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Justin Elze ✓

@HackingLZ · /tmp/.a · joined 26 Apr 2008

CTO @TrustedSec | Former Optiv/SecureWorks/Accuvant Labs/Redspin | Race cars

71 840Followers
4 909Following
65 733Posts total
70.7KViews on collected posts

Postingan terbaru

I have no idea how my wife did this 😂 It was leaking super slow which is how we noticed and the outside of wheel is fine https://t.co/YEwweS9F1b 115 views · 2 likes · 0 reposts · 3 replies Open on X →
What age range is most known for hacking? 1.6K views · 7 likes · 0 reposts · 1 replies Open on X →
Big picture the issues the labs had with cyber evals going outside the box weren’t that bad, but enough happened for everyone involved to improve for the future. As far as infosec goes this space is used to tripping, falling on its face, and getting better(sometimes it takes a 1.3K views · 9 likes · 0 reposts · 3 replies Open on X →
Saturday morning @ZackKorman “How Al reduced cybersecurity to bugs” https://t.co/2BXa7ZugBH 2.5K views · 28 likes · 5 reposts · 2 replies Open on X →
A lot of this is playing out because the people working on benchmarks, model alignment, and risk are not necessarily cyber practitioners, which is fine. But it becomes pretty apparent when the conversation moves from abstract risk and benchmark results to how systems actually get 2.1K views · 35 likes · 3 reposts · 6 replies Open on X →
@HackingLZ It'd be funny if it kept downgrading until Haiku 3.0 answers 1.1K views · 8 likes · 0 reposts · 1 replies Open on X →
@HackingLZ well yknow, 60% less of the odd 100 billion or so is still pretty big 157 views · 1 likes · 0 reposts · 0 replies Open on X →
They did the meme! "Safeguards. We’ve improved our safeguards to reduce false positives (where the system flags benign content). In cybersecurity, our newest safeguards block 60% fewer false positives than before. In part, this is because Fable 5.1 can now be used to discover h 4.5K views · 33 likes · 2 reposts · 1 replies Open on X →
This Model is amazing they really fixed the CVP guardrails. https://t.co/0lMAtuVCEe 57.4K views · 364 likes · 28 reposts · 38 replies Open on X →

Dibandingkan akun berukuran sama

9 postingan dari 90 hari terakhir, dibandingkan dengan rentang 10K–100K pengikut. di bawah akun sekelasnya baik pada jangkauan maupun interaksi.

Median tayangan1 625akun ini924median untuk 10K–100K
Jangkauan, %2.26%akun ini3.62%median untuk 10K–100K
Interaksi, %0.83%akun ini1.52%median untuk 10K–100K
MetrikAkun iniMedian untuk 10K–100KRasio
Median tayangan per postingan1 6259241.76×
Jangkauan (tayangan ÷ pengikut)2.26%3.62%0.62×
Tingkat interaksi0.83%1.52%0.55×

Akun lain pada rentang ini →   Bandingkan dengan akun lain →   Bagaimana tolok ukur ini disusun →

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

57.4K1 Sep
4.5K
157
1.1K
2.1K5 Sep
2.5K
1.3K
1.6K
115

Last 9 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.76%1 Sep
0.81%
0.64%
0.83%
2.12%5 Sep
1.42%
0.92%
0.55%
4.35%

Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.

What the audience does

Likes76.9%487 in total
Reposts6.0%38 in total
Replies8.7%55 in total
Quotes0.9%6 in total
Bookmarks7.4%47 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.

Akun serupa