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

Against accounts of the same size

9 posts from the last 90 days, next to the 10K–100K follower range. below its peers on both reach and engagement.

Median views1 625this account1 195median for 10K–100K

Reach, %2.26%this account3.80%median for 10K–100K

Engagement, %0.83%this account2.00%median for 10K–100K

Metric	This account	Median for 10K–100K	Ratio
Median views per post	1 625	1 195	1.36×
Reach (views ÷ followers)	2.26%	3.80%	0.60×
Engagement rate	0.83%	2.00%	0.42×

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

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 2.00%.

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.

Latest posts

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 05 Sep 2026 What age range is most known for hacking? 1.6K views · 7 likes · 0 reposts · 1 replies 05 Sep 2026 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 05 Sep 2026 Saturday morning @ZackKorman “How Al reduced cybersecurity to bugs” https://t.co/2BXa7ZugBH 2.5K views · 28 likes · 5 reposts · 2 replies 05 Sep 2026 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 05 Sep 2026 @HackingLZ It'd be funny if it kept downgrading until Haiku 3.0 answers 1.1K views · 8 likes · 0 reposts · 1 replies 01 Sep 2026 @HackingLZ well yknow, 60% less of the odd 100 billion or so is still pretty big 157 views · 1 likes · 0 reposts · 0 replies 01 Sep 2026 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 01 Sep 2026 This Model is amazing they really fixed the CVP guardrails. https://t.co/0lMAtuVCEe 57.4K views · 364 likes · 28 reposts · 38 replies 01 Sep 2026