Hands-on build guides with free AI tools, the latest in AI, and Forward Deployed Engineer playbooks — written for engineers who ship.
Build a local Python daemon that watches your clipboard, then uses free Ollama models like Llama 3.2 to summarize, translate, or rephrase any text on demand. No cloud, no API keys.
Stop dreading postmortems. Build a Python tool that ingests structured logs and Slack threads, then uses Google Gemini's free API to auto-draft a root cause analysis and polished markdown report.
A technical deep dive comparing Palantir's Forward Deployed Engineer (FDE) and Forward Deployed Software Engineer (FDSE) roles. We break down responsibilities, technical skills, compensation, and career trajectories to help you choose the right path.
Forward Deployed Engineer vs Consultant: Compare technical depth, ownership, compensation, and career trajectories. Learn which path aligns with your engineering ambition.
Explore the technical Venn diagram between Forward Deployed Engineering and DevOps. We break down code vs. config, on-call vs. on-site, and tooling to help you pivot.
Unfiltered Reddit insights on the AI engineer career path. We analyze real salary data, daily workflows, skill stacks, and growth hacks discussed by practitioners.
A data-backed breakdown of the technical, business, and security skills that land FDE roles. Learn why the best FDEs aren't pure software engineers—and how to build the exact profile top tech firms hire.
A deep-dive into the FDE work lifecycle: from scoping a messy customer problem to shipping a maintainable prototype. Tactical guide covering integration patterns, data pipelines, and on-site debugging.
Deep dive into Google's Forward Deployed Engineer role: responsibilities, compensation, interview prep, and how to build the hybrid engineering/customer skills to land the job.
Deep dive into the AI engineer job and salary landscape. Covers core responsibilities, required stack, and earning potential across experience levels with actionable data.
Unfiltered analysis of Palantir FDE experiences from Reddit threads. We break down real compensation, work-life balance, interview loops, and exit strategies for Forward Deployed Engineers.
Master the FDE interview process with this stage-by-stage breakdown. Learn what to expect in coding, system design, debugging, and executive rounds, plus tactical preparation tips.
A real playbook for deploying an LLM feature into a locked-down F500 bank. We cover the 3-week timeline, the PII redaction architecture, the compliance theater, and the $280k FDE comp context behind the decision to ship.
A concrete, day-by-day playbook of what a Forward Deployed Engineer actually does in a week. Real scenarios, tools, decisions, and comp context. No hype.
A raw, engineer-to-engineer breakdown of the FDE week: from high-stakes customer debugging and rapid prototyping to internal tooling. No fluff, just real scenarios, tools, and comp context.
A concrete playbook for Forward Deployed Engineers on how to parse ambiguous customer pain, scope ruthlessly, and ship a working prototype in five days. Real scenarios, tools, and comp context included.
Concrete 2026 FDE compensation data and negotiation frameworks. Learn to decode equity, benchmark total comp, and run the offer process like a technical sales cycle.
A concrete playbook for Forward Deployed Engineers debugging customer issues when you can't touch their stack. Real scenarios, tooling decisions, and comp context.
Claude Fable produced a counterexample to the Jacobian Conjecture—a 85-year-old math problem. We unpack the event, why it matters for FDEs building with LLMs, and how to harness reasoning models for hard technical work.
OpenAI Presence isn't just a demo—it's an operator that takes over your desktop. Here's how it works, why it matters for Forward Deployed Engineers, and how to build your own today.
New research shows you can dial reasoning effort up or down in LLMs without retraining. Learn how this inference-time control works, why it matters for cost-sensitive pipelines, and how to implement it today.
A custom Rust tokenizer claims a 1000x throughput leap over Hugging Face tokenizers. We dig into the architecture, the memory tricks that make it fly, and what it means for your inference pipeline.
Google's Gemini 3.6 Flash ignores temperature, top_p, and top_k. Here's the engineering impact on determinism, caching, and pipeline design—and how to adapt today.
Build a local tool that records system audio, transcribes with OpenAI Whisper, and uses Groq's LLM to output structured summaries with action items—all free.
1,102 articles and counting