Hands-on build guides with free AI tools, the latest in AI, and Forward Deployed Engineer playbooks — written for engineers who ship.
Deploy a production-grade WhatsApp bot that answers questions from your docs. Uses Cloudflare Workers AI, Pinecone free tier, and TypeScript—all free to run.
Build a Python agent that ingests your base resume and a job description, then uses Gemini's free tier to rewrite bullet points and output an ATS-optimized LaTeX PDF—all for free.
Build a local-first RAG chatbot that answers questions from PDFs and markdown notes with source citations using Groq, Qdrant free tier, LlamaIndex, and Streamlit.
A practical, high-signal guide to the xAI Forward Deployed Engineer interview loop: coding, system design, and the unique Grok integration stage. No fluff.
A practical guide to launching and scaling a remote AI engineering career. Covers skills, portfolio projects, job boards, salary data, and the no-experience entry path.
A practical engineer's guide to landing AI engineer jobs in NYC. Covers the real skill stack, portfolio projects that bypass HR filters, and NYC-specific networking tactics.
A rigorous, no-fluff guide on how to become a military engineer. Covers officer vs. enlisted routes, degree requirements, ASVAB scores, combat engineering reality, salary data, and career transitions.
Master the AI engineer career progression from Junior to Staff and beyond. Real-world ladder, salary data, skill checklists, and no-fluff strategies to accelerate your path.
A forward deployed engineer for AI bridges the gap between lab-grade models and messy enterprise reality. Learn the role, required skills, salary data, and why it's the most critical technical hire for AI-native companies.
Forward Deployed Engineers bridge impossible gaps between frontier AI models and enterprise reality. Here's why FDE is the most coveted, high-agency role in 2025.
Build a portfolio of forward deployed engineer projects that demonstrate rapid prototyping, customer empathy, and production-grade delivery without deep ML expertise.
Stop grinding DP blindly. We analyze real FDE interview loops at Palantir, Anthropic, and C3.ai to show exactly when LeetCode matters and when debugging customer environments matters more.
Forget LeetCode marathons. The FDE coding interview tests real-world integration, debugging, and API orchestration. Learn the 4 problem types, see real prompts, and prepare to build.
A step-by-step technical playbook for deploying an LLM feature at an enterprise customer, navigating InfoSec reviews, data residency, and air-gapped inference. Real decisions, architecture, and the career context for Forward Deployed Engineers.
Concrete tactics for FDEs to translate technical chaos into business confidence. Learn to scope, communicate risk, and present data without losing the room.
Stop measuring vanity activity. Forward Deployed Engineers own business outcomes: compress Time-to-Value to days, drive deep feature adoption, and design expansion paths that compound revenue. Real workflows, comp impact, and no fluff.
A concrete playbook on how AI-native startups weaponize FDEs to close enterprise deals. Covers the technical integration playbook, security review survival, and the multi-million dollar bridge from API to production.
A step-by-step FDE playbook for shipping an LLM-powered summarization feature into a locked-down bank. Covers data egress, zero-retention prompts, air-gapped deployment, and the SOC2 audit that almost killed the project.
A concrete playbook for Forward Deployed Engineers debugging production issues in locked-down customer environments. Techniques for remote diagnosis, evidence collection, and resolving critical bugs when you can't touch the system.
A solo engineer built a meta-reinforcement learning agent that trains child models, all for ~$1.3K in compute. Here's the architecture, why it matters for FDEs, and how to run it today.
Deja Vu gives coding agents persistent memory by syncing a SQLite database over SSH. Here's how it works, why it matters for self-hosted AI workflows, and how to wire it up today.
Forget the AI detector snake oil. We break down a practical approach using classical machine learning to catch LLM-generated text, why it outperforms neural networks, and how to build your own.
LM Studio Bionic lets you build and run AI agents entirely on local, open-weight models. No cloud APIs, no data leakage. Here's how it works, why it matters for forward-deployed engineers, and a hands-on guide to getting started.
Moonshot AI's Kimi K3 hits the open-source scene with frontier-level reasoning. We dissect its RL scaling recipe, multimodal MoE architecture, and what it means for engineers shipping real products.
1,102 articles and counting