AI 관련 Kodenique 블로그 글 — 소프트웨어 개발, 클라우드, AI, 디지털 전환 인사이트.
17개의 글
OpenClaw became GitHub's most-starred project in 2026. What it actually does, where it fits in a business, and the security guardrails to set up before connecting it to anything that matters.
What AI agents cost in 2026 — subscription, platform, and custom tiers — the ROI math that justifies them, and the cases where you shouldn't buy one.
AI-built prototypes are arriving at agencies for productionization. What breaks in vibe-coded software, when the prototype helps, and how to harden it.
Gartner expects over 40% of agentic AI projects to be cancelled by 2027. The real causes — integration, data, cost — and how to plan a project that survives.
Multi-agent AI systems and the Model Context Protocol in plain business language — what they are, when they pay off, and what to ask vendors before buying.
When a small, domain-specific language model beats a frontier LLM on cost, privacy, and accuracy — and when the general-purpose model still earns its price.
A realistic breakdown of what AI features cost in 2026 — discovery, data prep, build, evals, and the ongoing token and monitoring costs nobody quotes upfront.
Seven AI use cases for SMBs with honest effort and payoff ratings — support triage, document processing, forecasting, and more — plus where projects quietly fail.
The three tiers of AI adoption — SaaS tools, API-based custom apps, and fine-tuned models — compared on cost, control, and effort, with a decision tree for SMEs.
What AI genuinely accelerates in software development, what it doesn't, and how to tell whether your vendor uses AI responsibly.
Where AI support chatbots genuinely help, where they fail publicly, and how grounding, escalation design, and honest metrics separate the two outcomes.
How AI document processing works for invoices, contracts, and forms — what modern extraction can do, human-in-the-loop design, and an honest ROI calculation.
Retrieval-augmented generation in plain language — how it makes AI answer from your company's documents, what it enables, and why data hygiene beats model choice.
How SMEs move from dashboards nobody opens to AI-powered analytics that drive decisions — the maturity ladder, quick wins, and what a realistic pilot looks like.
The real AI security risks for businesses — data leakage, prompt injection, over-permissioned tools, and shadow AI — ranked honestly, with a policy outline.
A practical 90-day AI adoption roadmap for SMEs — audit, pilot, measure, scale — with the policy basics, common failure modes, and where each AI use case fits.
How our team uses AI-assisted coding, testing, and documentation — where it speeds us up, where humans stay in charge, and what clients gain from it.