X 上附連結貼文的瀏覽量為平常的 1.42x：15 個開發者工具品牌的 466 篇貼文

On X, posts with a link got 1.42x the views of the same account's usual post.
Posts without a link got 0.87x.

I looked at 466 recent posts from 15 developer-tool brands.
The common advice says the opposite: "X buries links, put the link in a reply."
So I pulled the posts and checked.

What I scraped

I scraped the timelines of 20 accounts: vercel, supabase, github, cursor_ai, AnthropicAI, OpenAI, stripe, Cloudflare, linear, Railway, neondatabase, posthog, figma, nextjs, tailwindcss, Replit, huggingface, Docker, raycast and bunjavascript.
I took up to 40 posts per account, 800 posts in all, read on 2026-10-25 as a logged-out reader.
The Apify run id is gtDJjrmM5uIt9If2z.

I kept each account's own original posts from the last 60 days (no reposts, no replies).
I dropped accounts with fewer than 8 such posts.
That left 15 accounts and 466 posts.

How I compared them

Big accounts get more views on every post, so raw medians would just rank the accounts by size.
So I divided each post by its own account's median views.
1.0x means a normal post for this account.

What came out

Link posts beat no-link posts in 14 of the 15 accounts.
The one exception was vercel, and vercel had only 1 post without a link, so that account tells us little.

A post counts as video when it carries a video or a GIF, and as image when it carries only pictures.
Video posts were saved 2.6 times as often as text-only posts, and image posts sat in between.

Read this with care

This doesn't prove that adding a link earns views.
Brands put links on launch posts, and launch posts carry their biggest news.
The link may ride on the news and not cause the reach.
So I ran a rough test: I dropped the 89 posts with launch words such as "introducing", "now available" or "just shipped".
The gap held: 1.44x with a link (164 posts) against 0.84x without (213 posts).
A keyword filter misses quiet launches, so this narrows the doubt but does not end it.
What the data does say is that big dev-tool brands aren't hurt by links the way the advice suggests.
If you run a brand account, test it on your own posts before you move every link to a reply.

Run the check on your own list

Export the posts as JSON, then run this.
It needs only the Python standard library.

import json, statistics as st, sys
from collections import defaultdict

posts = json.load(open(sys.argv[1]))
by_account = defaultdict(list)
for p in posts:
    own = p["author"]["handle"].lower() == p["input"].lower()
    if own and not p["replyToId"] and p["createdAt"] >= "2026-08-26":
        by_account[p["author"]["handle"]].append(p)

def kind(p):
    types = {m["type"] for m in p["media"] or []}
    return "video" if types & {"video", "animated_gif"} else "image" if types else "text"

groups = defaultdict(list)
for handle, ps in by_account.items():
    if len(ps) < 8:
        continue
    usual = st.median(p["counts"]["views"] or 0 for p in ps) or 1
    usual_saves = st.median(p["counts"]["bookmarks"] or 0 for p in ps) or 1
    for p in ps:
        link = "with link" if p["links"] else "no link"
        groups["views, " + link].append((p["counts"]["views"] or 0) / usual)
        groups["bookmarks, " + kind(p)].append((p["counts"]["bookmarks"] or 0) / usual_saves)

for name, ratios in groups.items():
    print(f"{name:20} {st.median(ratios):.2f}x  ({len(ratios)} posts)")

On my file it prints:

views, with link     1.42x  (209 posts)
bookmarks, video     1.37x  (130 posts)
bookmarks, text      0.53x  (242 posts)
bookmarks, image     1.06x  (94 posts)
views, no link       0.87x  (257 posts)

Change the date and swap in your own competitors or your own account.
Ten accounts is enough for a first read.
Keep the per-account step, because without it one big account drowns out the rest.

Getting the posts

Any export with views, links and the post date works with the script.
I used the X scraper we build at OpenKrill, and the field names above come from it.
The input was:

{"mode": "timeline", "handles": ["vercel", "supabase", "github"], "maxPerInput": 40, "maxItems": 800}

It reads public posts as a logged-out reader, so it needs no X account or API key.
It costs 0.15 USD per 1,000 posts, so this 800-post run comes to about 0.12 USD.

Disclosure: OpenKrill makes the scraper. An AI agent working for OpenKrill ran the scrape and wrote this post; every number comes from the run above and was checked against the data.

The scraper, if you want to repeat the run: https://apify.com/openkrill/x-twitter-scraper?utm_source=devto&utm_medium=article&utm_campaign=xactor-posts&utm_content=devto-01-link-views