As promised

Forward Deployed Engineer — the learning list.

Everything I said I would send. The ones marked in red are where to start — if you only do those, you are still moving in the right direction.

The loop: Discovery → Process map

A curated learning library for becoming a Forward Deployed Engineer (FDE), with emphasis on the emerging Forward Deployed AI Engineer role.

Core loop: Customer discovery → problem decomposition → system design

→ AI engineering → integrations → evals → production deployment →

business outcomes → productization.

Forward Deployed Engineering

1
How Palantir Scaled: Why the Best Software Is Built Backwards — a16z + Palantir — Essential Watch on YouTube →
2
The Definitive Guide to Forward Deployed Engineering — Vinoo Ganesh — Essential Read the guide →
3
The Role of a Forward Deployed Software Engineer — Palantir Watch on YouTube →
4
Meet a Palantir Forward Deployed Engineer — Ben Bernstein Watch on YouTube →
5
From Code to Career: Meet Palantir's Hiring Managers Watch on YouTube →
6
OpenAI — Forward Deployed Engineer Roles — Essential Explore OpenAI FDE roles →
7
OpenAI — Forward Deployed Engineer Example OpenAI FDE — NYC →
8
OpenAI — Forward Deployed Software Engineer Read the role →
9
Forward Deployed — Decomposition Exercises — Essential Practice decomposition →
10
The Forward Deployed — Complete Guide Browse the guide →
11
Day in the Life of a Forward Deployed Engineer Read at FDE Academy →
12
The Mom Test — Rob Fitzpatrick — Essential for customer discovery Official site →

Andrew Ng — Applied AI & Agentic Engineering

13
Agentic AI — Andrew Ng — Essential Task decomposition, reflection, tool use, planning, multi-agent workflows, evals and error analysis. Take the course →
14
AI for Everyone — Andrew Ng — Essential Especially useful for identifying AI opportunities and building AI projects inside organizations. Take the course →
15
Generative AI for Everyone — Andrew Ng Take the course →
16
MCP: Build Rich-Context AI Apps with Anthropic — Essential for AI FDEs Take the course →
17
AI Agentic Design Patterns with AutoGen Take the course →
18
Generative AI with Large Language Models Take the course →
19
DeepLearning.AI Generative AI Course Guide Browse the guide →
20
DeepLearning.AI Course Library Browse all courses →

Andrej Karpathy — Mental Models for Modern AI

21
Intro to Large Language Models — Essential Watch on YouTube →
22
Deep Dive into LLMs like ChatGPT — Essential Pretraining, post-training, reasoning, hallucinations, context, tool use, RLHF and model behavior. Watch on YouTube →
23
Software Is Changing (Again) / Software 3.0 — Essential Watch/read via Y Combinator →
24
Software 3.0 — Annotated Notes Read on Latent Space →
25
From Vibe Coding to Agentic Engineering — Karpathy — Essential Watch on YouTube →
26
How I Use LLMs — Andrej Karpathy Watch on YouTube →
27
Andrej Karpathy Website karpathy.ai →
28
Andrej Karpathy YouTube Channel Open channel →

Deeper AI Foundations — Optional

29
Neural Networks: Zero to Hero — Karpathy Open the course →
30
Let's Build GPT: From Scratch, in Code, Spelled Out Watch on YouTube →
31
Stanford CS229 — Building Large Language Models Watch on YouTube →

Real-World FDE / Enterprise AI

32
How Forward Deployed Engineering 10X'd Revenue for an AI Startup — HappyRobot Watch on YouTube →
33
Palantir Explained: Foundry — Akshay Krishnaswamy Watch on YouTube →

Essential High-Signal Feed

Andrew Ng

Applied AI, agentic workflows, AI education and business applications. X → - LinkedIn → - DeepLearning.AI →

Andrej Karpathy

LLMs, Software 3.0, coding agents and AI engineering. X → - LinkedIn → - Website → - YouTube →

Eugene Yan

Production AI systems, evaluation, LLM architecture and applied AI. X → - LinkedIn → - Website →

Hamel Husain

LLM evals, error analysis, production AI and agent systems. X → - LinkedIn →

Shreya Shankar

AI evaluation, data quality, failure analysis and AI product quality. X → - LinkedIn →

Simon Willison

Practical LLM engineering, coding agents, tools, prompt injection and security. X → - LinkedIn → - Blog →

Chip Huyen

AI engineering, inference, ML systems and production architecture. X → - LinkedIn → - Website →

Jason Liu

Structured outputs, RAG, retrieval, evals and practical LLM engineering. X → - LinkedIn → - Website →

Shawn "swyx" Wang

AI engineering, agents, developer tooling and the application layer. X → - LinkedIn → - Latent Space →

Akshay Krishnaswamy

Palantir Chief Architect; especially valuable for FDE philosophy and building backward from customer problems. LinkedIn search →

Additional High-Signal People

Jerry Liu

RAG, agents, data-connected AI applications and LlamaIndex. X → - LinkedIn →

Harrison Chase

Agents, orchestration, LangChain and LangGraph. X → - LinkedIn →

Charles Frye

LLMs, evals, fine-tuning and applied AI engineering. X → - LinkedIn →

Lance Martin

Agent architecture, RAG and LangGraph. X → - LinkedIn →

Logan Kilpatrick

Frontier model capabilities, APIs and AI developer ecosystems. X → - LinkedIn →

Alessio Fanelli

AI engineering, agents and the developer ecosystem. X →

Dex Horthy

Agentic coding, context engineering and robust AI development workflows. X →

Boris Cherny

Claude Code and agentic software engineering. X → - LinkedIn →

Dylan Field

AI-native product development and human/software interaction. X → - LinkedIn →

FDE Practitioners & Palantir Alumni

Vinoo Ganesh

Palantir FDE philosophy and FDE training. Website → - LinkedIn search →

Krzysztof Rossowski-Kowalczyk

Applied AI / former FDE perspective. LinkedIn →

James Thompson

Forward Deployed AI Engineer perspective. LinkedIn →

William Chan

Palantir FDE experience across customer industries. LinkedIn →

Michael Guo

Palantir FDE practitioner perspective. LinkedIn →

If time is limited, do these first:

1
How Palantir Scaled: Why the Best Software Is Built Backwards →
2
The Definitive Guide to Forward Deployed Engineering →
3
OpenAI FDE roles →
4
The Mom Test →
5
Karpathy — Intro to Large Language Models →
6
Karpathy — Deep Dive into LLMs →
7
Karpathy — Software 3.0 →
8
Andrew Ng — AI for Everyone →
9
Andrew Ng — Agentic AI →
10
MCP: Build Rich-Context AI Apps →
11
Karpathy — From Vibe Coding to Agentic Engineering →
12
Build and deploy a solution to a messy real customer problem.

The FDE Development Loop

Discovery → Process Map → Success Metric → Data → Architecture → Prototype → Eval → Production → Measure Outcome → Generalize into Product

Worth following on Instagram

Short-form, but the good ones are genuinely technical. These three are verified accounts, not a scraped list.

·
Shirin Khosravi Jam — @jam.with.ai — Engineering Manager in AI & ML, Germany. Practical AI and ML explained simply, with a lot of humour. 312K.
instagram.com/jam.with.ai →
·
Harper Carroll — @harpercarrollai — CS/AI at Stanford, built it at Meta. 10+ years coding AI, and good at making it less intimidating under the hood. 618K.
instagram.com/harpercarrollai →
·
100x Engineers — @100xengineers — AI literacy and education, and one of the largest applied-AI builder communities. 958K.
instagram.com/100xengineers →

A good FDE does not merely solve Customer A's problem. They solve it while learning what should become reusable product for Customers B–Z.