All field notes

When to embed a forward deployed engineer versus building the capability in-house—the trade-offs in speed, focus, cost, and long-term ownership.

Agencies deliver scoped projects from the outside; forward deployed engineers embed inside your team and own outcomes in production. This comparison covers ownership, embedding, speed, knowledge transfer, cost, and fit.

The most consequential architecture decision in an LLM system is who controls the sequence of steps: code or the model. This comparison covers determinism, cost, reliability, auditability, and a decision rule.

A dedicated development team acts as an extension of yours. When it beats project or staff-augmentation models, and how to build one in Pakistan with US oversight.

Prompt injection cannot be fully solved at the model layer, so defense is layered. This checklist covers content separation, detection, least-privilege tools, output validation, approval gates, monitoring, and adversarial testing.

A RAG system that demos well and fails in production usually skipped items on this list. Content curation, permissions, retrieval quality, grounding, evaluation, freshness, cost, and operations, each with evidence.

Smart contracts are hard to change and hold value, which makes pre-deployment security decisive. This checklist covers specification, access control, common vulnerability classes, upgradeability, oracles, testing, independent audit, and monitoring.

AI systems introduce controls SOC 2 auditors are now asking about. This checklist maps LLM applications and agents to the trust services criteria: security, availability, processing integrity, confidentiality, privacy, and change management.

A token launch is a legal, economic, technical, and operational event at once. This checklist covers legal structure, tokenomics, contract security, distribution and vesting, custody, liquidity, compliance, communications, and operations.

Most web application breaches exploit well-understood weaknesses. This checklist covers authentication, authorization, input handling, security headers, dependencies, secrets, logging, infrastructure, and testing before release.

Postings for the role surged and a16z called it the hottest job in tech. Why demand for forward deployed engineers exploded—and what it means for buyers.

Asia is the world's fastest-growing source of AI engineering talent. The leading hubs, the trade-offs, and how to secure production delivery with US oversight.

Any AI system that touches protected health information is inside HIPAA. This checklist covers scope and business associate agreements, minimum necessary, access, encryption, audit logging, risk analysis, breach procedures, and vendors.

Intellectual property is protected offshore by contract and by architecture together. This checklist covers contracts under your law, buyer-owned assets, access controls, data handling, confidentiality practices, and audit.

LLM applications introduce attack surfaces conventional security reviews miss. This checklist covers prompt injection, data leakage, output validation, tool scoping, supply chain, logging, and testing, each with evidence.

MLOps maturity is the difference between models that ship once and models that ship safely every week. This checklist scores versioning, reproducibility, pipelines, evaluation gates, registry, deployment, monitoring, and governance by level.

A mobile launch is a store submission, a rollout, and an operating commitment at once. This checklist covers store readiness, device quality, performance, security, analytics, crash reporting, staged rollout, and support.

Next.js gives you the tools for fast pages and the freedom to misuse them. This checklist covers rendering strategy, caching semantics, images and fonts, bundle size, Core Web Vitals budgets, data fetching, and real-user monitoring.

The first thirty days decide whether an offshore team becomes part of your engineering organization or a vendor at arm's length. This checklist covers contracts, access, security, tooling, overlap, first deliverables, and knowledge capture.

OpenAI runs Forward Deployed Engineering; Anthropic runs Applied AI. How the frontier labs embed engineers with customers—and why the model is now mainstream.

The contract is where outsourcing risk is either handled or hidden. This checklist covers scope and acceptance, IP, data protection, security, change control, exit, liability, and governing law for AI and software engagements.

Most AI projects are lost in the first two weeks, when outcomes stay vague and access never arrives. This checklist covers what must be true at kickoff: outcome, owner, data, access, security, evaluation plan, team, and cadence.

An AI RFP built from feature checklists rewards marketing. One built to reveal method rewards the vendors who will deliver. This checklist covers outcome, context, method questions, security, operations, terms, artifacts, and scoring.

Service levels for AI systems must cover quality and change, not only uptime. This checklist covers availability, latency, quality thresholds, oversight response times, incident handling, change notification, reporting, and remedies.