RTs not endorsement
Joined November 2016
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deadphoenix retweeted
AI Infrastructure for DevOps Engineers: The Ultimate Learning Roadmap
├── 01 Linux & Systems Fundamentals
│ ├── Linux, Bash, Python & Git
│ ├── Processes, Memory, CPU & GPU Basics
│ └── Networking, DNS, TLS & Load Balancing
│
├── 02 AI & LLM Fundamentals
│ ├── LLMs, Transformers, Tokens & Embeddings
│ ├── Training vs Inference
│ └── Model Sizes, Context Windows & GPU Requirements
│
├── 03 GPU Infrastructure
│ ├── NVIDIA GPUs, CUDA & GPU Drivers
│ ├── VRAM, GPU Utilization & Memory Bandwidth
│ └── GPU Monitoring, Scheduling & Capacity Planning
│
├── 04 Containers for AI Workloads
│ ├── Docker, Container Images & Registries
│ ├── NVIDIA Container Toolkit
│ └── GPU-Enabled Containers & Runtime Configuration
│
├── 05 Kubernetes for AI
│ ├── GPU Nodes, Device Plugins & Resource Requests
│ ├── Node Pools, Taints, Tolerations & Scheduling
│ └── Kueue, KubeRay & Distributed AI Workloads
│
├── 06 Cloud AI Infrastructure
│ ├── AWS, Azure & GCP GPU Instances
│ ├── VPCs, IAM, Private Networking & Storage
│ └── GPU Clusters, Autoscaling & High Availability
│
├── 07 LLM Serving & Inference
│ ├── Deploy Models with vLLM or NVIDIA Triton
│ ├── APIs, Batching, Streaming & Model Endpoints
│ └── Scaling, Load Balancing & Health Checks
│
├── 08 AI Infrastructure as Code
│ ├── Terraform for GPU Infrastructure
│ ├── Ansible for Host & GPU Configuration
│ └── Reusable Modules, Secrets & Environment Management
│
├── 09 CI/CD for AI Systems
│ ├── GitHub Actions, Jenkins & GitOps
│ ├── Automated Model Deployment & Image Builds
│ └── Versioning, Rollbacks & Release Strategies
│
├── 10 AI Platform Engineering
│ ├── Self-Service AI Environments
│ ├── Model APIs, Developer Portals & Shared Services
│ └── Multi-Tenancy, Resource Quotas & Isolation
│
├── 11 AI Observability & SRE
│ ├── Prometheus, Grafana & OpenTelemetry
│ ├── GPU Metrics, Latency, Throughput & Token Usage
│ └── SLOs, Alerting, Incident Response & Capacity Planning
│
├── 12 AI Security & FinOps
│ ├── IAM, Secrets, Network Policies & Model Access
│ ├── Container Security, Supply Chain & Data Protection
│ └── GPU Cost Optimization, Quotas & Scale-to-Zero
│
└── 13 Hands-On Projects
├── Deploy an LLM API using Docker and vLLM
├── Run GPU Workloads on Kubernetes
├── 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.
deadphoenix retweeted
Want to Become a Gold-Tier AWS Cloud/DevOps Engineer?
Build These 13 AWS Projects (with Architectures)☁️🚀
medium.com/aws-in-plain-engl…
deadphoenix retweeted
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?
deadphoenix retweeted
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.
deadphoenix retweeted
A man chases meaning, systems, structures.
A woman chases experience, sensation, and connection.
Let’s be honest:
women don’t follow ideologies— 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 believes—
But because she wants to belong to the one who does.
“She only loves what her love loves—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.
deadphoenix retweeted
Beautiful explanation
#VijayaDasami #HappyDussehra
deadphoenix retweeted
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
deadphoenix retweeted
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
deadphoenix retweeted
If you learn AI Agent Engineering, you will never be unemployed again.
I'd blindly follow these 12 stages.
STAGE 01 → 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 → 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 → 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 → 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 → 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 → 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 → 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 → 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 → 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 → 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 → 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 → 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.
📌 Bookmark this + send it to someone learning AI agents.
deadphoenix retweeted
Your 5-year experience might be the same 1 year repeated 5 times.
Here's how interviewers detect it.
They ask questions like:
1. Debugging
"Tell me about the hardest production bug you've fixed. How did you find the root cause?"
2. System Design
"Your database is overloaded. What metrics would you inspect before scaling?"
3. Trade-offs
"Why did you choose Kafka over a simple queue? What would make you change that decision?"
4. Failure Handling
"What happens if your service processes a payment but crashes before acknowledging the message?"
5. Performance
"Your API latency jumped from 100ms to 2 seconds. Walk me through your investigation."
6. Ownership
"Tell me about a technical decision you made that turned out to be wrong."
Notice the pattern?
They're not testing how many technologies you've used.
They're testing whether you've learned from the problems you've solved.
Experience is measured in years.
Engineering maturity is measured in decisions, trade-offs, and lessons learned.
deadphoenix retweeted
विषाद क्रोध भरे 🐍जैसा है। पुरुषार्थ करने के समय विषादग्रस्त होने से तेज नष्ट हो जाता है और कार्य सिद्ध नहीं होता।
Despair is like an angry 🐍. Becoming despondent when trying to achieve something drains your vigor and leaves the task unaccomplished.
- अंगद
deadphoenix retweeted
Intolerant of imperfection, she [Mahakali] deals roughly with all in man that is unwilling and she is severe to all that is obstinately ignorant and obscure;
#SriAurobindo #IntegralYoga #Consciousness
deadphoenix retweeted
how to get a job that’s not even listed yet:
> pick 5 startups you’d love to work at.
> go through their website, product, onboarding and customer complaints. use Claude to find gaps in their growth/product/ or whatever you wanna fix
> stalk the founders’ posts and interviews from the last 30 days like you would stalk your ex..
> figure out what problems they’re actively trying to solve.
> find an overlap with the gaps you identified
> build the smallest possible fix. a better onboarding flow, a landing page rewrite, or a new acquisition play.
> record a 2-min Loom explaining what you found, what you’d change, and why
> cold email the founder. lead with the problem, show your solution, and explain the potential impact.
> don’t waste the first paragraph introducing yourself (intro yourself at the end if you’ve got a great work showcase)
> follow up after 4 days with another useful observation
also im not making this up…
amol avasare cold emailed mike krieger explaining why Anthropic needed a growth team. there wasn’t even a growth role listed. he got hired and went on to become their head of growth
you don’t always need a job opening to get hired.
reply “gap” and i’ll send you the tools + Claude prompt to find such gaps at any startup.
deadphoenix retweeted
since many of you asked how i got this offer
i'm writing an article on how to get contract/freelance roles on platforms like mercor, micro1 etc
dropping soon!
