Varun Madupu 💙

Freelance | Flutter developer | AWS

[Varun Madupu 💙 @devmvk]

@f_a_r_i_n_x @Naveen_X69 Why did he abscond when he gave complaint two hours before

[Turing Post @TheTuringPost]

13+ Attention mechanisms you should know

▪️ Self-attention
▪️ Cross-attention
▪️ Causal attention
▪️ Linear Attention
▪️ Softmax attention
▪️ Sliding Window (local attention)
▪️ Global attention
▪️ FlashAttention
▪️ Multi-Head Attention (MHA)
▪️ Multi-Query Attention (MQA)
▪️ Grouped-Query Attention (GQA)
▪️ Multi-Head Latent Attention (MLA)
▪️ Interleaved Head Attention (IHA)

+ Slim Attention, KArAt, XAttention, Mixture-of-Depths Attention (MoDA)

Save the list and explore more about them here: https://t.co/yQVLSnQB61

[Karan🧋 @kmeanskaran]

These are actual high-demand next-level AI skills, not RAG:

- Data prep for instruction fine-tuning
- @UnslothAI ecosystem for fine-tuning, reasoning models, quantization, etc.
- Fine-tuning embedding models
- Backend design using FastAPI, Redis caching, queue workers, rate limiting
- Making LLM inference layer using @vllm_project
- @DeepSpeedAI for multi-GPU training
- Learn NVIDIA Triton for running any vision and object detection models along with LLMs
- Learn about Docker and CUDA setup
- Use of @huggingface ecosystem
- Distributed systems using @anyscalecompute
- Kubernetes and Terraform setup for serving your model
- Learn about @modal for low-config setup deployment
- Deploying LLM on Ollama for easier calls
- Context engineering
- Agent memory harness
- Multi-agent orchestration
- LLM as a judge

These skills are more focused towards inference engineering to ensure that your model runs smoothly in production.

Memory, inference, and orchestration matter a lot more than frontier models.

I am learning inference engineering and agent harness nowdays.

Totally worth exploring!

[Akshay Shinde @ConsciousRide]

90% of Applied AI Engineering interviews are just these 7 concepts repeated:

[Vivo @vivoplt]

Research papers you must read for AI Engineer interviews:

1. Attention is all you need (Transformers)
2. LoRA (Low rank adaption)
3. PEFT ( Parameter Efficient Fine Tuning)
4. VIT (Vision Transformers)
5. VAE (Variational Auto Encoder)
6. GANs ( Generative Adversarial Networks)
7. BERT ( Bidirectional Encoder Representation from Transformers)
8. Diffusion Models (Stable Diffusion)
9. RAG (Retrieval Augment Generation)
10. GPT (Generative Pre-trained Transformers)

[Makakmayum @makakmayum_sid]

In the era of vibe coding, study the fundamentals and read books to keep your fundamentals clear. My career has benefited a lot from reading these books:

1. Designing Data Intensive Applications
2. Operating Systems: Three Easy Pieces
3. A Philosophy of Software Design
4. System Design Interview: An Insider‘s Guide

[neural nets. @cneuralnetwork]

I came across this blog from Chip Huyen in 2022 where she discussed the ai courses to do. i feel they are relevant to this day!
Here they are -

STATS 202 :- data mining basics
CS109 :- probability foundations
CS231N :- computer vision DL
CS224N :- NLP deep learning
CS229 :- theoretical machine learning
CS221 :- intro artificial intelligence
CS228 :- probabilistic graphical models
CS234 :- reinforcement learning
CS238 :- decision theory RL
CS224W :- graph machine learning
CS246 :- large-scale data mining
CS230 :- applied deep learning
CS236 :- generative models
EE263 :- linear dynamical systems
CS336 :- robot perception
CS103 :- discrete math foundations
CS124 :- NLP basics
CS223A :- robotics fundamentals

i have done a few from here, would reccomend

[HackerNoon | Learn Any Technology @hackernoon]

“Build an AI agent” isn’t the right starting point.

Real systems need architecture, determinism, and integration—not just better prompts.

Here’s what actually matters: https://t.co/EQ3bs5eYgI

[Alexey Grigorev @Al_Grigor]

AI Engineering Buildcamp is project-driven.

You learn AI engineering by building.

15 projects you can build during the course 👇🏼

(You get lifetime access to the course if you sign up)

[Jahir Sheikh @jahirsheikh8]

Places to get your first 100 users:

1. Reddit (niche subreddits)
2. Indie Hackers
3. Hacker News
4. Product Hunt
5. BetaList
6. AlternativeTo
7. SaaSHub
8. Uneed
9. Launching Next
10. StartupBase
11. PitchWall
12. MicroLaunch
13. Dev Hunt
14. Side Project Ideas
15. Niche newsletters
16. Founder communities
17. Waitlist directories

[gxjo @gxjo_dev]

Best Skills That Actually Get You Hired (2026)

Backend – APIs (REST + GraphQL), Auth (JWT/OAuth, RBAC/ABAC), DB Design (SQL + NoSQL), Caching (Redis/CDN), Rate Limiting, Queues (Kafka/RabbitMQ), Event-driven architecture, Idempotency, API versioning, Observability (logs/metrics/traces), Background jobs, File storage (S3/GCS), WebSockets, gRPC, Feature flags

Frontend – State Management (Redux/Zustand/Signals), Performance (code splitting, lazy loading), Accessibility (a11y), Responsive design, Testing (unit + e2e), SSR/SSG (Next.js), Hydration, Edge rendering, API integration patterns, Error boundaries, Form handling, Design systems, Micro-frontends, Web security (XSS, CSP)

DevOps / Infra – Docker, CI/CD pipelines, Kubernetes (basics → deployments), Infrastructure as Code (Terraform), Cloud (AWS/GCP/Azure), Monitoring (Prometheus/Grafana), Logging (ELK), Tracing (OpenTelemetry), CDN + edge (Cloudflare), Secrets management, Autoscaling, Blue-green / Canary deploys, Cost optimization

System Design – Scalability patterns, Tradeoffs (latency vs consistency), Distributed systems, CAP theorem, Consistency models, Data partitioning (sharding), Replication, Load balancing, Caching strategies, API gateways, Service discovery, Fault tolerance, Circuit breakers, Backpressure, Multi-region design

Data / AI (increasingly expected) – Basic ML intuition, LLM APIs, Prompt engineering, Embeddings, Vector DBs (Pinecone/Weaviate), RAG pipelines, Data pipelines (ETL), Streaming (Kafka), Feature stores, Model evaluation basics

Security (non-optional now) – Auth flows, OWASP Top 10, Input validation, Rate limiting abuse, Secrets handling, HTTPS/TLS basics, Secure storage, API security, Dependency vulnerabilities

Testing / Reliability – Unit testing, Integration testing, E2E testing, Test coverage, Load testing, Chaos testing basics, Observability-driven debugging, SLOs/SLAs, Incident handling

Developer Productivity – Git (advanced workflows), Debugging complex systems, Reading large codebases, Writing clean PRs, Code reviews, Documentation, CLI tools, Automation scripting

AI-Assisted Development – Using tools like GitHub Copilot, Cursor IDE, ChatGPT effectively, Prompting for code, Code validation (not blind trust), Generating tests/docs, Refactoring with AI

Soft Skills (still underrated, still decisive) – Communication (clear + structured), Ownership, Breaking down problems, Asking good questions, Explaining tradeoffs, Writing design docs, Handling ambiguity, Prioritization

[Vivo @vivoplt]

Skills That Pay in the AI Era (2026)

AI/ML: Python, PyTorch, Transformers, Fine-tuning, Evaluation
AI Stack: RAG, Vector Databases, Embeddings, Prompt Engineering, Agents
Backend: Node.js, Go, Rust, API Design, System Design
Frontend: React, Next.js (for AI apps & dashboards)
APIs: REST, GraphQL, gRPC
Databases: PostgreSQL, Redis, Vector DBs
DevOps: Docker, Kubernetes, CI/CD, Terraform
Cloud: AWS/GCP/Azure, Serverless, Edge (Cloudflare, Vercel)
MLOps: Model serving, MLflow, Monitoring, Data pipelines
Security: Auth, API Security, Zero Trust
AI Productivity: Copilot, Cursor, Code review with AI
Core CS: Data Structures, Networking, OS, Scalability

[Vivo @vivoplt]

Don’t overthink it.
• Build a Calculator to master logic & loops
• Build a Weather App using live APIs
• Build a CRUD Web App with Flask + DB
• Build a Chatbot UI with Streamlit + GPT
• Build a File Organizer with os & shutil
• Build a Resume Parser using NLP
• Build a Stock Predictor using ML
• Build a Job Tracker that updates Notion

[Suraj Sharma @suraj_sharma14]

If you're hunting for remote work, Reddit is one of the most underrated job boards on the internet.

You just need to know where to look.

Bookmark this thread ↓

[Vaidehi @Ai_Vaidehi]

If I had to start System Design from scratch again, I’d ignore 90% of the internet…

…and just study these 40 articles.

No random YouTube hopping.
No endless tabs.
No confusion.

Just a clean, structured path that actually works.

This is the roadmap I *wish* I had during my interview prep 👇

You’ll learn:
• How to think in systems (not just memorize answers)
• Real trade-offs (scalability vs consistency, latency vs cost)
• How to design like a senior engineer

And the best part?
You can even:
→ Ask questions via voice in real-time
→ Get instant feedback
→ Practice HLD even as a beginner

Here’s the full breakdown:

1. HLD Basics → https://t.co/I6E3xWnnPV
2. Core Concepts & Trade-offs → https://t.co/guimX30aqb
3. Networking & DNS → https://t.co/mEQN53UdRK
4. Load Balancing & Scaling → https://t.co/kKWa0cgDfU
5. Application Architecture → https://t.co/pBzsfCiUVC
6. Databases → https://t.co/Aq4AJBSTWy
7. Caching → https://t.co/SjJ4m8qhhP
8. Async Processing → https://t.co/1S25lPgiEC
9. Communication Protocols → https://t.co/v5Lse3k0wP
10. Performance & Monitoring → https://t.co/eQOXMGYVqj
11. Cloud Design Patterns → https://t.co/20nRBjreAn
12. Reliability Patterns → https://t.co/bWuWBbzqEZ

Save this.
This is easily 50+ hours of scattered learning—compressed into one roadmap.

Follow this, and System Design will finally start making sense.

[Corey Ganim @coreyganim]

https://t.co/Xq5tVlMQd0

[Joseph Tsar @joseph_tsar_]

Full Course On How To Speak Brilliantly

[Chioma Amadi @Chioma__Amadi]

If you’re hunting for a remote job, you just need to figure out how Reddit works, and you’ll never be unemployed for a long time.

Here’s a list of subreddits you should bookmark right now:

[Jaynit @jaynitx]

In 2013, Yale professor Ben Polak gave a legendary 1-hour lecture on Game Theory.

It will change how you make decisions in negotiations, business, and life.

His frameworks:
• Dominance arguments
• Backward induction
• The proactive bias

12 lessons to make better decisions:

[Karan🧋 @kmeanskaran]

You don't need random papers, just learn these concepts sequentially to kickstart your LLM engineer journey.

Here’s what actually matters if you’re an engineer:
- Tokenization and embeddings
- Attention and transformer blocks
- Training and fine tuning
- LoRA and QLoRA
- DPO and alignment
- Quantization
- KV cache and inference systems
- FlashAttention and PagedAttention

Not theory.
Systems.

I wrote a complete guide covering everything step by step. If you want to build with LLMs, not just study them, this is for you.

Also breaking down inference and deployment at scale in upcoming posts on hands-on level.