03-27 00:02 · GitHub,开源,技术,AI,技术
Understand GitHub Trending projects quickly with AI.（用 AI 快速读懂 GitHub Trending 项目）
全球技术热点，AI 精选速递。
聚合 GitHub Trending、Hacker News、Product Hunt，用 AI 精选和解读全网最值得关注的技术项目。
Trending AI 是一款使用 Kotlin Multiplatform (KMP) 构建的跨平台应用。聚合 GitHub Trending、Hacker News、Product Hunt 三大技术信息源，通过 AI 智能摘要和每日精选，帮你高效发现全网最值得关注的技术项目。
- 🌐 多源聚合：GitHub Trending、Hacker News、Product Hunt 一站浏览。
- 🤖 AI 智能摘要：每个项目自动生成 AI 分析，快速了解核心价值。
- ⭐ 每日精选：AI 从全网筛选最值得关注的技术项目。
- 📅 历史回溯：查看任意日期的趋势榜单，不错过每一天。
Kotlin Multiplatform | Compose Multiplatform | Ktor | Material 3
本项目采用 MIT 协议开源 - 详情请参阅 LICENSE 文件。
▸ 展开全文
03-27 00:02 · GitHub,开源,技术,工具,后端,技术
Discover and compare open-source Agent Skills, tools & MCP servers — with quality scoring, trending analysis, and automated GitHub sync
Discover, evaluate, and compare 55,000+ open-source Agent Skills, MCP servers, and AI tools.
🌐 Live: agentskillshub.top · 📰 Newsletter · 𝕏 Follow
GitHub API ──▸ Collection ──▸ Cleaning ──▸ Evaluation ──▸ Scoring ──▸ Presentation
(6 phases) (classify) (6 dimensions) (0-100) (Web + API)
Every 8 hours, GitHub Actions triggers a 6-phase sync:
- Incremental sync on weekdays (only new/updated repos), full sync on Sundays
- Smart rate-limit handling: waits for actual GitHub reset time
- Deduplication by
repo_full_name
- Keyword-based category classification:
mcp-server
·claude-skill
·codex-skill
·agent-tool
·ai-skill
·llm-plugin
·youmind-plugin
- Project type inference (framework / skill / tool)
- Size categorization (micro ≤50KB → large >5MB)
Quality Analysis — 6 dimensions (services/quality_analyzer.py
):
Composite Score — 9 weighted signals (services/scorer.py
):
Also: Platform Inference (17 platforms), Token Estimation, Composability (TF-IDF + 8-signal skill pairing).
Dual-mode API client: Production uses Supabase PostgREST + RPCs directly; dev uses FastAPI backend.
Key optimizations:
get_landing_data()
RPC replaces 7+ API calls- Database views:
v_trending
,v_top_rated
,v_rising
, etc. - Full-text search via PostgreSQL tsvector + GIN index
- HTTP caching: 5min (trending) → 1hr (stats)
cd backend
python3.12 -m venv venv && source venv/bin/activate
pip install -r requirements.txt
cp .env.example .env # Add GITHUB_TOKEN
uvicorn app.main:app --reload
cd frontend
npm install
npm run dev
┌─────────── GitHub Actions ───────────┐
│ sync.yml (8h) · deploy.yml · newsletter.yml (Mon) │
└──────┬──────────────┬────────────────┬──┘
▼ ▼ ▼
Supabase GitHub Pages Resend API
(PostgreSQL) (React SPA) (Emails)
GITHUB_TOKEN=ghp_xxx # GitHub API (required)
SUPABASE_DB_URL=postgresql:// # Production database
RESEND_API_KEY=re_xxx # Email service
ADMIN_TOKEN=sk-xxx # Admin API auth
SYNC_INTERVAL_HOURS=8 # Sync frequency
- Scoring Algorithm — Full scoring design
- System Architecture — Detailed pipeline reference
▸ 展开全文
03-27 00:02 · GitHub,开源,技术,技术
Monitor your internet connection with health assessment, trend charts, speedtest tracking, and Home Assistant integration. Built for DOCSIS cable modems, also works (kind of) with fiber, DSL, and sate
Your ISP says everything is fine. DOCSight gives you the proof that it isn't.
DOCSight monitors your cable internet 24/7, documents every signal issue, and generates complaint letters with hard evidence your provider can't dismiss.
Available in 🇬🇧 🇩🇪 🇪🇸 🇫🇷
No router needed. Demo mode generates 9 months of realistic DOCSIS data so you can explore everything.
docker run -d --name docsight-demo -p 8765:8765 -e DEMO_MODE=true ghcr.io/itsdnns/docsight:latest
Open http://localhost:8765
and see what DOCSight can do.
docker run -d --name docsight -p 8765:8765 -v docsight_data:/data ghcr.io/itsdnns/docsight:latest
Open http://localhost:8765
, enter your router login, done.
Full installation guide | Example Compose Stacks
Most connection problems aren't one-time events. They come and go, making them nearly impossible to prove when you call your ISP.
DOCSight runs in the background and builds your case over time:
- Hour 1 - You see your current signal health and any active issues
- Week 1 - Trend charts reveal patterns your ISP can't see from a single snapshot
- Month 1 - The event log, incident journal, and correlation analysis paint a complete picture
- When you call your ISP - The complaint generator turns weeks of evidence into a professional letter with attached diagnostics
The longer DOCSight runs, the stronger your evidence gets.
4 languages (EN/DE/FR/ES) | Light/Dark mode | Themes | PWA/Offline | Setup wizard | Optional authentication | API tokens | System font toggle
Click to expand
16 modem families supported out of the box.
Works with any DOCSIS cable provider worldwide. Non-cable users can select Generic Router during setup.
Currently focused on the German cable market (BNetzA measurements, VFKD thresholds, complaint templates). The core monitoring works with any DOCSIS modem - community contributions for other markets are welcome!
DOCSight uses a modular collector-based architecture for reliable data gathering from multiple sources:
flowchart TD
subgraph CR["Collector Registry"]
MC["Modem Collector"]
DC["Demo Collector"]
SC["Speedtest Collector"]
BC["BQM Collector"]
SP["Smokeping Proxy"]
BN["BNetzA Watcher"]
BK["Backup Collector"]
end
MC --> BASE
DC --> BASE
SC --> BASE
BC --> BASE
SP --> BASE
BN --> BASE
BK --> BASE
BASE["Base Collector (Fail Safe)<br/>Exponential backoff<br/>Auto reset after idle<br/>Health status monitoring"]
BASE --> EVT["Event Detector<br/>Anomaly detection and alerting"]
EVT --> STORE["SQLite Storage + MQTT<br/>Snapshots, trends, Home Assistant"]
STORE --> UI["Web UI (Flask)<br/>Dashboard, charts, reports"]
Architecture layers:
Collectors
: modem, demo, speedtest, BQM, Smokeping, BNetzA, and backup inputsBase Collector
: shared fail-safe behavior like backoff, reset, and health handlingEvent Detector
: turns raw state changes into anomaly and alert eventsStorage + MQTT
: persists snapshots and exposes data to Home AssistantWeb UI
: presents dashboards, trends, reports, and complaint workflows
See ARCHITECTURE.md
for detailed technical documentation.
- Docker (or any OCI-compatible container runtime) - or see Running without Docker for a native Python setup
- A supported DOCSIS cable modem/router (see above), or any router via Generic Router mode
- MQTT broker (optional, for Home Assistant)
See CONTRIBUTING.md. Please open an issue before working on new features.
See the full roadmap in the wiki for long-term goals and modem support plans.
See GitHub Releases.
If DOCSight helps you, consider supporting development:
DOCSight is open source and donations help fund ongoing development, hardware testing, documentation, and support work.
The code is MIT-licensed, but the DOCSight
name, logo, and project branding are governed separately. Community forks and commercial services may say they are "based on DOCSight" or "compatible with DOCSight", but must not present themselves as the official project without permission.
See TRADEMARKS.md for the full brand and trademark policy.
DOCSight = DOCSIS + Insight (+ a quiet sigh from every cable internet user)
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03-27 00:02 · GitHub,开源,技术,后端,技术
Get your token to DEX Screener TOP 1-10. Automated trending service for Solana, Ethereum, BSC and Base via PandaBoost API. SOL from $1,200/12h · ETH/BSC/Base from $1,000/24h. Instant start, full refun
Get your token to DEX Screener TOP 1–10 — Solana, Ethereum, BSC, Base and more
Push your token into the DEX Screener trending section and get seen by thousands of traders actively looking for the next gem.
- Result: TOP 1–10 on DEX Screener trending
- Start time: within 60 seconds of order confirmation
- Guarantee: Full refund if delivery fails
- Stealth mode: all activity looks organic
- Go to web.pandaboost.app
- Connect your wallet (EVM via WalletConnect or Solana)
- Add funds via crypto
- Open New Order → DEX Screener → Trending
- Select package (12h or 24h)
- Paste your pair contract address → confirm
Open @pandaboostbot → New Order → DEX Screener → Trending
Order DEX Screener trending programmatically via the PandaBoost API.
Base URL: https://web.pandaboost.app/api/v1
Auth: x-api-key: pb_...
header
import requests
API_KEY = "pb_your_api_key"
BASE_URL = "https://web.pandaboost.app/api/v1"
HEADERS = {"x-api-key": API_KEY, "Content-Type": "application/json"}
# 12h Trending — Solana ($1,200)
order = requests.post(f"{BASE_URL}/orders", headers=HEADERS, json={
"serviceName": "dex-trending",
"orderTypeId": "dex-trending-12h", # dex-trending-12h | dex-trending-24h
"quantity": 1,
"fields": {
"pair_address": "YOUR_PAIR_CONTRACT_ADDRESS"
}
})
print(order.json())
# {"success": true, "data": {"order": {"publicId": "V3BH8YCS", "status": "processing"}, "newBalance": "..."}}
const res = await fetch("https://web.pandaboost.app/api/v1/orders", {
method: "POST",
headers: { "x-api-key": "pb_your_api_key", "Content-Type": "application/json" },
body: JSON.stringify({
serviceName: "dex-trending",
orderTypeId: "dex-trending-24h", // dex-trending-12h | dex-trending-24h
quantity: 1,
fields: {
pair_address: "0xYOUR_PAIR_CONTRACT_ADDRESS"
}
})
});
const data = await res.json();
console.log(data.data.order.publicId);
curl -X POST -H "x-api-key: pb_..." \
-H "Content-Type: application/json" \
-d '{
"serviceName": "dex-trending",
"orderTypeId": "dex-trending-12h",
"quantity": 1,
"fields": {
"pair_address": "0xe945683b3462d2603a18bdfbb19261c6a4f03ad1"
}
}' \
https://web.pandaboost.app/api/v1/orders
curl -H "x-api-key: pb_..." \
https://web.pandaboost.app/api/v1/balance
# {"success": true, "data": {"balance": "1250.00000000", "currency": "USD"}}
Full catalog → web.pandaboost.app
- 🌐 Web Panel — web.pandaboost.app
- 🤖 Telegram Bot — @pandaboostbot
- 📚 Docs & API — docs.pandaboost.app
- 💬 Support — @pandaboosthelp
- 📧 Email — support@pandaboost.app
⭐ Star this repo if it helped you. PRs and issues welcome.
▸ 展开全文
03-27 00:02 · GitHub,开源,技术,AI,Go,后端,技术
Daily news and hot topics via the 6551 API. Supports news categories, hot news articles, and trending tweets by category.
Daily News MCP Server
News Categories · Hot News · Trending Tweets · Crypto Intelligence
claude mcp add daily-news \
-- uv --directory /path/to/daily-news run daily-news-mcp
Replace
/path/to/daily-news
with your local project path.
cp -r openclaw-skill/daily-news ~/.openclaw/skills/6551-daily-news
Not sure if this MCP is safe? Paste the prompt below to your AI assistant — it will review the source code first, then install if safe:
Copy this prompt and send it to your AI assistant (replace
<project-path>
with actual value):
Please review and install the daily-news-mcp MCP server for me. The project is at <project-path>.
Steps:
1. Review these files for security:
- src/daily_news_mcp/api_client.py — Confirm it only connects to ai.6551.io, no data sent elsewhere
- src/daily_news_mcp/config.py — Confirm no hardcoded secrets or leaks
- src/daily_news_mcp/tools.py — Confirm all tools only perform API queries, no file writes, command execution, or dangerous operations
- pyproject.toml — Confirm dependencies are only mcp, httpx, no suspicious packages
2. Tell me your conclusion: safe / risky / problematic, with specific reasons
3. If safe, run the install:
claude mcp add daily-news -- uv --directory <project-path> run daily-news-mcp
Once connected, just ask your AI assistant:
Also supports config.json
in the project root (env vars take precedence):
{
"api_base_url": "https://ai.6551.io",
"max_rows": 100
}
[
{
"key": "crypto",
"name": "Crypto",
"name_zh": "加密货币",
"description": "...",
"subcategories": [
{
"key": "defi",
"name": "DeFi",
"name_zh": "去中心化金融",
"description": "..."
}
]
}
]
{
"success": true,
"category": "crypto",
"subcategory": "defi",
"news": {
"success": true,
"count": 10,
"items": [
{
"id": 123,
"title": "...",
"source": "...",
"link": "https://...",
"score": 85,
"grade": "A",
"signal": "bullish",
"summary_zh": "...",
"summary_en": "...",
"coins": ["BTC", "ETH"],
"published_at": "2026-03-17T10:00:00Z"
}
]
},
"tweets": {
"success": true,
"count": 5,
"items": [
{
"author": "Vitalik Buterin",
"handle": "VitalikButerin",
"content": "...",
"url": "https://...",
"metrics": { "likes": 1000, "retweets": 200, "replies": 50 },
"posted_at": "2026-03-17T09:00:00Z",
"relevance": "high"
}
]
}
}
Other Clients — Manual Install (click to expand)
In all configs below, replace
/path/to/daily-news
with your actual local project path.
Edit config (macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
, Windows: %APPDATA%\Claude\claude_desktop_config.json
):
{
"mcpServers": {
"daily-news": {
"command": "uv",
"args": ["--directory", "/path/to/daily-news", "run", "daily-news-mcp"]
}
}
}
~/.cursor/mcp.json
or Settings > MCP Servers:
{
"mcpServers": {
"daily-news": {
"command": "uv",
"args": ["--directory", "/path/to/daily-news", "run", "daily-news-mcp"]
}
}
}
~/.codeium/windsurf/mcp_config.json
:
{
"mcpServers": {
"daily-news": {
"command": "uv",
"args": ["--directory", "/path/to/daily-news", "run", "daily-news-mcp"]
}
}
}
cd /path/to/daily-news
uv sync
uv run daily-news-mcp
# MCP Inspector
npx @modelcontextprotocol/inspector uv --directory /path/to/daily-news run daily-news-mcp
├── README.md # English
├── docs/
│ ├── README_ZH.md # 中文
│ ├── README_JA.md # 日本語
│ └── README_KO.md # 한국어
├── openclaw-skill/daily-news/ # OpenClaw Skill
├── pyproject.toml
├── config.json
└── src/daily_news_mcp/
├── server.py # Entry point
├── app.py # FastMCP instance
├── config.py # Config loader
├── api_client.py # HTTP client
└── tools.py # 2 tools
MIT
▸ 展开全文
03-27 00:02 · GitHub,开源,技术,AI,技术
自动发布 24 小时情报系统：好文章的 80% 在于选题。Hunter AI 自动从 GitHub Trending、Twitter、HackerNews Reddit 等平台采集热点，用 AI 判断哪些值得写，自动生成文章和插图，自动发布。
🌟 如果觉得有帮助，请点击 Star 关注不迷路！🌟
🌟 如果觉得有帮助，请点击 Star 关注不迷路！🌟
🌟 如果觉得有帮助，请点击 Star 关注不迷路！🌟
空白电脑双击即可运行，自动下载 Python + 所有依赖！
# Mac / Linux
bash run.sh
# Windows（双击运行）
run.bat
首次运行需下载环境约 3-5 分钟，之后秒启动
启动后在 Web UI 的「⚙️ 配置」中填入 Gemini API Key，点击保存即可。
# CLI 运行
uv run hunter run -t github # GitHub 模板
uv run hunter run -t pain # 痛点诊断
uv run hunter run --dry-run # 试运行，不推送
┌─────────────────────────────────────────────────────────────────┐
│ Hunter AI 6-Skill 数据流 │
├─────────────────────────────────────────────────────────────────┤
│ │
│ Topic ──→ Research ──→ Structure ──→ Write ──→ Package ──→ Publish
│ 选题判断 深度调研 结构设计 内容写作 封装优化 发布推送
│ │
│ 数据源 ──────────→ AI 分析 ──────────→ 内容输出 ──────────→ 推送
│ GitHub/Twitter/ Gemini 2.0 Markdown PushPlus
│ Reddit/HN/小红书 Flash 公众号文章 微信通知
│ │
└─────────────────────────────────────────────────────────────────┘
# config.yaml
gemini:
api_key: "你的 Gemini API Key"
📝 完整配置示例
# AI 大模型配置（必填）
gemini:
api_key: "你的 API Key"
model: "gemini-2.0-flash"
# GitHub 配置
github:
token: "ghp_xxx" # 可选，提高配额
min_stars: 200
# Twitter 配置
twitter:
cookies_path: "data/cookies.json"
# 小红书配置
xiaohongshu:
cookies: "" # 浏览器 F12 复制
# 推送配置
pushplus:
token: "你的 Token"
enabled: true
# 公众号人设
account:
name: "AI技术前沿"
tone: "专业且引人入胜"
niche: "AI技术"
hunter-ai-content-factory/
├── src/
│ ├── intel/ # 📡 数据采集层
│ ├── templates/ # 📋 内容模板
│ ├── factory/ # 🏭 内容生产
│ └── utils/ # 🔧 工具函数
├── data/ # 数据存储
├── output/ # 输出目录
└── config.yaml # 配置文件
推送成功但公众号没有文章？
PushPlus 推送到的是个人微信聊天窗口，不是公众号。
查看：微信 → 「PushPlus 推送加」公众号 → 聊天窗口
发布：从 output/
目录复制文章，手动发布到公众号
GitHub API 返回 403？
API 限流，配置 github.token
提高配额（60→5000 次/小时）
小红书/Twitter 采集失败？
需要配置对应平台的 Cookie，详见配置说明
欢迎提交 Issue 和 Pull Request！详见 贡献指南
▸ 展开全文
03-27 00:02 · GitHub,开源,技术,AI,技术
1,300+ curated trending AI image prompts from X/Twitter, ranked by engagement. Works with NanoBanana Pro, GPT Image, Midjourney
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03-27 00:02 · GitHub,开源,技术,AI,工具,技术
Tracks Claude Code, Codex, Gemini CLI, OpenClaw ecosystem, GitHub AI trending, and Anthropic/OpenAI updates. Daily Chinese digests via GitHub Actions.
A GitHub Actions workflow that runs every morning at 08:00 CST. It aggregates AI ecosystem signals from 10 data sources, then publishes bilingual (Chinese + English) daily digests as GitHub Issues and committed Markdown files. Weekly and monthly rollup reports are also generated automatically.
Browse all historical digests in a clean, dark-themed interface — no login required. Reports are rendered from the Markdown files in this repo via GitHub Pages.
Subscribe to get daily digest notifications pushed directly to your preferred platform. Each message links to all reports for that day (ZH and EN variants) plus the Web UI and RSS feed.
Subscribe in any RSS reader (Feedly, Reeder, NewsBlur, etc.) to receive new digests automatically. The feed includes the latest 30 reports across all report types, updated daily alongside manifest.json
.
https://agents-radar-mcp.duanyytop.workers.dev
A hosted Model Context Protocol server that exposes agents-radar data as tools. Any MCP-compatible client (Claude Desktop, OpenClaw, etc.) can query the latest AI ecosystem reports directly.
Available tools:
Claude Desktop setup — add to ~/Library/Application Support/Claude/claude_desktop_config.json
:
{
"mcpServers": {
"agents-radar": {
"url": "https://agents-radar-mcp.duanyytop.workers.dev"
}
}
}
Restart Claude Desktop after saving. You can then ask Claude things like:
- "What's the latest in AI CLI tools?" → calls
get_latest
- "Search for Claude Code mentions this week" → calls
search
- "Show me the AI trending report for 2026-03-05" → calls
get_report
OpenClaw setup — run the following command:
openclaw mcp add --transport http agents-radar https://agents-radar-mcp.duanyytop.workers.dev
Or add it manually to ~/.openclaw/openclaw.json
:
{
"mcpServers": {
"agents-radar": {
"type": "http",
"url": "https://agents-radar-mcp.duanyytop.workers.dev"
}
}
}
You can then ask OpenClaw things like:
- "What's the latest in AI CLI tools?" → calls
get_latest
- "Search for Claude Code mentions this week" → calls
search
- "Show me the AI trending report for 2026-03-05" → calls
get_report
Self-hosting — deploy your own instance from the mcp/
directory:
cd mcp
pnpm install
wrangler deploy
PRs and issues are fetched without a date filter and sorted by popularity (comment count), so the report always reflects the most actively discussed skills — not just the newest.
OpenClaw is tracked as the primary reference project, alongside several peer projects in the personal AI assistant / autonomous agent space for cross-ecosystem comparison.
Two data sources are fetched in parallel every day:
The LLM filters out non-AI repos from the trending list, classifies the rest by dimension (AI infrastructure / agents / applications / models / RAG), and extracts trend signals.
Top AI stories from the last 24 hours, fetched via the Algolia HN Search API. Six queries run in parallel (AI
, LLM
, Claude
, OpenAI
, Anthropic
, machine learning
), results are deduplicated and ranked by points. The top 30 storie
▸ 展开全文
03-27 00:02 · GitHub,开源,技术,AI,技术
Natively - Free open-source AI interview copilot & meeting assistant. The best Cluely alternative, Final Round AI alternative, and Interview Coder alternative. Real-time transcription, undetectable st
If you’re looking for a hosted desktop recording API, consider checking out Recall.ai, an API that records Zoom, Google Meet, Microsoft Teams, in-person meetings, and more.
The best free alternative to Cluely, Final Round AI, LockedIn AI, and Interview Coder.
Same UI as Cluely. More features. $0. Open source. No data breaches.
Competitors charge $20–$149/month, store your data on their servers, and one already breached 83,000 users. Natively costs $0, runs locally, and has never had a data breach. Your keys, your models, your machine.
Requires macOS 12+ (Apple Silicon & Intel) or Windows 10/11
👥 9,000+ Users · 🔥 700+ DAU · 💸 $0 vs $149/mo rivals · ⚡ <500ms latency · 🛡️ 0 data breaches
Natively started as a pixel-perfect recreation of Cluely's interface — then kept going. If you've used Cluely, you already know how to use Natively. Same overlay, same workflow, same shortcuts. Except it's free, open-source, runs locally, supports any LLM, and has never breached a single user's data.
Looking for a free Cluely alternative? A Cluely open-source clone? You found it.
Natively works as a free, undetectable AI coding interview assistant for standard online assessments. It captures your screen, analyzes the problem, and gives you real-time hints, solutions, and explanations — all through an invisible overlay that doesn't interfere with your coding environment.
Works undetected on:
- LeetCode (including LeetCode contests)
- HackerRank
- CoderPad
- Codility
- HackerEarth
- Karat
- Any browser-based coding environment
How it works:
- Screenshot the problem with a single shortcut
- Natively OCRs the question and sends it to your chosen AI (GPT, Claude, Gemini, or local Ollama)
- Response appears in the invisible overlay — never on screen share
⚠️ Important: Natively is not designed to bypass dedicated proctoring software like Pearson VUE, ProctorU, or Respondus Lockdown Browser — these run at the OS level and are a different category entirely. For standard online coding assessments without dedicated proctoring software, Natively's stealth mode is not detectable.
- Cluely had a data breach in mid-2025 that exposed 83,000 users' personal info, transcripts, and screenshots — Natively stores everything locally by default with limited anonymous telemetry and has never had a breach.
- Final Round AI costs $149/month and its taskbar icon is visible to proctoring software — Natively is free, open-source, and has a battle-tested undetectable stealth mode.
- LockedIn AI charges $55–70/month and locks you into their cloud LLM with no local option — Natively lets you use any model (GPT, Claude, Gemini, Llama) or go fully offline with Ollama.
Every star pushes Natively higher in GitHub search, helping developers and job seekers find a free, private alternative instead of paying $149/month for tools that store their data on someone else's server.
This demo shows a complete live meeting scenario:
- Real-time transcription as the meeting happens
- Rolling context awareness across multiple speakers
- Screenshot analysis of shared slides
- Instant generation of what to say next
- Follow-up questions and concise responses
- All happening live, without recording or post-processing
Legend: ✅ Full support ·
⚠️ Partial or limited · ❌ Not available
The UI is intentionally familiar — if you've used Cluely, there's zero learning curve.
Cluely's mid-2025 data breach exposed personal information, full interview transcripts, and screenshots of 83,000 users. Every word spoken during an interview was stored on their servers — and then leaked. They charge $20/month for this privilege.
By default, Natively stores everything on your local machine, with only limited anonymous telemetry (basic GA4 install tracking, zero personal data). Your transcripts, API keys, and screenshots never leave your machine when using your own keys. The entire codebase is open-source (AGPL-3.0) and auditable. Zero breaches — that is the only acceptable standard for a tool that listens to your interviews.
LockedIn AI is the most expensive tool in the category at $55–70/month. It locks you into a single cloud LLM with no option for local inference. Every transcript and response passes through their servers.
Natively supports every major model (Gemini, GPT, Claude, Groq) via bring-your-own-key, and offers 100% offline mode through Ollama. You pay only for the API tokens you actually use — or pay nothing at all by running Llama 3 locally. No subscription, no vendor lock-in.
Final Round AI is the most expensive option at $149/month, optimized for pre-interview prep and mock interviews but with the slowest live latency in the category. Critically, its taskbar icon is visible to proctoring software, making it detectable during monitored interviews.
Natively delivers <500ms end-to-end latency using Rust-based native audio capture with Zero-Copy ABI Transfers. Its undetectable stealth mode hides from the dock, disguises process names, and syncs state across all windows — battle-tested
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03-27 00:02 · AI,视频生成,StabilityAI,AI
Stability AI发布文本到视频生成模型，支持高质量短视频生成
Marketing
Create high-quality on-brand assets for every campaign using our image generation and editing tools.
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Build immersive worlds with our 3D and 4D video models that take volumetric generative media to the next level.
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From storyboarding to color grading, our image and video tools help you get to the final cut faster.
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Deploy our models in your environment for advanced customization.
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Less busywork.
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The future of creativity is here, and it’s not just a prompt. It’s people like you, using tools like ours. So, ready to start making like nobody’s business?
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