LlamaIndex 🦙 engagement report
@llama_index - 122K followers on X
Measured over 16 original posts from a 30-day window, last computed on October 5, 2026.
Engagement
A typical post picks up 41 interactions against 122K followers, an engagement rate of 0.034%. Measured over 16 original posts, its engagement rate beats 36% of 21,185 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 12K times each, and 0.34% of those impressions turn into an interaction. That is about 9.89% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.2 post a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 16:00 UTC, and Monday is the busiest day of the week. Of the 16 posts sampled, 94% carry an image or video and 88% link out. The account's strongest tracked post pulled 1.4K interactions, about 33x its own typical post.
Measured over 16 original posts from a 30-day window, last computed on October 5, 2026.
Compared with accounts its own size
LlamaIndex 🦙's engagement rate beats 36% of the tracked X accounts closest to it in follower count (21,185 accounts, accounts of similar size (decile 8 of 10)). A percentile is spread evenly by construction, so 50 really is the middle of that group and 90 really is its top tenth.
On engagement per impression rather than per follower it beats 18% of the same group. When those two numbers disagree, the gap is about how far its posts travel rather than how people react to them.
Where this sits in the catalog
At 0.034%, LlamaIndex 🦙 sits above the 25th percentile of the 212,567 accounts in this comparison. That places it in the below the median band, which runs 0.025% to 0.146%.
Show the percentile table
| Percentile | Engagement rate |
|---|---|
| 10th percentile | 0.004% |
| 25th percentile | 0.025% |
| 50th percentile | 0.146% |
| 75th percentile | 0.696% |
| 90th percentile | 2.61% |
| 99th percentile | 82.7% |
This ruler is the whole measured catalog, not a size-matched group: it shows where the raw rate falls across every account we can measure, all of which are large. For a like-for-like comparison, read the size-band percentile above instead. See how the bands are built
Posting timing
This account posts most often around 16:00 UTC, and Monday is its busiest day of the week. The bars below are the catalog-wide pattern, with this account's own busiest slot marked. They do not show how this account performs at each hour: we keep one aggregate per account, not one per hour, so that measurement does not exist in our data.
Show engagement by hour posted, utc as a table
| Hour (UTC) | Vs author median | Posts |
|---|---|---|
| 00:00 UTC | -1% | 89K |
| 01:00 UTC | -2% | 90K |
| 02:00 UTC | -3% | 88K |
| 03:00 UTC | -4% | 94K |
| 04:00 UTC | -5% | 76K |
| 05:00 UTC | -4% | 75K |
| 06:00 UTC | -5% | 86K |
| 07:00 UTC | -5% | 93K |
| 08:00 UTC | -4% | 108K |
| 09:00 UTC | -4% | 124K |
| 10:00 UTC | -3% | 129K |
| 11:00 UTC | -3% | 141K |
| 12:00 UTC | -3% | 154K |
| 13:00 UTC | -3% | 167K |
| 14:00 UTC | -4% | 173K |
| 15:00 UTC | -2% | 176K |
| 16:00 UTC | -3% | 171K |
| 17:00 UTC | -3% | 159K |
| 18:00 UTC | -2% | 149K |
| 19:00 UTC | -2% | 141K |
| 20:00 UTC | -1% | 131K |
| 21:00 UTC | 0% | 116K |
| 22:00 UTC | -2% | 100K |
| 23:00 UTC | -1% | 90K |
Show engagement by day of week as a table
| Day | Vs author median | Posts |
|---|---|---|
| Sunday | +5% | 393K |
| Monday | +1% | 483K |
| Tuesday | -2% | 520K |
| Wednesday | -3% | 472K |
| Thursday | -2% | 430K |
| Friday | -3% | 447K |
| Saturday | +2% | 393K |
Formats this account uses
Its own posting mix on the left, and what each of those formats does across every account we track on the right. Only formats where the effect clears our publish test appear here, so an empty row is a format we could not measure rather than one that does nothing.
| Format | This account | Catalog effect | 95% interval | Accounts behind it |
|---|---|---|---|---|
| Image or video | 94% of posts | +111% | +108% to +115% | 34K |
| Outbound link | 88% of posts | -41% | -42% to -40% | 32K |
| Typical length | - | +15% | +14% to +16% | 32K |
- 94% of this account's sampled posts carry an image or video. Across the catalog, posts with an image or video run 111% above the same accounts' other posts.
- 88% of its posts carry a link off X. Across the catalog, posts with an outbound link run 41% below the same accounts' other posts, so a large share of this account's output sits in the weakest bucket we measure.
- Its average post runs 669 characters, which falls in the over 280 characters band. Across the catalog, posts over 280 characters run 15% above the same accounts' other posts.
These are catalog-wide differences applied to this account's own posting mix, not a measurement of how each format performs for this account specifically. We keep one median per account, not one per format per account, so the second thing is not something this data can tell you.
Best tweets
- Sep 20, 202633x their median
Introducing DocJev - a lightning-fast OSS library for document classification and splitting with jev ⚡️ Give a document alongside some natural language category rules. Jev will predict the document category (classify) or the boundaries between sub-documents (split). It is 6x faster than gpt-5.6-luna with equivalent accuracy. You can choose between different OCR backends: liteparse: the fastest/most accurate free+OSS text parser out there. Perfect for digitalized documents. The speedups vs. luna above are inclusive of liteparse times. LlamaParse: the most advanced VLM-based solution for complex docs. Using this will of course add latency on document preprocessing. But it’s a good option if you want to cache the digitalized representations of documents for a variety of downstream tasks. Check it out: https://t.co/PVE4hbEyFR If you want to dive deeper into liteparse or LlamaParse check the links below: liteparse: https://t.co/JNER0mVcB8 LlamaParse: https://t.co/XYZmx5TFz8
- Sep 14, 20262.9x their median
just-in-time OCR is all the rage. most pipelines parse every page before anyone asks a question. for an agent working through an ad-hoc data room, that's slow, expensive, and most of those pages never get read. the better pattern is just-in-time OCR in two passes: ✅️ LiteParse (free, OSS, Rust, 50+ formats) does a fast layout-aware first pass: spatial text, bounding boxes, headings, tables, and a per-page complexity flag. a full data room in 32 seconds. ✅️ LlamaParse zooms in on only the pages that need it, by page number, and returns cell-level tables, bounding boxes, and confidence scores. the rest fills in the background. pypdf and pdftotext can't do the first pass well. parsing everything up front can't do it cheaply. two passes gets you both. full breakdown with numbers: https://t.co/z8HWokXatj
- Sep 22, 20262.1x their median
The fastest PDF-to-Markdown parser just got faster. ⚡️ With LiteParse v2.14.6, text-based PDFs parse about 25% faster. Across realistic documents, LiteParse processed pages at 2.8ms/page and 1.5× faster than the next-fastest local parser. LiteParse is open source and runs locally in Python, Node.js, Rust, or directly in the browser. Grab v2.14.6 → https://t.co/UJy6KQ1Dyi Bench Docs → https://t.co/rVY3Cxyj8H
- Sep 3, 20261.7x their median
Document extraction just got a new gear. ⚡️ Introducing Turbo mode for Extract, our fastest way yet to pull structured data from documents. It runs roughly 4× faster than our Cost Effective Tier at comparable accuracy, with a median latency of 3.7 seconds a page. Turbo processes pages in parallel, so latency stays nearly flat as document size grows. Available in beta today. Read the full breakdown → https://t.co/6kErWWqXGG
- Sep 18, 20261.5x their median
Introducing the first in our Parsed by LlamaParse series. We're outlining the stakes of 'getting parsing wrong' in consequential documents. We’re starting with the U.S. Energy Information Administration’s September 2026 Short-Term Energy Outlook, a dense government report covering energy supply, demand, prices, and forecasts. Table 7a alone packs multiple years, quarters, row hierarchies, units, and footnotes into one electricity-industry table. Take 1,186. Parsed correctly, it means electricity sales to ultimate customers in Q3 2026, measured in billion kilowatthours. Parsed incorrectly, it could be assigned to the wrong quarter, metric, or unit , which means the error can flow straight into a dashboard, forecast, alert, or AI application. And the values aren’t the only thing that matters. Footnotes define the data too: “small-scale solar,” for example, refers to systems under one megawatt, not solar generation overall. LlamaParse preserves the structure and context that make document data usable downstream: whether you’re populating a database, updating a dashboard, running forecasting workflows, or building an AI app over complex documents. Source doc here: https://t.co/WOWZjpViNQ
- Sep 28, 2026
Document parsing requires a lot of on-the-fly decision making. We explored the early approaches to Jev and Jev-like models on several documents tasks like orientation detection, language detection, routing, and more. https://t.co/U9urKwcORE
- Sep 11, 2026
confidence scores in LlamaParse just got an upgrade 🦸♀️ our new high-effort mode provides granular page-level scores with text explanations to help you intimately understand parsing quality of your documents. we even refer back to the original document for an extra check when generating the score. use high-effort only when you need it, at 5 additional credits per page. try it on your docs: https://t.co/SqKn6MkJxh
- Sep 21, 2026
Grounded Confidence is here for Extract! 🦙 When your agents and workflows depend on extracted data, you need to know how accurate that data is. We’ve added confidence scores to give you a better read on extraction accuracy, field by field. Use them to decide which results your app can accept automatically and which need human review. Available on Cost Effective, Agentic, and Agentic Plus. Try it out on your docs -> https://t.co/v28mAfRAet
- Sep 28, 2026
Parsing forms still trips up the latest frontier VLMs. A form isn't text on a page. It's a set of fields, grouped into sections, each tied to a specific box. That's why forms need purpose-built parsing, not a bigger general model: ✅️ Detect every field, not just the obvious ones ✅️ Keep the hierarchy of sections and fields ✅️ Tie every value to the exact box it came from ✅️ Understand handwriting and checkmarks Our latest blogpost breaks down where VLMs fail on real W-2s, 1040s, W-9s and scanned W-4s, and a custom cookbook for LlamaParse to handle them at a fraction of the cost 👇 https://t.co/KPTrjhvuby
- Sep 9, 2026
Last week we brought LlamaParse to Claude. Now it’s available in ChatGPT, too 🦙 ChatGPT can already read PDFs. But document work gets harder when the information you need is buried in messy scans, dense tables, spreadsheets, and charts. Strike the right balance between burning tokens and accuracy, with a specialized API for document understanding. Turn complex documents into clean, structured context and let Astra run its magic. Use our connector to: ✅ Parse documents into clean Markdown, JSON, or HTML. ✅ Extract specific fields into a schema you define. ✅ Search across document collections to find the information you need. ✅ Classify files and split documents into logical sections. Live in the ChatGPT plugin directory now! → https://t.co/6nrupYikK5
Ranked by total interactions across everything we have tracked for this account, which is a longer history than the 30-day window the rates above use. The multiple compares each post to this account's own median.
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Reading these numbers
A typical post picks up 41 interactions against 122K followers, an engagement rate of 0.034%. Measured over 16 original posts, its engagement rate beats 36% of 21,185 tracked accounts of a similar size, which puts it in the middle of its size range rather than at either end. Posts are seen about 12K times each, and 0.34% of those impressions turn into an interaction. That is about 9.89% of the follower count, which is the gap between an audience on paper and an audience in a timeline. Posting runs at about 1.2 post a day over the last 30 days, with activity on roughly 77% of days. Most posts go out around 16:00 UTC, and Monday is the busiest day of the week. Of the 16 posts sampled, 94% carry an image or video and 88% link out. The account's strongest tracked post pulled 1.4K interactions, about 33x its own typical post.
- What is LlamaIndex 🦙's engagement rate on X?
- LlamaIndex 🦙 (@llama_index) has an engagement rate of 0.034%, based on the median interactions across 16 original posts from the last 30 days against 121,997 followers. Replies, reposts and quote-posts of other people are excluded from that sample.
- Is that a good engagement rate?
- At 0.034%, LlamaIndex 🦙 sits above the 25th percentile of the 212,567 accounts in this comparison. Those comparison accounts are all large ones, because our scanning cadence is weighted towards big accounts, so this is a ranking among peers of similar scale rather than a ranking across X.
- Does @llama_index have real engagement?
- Its engagement rate beats 36% of the tracked X accounts closest to it in follower count (21,185 accounts), which puts it in the middle of its size range group. Ranking inside a size band matters because engagement rate falls as accounts grow, so a raw rate would mostly re-measure the follower count. It is a starting point for a look at follower quality, not a verdict on it.
- When does @llama_index post?
- Most posts go out around 16:00 UTC, and Monday is its busiest day, at roughly 1.17 posts per day across the measured window.