SShortSingh.
Back to feed

Aspiring Ethical Hacker Shares How TryHackMe Sparked a Cybersecurity Journey

0
·3 views

A self-taught developer recently shared how a casual internet search for 'how to learn how to hack' led them to discover ethical hacking through TryHackMe. Initially misled by mobile apps and movie-style hacking stereotypes, they found the field more intuitive and methodical than expected. Over the past year, they completed multiple TryHackMe learning paths and are now building stronger fundamentals on HackTheBox. Alongside cybersecurity, they maintain interests in game development and AI applications in bioinformatics. They plan to publish beginner-friendly explanations of concepts they find confusing or interesting, inviting others to share their own learning journeys.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

Zero Dependency 2026: Hackathon Challenges Devs to Build Without Third-Party Packages

Hackathon Raptors is hosting Zero Dependency 2026, a 72-hour global online hackathon requiring participants to build functional software using only their programming language's standard library. The event was prompted by growing supply chain risks, including a 2025 report cataloguing over 454,600 new malicious packages and findings that nearly 20% of AI-suggested package names do not actually exist. Attackers have begun pre-registering these hallucinated package names to exploit developers who unknowingly attempt to install them. Participants can compete across six tracks spanning developer tools, parsers, web and networking, data storage, security utilities, and an open wildcard category. Judging will prioritize functional correctness, idiomatic code, and a verifiably empty third-party dependency manifest over presentation or speed.

0
ProgrammingHacker News ·

Red Hat argues CPUs can rival GPUs for LLM inference workloads

Red Hat has published a blog post challenging the assumption that GPUs are always the optimal hardware for large language model inference. The article argues that modern CPUs have advanced significantly and may be competitive for certain LLM inference tasks. This perspective encourages engineers to reconsider the default CPU-GPU split when deploying AI workloads. The post suggests that relying solely on GPUs may not always be necessary or cost-effective for inference at scale.

0
ProgrammingDEV Community ·

AWS Route 53 Explained: DNS Basics, Hosted Zones, Routing Policies and Resolvers

A systems engineer transitioning to cloud and DevOps has published a detailed technical guide covering AWS Route 53 and its core DNS concepts. The guide explains how DNS translates human-readable domain names into IP addresses, walking through each step from browser cache lookup to authoritative server response. It covers Route 53's role as AWS's fully managed DNS service, including public and private hosted zones, record types, and the significance of TTL in managing IP change propagation. The article also addresses all eight Route 53 routing policies, traffic policies, and resolver endpoints for hybrid AWS and on-premises environments. Topics such as split-horizon DNS, inbound and outbound resolver endpoints, and DNS forwarders are included alongside interview questions and hands-on practice tasks.

0
ProgrammingDEV Community ·

Developer builds Android read-aloud app with word highlighting after struggling to focus while reading

A developer who struggles with reading attention and focus built an Android app called ReadAloud, now available on Google Play, that reads text aloud while highlighting each word in sync. The app supports PDFs, EPUBs, DOCX files, plain text, and web articles, with features including offline listening, multilingual support, speed reading, and a vocabulary builder. Built with Kotlin and Jetpack Compose, one of its core technical challenges was synchronizing word-level highlighting across both on-device and cloud text-to-speech voices, which required two separate code paths. To protect cloud API keys from being extracted from the app binary, the developer implemented an HMAC-signed request system where keys are fetched from a server and decrypted on-device using a unique per-device derived key. Early testing with two volunteers helped identify usability issues and stabilize the app before wider release.