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Practical analysis of new AI models, tools, and product decisions from someone who actually ships them.
How long it takes to build an MVP in 2026: published timelines side by side, weeks per phase, web vs mobile, App Store and Google Play clocks, what AI changed, and how to read the timeline in a quote.
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What adding AI to an existing app costs in 2026: published price ranges side by side, where the engineering hours go, a monthly API bill worked out at October 2026 prices, on-device options, and the costs quotes leave out.
What a mobile app costs to build and run in 2026: price ranges from Clutch, Upwork, Bubble and agency guides side by side, cost by feature and platform, the new US store fees, and a three-year budget.
Freelance app developer rates in 2026 from Upwork, Arc, Toptal, Clutch, and BLS data: hourly rates by seniority and region, platform fees, freelancer vs agency vs in-house, and how to vet a developer.
What an AI agent costs to build and run in 2026: published prices from 122 AI firms, cost by agent type, monthly token math at October 2026 model prices, hidden costs, and agency vs freelancer.
What a forward deployed engineer does, how the role differs from software, solutions, and sales engineers, consultants, and customer success, what it pays, and why AI companies are hiring FDEs so fast. Sourced from job postings and 2026 data.
MVP pricing in 2026 from two studies of 146 agency sites: median prices, cost per week, fixed price vs hourly vs sprints, AI MVP markup, and how to compare quotes.
AI model routing matches each task with the right model, cost, and risk controls. Learn how to design rules, escalation, and useful metrics.
AI agent testing goes beyond the final answer. Learn how to evaluate trajectories, tool calls, side effects, cost, and regressions.
Skills, subagents, and multi-agent systems solve different problems. Use this decision framework to choose the smallest architecture that works.
An AI agent is only as reliable as its environment. Learn how sandboxes, identity, permissions, logs, and outcome verification fit together.
Context engineering selects the instructions, memory, files, and tool output an AI agent needs. Learn how to reduce noise without losing evidence.
MCP connects AI applications to data and tools. Learn how it works, how it differs from an API, and when the extra protocol is worth using.
An AI agent chooses actions, uses tools, and learns from environmental feedback. Here is how agents differ from chatbots and fixed workflows.
Jev returns typed decisions, not chatbot prose. See how it works, where it fits in AI agents, and why valid types do not guarantee sound judgment.