postgres proxy for reproducible race-condition tests)
Article URL: https://github.com/alt-f6/pg-interleave Comments URL: https://news.ycombinator.com/item?id=50437720 Points: 1 # Comments: 0
15:00
Work at the Frontier: AI’s effect on freelance work
New research from OpenAI and Upwork finds shifting demand for freelance work, new skill combinations, and larger budgets and more hours for AI-related projects.
14:44
Show HN: Willder, an open source context layer that syncs apps, agents and teams
Article URL: https://github.com/willderai/willder-core Comments URL: https://news.ycombinator.com/item?id=50436876 Points: 2 # Comments: 0
13:00
Video-DCI: How Far Are VLM Agents from Human Viewers in Direct Video Corpus Interaction?
arXiv:2611.09951v1 Announce Type: new Abstract: Humans answer complex visual questions by inspecting short moments, updating what they know, and redirecting attention toward missing evidence. Existing video benchmarks, however, often provide the relevant video, evaluate only final answers or isolated localization, or permit textual shortcuts, leaving multi-round visual evidence seeking underexplored. We introduce Video-DCI, a diagnostic benchmark and functionally matched human-agent protocol for Direct Video-Corpus Interaction. It comprises DCI-Loc for temporal evidence localization and DCI-QA for end-to-end discovery, retention, and composition of cross-video evidence. In DCI-QA, videos within each pool are topically related, yet every answer requires hidden temporal evidence from multiple videos. Given only the question and video pool, humans and agents iteratively inspect bounded local windows, retain supporting evidence, and answer with cited temporal spans. Human trajectories provide reference curves for evidence acquisition and use across rounds. Experiments reveal a consistent retrieval-to-grounding gap. On DCI-Loc, frontier VLMs often retrieve and expose the reference moment, yet convert far less of this exposure into precise final citations than the human reference. On DCI-QA, the loss begins earlier and compounds across videos. Ablation studies further confirm that iterative search outperforms one-shot retrieval. Multi-round search is therefore necessary but insufficient: current agents accumulate candidates and observations more readily than precise, jointly sufficient evidence.
09:26
Start and Restore GPU Workloads in Seconds with NVIDIA Snapshot
NVIDIA Snapshot, a new open-source component of the NVIDIA DSX AI Factory Platform, makes initialized GPU workloads reusable across inference...
08:24
Show HN: I abandoned my AVX2 128-bit fixed-point library
Article URL: https://github.com/arjavsharma91/v4q128 Comments URL: https://news.ycombinator.com/item?id=50434431 Points: 1 # Comments: 1
08:00
Speed Insights and Web Analytics now support custom environments
Data from custom environments in Speed Insights and Web Analytics is now correctly attributed to the respective custom environment, enabling separate tracking for staging, QA, or canary environments. This update allows users to view and query data via the dashboard, CLI, query builder, and aggregation API without additional configuration, available on Pro and Enterprise plans.
05:00
Vercel Sandbox snapshot creation is now up to 10x faster
Vercel Sandbox has improved snapshot creation speed by up to 10x, with median time dropping to under 0.4 seconds from 4.4 seconds, and restore speed improving by 50% to 250ms at p50. The enhancements leverage the Vercel Hive Snapshot (VHS) format with more efficient data indexing, enabling parallel read and write operations and faster restoration of both base images and user data. Persistent sandboxes now automatically snapshot upon session end and resume from the latest snapshot by default, using the new format for all new snapshots while existing ones retain the previous format.
04:13
Single-shot and agentic retrieval with Amazon Bedrock Managed Knowledge Base
Amazon Bedrock Managed Knowledge Base now supports single-shot RAG and retrieval-only requests through AgenticRetrieveStream, alongside agentic retrieval. This post explains how the maxAgentIteration parameter matches retrieval effort to each request, using an illustrative internal banking policy assistant.
01:31
Show HN: We Gave AI Coding Agents Production Memory
Hi HN, I’m building Codela to help coding agents learn from what happens after their code ships.I realized that my AI agent writes a change, the tests pass, and it gets deployed. Then in production a problem happens, I fix it, but what it did wrong and why never reaches the agent, so futures session usually make the same mistake.Codela checks every deploy against how your app actually behaves in production, learns from the ones that broke it, and teaches your agents so they don’t repeat the mistake.Would love any feedback on this, thanks! Comments URL: https://news.ycombinator.com/item?id=50428670 Points: 1 # Comments: 0
01:00
The observability UI as we know it is dead. So what comes next?
Your observability UI shouldn't limit the questions you can ask. Explore how ClickHouse Observability Apps turns a prompt into custom investigation experiences, why SQL makes it possible, and what we still need to get right.
00:41
What Frontier Labs Buy: A Data Deep Dive with Handshake AI — Garrett Lord & Team
Our first deep dive with an AI data company. In roughly a year, Handshake built an AI training business doing nearly $1 billion in gross annualized revenue. What are frontier labs buying? Garrett Lord joins us with the people building reinforcement lea...
00:23
What Is Eval-Driven Development? How to Improve AI Agents
Eval-driven development helps you build AI agents against a consistent set of metrics. Define good behavior, write the eval first, then compare experiments.
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