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@steelaudios: Being your gym audio plug. 🦍 drop a follow

steelAudios
steelAudios
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Region: US
Sunday 18 October 2026 23:43:16 GMT
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Music

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No Watermark .mp4 (2.29MB) No Watermark(HD) .mp4 (2.27MB) Watermark .mp4 (2.12MB) Music .mp3

Comments

the.grappler
Baki. :
song name
2026-10-18 23:46:36
0
hektor881
Engineering541s :
Linkin park (one step closer)
2026-10-19 01:10:49
2
motivationaye
aesthetics.aye :
Hel yeahh 💀💀🔥🔥
2026-10-24 14:07:55
0
dan3km0
данёк) :
anybody have soundcloud playlist like this? send me PLEASE
2026-10-20 19:52:09
0
To see more videos from user @steelaudios, please go to the Tikwm homepage.

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Tricky Software Interview Question! Bots flood a login endpoint with 50,000 failed attempts across 30,000 IP addresses, and real users start waiting ten seconds to sign in. How do failed logins slow down the whole system, and why can’t you just block the attackers? The slowdown comes from bcrypt. It’s built to be slow and CPU-heavy so stolen password hashes are hard to crack, which means every wrong password from a bot still costs real CPU time. Attackers spread stolen credentials across tens of thousands of IPs and stop just before the account lockout limit, so per-IP rate limiting never triggers while the CPU maxes out hashing passwords that were never going to work. The fix is to filter traffic before bcrypt runs. During the TLS handshake, a client’s fingerprint can reveal whether it’s an automated script or the real mobile app. Paired with device trust tokens from past logins, that signal sorts requests into tiers: trusted devices get verified right away, borderline requests get a CAPTCHA, and obvious bots get rejected without bcrypt ever running. Would you trust a TLS fingerprint to separate bots from real users? Tell us in the comments. #SystemDesign #CyberSecurity #CredentialStuffing #bcrypt #APISecurity #SoftwareEngineering #TechInterview #WebDevelopment #DevOps #KodeKloud
Tricky Software Interview Question! Bots flood a login endpoint with 50,000 failed attempts across 30,000 IP addresses, and real users start waiting ten seconds to sign in. How do failed logins slow down the whole system, and why can’t you just block the attackers? The slowdown comes from bcrypt. It’s built to be slow and CPU-heavy so stolen password hashes are hard to crack, which means every wrong password from a bot still costs real CPU time. Attackers spread stolen credentials across tens of thousands of IPs and stop just before the account lockout limit, so per-IP rate limiting never triggers while the CPU maxes out hashing passwords that were never going to work. The fix is to filter traffic before bcrypt runs. During the TLS handshake, a client’s fingerprint can reveal whether it’s an automated script or the real mobile app. Paired with device trust tokens from past logins, that signal sorts requests into tiers: trusted devices get verified right away, borderline requests get a CAPTCHA, and obvious bots get rejected without bcrypt ever running. Would you trust a TLS fingerprint to separate bots from real users? Tell us in the comments. #SystemDesign #CyberSecurity #CredentialStuffing #bcrypt #APISecurity #SoftwareEngineering #TechInterview #WebDevelopment #DevOps #KodeKloud

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