Justin Gardner ✓
@Rhynorater · Richmond, VA · joined 20 Oct 2015
Christian | Full-time Bug Bounty Hunter | Host of @ctbbpodcast | Advisor @CaidoIO | 4x LHE MVH | 🗣️ English, 日本語 | ♥️ @mariahchan_ ♥️
38 174Followers
2 453Following
6 152Posts total
969.4KViews on collected posts
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Yo @digitalocean - please check out support ticket #12726682
My replies to your email are not coming through, and you took action on my droplet.
7.9K views · 32 likes
· 1 reposts · 1 replies
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@Rhynorater Great stuff dude. I was listening to Chris Williamson podcast recent and he talked about "going pro" with podcasting based on the book "Going Pro: The Deliberate Practice of Professionalism
Book by Tony Kern" and how it changed everything for him. he even kept doing r
18.2K views · 79 likes
· 4 reposts · 4 replies
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These stats are based on my personal experience of learning bug bounty, the stats on my H1 performance tab, and my estimation of time spent scaled to full-time.
I currently spend about 90/10 hacking/learning ratio. Maybe even higher on the learning now with @ctbbpodcast prep.
26.4K views · 119 likes
· 3 reposts · 1 replies
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@ctbbpodcast That's how I'd do it, TBH! Feel free to correct my math if I hekked it up.
Retweet the main tweet, ya?
31.2K views · 129 likes
· 11 reposts · 17 replies
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Months 8-12 would be 100% hacking finding 15-20 bugs per month at 1k each.
So, if my calculations are correct, that would put me at:
17.5*1000*4 + 9000*2 + 5250*2 +2250*2 = $103,000
In reality, that number would likely be closer to $90,000 because of dupes and bounty flux.
26.1K views · 102 likes
· 5 reposts · 1 replies
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From here, I'd continue improving my knowledge for a couple months and then switch to 100% hacking to round off the year at 100k before returning to a 90% hacking 10% learning split indefinitely.
Month 6&7 would be 9k per month at 80% hacking 20% learning.
27.4K views · 105 likes
· 5 reposts · 1 replies
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From there, in, let's say month 5&6, I'd finish up all the topics on PortSwigger WSA and read through all the hacktivity.
Id then switch my allocation to 80% hacking and 20% learning. That learning would be largely oriented at code review & specialty subjects like postMessage.
33.1K views · 129 likes
· 5 reposts · 4 replies
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After months 4&5, at the above allocation, I'd be finding at least 12 bugs at $750 - $1k per bug (I'm getting better at finding impactful bugs - my current average bounty is $1.8k).
This will put me at ~ 9k a month starting from month 6. Having earned 15.5k already this year.
29.6K views · 107 likes
· 5 reposts · 1 replies
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I'd expect to find 1-5 bugs per month at an average of $750 per bug. So pulling roughly $2,250/month after month 2.
After completing all of the above bug types, I'd switch my allocation to 40% hacking 60% learning.
I'd then switch my focus to learning XSS, CSRF, and SSRF.
41.8K views · 178 likes
· 11 reposts · 2 replies
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After ramping up my hacking and completing the aforementioned bugs, I'd expect my bugs per month to increase to around 7 at $750 per bug, putting me at $5,250 with the 40/60 split mentioned above. That's roughly 80 hours over the month. Or one bug every 10 or so hours.
36K views · 143 likes
· 7 reposts · 1 replies
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*HTTP
*Browsers (function, security constraints, ect)
*Web architecture (APIs, reverse proxy, cloud, ect)
*Server-side (APIs, MVC structure, routing and handlers)
*Client-side (JS, HTML, CSS)
I estimate this would take around 1 months of full-time time study. Maybe 1.5 months.
47.8K views · 286 likes
· 20 reposts · 12 replies
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From here, I'd begin to start learning about priv escalation bugs, client-side access control bugs, IDORs, and paywall bypassed. I'd do this via PortSwigger Academy and Hacktivity reports.
After I covered one of these I'd switch my time allocation to 20% hacking 80% learning.
43.7K views · 228 likes
· 13 reposts · 2 replies
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All my current bug bounty knowledge is gone.
Here's how I get it back and make $100k in the first year:
First, I've got to learn the basics. For this, I will make sure I understand at a high level how the components I'm working with function.
I'll need to understand... https
559.7K views · 3.9K likes
· 1.1K reposts · 88 replies
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Face aux comptes de taille comparable
5 posts des 90 derniers jours, à côté de la tranche de 10K–100K abonnés. diffusé largement, mais peu de ces spectateurs réagissent.
Vues médianes8 356ce compte924médiane pour 10K–100K
Portée, %21.89%ce compte3.62%médiane pour 10K–100K
Engagement, %0.53%ce compte1.52%médiane pour 10K–100K
| Indicateur | Ce compte | Médiane pour 10K–100K | Rapport |
|---|---|---|---|
| Vues médianes par post | 8 356 | 924 | 9.04× |
| Portée (vues ÷ abonnés) | 21.89% | 3.62% | 6.05× |
| Taux d'engagement | 0.53% | 1.52% | 0.35× |
Autres comptes de cette tranche → Comparer avec un autre compte → Comment ces repères sont établis →
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
36K6 Sep
41.8K
29.6K
33.1K
27.4K
26.1K
31.2K
26.4K
18.2K
7.9K2 Sep
14.1K
8.4K
8.1K
9.9K3 Sep
Last 14 collected posts, oldest on the left. The scale is logarithmic: one post can outrun the rest a hundred times over.
Engagement rate per post
0.42%6 Sep
0.46%
0.38%
0.42%
0.40%
0.41%
0.50%
0.47%
0.48%
0.43%2 Sep
0.33%
0.53%
1.07%
0.70%3 Sep
Reactions — likes, reposts, replies and quotes — divided by views. Median for 10K–100K accounts is 1.52%.
What the audience does
Likes46.4%5 811 in total
Reposts9.2%1 154 in total
Replies1.1%140 in total
Quotes0.8%98 in total
Bookmarks42.4%5 308 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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