# The AI Radar Kit: see the 10 AI tools that matter each week before everyone else, and build your own radar in 20 minutes, free The exact method my AI uses to scan 400+ new AI launches a day and pick the few worth your time. Plus this week's 10 picks, and a working copy you can run yourself for $0. *By Dan Shipped (@danshipped). Last verified: October 4, 2026.* --- ## What's in this kit | Part | What you get | Time | |---|---|---| | **Start here** | A 5-minute win: a personal AI briefing for your niche | 5 min | | **1. This week's drop** | The 10 AI tools and launches my radar ranked highest, each tested against its source, with a 2-minute trial step and my verdict | 10 min read | | **2. What's working on video** | 3 video formats pulling 3–13× their creators' usual views right now | 5 min | | **3. How the radar decides** | The scoring formula in plain English (4 signals, 3 adjustments) | 5 min | | **4. Build your own radar** | `radar/`: a ~650-line script that runs free on GitHub every morning and sends you a digest on Telegram or email | 20 min setup | | **5. The "what matters" prompt** | Turns any pile of links into "the 3 that matter for *you*, and why" | 2 min | | **6. Find → content in 15 minutes** | Hooks by awareness stage + 3 script templates + a script-writing prompt | 15 min per post | Why free? Because I want you on the daily drop. If this kit saves you an hour this week, you'll stick around for the next one. --- ## Start here: your 5-minute win You'll get a personal briefing: the 3 AI launches from this week that matter for **your** work, why they matter, and what to do next. No setup. 1. Open [`prompts/what-matters-prompt.md`](prompts/what-matters-prompt.md) and copy the prompt. 2. Fill the 3 lines under **ABOUT ME** (what you do, who you serve, what you want). 3. Under **THE LINKS**, paste this week's list: ``` 1. affaan-m/ECC — https://github.com/affaan-m/ECC — Performance system for AI coding agents: skills, memory, security. +897 stars in a day. 2. Panniantong/Agent-Reach — https://github.com/Panniantong/Agent-Reach — Lets your AI agent read and search X, Reddit, YouTube, GitHub. +1,696 stars in a day. 3. DietrichGebert/ponytail — https://github.com/DietrichGebert/ponytail — Makes your AI coding agent write less, simpler code. #1 on GitHub Trending. 4. heygen-com/hyperframes — https://github.com/heygen-com/hyperframes — Write HTML, render video. Built for AI agents. +580 stars in a day. 5. KKKKhazix/AIHOT — https://github.com/KKKKhazix/AIHOT — Open-source framework for a self-running industry news site with AI-written daily reports. ~1,073 stars/day. 6. mksglu/context-mode — https://github.com/mksglu/context-mode — Cuts how much of your AI coding agent's memory gets eaten by tool output. 7. Louis-CFM/coucou — https://github.com/Louis-CFM/coucou — A tiny app in your Mac's notch that watches your coding agents. 8. mvschwarz/openrig — https://github.com/mvschwarz/openrig — Run persistent teams of Claude Code / Codex agents with roles. 9. NVIDIA/OpenShell — https://github.com/NVIDIA/OpenShell — NVIDIA's sandbox runtime that limits what AI agents can touch. 10. edenfunf/reelmimic — https://github.com/edenfunf/reelmimic — Show it a video you love; AI agents make a new one in the same style. 11. MAI-Transcribe-2-Streaming — https://the-decoder.com/microsoft-ai-releases-new-transcription-and-text-to-speech-models-for-voice-agents/ — Microsoft's new real-time speech-to-text model, 60 languages. 12. Meta Muse gadget SDK — https://www.theverge.com/tech/1004330/meta-muse-ai-gadgets-home-link — Meta open-sourced code to build your own Muse AI gadgets. ``` 4. Send. In under a minute you have a briefing written for you, not for "everyone into AI". Next week, paste your own radar's digest instead. That's what chapter 4 builds. --- ## 1. This week's drop: the 10 that matter **How these were picked.** My radar (Shipyard OS) scanned GitHub, Hacker News, Reddit, Hugging Face, 26 feeds and 80+ AI creators between September 27 and October 3, 2026, and scored every item on velocity, relevance, freshness and source trust (chapter 3). I took the top of the list, removed drama and duplicates, and checked every entry against its source on October 4, 2026: the repo exists, the description is accurate, the install command is the one in the official README. **How to read the verdicts.** - **SHIP**: worth 10 minutes of your time this week. - **WATCH**: real, but early, niche or heavy to set up. Bookmark it. - **SKIP**: I'd skip it unless you're the exact person it's built for. Star counts are a snapshot from October 3–4, 2026. They move fast. --- ### 1 · Ponytail — make your AI coding agent write less code **Link:** · MIT license **What it does:** A plugin/skill set that makes your coding agent climb a "laziness ladder" before writing anything: does this need to exist? Is it already in the codebase? Does the standard library do it? Only then does it write the minimum that works. It keeps validation, error handling, security and accessibility. **Who it's for:** Anyone using Claude Code, Codex, Cursor, Gemini CLI or similar who's tired of 300-line answers to 10-line problems. **The signal:** #1 on GitHub Trending, +1,281 stars in a day, 153K stars total. **Try it in 2 minutes (Claude Code):** send these as two separate messages: ``` /plugin marketplace add DietrichGebert/ponytail ``` ``` /plugin install ponytail@ponytail ``` Then ask your agent for something it usually over-builds ("add a date picker to this form") and compare. **Dan's verdict: SHIP.** The authors' own benchmark reports ~54% less code on average across 12 tasks (up to 94% where agents over-build). That's their number, not mine, and they openly corrected an earlier, rosier claim. That honesty is why I'd try it. The plugin runs small Node.js hooks, so `node` must be on your PATH. --- ### 2 · Agent Reach — let your AI agent read X, Reddit, YouTube and GitHub **Link:** · MIT · English README at `docs/README_en.md` **What it does:** One installer that gives your agent working access to platforms that normally block it: any web page as clean Markdown, YouTube subtitles and search, GitHub, RSS, X (with your cookie), Reddit (via your browser session), plus Chinese platforms. It picks a backend per platform and switches when one breaks. It includes `agent-reach doctor` to show what works. **Who it's for:** Researchers, marketers and builders who want their agent to "go read what people are saying about X". **The signal:** +1,696 stars in a day (the fastest repo on the list), ~90K total. **Try it in 2 minutes:** paste this into Claude Code (or any agent that can run commands): ``` Install Agent Reach: https://raw.githubusercontent.com/Panniantong/agent-reach/main/docs/install.md ``` By default the install only checks your environment; it changes your system only with `--system`. **Dan's verdict: SHIP, carefully.** The zero-config parts (web pages, YouTube, GitHub, RSS) are the 2-minute win. X and Reddit need cookies from a logged-in browser, so use a secondary account and read what you're granting. Note its own README says Reddit has "no zero-config path" anymore. That matches what I found building the radar (chapter 4). --- ### 3 · HyperFrames — write HTML, render video **Link:** · Apache-2.0 · by HeyGen **What it does:** An open-source framework that turns HTML, CSS and animations into deterministic MP4 videos. You (or your AI agent) write a web page; it renders frame-perfect video. Ships as Claude Code skills plus a CLI with ready-made blocks (transitions, social overlays, animated charts). **Who it's for:** Creators who want motion graphics, title cards or animated captions without After Effects, especially if you already let Claude write code for you. **The signal:** +580 stars in a day, 56K total. A Reddit post titled "Claude made us a launch video entirely in code" hit r/ClaudeAI's top list the same week. Code-made video is having a moment. **Try it in 2 minutes (needs Node.js):** ```bash npx hyperframes init my-video cd my-video npx hyperframes preview ``` Or in Claude Code: `claude plugin marketplace add heygen-com/hyperframes`, then `claude plugin install hyperframes@hyperframes`, then ask for "a 10-second title card for my channel". **Dan's verdict: SHIP** if you make short-form content. The learning curve is "can you describe what you want". The limit: it's for graphics and text motion, not for editing your camera footage. --- ### 4 · context-mode — stop tool output from eating your agent's memory **Link:** · Elastic License 2.0 (free to use; you can't resell it as a hosted service) **What it does:** An MCP server + hooks that keep raw tool output (web snapshots, logs, long file reads) out of your agent's context window, and track your session in a local SQLite database so the agent picks up where it left off after the conversation compacts. Supports Claude Code and 16 other platforms. **Who it's for:** People who run long Claude Code sessions and watch them get "forgetful" after 30 minutes. **The signal:** +256 stars in a day, 25K total, steady for weeks. **Try it in 2 minutes (Claude Code):** ``` /plugin marketplace add mksglu/context-mode ``` ``` /plugin install context-mode@context-mode ``` MCP-only alternative: `claude mcp add context-mode -- npx -y context-mode` **Dan's verdict: SHIP** for heavy agent users. The README's "315 KB becomes 5.4 KB" is their best case, so expect less on normal sessions. Skip it if you mostly chat with AI rather than run agents. --- ### 5 · AIHOT — the open-source "AI news site that writes itself" **Link:** · MIT · live demo: **What it does:** The full engine behind a Chinese AI-news site: collects from many sources, has an LLM pre-screen and score each item twice, writes headlines and summaries, groups the same story from different outlets into one "event", ranks events by how many independent sources cover them, and publishes a daily report at 08:00 plus weekly and monthly roundups. **Who it's for:** Anyone who wants to run a niche news site or newsletter (legal AI, HR tech, crypto…) on autopilot. **The signal:** ~1,073 stars/day since it was created on September 28; 5.4K stars in under a week. **Try it in 2 minutes:** open and look at how it groups stories into events. To run your own you need Docker and an OpenAI-compatible API key: ```bash git clone https://github.com/KKKKhazix/AIHOT.git myhot && cd myhot node scripts/init-env.ts --llm-key docker compose up -d --build ``` **Dan's verdict: WATCH.** It's the most complete open-source radar I've seen this year, and its "count independent sources, not articles" rule is exactly what the kit's script does (chapter 3). But the README, UI and prompts are in Chinese, and it needs a server. If you want the 20-minute version, use `radar/` in this kit. --- ### 6 · Coucou — a tiny friend in your notch that watches your agents **Link:** · MIT **What it does:** A small macOS app that lives in the notch (top of the screen on Windows/Linux) and shows what your coding agents are doing: live Claude Code sessions, permission requests you can answer from the notch, plan usage, diffs and pull requests. Supports Claude Code, Codex, Cursor, Gemini CLI and more. **Who it's for:** Mac users who run agents in the background and keep missing "waiting for your approval" prompts. **The signal:** +531 stars/day since its first release on September 27; it shipped six versions in its first week (v0.1.5 on October 4). **Try it in 2 minutes:** download `Coucou.zip` from the [Releases page](https://github.com/Louis-CFM/coucou/releases), move it to Applications, open it, then click **Install hooks** in its settings. It backs up `~/.claude/settings.json` and shows you the diff before writing. **Dan's verdict: SHIP if you're on a Mac and run Claude Code daily; SKIP otherwise.** Windows installer is paused (Microsoft Defender false positive under review, per the README); Linux is a beta. --- ### 7 · OpenRig — persistent teams of AI agents with roles **Link:** · Apache-2.0 **What it does:** Turns Claude Code and/or Codex sessions into a standing team: an "owner" agent does the work, a "checker" agent reviews it, they share context and keep their roles across sessions. Runs in tmux on macOS or Linux. **Who it's for:** Developers already comfortable with Claude Code or Codex who want a second agent reviewing the first one's work. **The signal:** +683 stars in a day, 4.6K total, 15th on GitHub Trending. **Try it in 2 minutes (safe preview, changes nothing):** ```bash npm install -g @openrig/cli rig setup --dry-run ``` **Dan's verdict: WATCH.** The idea (no agent grades its own work) is right. But it needs Node 22/24 and tmux, writes provider hooks and permission settings, and native Windows isn't supported. Read "what OpenRig changes on your machine" in the README before the real setup. --- ### 8 · NVIDIA OpenShell — a sandbox that limits what your AI agents can touch **Link:** · Apache-2.0 · docs: **What it does:** Runs each agent in an isolated sandbox. You write a policy (which files, which websites, which APIs); OpenShell enforces it on every file access and network call. Agents never see your real credentials: they're added only to requests going to approved endpoints. Before a policy change, it flags risky new access for human review. **Who it's for:** Anyone giving an agent real API keys, a company laptop or a production server. **The signal:** +594 stars in a day, 14.7K total. **Try it in 2 minutes (Linux, Apple Silicon Mac, or WSL 2; needs Docker, Podman or host virtualization):** ```bash curl -LsSf https://raw.githubusercontent.com/NVIDIA/OpenShell/main/install.sh | sh openshell sandbox create --name demo ``` **Dan's verdict: WATCH** for most people, **SHIP** if you run agents with access to money, customer data or production. It collects anonymous usage telemetry by default; turn it off with `OPENSHELL_TELEMETRY_ENABLED=false`. --- ### 9 · ReelMimic — show it a video you love, get a new one in the same style **Link:** · MIT **What it does:** Breaks down a reference video (shot lengths, BPM, transitions, colors, framing), proposes a storyboard you approve, then splits production across up to 6 AI agents, with every shot reviewed by a different agent. Runs on your computer with your own Claude Code or Codex. It learns the *style*; it doesn't copy the original footage. **Who it's for:** Creators who want animated 2D content (motion graphics, watercolor, pixel art, paper cut-out…) in a style they've seen work. **The signal:** ~210 stars/day since September 28. It's built on HyperFrames (#3), which is a second signal: tools are stacking on it. **Try it in 2 minutes (setup only):** ```bash git clone https://github.com/edenfunf/reelmimic.git && cd reelmimic ./install.sh && ./start.sh ``` Then open and drop in a reference to see the breakdown. Needs Node 22.18+, Python 3.10+, FFmpeg and Chrome. **Dan's verdict: WATCH.** The README says a 30–60 second video takes 1–3.5 hours after you approve the plan, and it's 2D only. Impressive, not yet a daily tool. --- ### 10 · Microsoft MAI-Transcribe-2-Streaming — real-time speech-to-text, 60 languages **Source:** [The Decoder, October 2, 2026](https://the-decoder.com/microsoft-ai-releases-new-transcription-and-text-to-speech-models-for-voice-agents/) **What it does:** Microsoft AI's first real-time transcription model. First partial results in about 100 ms, 60 languages, ranked first for accuracy on Artificial Analysis's streaming leaderboard at launch. Released with MAI-Voice-2.1 (text-to-speech with voice cloning). **Who it's for:** Anyone building voice agents, live captions, or call and meeting tools. **The signal:** Covered by The Decoder and MarkTechPost within hours of release; big-lab launch. **Try it in 2 minutes:** it's available in Microsoft Foundry and Microsoft's MAI Playground (free to test with a Microsoft account). Search "MAI Playground", sign in, and speak into the transcription demo. **Dan's verdict: WATCH** unless you're building voice products, then **SHIP** a test this week. Intro pricing is $0.54 per hour of audio through the end of the year, per The Decoder. Check Microsoft's pricing page before you build a business case on it. --- ### Also on the radar this week (one line each) - **affaan-m/ECC** (): "agent harness performance system" with skills, memory and security for Claude Code, Codex, Cursor. 272K stars, +897/day. Huge, but a lot to absorb. WATCH. - **mattpocock/skills** () and **obra/superpowers** (): skill libraries for coding agents, both trending again. The skills wave isn't slowing. - **feder-cr/dots** (): open-source AI agent with its own browser. 2.6K stars in 5 days. - **Meta Muse gadget SDK** (): build your own hardware gadget around Meta's Muse agent (e-ink display, HDMI stick). Fun weekend project. - **OpenAI's GPT-6 model guide** (): official guide to choosing GPT-6 models and reasoning effort. Read it if you build on OpenAI. **Skipped on purpose:** the drama story of the week (an OpenAI safety lead quitting) and a viral "uncensored" home-PC model. Both were loud on Reddit and Hacker News. Neither gives you anything to use today. The radar's noise filter dropped them, and chapter 3 explains how. --- ## 2. Three video formats beating their creators' averages right now My radar also tracks AI creators on YouTube and flags **outliers**: videos that pull far more views than that creator's median video. An outlier score of 5× means five times their typical views. Same creator, same audience, same week: the only thing that changed is the format, so outliers tell you what the market wants *right now*. These are the three patterns that kept showing up (views as captured by the radar between July and October 2026): ### Format 1: The day-one test, with a number | Video | Creator | Views | vs. their median | |---|---|---|---| | GPT-6 Astra: 20 Real Examples From Useful to Almost Impossible | The AI Advantage | 389K | **12.8×** | | I Spent 100 Hours Using GPT-6 Astra (This Feels Like AGI) | Riley Brown | 745K | 8.4× | | GPT-6 Astra Is Finally Here (And It's REALLY Good) | Matt Wolfe | 500K | 4.8× | | I Tested Jev on 12 Real Use Cases. My Honest Thoughts. | Nate Herk | 354K | 2.6× | **What it is:** a hands-on test of something brand new, with a specific count in the title (20 examples, 100 hours, 12 use cases). **Why it works:** two desires at once. "Don't fall behind" (it's new) and "don't waste my time" (someone tested it for me). The number is proof of effort and tells the viewer exactly what they get. It only works in the first days after a launch, which is why a radar matters: you need to know on day one. **Your short-form version:** "I tested [new thing] on [N] real tasks. Here are the 3 that worked." Show 3, link the rest. ### Format 2: Copy the expert's workflow | Video | Creator | Views | vs. their median | |---|---|---|---| | Matt Pocock's Agentic Engineering Workflow (just copy him) | David Ondrej | 450K | **7.9×** | | Ex-NASA dev reveals his Agentic Engineering Workflow | David Ondrej | 333K | 5.8× | | Google Just Dropped a Masterclass on Agentic Engineering | Cole Medin | 163K | 5.6× | | Pi Agent dev reveals his Agentic Engineering Workflow | David Ondrej | 236K | 4.1× | **What it is:** someone with clear authority (a known engineer, an ex-NASA dev, Google) shows their actual setup, and the title tells you to copy it. **Why it works:** borrowed credibility plus the shortest path to the result. The viewer doesn't want to *learn* agentic engineering; they want to *have* a setup that works. "Just copy him" removes the effort. David Ondrej has four videos on this list with the same skeleton. When a format works, repeat it with a new expert. **Your short-form version:** "This is how [credible person] uses [tool]. Steal it." Show the 3 settings or the one file that matters. ### Format 3: Compressed mastery | Video | Creator | Views | vs. their median | |---|---|---|---| | 1000+ hours of Learning Claude in 15 Minutes (Beginner to Pro) | Dan Martell | 1.3M | **6.5×** | | Obsidian in 24 Minutes | Tina Huang | 706K | 3.5× | | Every Size Local AI In 24 Minutes | Tina Huang | 679K | 3.3× | | Hermes Agent Fundamentals In 29 Minutes | Tina Huang | 604K | 3.0× | **What it is:** a huge input (1,000 hours, a whole tool, "every size") squeezed into a small, exact time. **Why it works:** it's a price reduction. The viewer "buys" 1,000 hours of someone else's learning for 15 minutes of their own. The exact minute count makes it believable. Tina Huang uses the "[Topic] in [N] Minutes" title three times in this list, each time at 3× or more. **Your short-form version:** "[N] hours of [skill] in 45 seconds." Three rules, one on-screen example each. **The pattern under all three:** none of these titles lead with the tool's features. They lead with effort saved (tested for you, copy him, in 15 minutes). Same lesson as the scoring formula: **usable beats impressive**. --- ## 3. How the radar decides what matters Every item gets a score from 0 to 100. Here's the formula, in plain English. It's a simplified version of the one running in my own radar, and it's exactly what `radar/radar.mjs` does. ### The 4 signals | Signal | Weight | Question it answers | How it's measured | |---|---|---|---| | **Velocity** | 36% | Is it spreading *fast*? | Speed, not size. GitHub: stars per day. Hacker News: points per hour. Hugging Face: trending score. Each is compared with "very hot" for that source (800 stars/day, 40 points/hour…) and square-rooted, so one rocket doesn't flatten everything else. | | **Relevance** | 36% | Is it *my* niche? | Your keyword list. Each match adds its weight ("claude" = 1.3, "agent" = 1.0, "free" = 0.2), negatives subtract ("hiring" = −1.2), and the total is squashed to 0–1 so ten weak matches can't beat two strong ones. | | **Freshness** | 14% | Is it new? | Loses half its value every 36 hours. A 3-day-old post is worth 1/4 of a fresh one. | | **Trust** | 14% | Is the source reliable? | A number you set per source: OpenAI's blog 0.95, a general news site 0.6. | **Score = 100 × (0.36 × Velocity + 0.36 × Relevance + 0.14 × Freshness + 0.14 × Trust)** Blogs and changelogs have no stars or upvotes, so for them velocity = 0.85 × trust × freshness. A fresh post from a source you trust *is* the signal. ### The 3 adjustments that make it smart 1. **The off-niche brake.** Relevance under 0.2? Score × 0.6. Under 0.5? Capped at 67, so it can never reach the top tier. A viral story outside your niche is still outside your niche. 2. **The echo bonus.** The same link or headline on several sources gets +6 points per extra source (max +18). One blog saying it is a claim; four sources saying it is news. (AIHOT, tool #5, uses the same idea: count independent sources, not articles.) 3. **The "can I use it?" boost.** Open-source repos and "Show HN" launches × 1.1 (you can try them today). A big lab + a launch verb ("OpenAI releases…") × 1.12. Before scoring, the **noise filter** throws out: questions ("Is it just me or…?"), Ask HN threads, weekly discussion threads, memes, "uncensored" model re-uploads. After scoring, **memory** hides anything you already saw in the last 10 days, and **variety** stops one source from taking more than 3 of the top 10 slots. ### A worked example (real numbers from the test run) `DietrichGebert/ponytail` on GitHub Trending, +1,281 stars today: - Velocity: √(1,281 / 800) → capped at **1.0** - Relevance: "AI agent" (1.0) + "AI" (0.6) = 1.6 → **0.63** - Freshness: trending pages have no date, so we assume 12 hours → **0.79** - Trust: **0.65** - Base: 100 × (0.36 + 0.227 + 0.111 + 0.091) = **79**, × 1.1 (open-source repo) = **87**. Top 5. A blog post titled "Our customer story: how Acme scales with AI" from a 0.7-trust news site, 6 hours old: fresh and trusted, but "customer story" subtracts 0.9 from relevance (which drops to 0), the off-niche brake kicks in, and it lands at about 25. Below the cut. That's the point. ### What the formula can't do It can't tell you whether a tool is *good*. Stars measure attention, not quality. That's why every item in chapter 1 has a verdict from a human who opened the repo. Use the radar to decide **what to look at**, not **what to believe**. --- ## 4. Build your own radar (free, 20 minutes) The `radar/` folder is a complete, working radar. One file, no dependencies, runs on GitHub's free servers every morning. **What it reads (51 sources, all free, all checked live on October 4, 2026):** - **Hacker News**: top + Show HN, via the official API - **GitHub**: new repos rising fastest (search API) + today's Trending page - **Hugging Face**: trending models and Spaces - **45 RSS/Atom feeds**: OpenAI, Anthropic, Google DeepMind, Google AI, Hugging Face, NVIDIA, Meta, AWS, Ollama · release feeds for Claude Code, Codex CLI, Gemini CLI, Ollama, Cursor · GitHub, Vercel, Lovable, Replit, n8n, Supabase, Cloudflare · TechCrunch, The Verge, Ars Technica, MIT Tech Review, Wired, The Decoder · Simon Willison, Latent Space, Ben's Bites, TLDR AI, The Rundown, Import AI, One Useful Thing, Interconnects, Last Week in AI, Lenny's · Product Hunt, Hugging Face Daily Papers · 3 AI YouTube channels - **Reddit**: included but off by default (see Limits) **What it does each run:** fetch → drop noise → merge duplicates → score → skip what you've seen → send the top 10 + 5 "also moving" to Telegram and/or email → save `digests/YYYY-MM-DD.md` in your repo. **Tested:** on October 4, 2026, a full run scanned 410 items from 50 sources in about 3 seconds. You can read the real output in [`radar/digests/example-2026-10-04.md`](radar/digests/example-2026-10-04.md). **Setup:** follow [`radar/SETUP.md`](radar/SETUP.md). Five steps: create a private repo, upload 3 files, paste the workflow, make a Telegram bot (or a free Resend key for email), add 2 secrets, click Run. If you have Node.js, you can preview it first with `node radar.mjs --dry-run`: it prints today's digest without sending anything. **Make it yours in 1 minute:** edit the `keywords` in `config.json`. A Shopify agency adds `shopify|ecommerce|klaviyo` at weight 1.2. A video editor adds `premiere|davinci|capcut|caption`. The formula does the rest. --- ## 5. The "what matters and why" prompt The radar finds 10 things. You still need to know which 3 matter *for you*. That's this prompt's job: [`prompts/what-matters-prompt.md`](prompts/what-matters-prompt.md). It makes the AI: - score every link on **relevance ×2, usability, momentum** (the same logic as the radar, done by an LLM that knows your context), - drop anything under 6/10 and merge duplicates, - return **3 picks** with "what it is", "why it matters for you", "do this next (≤10 minutes)" and a confidence level, - list what to skip and why, - give you **one content idea** that starts from your audience's problem, not the tool's name. It refuses to invent numbers and tells you when it only judged from the title. Paste it once into a Claude Project, a custom GPT or a Gemini Gem, and each morning you just drop in the digest. --- ## 6. Turn a radar find into content in 15 minutes Knowing first is only an advantage if you *publish* first. Here's the 15-minute loop I use. ### The 15-minute timer | Minutes | Do | |---|---| | 0–3 | Pick **one** find from your digest. Rule: you can show it on screen in under 30 seconds. | | 3–8 | Try it. Screen-record the moment it works (or fails). That clip is your video. | | 8–12 | Write the script: pick a template below, or paste [`prompts/find-to-script-prompt.md`](prompts/find-to-script-prompt.md) with your notes. | | 12–15 | Record the voiceover over the clip. Burn in captions. Post. | ### Hooks by awareness stage How much does your viewer already know? Pick the stage, then the formula. (This is Eugene Schwartz's awareness scale, applied to AI content. Cold feeds = stages 3–5.) | Viewer stage | Start from | Formula | Example (from this week's drop) | |---|---|---|---| | **5 · Doesn't know they have the problem** | Their identity | "If you [identity/habit], this is for you." | "If you let AI write your code, watch this before your next commit." | | **4 · Feels the problem, knows no solution** | The pain | "[Pain in their words]? [Stop / Here's the fix]." | "Your AI agent writes 300 lines for a 10-line fix? There's a fix." | | **3 · Wants the result, doesn't know the tool exists** | The desire + "there's a way" | "There's a way to [result] in [time], free." | "There's a way to make motion graphics by just describing them." | | **2 · Knows the category, not convinced** | Superiority / proof | "Everyone uses [X] for this. [New thing] does [specific better result]." | "Everyone's adding agent skills. This one made the agent write 54% less code." | | **1 · Knows you, wants the thing** | Name + offer | "Comment [WORD] and I'll send you [specific asset]." | "Comment SHIP and I'll send you this week's 10 tools and the radar." | **Sophistication check:** "5 AI tools you need" is dead; everyone's seen 500 of them. When the claim is tired, say **how** ("My radar flagged this at 6 a.m. Here's why it's spreading.") or speak to **identity** ("Builders don't wait for the newsletter."). ### Script template 1: THE FIND (30 seconds, cold audience) ``` HOOK (0-2s) [Stage 3-4 hook: pain or desire, NOT the tool name] ON SCREEN: [same promise, ≤8 words] YOU (2-5s) "If you [specific situation], this is for you." SHOW (5-18s) [Screen recording: the result happening. Narrate what we see, one sentence per cut.] PROOF (18-23s) "It's free, open source, and [the one number: +1,281 stars in a day / #1 on GitHub]." CTA (23-30s) "Comment [WORD] and I'll send you the link plus 9 more from this week." ``` **Filled example (Ponytail):** > "Your AI writes 300 lines for a 10-line fix?" / "If you code with Claude or Cursor, this is for you." / [recording: same prompt, before and after the plugin, the diff shrinking] "Same request. Before: a new library and a wrapper. After: one line." / "It hit #1 on GitHub this week. Free." / "Comment SHIP and I'll send you the install plus 9 more." ### Script template 2: THE VERDICT (35 seconds, warm audience) ``` HOOK (0-2s) "I tested [tool] so you don't have to." OR "[Tool] is everywhere. Here's the truth." GOOD (2-14s) "What's good: [specific result you got, with a number or a visible output]." BAD (14-22s) "What's not: [one honest limit: setup time, platform, price, quality]." WHO (22-28s) "Use it if you [X]. Skip it if you [Y]." VERDICT (28-35s) "My verdict: [SHIP / SKIP / WATCH]. Follow, tomorrow's test is already in the queue." ``` Admitting the limit is what makes the "good" believable. ReelMimic is a perfect verdict video: stunning output, 1–3.5 hours per clip. Say both. ### Script template 3: THE MECHANISM (40 seconds, audience tired of tool lists) ``` HOOK (0-3s) "My AI scans [400+] new AI launches every day. This morning it flagged one thing." SIGNAL (3-12s) "[Tool] went from 0 to [N] stars in [days]. [N] sources covered it in 24 hours." WHY (12-25s) "Here's why it's spreading: [the one change it makes for the viewer]." SO WHAT (25-33s) "What it means for you: [concrete action or consequence]." CTA (33-40s) "Want the radar? Comment [WORD]. It's free and you can build it in 20 minutes." ``` This one sells **you** as the source, not just the tool. It's the format behind my launch reel. ### Before you post: 5 checks 1. One idea, one CTA? 2. Does the hook start from the viewer's problem or desire, not the tool's name? 3. Is every number from the source (stars, price, benchmark)? No invented stats. 4. Did you say one honest limit? 5. Can someone understand it with the sound off? (Captions + on-screen hook.) --- ## Going further - **Add your competitors.** Every YouTube channel has a feed: `https://www.youtube.com/feeds/videos.xml?channel_id=CHANNEL_ID`. Add the 10 creators in your niche to `sources.json` and the radar shows you what they posted. To find a channel ID, open the channel page, view source and search for `channel_id`, or use any "YouTube channel ID finder" site. - **Track the tools you depend on.** Add `https://github.com/OWNER/REPO/releases.atom` for every open-source tool in your stack. You'll know about breaking changes before your users do. - **Share it.** Point the Telegram delivery at a channel and you have a daily newsletter for your team or audience. Swap the footer in `radar.mjs` for your name. - **Run it twice a day.** Add a second line under `schedule:`, e.g. `- cron: "0 17 * * *"`. Memory makes sure you never see an item twice. - **Let an LLM write the summary.** The digest is plain text, so any automation (Zapier, n8n, Make) can forward it to Claude or ChatGPT with the "what matters" prompt and post the result back to you. - **Tune with real data.** After a week, look at the items you actually clicked. Raise the weight of the keywords they share. Lower `trust` for sources that never make the top 10. ## Limits (read this, it'll save you time) - **Reddit is basically closed.** Reddit started returning 403 errors on its anonymous `.json` endpoints in 2026, and new API apps need approval under its Responsible Builder Policy. The public RSS works from some home connections but is usually blocked from cloud servers like GitHub's. The kit includes a Reddit source, off by default. If it fails, the run continues without it. - **No X/Twitter.** X's API isn't free at any useful level. Nothing in this kit touches it. - **The GitHub Trending reader scrapes a web page.** If GitHub redesigns that page, that one source returns 0 items until the pattern is updated. The search-API source keeps working. - **Feeds die.** Every feed in `sources.json` worked on October 4, 2026. Some will move or break. The digest's "Source health" section tells you which one, so you can disable or replace it. - **Keywords aren't understanding.** The formula matches words, not meaning. "Agent" matches "travel agent". Use `negative` and `noise` to correct it, and the "what matters" prompt for real judgment. - **Stars aren't quality.** Some fast-rising repos are hype, abandoned in a month, or worse. Read the code (or have your agent read it) before you give anything access to your machine, your cookies or your keys. - **This week's drop ages fast.** Star counts are a snapshot from October 3–4, 2026. The method doesn't age; the list does. - **Not advice.** Tool verdicts are my opinion after reading the source and the README. Check licenses and your company's rules before using any of this at work. --- ## Want this done automatically? The free kit is the do-it-yourself path: 51 sources, your keywords, your GitHub. I'm considering opening my full radar as **Radar Pro**: GitHub, Product Hunt, Reddit, Hacker News, Hugging Face, 26 feeds and 80+ AI creators, real-time alerts when something breaks out, competitor-outlier tracking (the chapter 2 data, every week, for your niche), and a human verdict on every pick. One daily digest in Telegram or email, around $9–19 a month. It doesn't exist for the public yet. If you'd use it, **reply YES to my DM** (or comment YES on the post) and I'll build it if enough of you want it. Early repliers get it first, at the lowest price. **Follow @danshipped for the next drop.** Tomorrow's tool is already in the queue. --- *Made by Dan Shipped (@danshipped). Last verified: October 4, 2026. Code: MIT license. Tool descriptions and install commands come from each project's official README as of that date; star counts come from GitHub's API and my radar's snapshot on October 3–4, 2026; YouTube view counts come from my radar's competitor tracking.*