From the Team
Engineering insights, product updates, tutorials, and industry analysis from the Transactional team.
Agentic AI is the Biggest Security Risk Nobody is Talking About
Autonomous AI agents are proliferating across enterprises. The security implications of giving AI tools, credentials, and decision-making power are massive and largely unaddressed.
MCP Security: What Developers Need to Know
Security analysis of the Model Context Protocol ecosystem. Authentication gaps, tool poisoning risks, excessive permissions, and a security checklist for developers adopting MCP servers.
RAG is Probably the Weakest Link in Your AI Security Chain
Retrieval-Augmented Generation pipelines introduce unique security vulnerabilities that most teams overlook. Data poisoning, prompt injection via context, and access control gaps are endemic.
Prompt Injection Nearly Broke Production AI. These Patterns Can Save You.
Prompt injection incident analysis and proven defense patterns: input sanitization, output validation, system prompt hardening, sandwich defense, and canary tokens.
15 Years of Shipping Software Taught Me Email Deliverability is an Infrastructure Problem
Why developers treat email as an afterthought and what goes wrong at scale. IP warming, feedback loops, bounce handling, reputation management, and the architecture of a reliable email pipeline.
JWT Authentication in 2026: What Most Tutorials Get Wrong
Common JWT mistakes that compromise security, and the correct patterns for token storage, rotation, and validation. Includes Express and Next.js code examples.
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Stop Writing Raw HTML Emails. Use React Email Templates Instead.
Build maintainable, responsive email templates using React Email instead of struggling with raw HTML tables. Includes password reset and order confirmation examples.
We Were Flying Blind on LLM Costs Until We Started Tracing Every Token
How we built token-level tracing to gain visibility into LLM costs, latency, and performance across providers. Architecture of the observability pipeline and the cost surprises we caught.
Traditional APM Cannot Track AI Errors. Here is What We Built Instead.
Why Sentry and Datadog fail for AI-specific errors like hallucinations, context overflows, and model degradation. Architecture of an AI-native error tracking system.
Your AI Should Never Go Down. Here is How to Set Up Fallback Routing.
Configure fallback chains across LLM providers so your AI features stay up when any single provider goes down. Includes TypeScript implementation with health checks and cost-aware routing.
Track Your LLM Costs in Real-Time Before They Surprise You
Set up real-time cost tracking for LLM API calls with token counting, dashboards, alert thresholds, and budget controls. Practical TypeScript examples included.
We Built an AI Gateway That Routes Across 13 LLM Providers. Here is How.
Architecture deep-dive into building a unified LLM proxy that routes requests across OpenAI, Anthropic, Google, Mistral, and more with load balancing, failover, and schema normalization.
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