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LlamaIndex 🦙 ✓

@llama_index · joined 18 Dec 2022

The most accurate agentic OCR platform for production AI. LlamaParse: https://t.co/yQGTiRSNvj Docs: https://t.co/us6GCS1Clb

120 509Followers
38Following
4 031Posts total
380.5KViews on collected posts

Latest posts

Does paying 5x more per page actually get you better document extraction? We ran the data to find out. We evaluated 14 frontier systems across 370 enterprise documents, then plotted their accuracy against cost per page. The biggest takeaway? Higher cost does not equal better htt 1.6K views · 5 likes · 2 reposts · 1 replies Open on X →
@llama_index https://t.co/XrTgDFDrPs 0 views · 0 likes · 0 reposts · 0 replies Open on X →
@llama_index 4× faster with comparable accuracy is a pretty big upgrade ⚡️ Definitely worth trying. 80 views · 0 likes · 0 reposts · 0 replies Open on X →
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 https:// 15.8K views · 43 likes · 11 reposts · 6 replies Open on X →
ExtractBench is now live on @Kaggle. It tests schema-guided document extraction on the documents most likely to break downstream agents and workflows, including long record lists, noisy scans, handwriting, and complex tables. The benchmark covers 370 enterprise documents across 9.1K views · 31 likes · 6 reposts · 3 replies Open on X →
LlamaParse is officially a verified connector on Claude! 🦙 Claude is perfectly fine for the occasional text-only PDF. But most of the context your agent needs lives in messy PDFs, spreadsheets, scanned forms, dense tables, and charts. Feeding those docs straight to the model h 12.8K views · 39 likes · 4 reposts · 7 replies Open on X →
@llama_index Huge boost for document parsing. More credits, more building. 31 views · 0 likes · 0 reposts · 0 replies Open on X →
@llama_index tripling credits only for the first month is a smart hook to get teams to actually test the pro parsing at scale 77 views · 0 likes · 0 reposts · 0 replies Open on X →
🦙☀️ It’s a hot Llama summer, and it pays to be a PRO at document parsing. For a limited time, upgrade to the LlamaParse Pro plan and we'll triple your credits: 400K → 1.2M credits in your first month. That's $1,000 (~80k pages) of parsing, free. No code, applied https://t.co/cF
341.1K views · 28 likes · 3 reposts · 8 replies Open on X →

Against accounts of the same size

8 posts from the last 90 days, next to the 100K–1M follower range. shown widely, but few of those viewers react.

Median views5 303this account5 983median for 100K–1M
Reach, %4.40%this account1.49%median for 100K–1M
Engagement, %0.20%this account0.93%median for 100K–1M
MetricThis accountMedian for 100K–1MRatio
Median views per post5 3035 9830.89×
Reach (views ÷ followers)4.40%1.49%2.95×
Engagement rate0.20%0.93%0.22×

Others in this range →   Compare with another account →   How these benchmarks are built →

Growth & engagement

How the posts we collected actually performed: views and reaction rate post by post, what the audience did with them, and where the follower count goes.

Views per post

341.1K13 Aug
77
3121 Aug
12.8K31 Aug
9.1K2 Sep
15.8K3 Sep
80
1.6K4 Sep

Last 8 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.01%13 Aug
0.00%
0.00%21 Aug
0.41%31 Aug
0.45%2 Sep
0.39%3 Sep
0.00%
0.52%4 Sep

Reactions — likes, reposts, replies and quotes — divided by views. Median for 100K–1M accounts is 0.93%.

What the audience does

Likes53.7%146 in total
Reposts9.6%26 in total
Replies9.2%25 in total
Quotes2.6%7 in total
Bookmarks25.0%68 in total

Share of every reaction we collected for this account. Replies mean argument, reposts mean endorsement, bookmarks mean the post was worth keeping.

The follower curve appears once this account has two daily snapshots — we take one a day, and this one is on its first.

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