# 5,000 stars: how one person turned Karpathy's post into a skill 9 hours later. Why answer-me-with-html works · GitHub open-source project

> Source: https://stringers.io/en/github/gh_QingYunA__answer-me-with-html.html (Stringers / 什么值得做, generated 2026-11-08)
> Stringers answers one question every day: what small product can one or two people build in a few months and actually make money from. Numbers come from 15 databases we collect daily, each with source and date.
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> It launched 9 hours after Karpathy's post, shipped all four forms from the post at once, and used measured data to show output tokens drop to about 1/8 and time from 31 seconds to 12 seconds, directly answering the "slow and token-hungry" worry; add the LINUX DO promo thread, skill directory sites, and AI news reposts.

[GitHub open-source project](https://stringers.io/en/github/index.md) · written 11-6

QingYunA/answer-me-with-html · QingYunA

# Karpathy posted it, and within 9 hours one person turned it into a 5,000-star skill: why answer-me-with-html won

**What it is** A skill for Claude Code, Codex, and Cursor: when faced with a complex question, instead of replying with a wall of text, it produces a single offline HTML page with charts. The model only writes a Markdown draft; a bundled program handles the layout and drawing.

**Why it blew up** It shipped 9 hours after Karpathy's post, covered all four forms from the post at once, and used measured data to answer the "slow and token-hungry" worry: output tokens dropped to about 1/8, and time fell from 31 seconds to 12 seconds. Then came a LINUX DO promo post, skill directory sites, and reposts on AI news sites.

**B** **How you could follow** Stop building yet another "answer in HTML". Move the "model writes a draft + program renders" pattern to outputs like slide decks, charts, explainer videos and review reports. Ship it the same day the next big-name post comes out, with reproducible comparison data and a one-line install.

Grade B: the playbook and structure are clear and transfer to other output formats. But this single direction is already crowded with long-established projects of ten-thousand-star scale, and the Karpathy wave it rode was a one-off. Followers are betting on the next post of similar magnitude and on their own renderer skill. Circulating in small circles

Data (updated daily) · Repo created 2026-10-02 · 5,993 stars · 7 days +1,029

GitHub · stars

Data 2026-10-02 → 2026-11-08 (38 days, 32 observations). Points before 10-28 come from deeply / archive backfill, not every day has one

On the night of Oct 2026 2, Karpathy posted a long thread on X saying we'll spend a lot of time "reading the model's output," and laid out a ladder from low to high: controlled English ASD-STE100, diagrams, HTML web pages, and 3b1b-style explainer videos. 9 hours later, a repo called answer-me-with-html appeared on GitHub. The same thread spawned a dozen or so skills, most stalling at dozens to a hundred-odd stars. This one reached 5,384 stars in 35 days (daily star log, 11-06; the GitHub page showed just over 5,700 that day). The difference isn't timing alone; it's one engineering decision and one packaging choice. Below, I lay out what it got right, and the few moves that people wanting stars for their own open-source projects can copy.

### What the post said, and what the project did with it

Karpathy's original post escalated in layers: first have the model write in ASD-STE100 (a controlled English originally made for aircraft maintenance manuals), "but better yet" have it draw diagrams, "better still" have it output HTML web pages, "better still" have it make 3b1b-style explainer videos, "and this is starting to be usable."

The author, Cyan (GitHub account QingYunA, 84 followers, with previous best work at only 65 stars), packed all four levels into one skill that same day. Once installed, when you ask Claude Code, Codex or Cursor things like "explain the TCP three-way handshake", "how are this repo's modules organized", or "should the cache use Redis or Memcached", you get back not a wall of terminal text but a single offline HTML file: sequence diagrams, state diagrams, comparison tables and conclusions. The README's first line reads "Super Fast | ASD-STE100 | Explainer Videos | One File, Offline", and the topics include ste100. Every keyword in the post is covered.

### How it differs from the dozen-plus similar projects: the model only writes the draft

After reading Karpathy's post, anyone who's tried this at all has the same first reaction: having the model hand-write a whole HTML page is slow and token-expensive. Similar projects from the same period were almost all a single prompt that had the model write the page itself. Even visual-explainer, which came out half a year earlier and already has 10,820 stars, has the model hand-write the entire page.

answer-me-with-html switched route: the model writes only a short Markdown draft, marking the structure with a few component blocks (timelines, flows, ER diagrams and so on). A bundled CLI turns the draft into a page in about 50 milliseconds; SVG coordinates, CSS and labels are all generated by the program. The README spells out the math: across 9 pages the model wrote by hand, an average of 4,893 tokens, 47% of which were SVG coordinates and paths, with text only 21%. With this skill, the same questions average 612 tokens, about 1/8, and time drops from 31 seconds to 12 seconds. The data and rerun scripts are in the repo, and it also honestly notes that the bill only drops about 15%, because every turn still reads the system prompt and context.

This directly answers everyone's first worry. On 5 Oct, under the LINUX DO promo post, one commenter's first reply was: "Output tokens are worth far more than input tokens."

### How the stars came

| Timeline | What happened | Daily new stars (Trendshift) |
|---|---|---|
| 10-02 | Karpathy's post (00:37 UTC), repo created (09:57 UTC) | 0 |
| 10-03 | Several skill directory sites listed it the same day | +67 |
| 10-04 | Trendshift all-languages #8, JavaScript #2 | +731 |
| 10-05 | The author posted on LINUX DO: "1k stars in a day! This Skill makes Agents output visual results!" | +437 |
| 10-06 | Covered by AlphaSignal, Hermes AI News | +293 |
| From ¥10-14 | 50~100 per day, with small spikes from releases and new articles | — |

Who drove the +731 on 10-04 can't be pinned down from outside (GitHub doesn't show star sources; no big account visibly reposted it on X). The channels that followed are visible:

- **LINUX DO open-source promo thread** : 3.1k views, 242 likes, 67 replies. The README specifically credits the community. The post said "7.4x fewer tokens, 3.6x faster." The README later switched to the more conservative 8x and 2.6x. The numbers got revised on re-testing and weren't inflated.

- **Passive listings on skill directories** : skills.sh, skillsllm, skillsdirectory and ten-plus other sites. The install command `npx skills add` runs an installer that supports 70 agents, which is a distribution channel in itself. 11-03 The author also forked 6 awesome-skills lists, apparently to get listed via PRs.

- **News sites and Chinese tech blogs** : AlphaSignal, Juejin, CSDN, WeChat Official Accounts. Titles mostly name Karpathy directly. Third-party YouTube Shorts and traditional-Chinese Threads / Instagram explainers also circulated.

It didn't stop after the first wave. In 36 days it merged 243 PRs and iterated from v0.4 to v0.5.10. From 10-17 to 11-06 daily stars rose from 3,514 to 5,384, and the last 7 days added +681, faster than the 457 of the prior 7 days (daily star log, 11-06). Interest held because releases and new articles kept feeding it.

### Packaging is part of the product

- The README's first screen has one image: a full screen of terminal text on the left, and a one-page diagram of the same question on the right. You get it without reading.

- A 24-second demo video with music, a bilingual (Chinese/English) README, and a standalone website.

- The smartest move is the install: "Paste this into your agent: read INSTALL.md and follow it." INSTALL.md is written for the agent. After installing, it verifies the page itself with a TCP three-way handshake. The user doesn't have to do anything.

- Four entry points: Claude Code plugin marketplace, DeepSeek Harness plugin, npx skills, and manual copy.

### Who's competing, and what space is left

| Project | star(11-06) | How it differs |
|---|---|---|
| nicobailon/visual-explainer | 10,820 | Models hand-write whole HTML pages, leaning toward diff review and plan review; +117 in the last 7 days |
| nexu-io/html-anything | 9,428 | Markdown to finished WeChat / Xiaohongshu pages, aimed at publishing |
| danyuchn/asd-ste100-skill | 6,099 | Only does the first rung of the ladder (STE100 writing), and it also picked up the traffic from 10-02 |
| plannotator/effective-html | 3,964 | Rides the "HTML replaces Markdown" idea for spec and plan docs |
| dualface/ste-zh | 874 | A Chinese version of STE100 |
| Contemporaneous skills like understanding-ladder and karpathy-output-style | 24~121 | Prompt-only: no renderer, no benchmark |

"Have the agent answer in HTML" is already crowded. An established project with ten thousand stars sits at the top, and this one soaked up most of the Karpathy wave. Someone has already posted a derivative on Juejin titled "reworked from answer-me-with-html." Building another general "answer-as-a-page" skill is unlikely to earn the same stars.

The open space is this pattern elsewhere: "the model writes a structured draft, a deterministic program handles layout and drawing." It applies anywhere models currently write finished output directly and do it slowly and unreliably: slide decks, data charts, posters, explainer videos, code review reports. For its own explainer-video part, the README's small test shows about 18x fewer output tokens and 12x faster, which suggests the approach carries over to other formats.

### If it were you

**One-line verdict:** Don't build another "answer in HTML." Copy its three moves (do the full version the same day as a big-name post, answer the first objection with reproducible data, and use the "model drafts + program renders" structure), then apply them to a different output format.

- **Who it suits:** Developers who can write some frontend or rendering code and already use agents like Claude Code or Codex daily. You need to watch a few big X accounts and be able to start building the day a new claim appears.

- **Who it doesn't suit:** People who only write prompts and won't write a renderer. A prompt-only skill from the same wave stalled at a few dozen stars. It also isn't for anyone aiming to make money directly from it; it only has Ko-fi and Afdian tip links.

- **How to build it:** Pick an output the model is slow and clumsy at when writing it directly (slide decks, charts, short videos, PR review reports). Write a deterministic render CLI, and have the model produce only a draft of a few hundred tokens. Ship a reproducible token and timing comparison in the repo, put a before-vs-after image on the README's first screen, and add a 20-odd-second demo video plus an INSTALL.md written for agents.

- **How to get users:** For Chinese, post an open-source promo thread on LINUX DO following its rules (tagging, crediting the community). Also list on npx skills and the Claude Code plugin marketplace so the directories pick it up passively. For English, pitch AI news outlets like AlphaSignal and open PRs to awesome-skills lists. Put the original post's author name in the title.

- **When to move:** The Karpathy wave is already a month old, so don't chase it. Wait for the next post of similar scale that says "try using models like this." Ship within 24 hours of it, and finish the community post before day three.

- **Signs you've misread it:** If your renderer version can't get even 200 stars in a week while prompt-only versions of the same idea climb faster, then the win came from timing and the author's luck, not from the "draft + renderer" structure. Stop putting time into this direction.

[GitHub repo ↗](https://github.com/QingYunA/answer-me-with-html)

**Research notes: competitor table, acquisition evidence, full research report**

Competitors

| Name | Where | Launched | Scale | How it differs from this one |
|---|---|---|---|---|
| nicobailon/visual-explainer | Open source | 2026-02-16 | 10,820★,gain_7d 117 | Models hand-write whole HTML pages, leaning toward diff review and plans. No render CLI, no token savings. |
| nexu-io/html-anything | Open source | 2026-05-11 | 9,428★ | Markdown to finished WeChat / Xiaohongshu pages, aimed at publishing, not answering questions |
| plannotator/effective-html | Open source | 2026-06-09 | 3,964★ | Rides Thariq's "HTML replaces Markdown" idea for spec and plan docs |
| danyuchn/asd-ste100-skill | Open source | 2026-07-20 | 6,099★,gain_7d 291 | Only does the STE100 writing level, and it also picked up Karpathy's traffic |
| Contemporaneous skills from the Karpathy post, such as ChangWenC/understanding-ladder | Open source | 2026-10-02~10-05 | 24~121★ | Prompt-only: the model picks the format. No renderer, benchmark or packaging. |
| Claude.ai Artifacts / ChatGPT Canvas | Big-company features | 2024 | Big | Only in the chat web app. Terminal coding agents have no native "answer as a page." |

Where users come from

- **Verified** **Riding a big-name post (Karpathy's 10-02 X post)** : the repo was created 9 hours after the post went out. The LINUX DO thread and AlphaSignal both credit Karpathy for the idea. Topics include ste100.

- **Verified** **GitHub Trending / Trendshift** : 10-04 Trendshift all-languages #8, JS #2, +731★ that day

- **Verified** **LINUX DO open-source promo thread** : posted at linux.do/t/topic/2984649,10-05, 3.1k views, 242 likes, 67 replies. The README credits LINUX DO.

- **查实** **技能目录站 / npx skills / 插件市场** :10-03 起被 skillsllm、skillsdirectory、skills.sh、typingmind 等十多个站收录;作者 11-03 fork 了 6 个 awesome 技能列表

- **Verified** **AI newsletters / news sites / Chinese tech blogs** : AlphaSignal, Hermes 10-06, Juejin 10-14 and 11-06, CSDN 10-08, WeChat Official Account articles on Karpathy

- Inferred **Short video / social reposts** : YouTube Shorts from Forkcast, traditional-Chinese explainers on Threads / IG / FB / YouTube, LinkedIn. Whether these came from the author's own accounts is unverified.

- Inferred **Big-account reposts on X** : not found in the x-radar corpus

## Why QingYunA/answer-me-with-html worked

> Data as of 2026-11-06. Channel: GitHub. In one line: an agent skill that stops Claude Code / Codex / Cursor from replying with long walls of text on complex questions, and instead produces a single-file HTML page with charts. The model writes a Markdown draft; the bundled CLI handles layout and drawing.

### 1. Verdict

It is **the fastest and most complete version of "boxing up" Karpathy's 2026-10-02 X post** . Karpathy posted early that day (00:37 UTC), arguing that understanding LLM output has a ladder from low to high: ASD-STE100 controlled English → diagrams → HTML web pages → 3b1b-style explainer videos. The author created this repo 9 hours later (09:57 UTC) and packed all four rungs into one skill. The topics literally say `ste100`, and the README's first line reads "ASD-STE100 | Explainer Videos."

The same post spawned a batch of skills, most reaching a few dozen to a hundred-plus stars. This one reached 5,700+. The main reason is **engineering design** . Others have the model write HTML directly; this one has the model write Markdown and the CLI generate HTML/SVG. The README backs it with measured data (output tokens about 1/8, time 31s→12s). That answers the first worry readers had after Karpathy's post: it's slow and burns tokens. Growth came from the LINUX DO promo thread, automatic listings on skill directories and AI news sites, and Chinese and traditional-Chinese short-video explainers.

### II. Growth data

| Timeline | Event | New stars (Trendshift daily table) |
|---|---|---|
| 10-02 | Karpathy's post (00:37 UTC); repo created (09:57 UTC) | 0 |
| 10-03 | Directories like skillsllm, skillsdirectory and aiezz listed it the same day | +67 |
| 10-04 | Trendshift all-languages #8, JavaScript #2 | **+731** |
| 10-05 | The author posted on LINUX DO: "1k stars in a day! This Skill makes Agents output visual results!" | +437 |
| 10-06 | Covered by AlphaSignal, Hermes AI News | +293 |
| 10-07~10-13 | Daily +140~280 | — |
| From ¥10-14 | Falling back to 50~100,10-19/20 per day, with small spikes around 206/197 (+10-29/30/+142), 166 (+11-06/+299) | — |

- Our GitHub repo: 35 days, 5,384 stars, gain_7d 681, previous 7 days 457. Single-day peak 247 (our log shows 10-11, which differs from Trendshift's counting). A GitHub API check 11-06 shows 5,703 stars and 370 forks.

- Trendshift: 36 days, +5,463 stars, 243 PRs merged. 23 contributors, with the author making 255 commits. The commit rhythm looks agent-driven: v0.4.x → v0.5.11 across more than 21 versions.

- The author's GitHub accounts are Cyan / QingYunA, with 84 followers, created 2018 years ago. Their earlier project agent-html (2026-09-08, a single-file HTML design-system skill) has only 65 stars and serves as this project's predecessor. The author has no big-account backing.

### III. Competitors

| Product | Form | Launched | Scale | How it differs |
|---|---|---|---|---|
| nicobailon/visual-explainer | Open-source skill | 2026-02-16 | 10,820★ | Same "complex content → HTML page / slides" idea, but the model hand-writes the whole HTML page. Focuses on diff review and plan auditing. No render CLI, and token savings aren't the pitch. |
| nexu-io/html-anything | Open source | 2026-05-11 | 9,428★ | Markdown → WeChat / Xiaohongshu finished pages, aimed at content publishing, not answering questions |
| zarazhangrui/codebase-to-course, beautiful-html-templates | Open source | 2026-03 / 2026-05 | 5,770★ / 4,937★ | A course-page generator for codebases, HTML templates, narrower scope |
| plannotator/effective-html | Open source | 2026-06-09 | 3,964★ | Echoes Thariq's "HTML replaces Markdown", leans toward plan and spec docs |
| danyuchn/asd-ste100-skill | Open-source skill | 2026-07-20 | 6,099★(gain_7d 291) | Only does the first rung of Karpathy's ladder (STE100 writing), and still picked up the traffic from 10-02 |
| ChangWenC/understanding-ladder | Open source | 2026-10-04 | 121★ | A "ladder" skill from the same post, auto-picks text / diagram / HTML / video storyboard, no renderer |
| ashryaagr/karpathy-output-style | Open source | 2026-10-02 (2.5 hours earlier than it) | 53★ | An output style from the same post, pure prompting |
| JAICHANGPARK/ASD-STE100 | Open source | 2026-10-02 | 24★ | The same post, four modalities plus Mermaid, pure prompting |
| ajithraghavan's Karpathy skill (dev.to, Show HN 10-06) | Open source | 2026-10-05 | Unverified (small) | The same post, picks format on demand; its Show HN didn't take off |
| Claude.ai Artifacts, ChatGPT Canvas | Big-company features | 2024 | Big | Only inside chat web pages; terminal agents (Claude Code / Codex) have no native "answer as a page" |
| Thariq Shihipar's (Anthropic Claude Code lead) "HTML replaces Markdown" approach | A trend, not a product | 2026-05 | Covered by Lenny's, InfoQ | The model hand-writes HTML: slow, token-hungry, exactly the pain point it attacks |

Data source: our github repo_profile (2026-11-06), GitHub API.

**Key contrast** : Among the 10 skills that rode the Karpathy post, only this one passed 1,000 stars (apart from danyuchn/asd-ste100-skill, which was already around). Same timing, same audience, results two orders of magnitude apart, so the gap isn't timing. It's the product and the packaging.

### 4. Growth levers (evidence)

1. **Riding Karpathy's post (confirmed)** : the LINUX DO post's author wrote in their own words that the idea comes from Karpathy's latest X post of Oct 2; AlphaSignal's coverage also cites Karpathy. A Chinese WeChat article titled it directly: 《大神 Karpathy 教过一招:让 AI「用 HTML 输出」…于是就有了开源 Skill《answer-me-with-html》》.

2. **GitHub Trending / Trendshift (confirmed)** : 10-04 hit #8 on Trendshift's all-languages chart, then intermittently charted on the JS chart up to 10-30. Our database records trending_days_any=0, different criteria (trending only counts repos about 60 days old).

3. **LINUX DO (confirmed)** : the author 10-05 posted an open-source promo thread (account qingyun7z), 3.1k views, 242 likes, 67 replies; the README's Community section specifically thanks LINUX DO (a requirement of the forum's open-source promotion). The thread was still being bumped as of 10-30.

4. **Skill directory and aggregator sites auto-listing (confirmed)** : skillsllm, skillsdirectory (grade A), skills.sh, typingmind, hotskills, skillstore, aiagents.org, frontiernow, iyooo, dshpacks, plugandship, and more. The install method `npx skills add` (vercel-labs/skills, supports 70 different agents) is itself a distribution channel. The author 11-03 forked 6 awesome-claude-skills / awesome-agent-skills lists in one go, clearly aiming to get listed via PR (the forks are visible; whether the PRs were merged is unverified).

5. **AI news sites / newsletters (confirmed)** : AlphaSignal (10-06), Hermes AI News (10-06), aiidelist (10-04), a CSDN gitblog component tutorial (10-08), Juejin (10-14 adapted article, 11-06 another piece 《神级 Skill,让 AI 用 HTML 代替 Markdown 回复》).

6. **Short video / social reposts (confirmed they exist, authors unverified)** : YouTube Shorts from Forkcast (a third-party faceless GitHub recommendation channel, about 2.7k★ at the time of posting), traditional-Chinese Threads (@derekhsu) / Instagram / Facebook / YouTube Shorts explainers, LinkedIn reshares. Some posts say "1.5K+ GitHub stars", which means they were made early.

7. **Reddit / HN (weak)** : an r/ClaudeCode thread 《Does an "answer with HTML" skill actually save tokens? I tested it》 (403, content not retrievable); the Show HN 10-14 only got 1 points, HN didn't drive volume.

8. **No evidence found of big X accounts reposting it** : the repo name doesn't turn up in the x-radar corpus, unverified.

### 5. Why it works

1. **Timing** : Karpathy's 10-02 post supplied a ready-made need and vocabulary ("have an LLM answer you with HTML / explainer videos"), and it launched within 9 hours, catching the first wave of search and discussion. Before that, Thariq's "HTML is the new Markdown" had softened users up for half a year.

2. **Shipped all four rungs of Karpathy's ladder at once** : STE100 writing, diagrams, HTML, and 3b1b style videos all included, while most competitors in the same period did only one rung. A search on any of these keywords could land on it.

3. **The real technical difference** : "the model writes Markdown plus component blocks (sequence / flow / ER…), and the CLI renders an offline single-file HTML in 50ms". The README proves it with reproducible benchmarks: output tokens 4,893→612, time 31s→12s. The first reaction from LINUX DO users was "output tokens are worth more than input tokens". This is the biggest difference from the "let the model hand-write HTML" skills (including visual-explainer at 10k★).

4. **The packaging is first-rate open-source marketing** : the README's first screen is a "wall of text vs one page of image" comparison, plus a 24-second demo video with music, a bilingual Chinese/English README, the site answer-me-with-html.com, a one-liner install that says "have your agent read INSTALL.md and install it itself" (an install doc written for agents to read), and multiple entry points: the Claude Code plugin marketplace, a DeepSeek Harness plugin, npx skills.

5. **High-frequency iteration plus a community feedback loop** : 243 PRs merged in 36 days. A Tailscale approach suggested by a LINUX DO commenter was built into the product on the spot. Ongoing fixes to details like flow diagram crossings kept up its reputation and secondary spread (small peaks at 10-19, 10-29, 11-06).

### VI. Differentiation

- The core difference is **a rendering pipeline that separates content from layout** : the LLM produces only a semantic draft, and a deterministic CLI handles layout, themes, and SVG geometry. This makes it fast, token-cheap, consistently good-looking, and usable as an offline single file.

- Is this why it took off? **Yes, and it was the decisive factor relative to same-period competitors.** Timing (Karpathy) was shared by everyone in the category; only this one preemptively answered the inevitable objection "it's slow and expensive" with data, and on top of that, packaging far beyond its peers' READMEs and videos. It came 8 months after visual-explainer and is about half its size, yet its 30-day growth far outpaces it (visual-explainer gain_7d 117, this one 681).

### VII. Visibility and copycats

- **Visibility: circulating in small circles** . The Chinese developer community (LINUX DO, Juejin, CSDN, WeChat official accounts) and AI skill directories / newsletter circles have all covered it; AlphaSignal reported on it. I found no evidence of Karpathy himself or big X accounts naming it in reposts, and no mainstream tech media coverage. As an open-source project it has no revenue to speak of (only a Ko-fi / Afdian tip jar).

- **Followers** : jerrywu001/ask-report-html (10-14 Juejin post, "adapted from answer-me-with-html", outputs an HTML report after writing code); parallel skills spawned by the same Karpathy post: understanding-ladder, karpathy-output-style, JAICHANGPARK/ASD-STE100, dualface/ste-zh (874★), lemonhall/asd-ste100-skill-zh, mzopedia/simplified-technical-chinese. mzopedia is also a contributor to this repo.

### VIII. Takeaways

- **Chasing a big-V post has to be fast, and cover everything** : on the same day, implement every idea in the post as one installable thing, and put the post's key terms in the README's first line (SEO and topics).

- **Answer the first objection in advance** , with reproducible benchmarks (tokens, time) rather than adjectives.

- **The "LLM emits a structured draft + deterministic renderer" pattern is transferable** : slides, charts, posters, videos, reports, anywhere that "have the model write the artifact code directly" is slow and unstable.

- **Distribution mix** : the LINUX DO open-source promo thread (the most effective cold-start spot for Chinese developers) + `npx skills` / plugin marketplaces + passive listings from directory sites + awesome-list PRs + a bilingual README and demo video.

- **An INSTALL.md written for agents to read** , "copy one line and let your agent install it", bringing install friction to zero.

### 9. What I'm still unsure about

- Where did the 10-03→10-04 +731 on that day come from: a repost on X, replies under Karpathy's post, or GitHub's own traffic? The x-radar corpus doesn't cover it, and the stargazers API is hidden (404), so it can't be broken down by source.

- Whether the traditional-Chinese short videos / Threads / Instagram posts are the author's own content or third-party, unverified.

- Actual usage: stars aren't installs; skills.sh install counts weren't retrieved.

- How many merged PRs were AI-written, and whether the awesome-list PRs were merged, unverified.

### Sources

- Repo README / GitHub API: [github.com/QingYunA/answer-me-with-html](https://github.com/QingYunA/answer-me-with-html) (2026-11-06 read)

- Karpathy post: [x.com/karpathy/status/2105819303471976479](https://x.com/karpathy/status/2105819303471976479) (2026-10-02 00:37 UTC)

- Trendshift daily table: [trendshift.io/repositories/278180](https://trendshift.io/repositories/278180)

- LINUX DO promo thread: [linux.do/t/topic/2984649](https://linux.do/t/topic/2984649) (2026-10-05)

- AlphaSignal:[alphasignal.ai/news/answer-me-with-html-turns-ai-replies-int…](https://alphasignal.ai/news/answer-me-with-html-turns-ai-replies-into-visual-explainers-87-faster)(2026-10-06)

- Hermes AI News:[hermes-ai.net/news/answer-me-with-html-turns-ai-replies-into…](https://hermes-ai.net/news/answer-me-with-html-turns-ai-replies-into-visual-explainers-87-faster/)

- Juejin adapted article: [juejin.cn/post/7696396994588983306](https://juejin.cn/post/7696396994588983306); Juejin recommended article: [juejin.cn/post/7704902910831853587](https://juejin.cn/post/7704902910831853587)

- YouTube Shorts Forkcast:[www.youtube.com/shorts/IIkBH8xVqaI](https://www.youtube.com/shorts/IIkBH8xVqaI)

- Thariq "HTML replaces Markdown": [www.lennysnewsletter.com/p/how-i-ai-html-is-the-new-markdown](https://www.lennysnewsletter.com/p/how-i-ai-html-is-the-new-markdown), [www.infoq.com/news/2026/06/anthropic-html-markdown-agent/](https://www.infoq.com/news/2026/06/anthropic-html-markdown-agent/)

- Competitor stars: our github repo_profile (2026-11-06)

This article was written by AI from public data and the data we collect daily. Numbers are sourced, and inferences are labeled.