Stop Building Chatbots: Build an MCP Control Plane Before Your LLM Agent Becomes an Incident
MCP is turning “agent tools” into a software supply chain. Treat it like one—or expect outages, data leaks, and runaway spend.
Priya Sharma
Technology
Deep dives into software architecture, developer tools, programming best practices, databases, infrastructure, and the technical decisions that define modern software.
81 articles
Priya Sharma
The Post-ChatGPT Stack: Why 2026 Will Belong to Teams That Treat AI as Infrastructure, Not a Feature
LLM features are easy. Operating AI safely, cheaply, and repeatably is hard—and it’s where defensible advantage is forming.
Sarah Chen
Stop Shipping “AI Features.” Start Shipping Model Contracts: The 2026 Playbook for Reliable LLM Systems
The hard part of AI products isn’t prompts or models. It’s contracts: what the model may do, must never do, and how you prove it—every build.
David Kim
Stop Training Bigger Models: 2026 Is the Year of Model Routers, Not Monoliths
The winning AI stacks in 2026 won’t bet on one model. They’ll route tasks across many—cheap, fast, private, and compliant—without users noticing.
Priya Sharma
Stop Fine-Tuning Everything: The 2026 Stack Is Retrieval, Tooling, and Policy—Not Bigger Models
Founders keep paying the “model tax” when their real problem is data access and execution. The winning 2026 AI stack is retrieval + tools + governance.
Sarah Chen
Stop Fine-Tuning Everything: 2026 Is the Year of Deterministic AI Systems (and Boring Wins)
Most AI products fail at the same place: reliability. In 2026, the winners will build deterministic wrappers around probabilistic models—and treat “LLM output” as untrusted input.
Jessica Li
AI Agents Are Becoming Employees. Your IAM Stack Isn’t Ready.
Most teams are shipping “agents” with hard-coded API keys and vague permissions. Treat agents like identities—before audit, breach, or bill shock forces you to.
Michael Chang
The AI Agent Trap: Why 2026 Will Belong to Transactional AI, Not Chatty Bots
Most “agent” demos die the moment a real system demands auth, idempotency, and audit trails. The winners in 2026 will ship transactional AI with boring reliability.
Michael Chang
Your AI Is a Root User Now: The New Ops Stack for Tool-Calling Agents
Tool-calling agents quietly turned LLMs into operators with production privileges. The winners in 2026 will treat agent access like root access—audited, least-privileged, and throttled.
Alex Dev
Stop Fine‑Tuning Everything: 2026’s Winning AI Stack Is Retrieval, Tooling, and Logging
Fine-tuning has become the default move. It’s usually the wrong one. Here’s the practical 2026 stack: retrieval, tool calling, strict evals, and audit-grade logs.
Sarah Chen
Stop Shipping “AI Features.” Start Shipping Model-Control Planes.
In 2026, the winners aren’t the teams with the fanciest model. They’re the ones with routing, evals, safety, and cost controls built like real infrastructure.
Alex Dev
Your AI Agent Isn’t Ready for Production Until It Has an Identity, a Budget, and an Audit Trail
Most “agent” rollouts fail for boring reasons: no identity, no spend limits, and no receipts. Fix those three and agents become operators, not demos.
Alex Dev
The AI Feature That Will Get You Sued in 2026: Training on Your Own Customer Data
Fine-tuning on “your data” is turning into a liability pattern. The winners are shifting to retrieval, logging, and provable boundaries—not bigger models.
Marcus Rodriguez