@t__node

RTs not endorsement

Joined November 2016
  • Tweets43.8K
  • Following3.2K
  • Followers304
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deadphoenix retweeted
WebDeveloperMentor
@MentorWebDev2h
Backend Skills Ranked by Importance 🎯 🟒 GraphQL Federation - Nice to Know 🟒 gRPC & Protobuf - Nice to Know 🟒 Event Sourcing & CQRS - Nice to Know 🟒 Service Mesh Basics - Nice to Know 🟒 WebSockets & SSE - Nice to Know πŸ”΅ Caching (Redis) - Useful πŸ”΅ Message Queues - Useful πŸ”΅ Docker & Containers - Useful πŸ”΅ CI/CD Pipelines - Useful πŸ”΅ Rate Limiting - Useful πŸ”₯ Database Indexing - Important πŸ”₯ Logging & Monitoring - Important πŸ”₯ Writing Automated Tests - Important πŸ”₯ Error Handling & Retries - Important πŸ”₯ REST API Design - Important 🚨 SQL & Data Modeling - Essential 🚨 Authentication & Authorization - Essential 🚨 Input Validation & Security - Essential 🚨 HTTP & Networking Fundamentals - Essential 🚨 Git & Version Control - Essential Which one would you move up or down? πŸ‘€
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Caching & Redis - Ultimate Roadmap (BOOKMARK NOW)! 🟒 01 Caching Foundations πŸ”Ή Why Caching Works (Latency vs Load) πŸ”Ή Cache Hit, Miss & Hit Ratio πŸ”Ή Where to Cache: Browser, CDN, App, DB πŸ”Ή What Is Safe to Cache πŸ”Ή Cache Cost vs Complexity 🌐 02 HTTP & CDN Caching πŸ”Ή Cache-Control, ETag & Expires πŸ”Ή Conditional Requests (304) πŸ”Ή CDN Edge Caching πŸ”Ή Stale-While-Revalidate πŸ”Ή Cache Busting & Versioned Assets 🧠 03 Caching Patterns πŸ”Ή Cache-Aside (Lazy Loading) πŸ”Ή Read-Through & Write-Through πŸ”Ή Write-Behind (Write-Back) πŸ”Ή Refresh-Ahead πŸ”Ή Choosing the Right Pattern ⚑ 04 Redis Fundamentals πŸ”Ή In-Memory Data Store Basics πŸ”Ή Strings, Hashes & Lists πŸ”Ή Sets & Sorted Sets πŸ”Ή Keys, TTL & Expiration πŸ”Ή Redis CLI & Client Libraries 🧱 05 Advanced Redis Data Types πŸ”Ή Streams πŸ”Ή Pub/Sub πŸ”Ή Bitmaps & HyperLogLog πŸ”Ή Geospatial Indexes πŸ”Ή Lua Scripts & Transactions πŸ—‘οΈ 06 Eviction & Invalidation πŸ”Ή TTL Strategies πŸ”Ή LRU, LFU & maxmemory Policies πŸ”Ή Cache Invalidation Strategies πŸ”Ή Event-Driven Invalidation πŸ”Ή Versioned Keys & Tags πŸ’₯ 07 Cache Failure Modes πŸ”Ή Cache Stampede (Thundering Herd) πŸ”Ή Cache Penetration & Avalanche πŸ”Ή Hot Keys πŸ”Ή Request Coalescing & Locks πŸ”Ή Negative Caching πŸ” 08 Real-World Use Cases πŸ”Ή Session Storage πŸ”Ή Rate Limiting πŸ”Ή Leaderboards & Counters πŸ”Ή Job Queues & Background Workers πŸ”Ή Distributed Locks πŸ—οΈ 09 Scaling Redis πŸ”Ή Persistence: RDB vs AOF πŸ”Ή Replication & Failover πŸ”Ή Redis Sentinel πŸ”Ή Redis Cluster & Sharding πŸ”Ή Consistent Hashing πŸ“Š 10 Performance & Observability πŸ”Ή Memory Optimization πŸ”Ή Pipelining & Batching πŸ”Ή SLOWLOG & Latency Monitoring πŸ”Ή Hit Ratio Dashboards πŸ”Ή Benchmarking & Load Testing πŸ›‘οΈ 11 Security & Operations πŸ”Ή Auth, ACLs & TLS πŸ”Ή Network Isolation πŸ”Ή Backups & Restore πŸ”Ή Managed Redis (ElastiCache, Upstash) πŸ”Ή Redis vs Memcached vs Valkey πŸ”₯ 12 Production Caching πŸ”Ή Multi-Layer Cache Architecture πŸ”Ή Consistency Trade-offs πŸ”Ή Cache Warming πŸ”Ή Capacity Planning & Cost πŸ”Ή Backend β†’ Performance Engineer Path Cache smartly, cut latency & scale without melting your database.
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deadphoenix retweeted
Mansi πŸ‘©β€πŸ’»
@MansiCodez2h
Most developers learn Kubernetes too early. Before Kubernetes, understand: β†’ Processes β†’ Threads β†’ Networking β†’ HTTP β†’ Containers β†’ Linux β†’ Distributed systems Otherwise you're just memorizing commands.
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Elon Musk again repeats that money would become useless in the future. On the same note, he is expecting a $100 trillion valuation for his companies in the next 10 years, while owning more than one third of the stake, with a majority of the controlling voting shares. Follow what people do, not what they say.
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deadphoenix retweeted
Mashood K
@fromcodetocloudOct 20
FORGET TOY PROJECTS FOR A MOMENT These 7 GitHub repositories can help you understand how real engineering systems are built. 1. Kubernetes github.com/kubernetes/kubern… 2. Prometheus github.com/prometheus/promet… 3. Cilium github.com/cilium/cilium 4. Envoy github.com/envoyproxy/envoy 5. Backstage github.com/backstage/backsta… 6. Argo CD github.com/argoproj/argo-cd 7. OpenTelemetry Collector github.com/open-telemetry/op… Here's the trick: Don't try to understand the entire codebase. Pick one component, trace how a request flows through it, and follow the code. Reading production code is a skill worth developing.

GitHub - kubernetes/kubernetes: Production-Grade Container Scheduling and Management

Production-Grade Container Scheduling and Management - kubernetes/kubernetes

github.com
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Caste or IIT Rank 1 holders
Indic-Antidote@Antidote1xOct 19
IIT Rank 1 by caste[1960-2022] 1960 - Kayastha 1961 - Brahmin 1962 - Brahmin 1963 - Brahmin 1964 - Brahmin 1965 - Brahmin 1966 - Baniya 1967 - Brahmin 1968 - Brahmin 1969 - Brahmin 1970 - Baniya 1971 - Brahmin 1972 - Brahmin 1973 - Karmakar 1/4
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Production Bugs Ranked by Debugging Difficulty πŸ› 🟒 Typo in an Env Variable Name - Obvious 🟒 Missing Null Check - Obvious 🟒 Wrong API Endpoint URL - Obvious 🟒 Expired SSL Certificate - Obvious 🟒 Forgotten Database Migration - Obvious 🟑 Off-by-One Pagination Error - Tricky 🟑 Timezone & DST Bugs - Tricky 🟑 CORS Misconfiguration - Tricky 🟑 N+1 Query Slowdowns - Tricky 🟑 Stale Cache Serving Old Data - Tricky πŸ”₯ Memory Leak After 3 Days Uptime - Brutal πŸ”₯ Database Deadlocks Under Load - Brutal πŸ”₯ Connection Pool Exhaustion - Brutal πŸ”₯ Async Race Conditions - Brutal πŸ”₯ Flaky Tests Only in CI - Brutal πŸ’€ Intermittent Bug That Vanishes in Debug Mode - β€œWorks on My Machine” πŸ’€ Clock Drift Across Distributed Nodes - β€œWorks on My Machine” πŸ’€ Heisenbug in Multi-Threaded Code - β€œWorks on My Machine” πŸ’€ Data Corruption From Partial Writes - β€œWorks on My Machine” πŸ’€ Bug Only Reproducible in Prod Traffic - β€œWorks on My Machine” Which bug cost you the most sleep? πŸ‘€
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AI Infrastructure for DevOps Engineers: The Ultimate Learning Roadmap β”œβ”€β”€ 01 Linux & Systems Fundamentals β”‚ β”œβ”€β”€ Linux, Bash, Python & Git β”‚ β”œβ”€β”€ Processes, Memory, CPU & GPU Basics β”‚ └── Networking, DNS, TLS & Load Balancing β”‚ β”œβ”€β”€ 02 AI & LLM Fundamentals β”‚ β”œβ”€β”€ LLMs, Transformers, Tokens & Embeddings β”‚ β”œβ”€β”€ Training vs Inference β”‚ └── Model Sizes, Context Windows & GPU Requirements β”‚ β”œβ”€β”€ 03 GPU Infrastructure β”‚ β”œβ”€β”€ NVIDIA GPUs, CUDA & GPU Drivers β”‚ β”œβ”€β”€ VRAM, GPU Utilization & Memory Bandwidth β”‚ └── GPU Monitoring, Scheduling & Capacity Planning β”‚ β”œβ”€β”€ 04 Containers for AI Workloads β”‚ β”œβ”€β”€ Docker, Container Images & Registries β”‚ β”œβ”€β”€ NVIDIA Container Toolkit β”‚ └── GPU-Enabled Containers & Runtime Configuration β”‚ β”œβ”€β”€ 05 Kubernetes for AI β”‚ β”œβ”€β”€ GPU Nodes, Device Plugins & Resource Requests β”‚ β”œβ”€β”€ Node Pools, Taints, Tolerations & Scheduling β”‚ └── Kueue, KubeRay & Distributed AI Workloads β”‚ β”œβ”€β”€ 06 Cloud AI Infrastructure β”‚ β”œβ”€β”€ AWS, Azure & GCP GPU Instances β”‚ β”œβ”€β”€ VPCs, IAM, Private Networking & Storage β”‚ └── GPU Clusters, Autoscaling & High Availability β”‚ β”œβ”€β”€ 07 LLM Serving & Inference β”‚ β”œβ”€β”€ Deploy Models with vLLM or NVIDIA Triton β”‚ β”œβ”€β”€ APIs, Batching, Streaming & Model Endpoints β”‚ └── Scaling, Load Balancing & Health Checks β”‚ β”œβ”€β”€ 08 AI Infrastructure as Code β”‚ β”œβ”€β”€ Terraform for GPU Infrastructure β”‚ β”œβ”€β”€ Ansible for Host & GPU Configuration β”‚ └── Reusable Modules, Secrets & Environment Management β”‚ β”œβ”€β”€ 09 CI/CD for AI Systems β”‚ β”œβ”€β”€ GitHub Actions, Jenkins & GitOps β”‚ β”œβ”€β”€ Automated Model Deployment & Image Builds β”‚ └── Versioning, Rollbacks & Release Strategies β”‚ β”œβ”€β”€ 10 AI Platform Engineering β”‚ β”œβ”€β”€ Self-Service AI Environments β”‚ β”œβ”€β”€ Model APIs, Developer Portals & Shared Services β”‚ └── Multi-Tenancy, Resource Quotas & Isolation β”‚ β”œβ”€β”€ 11 AI Observability & SRE β”‚ β”œβ”€β”€ Prometheus, Grafana & OpenTelemetry β”‚ β”œβ”€β”€ GPU Metrics, Latency, Throughput & Token Usage β”‚ └── SLOs, Alerting, Incident Response & Capacity Planning β”‚ β”œβ”€β”€ 12 AI Security & FinOps β”‚ β”œβ”€β”€ IAM, Secrets, Network Policies & Model Access β”‚ β”œβ”€β”€ Container Security, Supply Chain & Data Protection β”‚ └── GPU Cost Optimization, Quotas & Scale-to-Zero β”‚ └── 13 Hands-On Projects β”œβ”€β”€ Deploy an LLM API using Docker and vLLM β”œβ”€β”€ Run GPU Workloads on Kubernetes β”œβ”€β”€ Build an AI Infrastructure CI/CD Pipeline └── Deploy a Monitored, Secure & Cost-Optimized AI Platform AI infrastructure isn't just about deploying models. It's about building the platform that makes AI reliable, scalable, secure and cost-efficient. Save this roadmap. Learn one layer at a time. Automate everything you can. Build real projects. The goal isn't just to become a DevOps Engineer who uses AI. It's to become the engineer who can run AI in production.
Tech Fusionist | Kushal Gangil
@techyoutbeOct 19
Interview Experience at Nvidia (Senior DevOps Engineer) πŸ‘‡πŸ‘‡
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Want to Become a Gold-Tier AWS Cloud/DevOps Engineer? Build These 13 AWS Projects (with Architectures)β˜οΈπŸš€ medium.com/aws-in-plain-engl…

Want to Become a Gold-Tier AWS Cloud/DevOps Engineer?

Build These 13 AWS Projects (with Architectures)β˜οΈπŸš€

aws.plainenglish.io
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Interview Experience at Nvidia (Senior DevOps Engineer) πŸ‘‡πŸ‘‡
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deadphoenix retweeted
Tanuj
@tanujDE318020h
Amazon system design interview: A product has 10,000 units in stock. One million users try to buy it in 30 seconds. Design A: Database transactions with row-level locking. Design B: Redis-based inventory reservation. Design C: Queue-based purchase processing. How would you prevent overselling without making checkout unbearably slow?
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deadphoenix retweeted
Shubh Jain
@shubh1923h
Tech professionals moving from the US back to India: - 2023: 7,200 - 2024: 9,800 - 2025: 15,100 - 2026: 12,700, and that's only till September About 14,500 still moved the other way this year. What returnees are running into: - pay at 25% to 33% of their US salary, for many - overseas experience alone isn't the edge it was - demand is for AI, ML, data, cloud and security skills Coming back isn't the hard part. Coming back with a skill India is short of is.
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A man chases meaning, systems, structures. A woman chases experience, sensation, and connection. Let’s be honest: women don’t follow ideologiesβ€” they follow men who hold them. Women are not drawn to philosophies; they are drawn to power, presence, and polarity. She doesn’t love communism, capitalism, religion, or atheism. She loves the man who speaks to them with conviction. - If he’s a communist, she becomes one. - If he’s a capitalist, suddenly she’s quoting Adam Smith. - If he’s a yogi, she’s chanting mantras. - If he’s a nihilist, she’s reading Nietzsche on Instagram stories. Not because she believesβ€” But because she wants to belong to the one who does. β€œShe only loves what her love lovesβ€”and nothing else.” This is the brutal essence of feminine nature. If her man loves God, she’ll pretend to worship. If her man whom she love hates certain ideologies, she’ll hate her too. She doesn’t care for flags, blood, or heritage. She mirrors the object of her emotional investment.
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Beautiful explanation #VijayaDasami #HappyDussehra
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deadphoenix retweeted
Hindutva Knight
@HPhobiaWatchOct 20
This is cool use of AI Happy Dussehra
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deadphoenix retweeted
Easy Talks
@EasyAsPie372040Oct 19
YES ! Big NO to Arrange Marriage I can fully relate to this reel as I have seen something ditto in my extended family Very rich yet they claim that we don't have maid culture at a home and will never have it Even they don't allow daughter in laws to use washing machine & and few yrs back when one of the DIL got COVID, they literally left her on her own to survive Even the husband is so Miser, that he hardly spent rs 20... Never ever saw their children's having dry fruits. They only believe in saving and depositing money in the bank account. And if you look at 4 kids in their family you will clearly see 3 are anemic
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Signs of extremely low intelligence: 1. Being Muslim
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deadphoenix retweeted
Indic-Antidote@Antidote1xOct 19
IIT Rank 1 by caste[1960-2022] 1960 - Kayastha 1961 - Brahmin 1962 - Brahmin 1963 - Brahmin 1964 - Brahmin 1965 - Brahmin 1966 - Baniya 1967 - Brahmin 1968 - Brahmin 1969 - Brahmin 1970 - Baniya 1971 - Brahmin 1972 - Brahmin 1973 - Karmakar 1/4
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deadphoenix retweeted
Aryan
@Just_Aryan_Oct 19
Chatgpt gone wrong πŸ™ŒπŸ˜‚πŸ«‘
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If you learn AI Agent Engineering, you will never be unemployed again. I'd blindly follow these 12 stages. STAGE 01 β†’ Agent Architecture & State Machines ↳ Learn: DAGs, LangGraph, Temporal, checkpointing. ↳ Build: An agent that loops, branches, pauses and resumes. ↳ Why: Chat loops break. Production agents need durable state. STAGE 02 β†’ Tool Use & MCP ↳ Learn: Function calling, JSON-RPC, Pydantic, MCP, OAuth. ↳ Build: A secure MCP server with read-only Postgres access. ↳ Why: Agents need standardized tools, not fragile wrappers. STAGE 03 β†’ Trajectory Evals & Observability ↳ Learn: Tracing, golden datasets, step-level grading, LLM judges. ↳ Build: CI that evaluates 50 scenarios, tool paths and failures. ↳ Why: Correct outputs can hide dangerous execution paths. STAGE 04 β†’ Memory & Context Engineering ↳ Learn: Working, episodic, semantic memory, token budgets. ↳ Build: An agent that compresses 50 turns into useful memory. ↳ Why: Unlimited context doesn't mean better reasoning. STAGE 05 β†’ Agent Security & Guardrails ↳ Learn: Prompt injection, tool poisoning, least privilege. ↳ Build: A security layer blocking unauthorized agent actions. ↳ Why: One compromised agent can threaten the entire system. STAGE 06 β†’ Sandboxed Execution ↳ Learn: E2B, gVisor, Docker, network isolation. ↳ Build: An agent that executes Python inside a secure sandbox. ↳ Why: Never give an LLM unrestricted host access. STAGE 07 β†’ Human-in-the-Loop ↳ Learn: Async approvals, webhooks, durable pauses, audit logs. ↳ Build: A $10k invoice agent that waits for Slack approval. ↳ Why: High-stakes actions need human accountability. STAGE 08 β†’ Computer Use & GUI Automation ↳ Learn: DOM parsing, screenshots, Playwright, visual grounding. ↳ Build: An agent that navigates a portal and downloads CSVs. ↳ Why: Legacy software doesn't always have APIs. STAGE 09 β†’ Self-Correction Loops ↳ Learn: Validators, tracebacks, bounded retries, error analysis. ↳ Build: A coding agent that fixes errors and retries up to 3 times. ↳ Why: Reliable agents recover instead of giving up. STAGE 10 β†’ Graceful Degradation ↳ Learn: Circuit breakers, timeouts, browser fallbacks, model routing. ↳ Build: An agent that uses browser automation when APIs fail. ↳ Why: Production systems break. Reliable agents adapt. STAGE 11 β†’ Multi-Agent Orchestration & A2A ↳ Learn: A2A protocols, typed handoffs, agent topologies. ↳ Build: Researcher + Coder + Reviewer with typed A2A handoffs. ↳ Why: Poor handoffs turn collaboration into chaos. STAGE 12 β†’ Public Teardowns & Portfolio ↳ Learn: ADRs, cost-per-task, p95 latency, benchmarking. ↳ Build: A public teardown showing costs, failures and evals. ↳ Why: Real engineering beats another LangChain demo. The goal isn't to build an agent that works once. It's to build one that works reliably, securely and at scale in production. πŸ“Œ Bookmark this + send it to someone learning AI agents.
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