Techsarrion
@Techsarrion
Cloud/Sec | Everyday Learner #BlackTechTwitter
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If you’re broke but serious, your stack should be: CompTIA Security+ (free study guides everywhere) TryHackMe + Blue Team Labs LinkedIn profile with action verbs 20 job applications per day That’s your $70K plan.
Kubernetes is an open source tool that helps you manage all the parts of your apps in containers. So if you have an app with a bunch of microservices, for example, Kubernetes lets you wrangle them more easily. In this handbook, @POnukwili teaches you about pods, workflows,…
resourcefulness - knowing how to get answers, where to look for them. That kind of stuff. I think in general people don't have this skill.
A harsh truth to remember when applying for a Job: Apply for it and forget it. After you apply, it's done, gone. Don't ever sit around with your hopes pinned on hearing from that one dream job.
Being educated removes a certain level of jealousy that comes with illiteracy.
Be one of the first to fight. Register now for the Closed Alpha from November 21-24. PS5 and Xbox X|S ClosedAlpha.Invinciblevs.com
Master the elements with @jwonggg , our official ambassador for Avatar Legends: The Fighting Game! Stay tuned for more on the Closed Alpha Playtest and check out our exclusive gameplay trailer! Wishlist Now on Steam: store.steampowered.com/app/2424420/Av…
Most people rent their AI infrastructure from the cloud. I decided to build my own. In just 6 hours, I stood up a fully GPU-accelerated AI/ML lab on an Nvidia DGX Spark running a production-grade MLOps stack. In this video, I’m not showing you the build step by step this is…
youtube.com
YouTube
I Built an AI/ML Lab on a DGX Spark — This Changes Everything (Part 1)
I follow: @SymoneBeez @KanikaTolver @teneikaask_you @princessxap @T3chnicalB @sp4rtan300 @Tomio_B @iamKierraD @TechWithDre @techgirl1908 @TheImmaturedad @Ant_0103 @itsthewealth4me @melTechFlow @DeedsNPassports @_mstrdom @phishfinding @melTechFlow @BoydClewis
I miss the old timeline sometimes, whole lot of information was flying left and right. Collaboration was in the air. Networking was at an all time high. A lot of people’s lives were forever changed💵
You can never trust someone who tried to get you to join a MLM, no matter how long ago it was.
Slowly getting into Kubernetes Security Just learned about Encryption at Rest By default, Kubernetes Secrets are stored in base64 Encoding and the data stored in etcd is unencrypted by default If you have the base64 command installed, you can take your Secret encoding and…
Twitter / X family, I'm struggling to find employment since the tariff / DOGE purge fucked up my income. Coming up on 4 months of unemployment after 100s of applications. Non-renewal of my lease with, 3 school-aged children. To say Trump/Elon can kma is an understatement.
Here’s how I would learn data engineering in 2025: 1. The basics: - learn SQL — SELECT, FROM, WHERE, GROUP BY, JOIN, HAVING, etc - learn Python — data structures: objects, arrays, tuples, namedtuples — algorithms: recursion, loops 2. Intermediate - learn distributed…
It costs $0.00 to support a 43 year old game developer. 🧑🚀
this guy is getting paid $35,000 to set up an internal “ChatGPT” for a law firm. > locally hosted Llama for LLM > N8N to connect it all. we’re living in the AI gold rush.
Killer project for those building AI Agents! Here’s how you can run agents at scale on their own cloud computers. (This is a must if you build agents that run code and want to ensure they don't mess up your environment.) E2B is an open-source infrastructure that gives AI agents…
github.com
GitHub - e2b-dev/E2B: Open-source, secure environment with real-world tools for enterprise-grade...
Open-source, secure environment with real-world tools for enterprise-grade agents. - e2b-dev/E2B
Weekly restock: I have the following cert exam vouchers available for 35% off retail: - Security+ - CASP+ - Cloud+ DM me if you are interested, must be located in the US. RTs for visibility are appreciated🤟
Advice for MLOps beginners 💡 → Stop thinking in terms of specific tools (aka feature stores, experiment trackers, Kubernetes...) → Instead, think in terms of pipelines. ML app = Feature pipeline + Training pipeline + Inference/Deployment pipeline
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