next wave of Al engineering may happen: harnesses, agent orchestration, feedback loops, multi-agent systems and eventually systems that can operate with much less human intervention.
HARNESS
↓ ↓ ↓
Coding Agent Research Agent Data Agent
↓
Agent orchestration
↓
Final outcome
The harness becomes the platform on which developers build agent systems
If you remove all constraints, remove all the laws of physics and you just think from first principal, what's the most extreme postive customer experience you can give.
Work from there backward and make it possible
Startup success is not a consequence of good genes or being in the right place at the right time. Startup success can be engineered by following the right process, which means it can be learned , and can be taught.
Entrepreneurship is a kind of management
This Independence Day 🇮🇳, I asked, "What will the next century of young Indians look like?"
$0.01 Drone Deliveries. UAVs that never land. Groceries in under 10 minutes.
Meet The 22nd Century Indian. A Documentary on a New India.
For people who keep asking what to build in AI Engineering
> Build your own Reasoner (Chain of Thought implementation)
> Build your own Agent loop (ReAct pattern)
> Build your own Inference Server (in C++/Rust)
> Build your own Transformer from scratch (Attention is all you need)
> Build your own Vector Database (HNSW index)
> Build your own RAG pipeline
> Build your own Flash Attention kernel (CUDA)
> Build your own Quantization library (Int8/FP4 implementation)
> Build your own Mixture of Experts (MoE) routing layer
> Build your own Distributed training loop (FSDP/Tensor Parallelism)
> Build your own KV Cache paging system (like vLLM)
> Build your own Speculative Decoding system
> Build your own State Space Model (Mamba implementation)
> Build your own RLHF pipeline (PPO implementation)
> Build your own Small Language Model (SLM)
> Build your own Matrix Multiplication kernel
> Build your own LoRA (Low-Rank Adaptation) trainer
> Build your own Code interpreter sandbox
> Build your own DPO (Direct Preference Optimization) loss function
> Build your own Graph RAG system
> Build your own Model merger (Model Soups/Spherical Linear Interpolation)
> Build your own Interpretability tool (SAE - Sparse Autoencoders)
> Build your own Synthetic data generator
> Build your own Function Calling router
> Build your own Structured Output parser (Context Free Grammars)
> Build your own Multi-modal projector (CLIP implementation)
> Build your own LLM Eval harness
> Build your own Guardrails system (Input/Output filtering)
> Build your own Prompt caching mechanism
> Build your own Tokenizer (BPE implementation)
> Build your own Autograd engine (like Micrograd)
> Build your own Diffusion model (UNet + Scheduler)
> Build your own Vision Transformer (ViT)
> Build your own Whisper-style ASR model
> Build your own Text-to-Speech pipeline
> Build your own Semantic Router
> Build your own Knowledge Graph builder
> Build your own Data curation pipeline (MinHash/Deduplication)
> Build your own AI Gateway (Load balancing/Failover)
> Build your own Parameter Efficient Fine-Tuning (PEFT) library
> Build your own Text-to-SQL engine
> Build your own Recommendation system (Two-tower architecture)
> Build your own Embedding model
> Build your own Logit Processor
> Build your own Softmax kernel optimization
> Build your own Adversarial attack generator
> Build your own Audio Spectrogram transformer
> Build your own Neural Architecture Search
> Build your own Model Distillation pipeline
> Build your own Feature Store
> Build your own Database driver (for Vectors)
Major green flags in an AI Engineer:
1/ builds before talking about building
2/ deeply understands how models actually work
3/ ships fast and iterates even faster
4/ constantly experiments with new models, tools & techniques
5/ has built things outside of tutorials and courses
6/ can go from research paper → working prototype
7/ understands the difference between a demo and a production system
8/ obsessively measures model performance instead of trusting vibes
9/ knows when to use an API vs. fine-tuning vs. building from scratch
10/ cares about latency, cost, reliability & evals not just accuracy
11/ reads papers but actually implements the interesting parts
12/ comfortable debugging things that don't have Stack Overflow answers
13/ constantly thinks about how to make AI systems more useful
14/ doesn't chase every new model release knows what actually matters
15/ can explain a complex AI system in simple language
If you hit a good chunk of these, tell me what you're building.
As an Amazon VP, I oversaw the promotions of 270+ engineers. Some were promoted from mid-level to senior, others from senior to staff or principal. Here’s how I think about promoting L5, L6, and L7 engineers.
instead of doomscrolling:
study and read about Naval Ravikant, cognitive biases, human nature, philosophy, Charlie Munger, Ancient History, Greek Mythology, psychology, the nervous system, Julius Caesar, Alexander the Great, the Hermetic Laws of the Universe, mental models, polymathy, coding, persuasion, ethics, emotional intelligence, Marcus Aurelius, Napoleon, and The Art of War.