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Followers

119.8K

Posts

Following

Account age

3 yrs

🎁 Free analysisfor LlamaIndex 🦙

🕵️Fake Followers CheckReal-audience % & bot risk→ 📊Engagement rateBenchmarked vs. peers→ 💰What they chargeFair sponsored-post price→

Known for

🦙☀️ 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

107.1K views 🦙☀️ 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

Introducing 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗕𝗲𝗻𝗰𝗵: the most comprehensive benchmark for information extraction from complex enterprise documents. Our applied research team tested:

14 systems — frontier VLMs, coding agents, extraction APIs — on 370 enterprise docs, 4,869 pages, 67 doc types.

31.5K views Introducing 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗕𝗲𝗻𝗰𝗵: the most comprehensive benchmark for information extraction from complex enterprise documents. Our applied research team tested: 14 systems — frontier VLMs, coding agents, extraction APIs — on 370 enterprise docs, 4,869 pages, 67 doc types.

Most document extraction APIs can't tell you where a value came from.

For ExtractBench, we scored grounding strictly: a field only counts if the value AND its citation are correct, word-level box at IoU 0.5. A perfect box around a wrong value earns nothing.

Results: VLMs and

20.1K views Most document extraction APIs can't tell you where a value came from. For ExtractBench, we scored grounding strictly: a field only counts if the value AND its citation are correct, word-level box at IoU 0.5. A perfect box around a wrong value earns nothing. Results: VLMs and

Every document extraction system has a perception blind spot. We mapped them.

For ExtractBench, we tested 14 systems on documents that weren't born digital: 1950s regulatory filings, hand-filled tax forms, and pages degraded with fax thresholding, photocopier tone curves, sensor

11.1K views Every document extraction system has a perception blind spot. We mapped them. For ExtractBench, we tested 14 systems on documents that weren't born digital: 1950s regulatory filings, hand-filled tax forms, and pages degraded with fax thresholding, photocopier tone curves, sensor

📊 Post performance

8.3K

Avg views / post

6.9%

Reach vs followers

Dec 2022

On X since

🔥 Top post: 🦙☀️ It’s a hot Llama summer, and it pays to be a PRO at document · 107.1K views

📊 Activity & format

Posting cadence

5.3 / week

Posts several times a week.

Content mix

Mostly video

Recent: 5 video · 2 image · 2 text.

Follower / following

3.2K×

Follows 37 back. A strong ratio — an audience that follows them, not a follow-for-follow network.

Verification

✓ X Premium

A paid X Premium (Blue) verified account.

Recent posts

View on X ↗

🔥 Top post 🦙☀️ 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 107.1K❤ 16🔁 4

▶ Most parsers treat tracked changes as noise. Result: a deleted clause comes back as live text, and your pipeline reads a document that says the opposite of what it means. LlamaParse now handles revision tracking. You get clean markdown of… 8.3K❤ 31🔁 11

▶ Most document extraction APIs can't tell you where a value came from. For ExtractBench, we scored grounding strictly: a field only counts if the value AND its citation are correct, word-level box at IoU 0.5. A perfect box around a wrong v… 20.1K❤ 35🔁 15 Get ready to break racks and agents. If you're in SF, next Thursday we're co-hosting a billiards night for AI engineers and founders with Judgment Labs , Modal, and ClickHouse. Limited spots available. RSVPs are being approved now 👇️ 4.5K❤ 15🔁 9

▶ Every document extraction system has a perception blind spot. We mapped them. For ExtractBench, we tested 14 systems on documents that weren't born digital: 1950s regulatory filings, hand-filled tax forms, and pages degraded with fax thre… 11.1K❤ 22🔁 12

▶ The most dangerous document extraction failure isn't a wrong value. It's a missing row that looks like nothing is wrong. We released ExtractBench yesterday: 370 enterprise docs, 14 systems. The hardest test: long-list completeness. An unc… 5.5K❤ 35🔁 11

▶ Introducing 𝗘𝘅𝘁𝗿𝗮𝗰𝘁𝗕𝗲𝗻𝗰𝗵: the most comprehensive benchmark for information extraction from complex enterprise documents. Our applied research team tested: 14 systems — frontier VLMs, coding agents, extraction APIs — on 370 enterprise docs… 31.5K❤ 80🔁 28 SF builders, it's time to ditch your agents and head out on a 🏃 Join us for a run tomorrow with the Lore community at 6pm at Sue Bierman Park (across from the Ferry Building) for a casual 5k down the Embarcadero. All paces welcome, found… 4.8K❤ 12🔁 8

POV: It's the last day at @Ai4conferences and your: 🧢 colorful hats and stickers are all over the conference floor 👨‍🏫 CEO, @jerryjliu0 , delivered a speech to a full room 🍽️ executive dinner brought in some of the best and brightest … 5K❤ 8🔁 3

💡 Facts

🗓️Joined X in 2022 — 3 years ago.

👁️Averages 8.3K views per post.

📤Posts about 5.3× per week.

💬0.03% engagement rate.

🏅Earned the 100K Followers for passing 100K followers.

🕵️ Fake follower check

Estimated

46/100

Fair Credibility score

69%

Mixed Real audience

Medium Fake-follower risk

High Data confidence

Est. 69% real, active audience · Medium fake-follower risk.
Low engagement (~6.9% of followers engage each post) — a sign of an inflated or inactive audience.
Organic base — far more followers than accounts it follows.
Verified account.
Established account (3+ years old).
0.03% engagement — below the ~14.4% typical for this size.

Heuristic estimate from engagement, follower ratios, account age & growth — a screening signal, not a guarantee.

About

The most accurate agentic OCR platform for production AI. LlamaParse: https://cloud.llamaindex.ai/ Docs: https://developers.llamaindex.ai/python/cloud/

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🔀 Audience overlap

Estimated

Estimated shared audience with similar creators — useful for avoiding overlap (or doubling down) when planning a campaign.

OML 18%

H & FW Dept Odisha 18%

FOX 32 News 14%

Salesforce 13%

PTA 13%

Wembley Stadium 11%

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