OpenSourced

Here To Openly Source Your Development Needs, Git it? 🔍

[Akhilesh Mishra @livingdevops]

Everyone is sharing DevOps roadmaps.
Most are just lists of tools.

But DevOps isn't about learning 20 tools at once. It's about learning the right things in the right order.

If I were starting in DevOps today, knowing what companies actually hire for, this is the roadmap I'd follow from Day 1 to landing a DevOps role.

I put together the complete roadmap here 👇

Full roadmap 👇
https://t.co/GDW4YzUJY7

[Tech Fusionist @techyoutbe]

I'm giving away FREE copies of my AWS Compute Architecture Playbook to 20 learners (100% FREE).

Inside you'll get:
✅ 10 production-grade AWS architecture projects
✅ Security, WAF & IAM design patterns
✅ Reliability, performance & cost optimization insights
✅ Real-world architecture diagrams
✅ Serverless, Containers, Kubernetes, Batch & Edge Computing use cases

Share - "How will this guide help your career?"

Examples:
• Upskill in AWS Architecture
• Prepare for Cloud/DevOps interviews
• Land your first cloud job
• Switch into DevOps or Cloud
• Become a Solutions Architect
• Learn production-grade AWS designs

Will select 20 learners who genuinely need it.
Winners announced in 24 hours.

Good luck 🚀

[0xor0ne @0xor0ne]

lang Hardening Cheat Sheet (2026 edition)

https://t.co/f451C7NKzV

By @0xTRIKKSS and @bcreusillet

#infosec

[GitHub Projects Community @GithubProjects]

Goravel provides a full-featured web framework for Go that follows Laravel's conventions, making it easier for PHP developers to transition to Go.

- Artisan Console for CLI commands and automation
- Authentication with JWT and Session drivers
- ORM with model factories for testing
- gRPC server and client support

[Kubernetes with Naveen 🇮🇳 @NaveenS16]

Securing the runtime execution of your #containers is one of the most critical aspects of #Kubernetes defense, yet it is shockingly easy to get wrong. By default, containers often run with far more privileges than they actually need to do their jobs.

🧵👇#DevOps

[Karen Payne MVP @KarenPayneMVP]

Example for removing a property from json.

https://t.co/WciCp6PLVp

[Dan Kornas @DanKornas]

LLM inference is where AI prototypes start getting expensive.

ai-inference-resources is a curated learning guide for engineers working on AI inference systems.

It helps you build a real study path by grouping resources across LLM serving, GPU kernels, attention, quantization, distributed inference, and production deployment instead of leaving you to stitch together random tutorials.

Key features:

• Tiered reading path – README recommends Tier 1 across all topics first, then Tier 2, then Tier 3
• 18-topic map – covers LLM fundamentals, serving systems, attention/memory optimization, quantization, CUDA, benchmarking, and more
• Serving-system focus – includes resources on vLLM, SGLang, inference engines, routing, batching, and deployment tradeoffs
• Systems-level depth – goes beyond model APIs into GPU kernels, multi-GPU inference, compiler/DSL approaches, and hardware co-design
• Easy contribution flow – accepts PRs or issues with a link, short description, and suggested category/tier placement

It’s open-source (MIT license).

Link in the reply 👇

[Real Python @realpython]

🐍📺 Rock, Paper, Scissors With Python: A Command-Line Game [Video] — https://t.co/tKjJEQhJlB
#python

[Infinite Logiz @Infinite_Logiz]

📘 Advanced Definite Integral Solved Step by Step ✍️

A beautiful logarithmic and trigonometric integral simplified using substitution, symmetry, and smart calculus techniques. 🚀

Problem:
Evaluate
I = ∫ from 0 to π/4 [ ln(1 + tan(x)) / sin(x) ] dx

In this handwritten solution, we use:
✔️ Transformation of variables
✔️ Trigonometric identities
✔️ Symmetry methods in definite integrals
✔️ Step-by-step simplification

This problem looks extremely difficult at first, but careful manipulation reveals an elegant final answer.

Final Answer:
I = 2ln(2) − π/2 📐

Perfect for:
🎯 Engineering Mathematics
📚 Calculus learners
⚙️ Competitive exam preparation
🧠 STEM enthusiasts

Mathematics becomes beautiful when complex expressions simplify into elegant results.

#Calculus #Integration #Mathematics #STEM #LearnMath

[Python Coding @clcoding]

Celebrate World Environment Day with Python!

[Java Code Geeks @javacodegeeks]

Convert ByteBuffer to Byte Array in #Java - #Java Code Geeks https://t.co/Zy4xLuSr9u

[PyCoder’s Weekly @pycoders]

nbpipe: Run Sequences of Jupyter Notebooks as a Workflow https://t.co/EEt08otMLb

[Dhanian 🗯️ @e_opore]

REDUNDANCY IN SYSTEM DESIGN

Redundancy is the practice of duplicating critical components in a system so that if one component fails, another can immediately take over.

The goal is simple:

Remove single points of failure.

Without redundancy, one failed server, database, or network device can bring down an entire application.

HOW REDUNDANCY WORKS

1. SERVER REDUNDANCY

Instead of running a single server:

→ Server A
→ Server B
→ Server C

A load balancer distributes traffic across all servers.

If one server crashes, the others continue serving users.

2. DATABASE REDUNDANCY

Data is copied across multiple database instances.

Example:

→ Primary Database
→ Replica Database

If the primary fails, a replica can become the new primary.

3. NETWORK REDUNDANCY

Multiple network paths are created between systems.

If one route fails, traffic automatically switches to another route.

4. STORAGE REDUNDANCY

Data is stored in multiple locations.

Examples:

→ RAID configurations
→ Cloud storage replication
→ Distributed file systems

This prevents data loss during hardware failures.

5. GEOGRAPHIC REDUNDANCY

Applications are deployed across multiple regions or data centers.

Example:

→ US Region
→ Europe Region
→ Asia Region

If an entire region goes offline, users can be routed to another region.

ACTIVE-ACTIVE VS ACTIVE-PASSIVE

Active-Active:
→ Multiple systems handle traffic simultaneously
→ Better performance
→ Higher availability

Active-Passive:
→ One system serves traffic
→ Backup waits on standby
→ Simpler architecture

REAL-WORLD EXAMPLES

→ Google runs services across multiple data centers
→ Netflix deploys infrastructure across multiple AWS regions
→ Banking systems maintain backup databases and servers

BENEFITS OF REDUNDANCY

→ High availability
→ Better reliability
→ Reduced downtime
→ Improved disaster recovery
→ Greater fault tolerance

CHALLENGES

→ Increased infrastructure costs
→ More operational complexity
→ Data synchronization issues
→ Additional monitoring requirements

RULE OF THUMB

If a component is critical to your system, assume it will eventually fail and build redundancy before it does.

Grab the System Design Ebook:
https://t.co/WIMretQFPE

Follow @e_opore for more updates.

[Java @java]

This #JavaOne talk breaks down how Java applications can layer internal, distributed, and semantic caches. We'll explore in-process caching with Caffeine for ultra-low-latency access, distributed caching with Redisson and Valkey for shared cache and semantic caching using Vector Similarity Search to reduce latency and cost while scaling LLM access. https://t.co/HAlcrlVv5q

[.NET @dotnet]

Agent Skills in .NET just leveled up 🤖⚡
Author skills as files, classes, or inline code — mix them freely, add script execution, and gate risky scripts with human approval. Perfect for fast‑moving, multi‑team agent apps.

Read → https://t.co/uJeS7kTjGi

[Edgar C Francis @edgarcfrancis]

CCNP CCIE EBGP Mock LAB configuration

https://t.co/sZpqe9FHCo

[osk @ogsvg]

There's too much BS being spread about peptides... that's why I created Retpides. The first of it's kind evidence based wiki for research peptide information.

Dont fall victim to affiliate marketing... see the data for yourself!

[freeCodeCamp.org @freeCodeCamp]

A Flutter app can look polished and still fall apart once real users start using it.

In this guide, Nicholas explains what production-ready actually means beyond “it works on my machine.”

You'll learn about defensive networking, retry logic, offline support, scalable state management, performance fixes, and lots more.

https://t.co/cIfCuXgdgF

[kubesimplify @kubesimplify]

Migrated AI workloads to K8s 1.36. Performance dropped 18%.

Root cause: scheduler preemption got more aggressive. Training jobs were getting killed by inference spikes.

Fix:
PreemptionPolicy: Never (on the training PriorityClass)

15 min to fix. 18% perf back.
Read the changelog. 📝

[David Bombal @davidbombal]

Pass your Cisco CCNA with David

#CCNA #Devnet #Cisco #CCNP