DEV Community: LMSlay

Instagram Reels to text in bulk (no login): transcripts, hooks and on-screen text

LMSlay — Fri, 09 Oct 2026 06:47:44 +0000

Disclosure: I build the Apify Actor used in this post (TubeText Labs).

Instagram doesn't give you transcripts for Reels, and copying captions misses what people actually say. If you research hooks, repurpose content, or feed creators' videos into an LLM, you want the spoken words as text – for many reels at once. Here's a way to do it without logging in.

A creator's latest reels → text

curl -X POST "https://api.apify.com/v2/acts/tubetext~instagram-reels-transcript/run-sync-get-dataset-items?timeout=300" \
  -H "Authorization: Bearer $APIFY_TOKEN" -H "Content-Type: application/json" \
  -d '{"urls": ["@natgeo"], "maxReelsPerProfile": 10}'

You can mix @usernames, profile links and single reel links in the same list.

Each row has the transcript (plain, timestamped, or SRT), the opening hook line, the caption, plays, likes and posting time. The speech is transcribed with Whisper, so it handles 90+ languages.

Things that save you money

Music-only reels and songs are free. A reel that's only a song comes back as no_speech or lyrics (with the song text) and isn't charged.
Only new reels. Turn on Only new reels and schedule the run daily: each run transcribes just what the creator posted since last time.
Optional on-screen text. Many reels put the message in captions on screen, not in the audio. includeOnScreenText reads those overlays with a vision model (+$0.002 per reel, only when text is found) – useful for recipe and listicle reels with no speech at all.

Speed and cost

About 15 reels per minute with default settings, $0.006 per reel (the first 3 minutes of audio are included; almost every reel is shorter). Private, deleted and failed reels are free.

Ideas

Build a swipe file of the opening hooks of the 50 latest reels in your niche.
Turn a coach's reels into a searchable knowledge base for an AI assistant.
Track competitors daily with Only new reels and send new transcripts to Slack.

Actor: https://apify.com/tubetext/instagram-reels-transcript

Google Hotels prices per booking site (and a 90-day price calendar) as JSON

LMSlay — Fri, 09 Oct 2026 03:51:29 +0000

Disclosure: I build the Apify Actor used in this post (TubeText Labs).

Google Hotels shows, for every hotel, what each booking site charges for your dates – Booking.com, Expedia, Hotels.com, the hotel's own site and many smaller agencies. There is no public API for those prices (Google's Hotel Prices API is for partners sending prices in, not for reading them). Here's how to get them as JSON, plus a 90-day price calendar for a single hotel.

1. Every hotel in a city, with prices per booking site

curl -X POST "https://api.apify.com/v2/acts/tubetext~google-hotels-scraper/run-sync-get-dataset-items?timeout=300" \
  -H "Authorization: Bearer $APIFY_TOKEN" -H "Content-Type: application/json" \
  -d '{"locations": ["Lisbon"], "checkInDate": "2026-11-20", "checkOutDate": "2026-11-22",
       "currency": "USD", "maxHotelsPerLocation": 20, "includeBookingOffers": true}'

Each hotel comes back with its rating, star class, location and the lowest price. With includeBookingOffers it also gets a bookingOffers list – vendor, nightly and total price, room types, whether it's the official site and whether it's an ad – cheapest first, plus cheapestVendor. In our Lisbon test each hotel had between 9 and 28 booking sites, and for one of them the cheapest offer came from a small agency rather than a big OTA.

2. When is this hotel cheapest? A price calendar

Take any hotel's googleHotelsUrl from step 1 and ask for a calendar:

{
  "calendarHotelUrls": ["https://www.google.com/travel/hotels/entity/…"],
  "checkInDate": "2026-11-20",
  "calendarDays": 30,
  "calendarNights": 1,
  "currency": "USD"
}

You get one row per check-in date with lowestPricePerNight, lowestVendor and all vendor prices. Dates with no rates come back as no_rates and aren't charged.

Things to know

Prices are what Google shows US visitors, taxes and fees included (priceIncludesTaxesAndFees tells you per row). Visitors from other countries can see different rates.
Google returns at most about 420 hotels per search. For more, split a city into neighbourhoods.
Price monitoring is just a schedule: run the same hotels and dates daily and compare pricePerNight.

Cost

$3 per 1,000 hotels with a price, +$2 per 1,000 for the booking-site breakdown, and $3 per 1,000 priced calendar dates. Sold-out hotels, no-rate dates and failed searches are free. A 30-day calendar for 5 hotels is at most 150 dates, about $0.45.

Actor: https://apify.com/tubetext/google-hotels-scraper

How to get TikTok Shop product data (price, sales, stock per SKU) as JSON in 2026

LMSlay — Fri, 09 Oct 2026 03:41:28 +0000

Disclosure: I build the Apify Actor used in this post (TubeText Labs).

TikTok Shop has no public API for product data. Most "TikTok Shop spy" dashboards build on the same public product-page numbers: the price, the "sold" count and the shop stats. If you only need that data for your own research, a dashboard subscription is overkill. Here's how to get it as JSON.

What you can get from a public TikTok Shop product page

price, original price and discount
items sold (TikTok's "sold since listing" counter)
rating and review count
every variant (SKU) with its own price and stock
specifications, shipping, delivery estimate
the shop's stats: rating, followers, products, total sold, number of videos

One API call

curl -X POST "https://api.apify.com/v2/acts/tubetext~tiktok-shop-product-scraper/run-sync-get-dataset-items?timeout=300" \
  -H "Authorization: Bearer $APIFY_TOKEN" -H "Content-Type: application/json" \
  -d '{"urls": ["https://www.tiktok.com/shop/pdp/1729527313880355335"]}'

You can also pass a store page (you get its first 20 products) or a category page (a sample of up to 100 products).

Real output (shortened)

{
  "title": "Goli Ashwagandha & Vitamin D Gummy - Mixed Berry…",
  "price": 19.47,
  "originalPrice": 24.7,
  "soldCount": 1304190,
  "rating": 4.5,
  "reviewCount": 94525,
  "skus": [{"options": "Size: 1 Bottle", "price": 19.47, "stock": 336485}],
  "shopFollowers": 601031,
  "shopSoldCount": 5777630
}

Turning it into "daily sales"

TikTok only shows a running total, so daily sales are the difference between two snapshots:

# yesterday.json and today.json are two runs over the same product URLs
import json
y = {p["productId"]: p for p in json.load(open("yesterday.json"))}
for p in json.load(open("today.json")):
    if p["productId"] in y:
        print(p["title"][:50], p["soldCount"] - y[p["productId"]]["soldCount"], "sold in 24h")

Schedule the run daily on Apify (or from n8n/Make), and you have a small competitor tracker. Per-SKU stock tells you which variant is actually moving.

Limits (so you don't waste time)

US TikTok Shop only for now.
No keyword search yet: use product, store or category links.
Store pages return their first 20 products.
"Sold" is TikTok's displayed counter, not audited sales.

Pricing is $4 per 1,000 products with full details ($1.50 per 1,000 if you only need listing cards), and failures are free.

Actor: https://apify.com/tubetext/tiktok-shop-product-scraper

youtube-transcript-api RequestBlocked on your server? What actually works in 2026

LMSlay — Fri, 09 Oct 2026 01:47:40 +0000

Disclosure: I build one of the hosted options mentioned below (TubeText Labs on Apify).

youtube-transcript-api works on your laptop and then fails on the server with RequestBlocked, IpBlocked or a misleading TranscriptsDisabled. Here's why, and the four fixes in order of effort.

Why it breaks on servers

YouTube answers requests from datacenter IP ranges (AWS, GCP, Azure, DigitalOcean…) with "Sign in to confirm you're not a bot". The library turns that into RequestBlocked; HTTP 429 becomes IpBlocked. Even home IPs get rate-limited after a few dozen fast requests.

Fix 1 – upgrade and slow down

Use youtube-transcript-api 1.2.4+ and add backoff between requests. This is enough for a handful of videos from a home connection.

Fix 2 – run the fetch from a non-cloud IP

Move only the transcript step to a machine with a residential IP (your own box) and push results to your server. Free, but you maintain it.

Fix 3 – rotating residential proxies

The library supports proxy configs (e.g. WebshareProxyConfig). Rotate on every retry: retrying through the same blocked IP just fails slower.

Fix 4 – call a hosted transcript API

If you'd rather not run proxies, use a hosted scraper. With the Apify Actor I maintain, one HTTP call returns the transcripts:

curl -X POST "https://api.apify.com/v2/acts/tubetext~youtube-transcript-fast/run-sync-get-dataset-items?timeout=300" \
  -H "Authorization: Bearer $APIFY_TOKEN" -H "Content-Type: application/json" \
  -d '{"urls": ["https://www.youtube.com/watch?v=aircAruvnKk"]}'

It takes videos, Shorts, playlists and whole channels; when a video has no captions it falls back to Whisper AI (in our 47-video test runs, 16 of 17 caption-less videos got AI transcripts – the other was music only). Failed videos are free; transcripts are $0.004 each. It's not magic: YouTube can still put an individual popular video behind a login wall.

Which one to pick

A few videos, occasionally → Fix 1 or 2.
You already run proxies → Fix 3.
Production pipeline, n8n/Make, or you need captions and AI fallback → a hosted API.

Questions about a specific error? Drop it in the comments.

I reverse-engineered 8 TikTok Shop videos with one API call. Here's what the data says

LMSlay — Thu, 08 Oct 2026 06:55:00 +0000

Disclosure: I built the Apify Actor used in this post (TubeText Labs).

If you run UGC ads or build content for TikTok Shop, you've probably done this by hand: watch a winning video 10 times, pause on every cut, and write down the hook, the product shot and the CTA. I wanted that as JSON, so I could do it for 50 videos at a time and feed it into a sheet or an LLM.

So I took 8 recent videos from #tiktokmademebuyit and ran each one through an AI breakdown. Each video took one API call. Here's the setup, a real output, and what the 8 videos had in common.

The call

The Actor runs on Apify. run-sync-get-dataset-items waits for the result, so you don't need polling or webhooks:

curl -X POST "https://api.apify.com/v2/acts/tubetext~video-breakdown-ai/run-sync-get-dataset-items?timeout=300" \
  -H "Authorization: Bearer $APIFY_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"urls": ["https://www.tiktok.com/@someone/video/123..."]}'

Python is the same request:

import os, requests

r = requests.post(
    "https://api.apify.com/v2/acts/tubetext~video-breakdown-ai/run-sync-get-dataset-items",
    params={"timeout": 300},
    headers={"Authorization": f"Bearer {os.environ['APIFY_TOKEN']}"},
    json={"urls": ["https://www.tiktok.com/@someone/video/123..."]},
    timeout=320,
)
for video in r.json():
    print(video["hook"]["type"], video["pacing"]["cutsPerMinute"], video["commercialIntent"]["type"])

In n8n or Make, it's a single HTTP Request node with the same URL and body.

What comes back (real output, shortened)

This is a 7-second pet-hair laundry video with 1.1M views:

{
  "format": "UGC ad",
  "hook": {
    "type": "problem",
    "text": "A close-up of a black shirt covered in pet hair, with the on-screen text 'your sign to get your laundry fish 🐟'",
    "why_it_works": "It immediately identifies a common pain point for pet owners (pet hair on clothes) and offers a specific, intriguing solution."
  },
  "commercialIntent": {
    "type": "brand ad/sponsored",
    "evidence": "The creator tags @Indigopetco and uses the hashtag #tiktokmademebuyit, indicating a partnership or promotion of the specific product."
  },
  "products": [{ "name": "laundry fish", "brand": "Indigopetco", "first_seen": 0.63, "how_shown": "held to camera and placed into the washing machine" }],
  "pacing": { "cutCount": 3, "avgShotSeconds": 1.8, "cutsPerMinute": 25.7, "cutTimes": [0.63, 1.1, 4.07] },
  "structure": [
    { "beat": "hook",  "start": 0,    "end": 0.63, "description": "The creator shows a black shirt covered in pet hair to establish the problem." },
    { "beat": "setup", "start": 0.63, "end": 1.1,  "description": "The creator introduces the 'laundry fish' sponges and places them into the washing machine." },
    { "beat": "value", "start": 1.1,  "end": 4.07, "description": "The washing machine is shown running with the clothes and sponges inside." }
  ],
  "keyMoments": [{ "time": 4.07, "what": "The reveal of the sponges covered in pet hair.", "why_it_matters": "It provides the visual proof that the product actually works, satisfying the viewer's curiosity." }],
  "engagementRate": 0.0756
}

The full output also has a shot-by-shot list (framing, action, on-screen text and the spoken line for each shot), the full transcript, sound info, audience and tone.

What the 8 videos had in common

Small sample, so treat these as patterns to test, not laws.

Every hook was one of three types. Bold claim (4 videos), visual surprise (2), problem first (2). None opened with a slow intro or a logo.
The product shows up fast. It was on screen within 1.5 s in 7 of 8 videos. The one exception, a 656K-view holiday product, delayed it until 4.5 s behind a cut-off line of text ("…doesn't exi-"), which is a curiosity gap.
More cuts ≠ more engagement. The most-viewed video (14.1M) is 8 seconds long with zero cuts: one hand, one gadget, one demo. The fastest-cut video (27.9 cuts/min, 1.9M views) had the lowest engagement of the 8, at 0.6%.
The sponsored ad beat the affiliate ones. The laundry video above, which the AI flagged as a sponsored brand ad, had the highest engagement at 7.6%. The four videos flagged as TikTok Shop affiliate averaged 2.4%. Its structure is problem → product → proof in about 4 seconds.

Honest limitations

Cut detection is measured from the video, but descriptions come from a vision-language model. Inside one long continuous shot, details can get merged.
Song titles come from TikTok's sound info, not from recognising the music.
Commercial intent is the model's judgment based on the caption, hashtags, tags and what's on screen, with the evidence quoted. Treat it as a strong hint, not ground truth.
It costs $0.03 per video (up to 3 minutes); invalid, private or unavailable videos aren't charged. It also works on Instagram Reels (beta) and YouTube, including channels and playlists.

Try it

Actor: https://apify.com/tubetext/video-breakdown-ai
An idea to build on top: pull your competitors' outlier videos every week, break them down, and have an LLM write three new hook variations for each one.

If you try it on your niche, I'd love to hear which fields are useful and which are missing.