@itu_toma: mencari spot buat menikmati pagi bersama @alltrek @eigeradventure @tokokopituku #cargobike

Itu_Toma|kuliner|motocamp

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Thursday 20 August 2026 01:48:17 GMT

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SakRek :

bang sabtu pagi mau ikut goes ga dari bintaro ke bsd ada acara sepeda

2026-08-20 08:50:56

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ANGLER_JAH420 (RIZA) :

mewah🔥🔥🔥

2026-08-20 02:03:10

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Alltrek indonesia :

Asoy dan santay yaa 😍

2026-08-20 02:20:02

0

mamad shop :

bikin sepeda cargo berapa om

2026-08-20 05:50:24

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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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