ProjectDiscovery ✓

@pdiscoveryio · joined 28 Jul 2019

Real, exploitable vulnerabilities. No noise. Nuclei scans fast. Neo closes the loop. @pdnuclei × @neo_ai_engineer

43 679Followers

144Following

2 945Posts total

176.8KViews on collected posts

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ProjectDiscovery ✓@pdiscoveryio21 Sep

something is coming soon for hackers and dreamers. https://t.co/qyQHE0SUTs

0:18

4K views · 77 likes · 12 reposts · 1 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio19 Sep

“A one-week job… to a few hours.” NahamSec shares how AI changed his hacking workflow from reviewing source code to catching things he might have missed during recon. Watch full conversation👇 https://t.co/hlIhgZhxVr

1:00

2.9K views · 21 likes · 1 reposts · 0 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio18 Sep

https://t.co/9V520l8oSf 2.3K views · 16 likes · 4 reposts · 0 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio17 Sep

How does reward hacking during training turn into a rogue model? How did this lead to the OpenAI Hugging Face incident? In this conversation, @ehrishiraj and @KoyalwarTarun unpack what the early reports missed: the role of misalignment during training, and how reward hacking

23:33

2.5K views · 17 likes · 3 reposts · 2 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio17 Sep

@pdiscoveryio @NahamSec @ehrishiraj Finding a bug faster is useful. Finding the same bug faster has a different payout. 26 views · 0 likes · 0 reposts · 0 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio16 Sep

How is AI changing bug bounty hunting, and where do human hackers still make the difference? @NahamSec and @ehrishiraj discuss AI agents, ethical hacking, and the future of vulnerability research from reconnaissance and code review to building security agents and verifying http

63:29

6K views · 26 likes · 9 reposts · 1 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio14 Sep

@pdiscoveryio The useful distinction isn't rogue agent vs bad eval. In both cases, defenders need the execution chain, blast radius, and a clearly assigned owner. That's where AI incident response starts. 49 views · 1 likes · 0 reposts · 0 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio14 Sep

@pdiscoveryio The key lesson for defenders is that AI-assisted attacks can evolve through feedback, not just execute a fixed playbook Detecting the attack’s adaptation loop may matter more than detecting any single payload. 54 views · 0 likes · 0 reposts · 0 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio14 Sep

@pdiscoveryio does this cover off the failures around (missing) controls in the test environments? 388 views · 2 likes · 0 reposts · 1 replies Open on X →

ProjectDiscovery ✓@pdiscoveryio14 Sep

Too much has been written about the OpenAI and Hugging Face incident to keep up with. We wrote the simplified version for security researchers and engineers: -> what actually happened inside training? -> how the attack evolved? ->what it means for defenders? Read: https://t.c

158.5K views · 54 likes · 15 reposts · 3 replies Open on X →

เทียบกับบัญชีขนาดเดียวกัน

10 โพสต์จาก 90 วันที่ผ่านมา เทียบกับช่วง 10K–100K ผู้ติดตาม แสดงในวงกว้าง แต่มีผู้ชมตอบสนองน้อย.

ยอดดูมัธยฐาน2 418บัญชีนี้927ค่ามัธยฐานของ 10K–100K

การเข้าถึง, %5.53%บัญชีนี้3.63%ค่ามัธยฐานของ 10K–100K

การมีส่วนร่วม, %0.77%บัญชีนี้1.52%ค่ามัธยฐานของ 10K–100K

ตัวชี้วัด	บัญชีนี้	ค่ามัธยฐานของ 10K–100K	อัตราส่วน
ยอดดูมัธยฐานต่อโพสต์	2 418	927	2.61×
การเข้าถึง (ยอดดู ÷ ผู้ติดตาม)	5.53%	3.63%	1.52×
อัตราการมีส่วนร่วม	0.77%	1.52%	0.51×

บัญชีอื่นในช่วงนี้ → เปรียบเทียบกับบัญชีอื่น → ค่าอ้างอิงเหล่านี้คำนวณอย่างไร →

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

158.5K14 Sep

388

6K16 Sep

2617 Sep

2.5K

2.3K18 Sep

2.9K19 Sep

4K21 Sep

Last 10 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.

Engagement rate per post

0.05%14 Sep

0.77%

0.00%

2.04%

0.63%16 Sep

0.00%17 Sep

0.91%

0.87%18 Sep

0.77%19 Sep

2.24%21 Sep

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

What the audience does

Likes60.3%214 in total

Reposts12.4%44 in total

Replies2.3%8 in total

Quotes2.3%8 in total

Bookmarks22.8%81 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.

โพสต์ล่าสุด

เทียบกับบัญชีขนาดเดียวกัน

Growth & engagement

Views per post

Engagement rate per post

What the audience does

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