The notes I would have wanted at fifteen.
Long form pieces on shipping production systems, scaling SaaS, hiring engineers, AI integration, and the engineering decisions behind Yashveer Labs.
799 posts · page 30 of 34
- AI Integration and Vibe Coding Rescue
Why Most AI Roadmaps Fail in the First Quarter
AI roadmaps fail for predictable reasons. The team underestimates the gap between a working demo and a working product, picks vanity features over real ones, and runs out of patience before the second iteration. The pattern is recognizable, and avoidable.
12 min read
- AI Integration and Vibe Coding Rescue
The AI Onboarding Assistant: A High Value SaaS Feature
Why an AI onboarding assistant is one of the highest ROI AI features you can ship, how to build one that works, and the failure modes to avoid.
12 min read
- AI Integration and Vibe Coding Rescue
Voice AI Agents for Service Businesses: A Builder's Guide
Voice AI agents for service businesses are past the demo stage. The gap between a working prototype and a reliable production system is specific and bridgeable. Here is how the build actually goes.
12 min read
- AI Integration and Vibe Coding Rescue
The Failure Modes of Autonomous AI Workflows
The ways autonomous AI workflows fail in production -- and how to design around the failure modes before they become expensive incidents.
12 min read
- AI Integration and Vibe Coding Rescue
Token Economics: Why Your AI Bill Surprised You and How to Fix It
AI API costs are predictable once you understand how tokens work and where they go. Most teams that get surprised by their bill are making the same four or five mistakes, and the fixes are not complicated.
12 min read
- AI Integration and Vibe Coding Rescue
The Privacy and Data Boundary Problem in AI Integrations
Every AI integration creates a new data flow. Most teams build the feature first and draw the boundary second. By then, customer data has already traveled somewhere it should not have, and fixing it costs more than building it right the first time.
12 min read
- AI Integration and Vibe Coding Rescue
The Difference Between an AI Wrapper and an AI Product
What separates an AI feature duct-taped onto a prompt from a product that creates durable value and survives model commoditization.
12 min read
- AI Integration and Vibe Coding Rescue
The AI Output Validation Problem: Why It Is Bigger Than You Think
AI output validation is the engineering problem most teams skip until a hallucination ships to production. Here is the architecture that catches it before users do.
12 min read
- AI Integration and Vibe Coding Rescue
Vector Databases Compared: Pinecone, Weaviate, pgvector, Qdrant
Pinecone, Weaviate, pgvector, and Qdrant each solve vector search differently. The right choice depends on your existing stack, your query patterns, and how much operational overhead your team can absorb.
12 min read
- AI Integration and Vibe Coding Rescue
The Top Five Architectural Failures in AI Assisted Codebases
AI tools write code fast. They also repeat the same five structural mistakes across almost every codebase I inherit. Knowing the pattern lets you catch it before it compounds.
12 min read
- AI Integration and Vibe Coding Rescue
Why AI Generated Code Breaks in Production
AI generated code passes local tests and breaks in production for specific, repeatable reasons. Understanding those reasons is more useful than a general skepticism about the tools.
12 min read
- AI Integration and Vibe Coding Rescue
The Prompt as a Spec: How to Build Software With AI Tools Responsibly
Teams that get consistent results from AI coding tools treat the prompt like a specification document. Teams that get inconsistent results treat it like a search query. The difference is in the discipline, not the tool.
11 min read
- AI Integration and Vibe Coding Rescue
The Founder Who Vibe Coded Their MVP: A Postmortem and Rescue Plan
What happens when founders build an MVP with AI assistants without engineering oversight -- and the practical steps to rescue the resulting codebase.
12 min read
- AI Integration and Vibe Coding Rescue
When AI Code Generation Stops Saving You Time and Starts Costing You
AI code generation has a break-even point. Past it, the debugging time, the structural debt, and the context loss start outweighing the speed gains. Knowing where that line is changes how you use the tools.
12 min read
- AI Integration and Vibe Coding Rescue
Vibe Coding Rescue: How to Take Over a Codebase Written by ChatGPT
Most AI generated codebases are not unworkable. They just have patterns that degrade fast under real usage. The rescue process is methodical, not heroic, and it starts with understanding what the AI was optimizing for.
12 min read
- AI Integration and Vibe Coding Rescue
The Last 20 Percent: Why Your AI Generated SaaS Fails at Stripe and Security
AI code generation handles the easy 80 percent. The last 20 -- payments, auth, and security -- is where generated SaaS products silently fail.
12 min read
- SaaS Architecture and Scaling
The SaaS Status Page: Build, Buy, or Both
A status page is the first thing customers open when something goes wrong. Whether you build it yourself, buy a hosted service, or layer both, the decision has real consequences for trust, cost, and incident response.
12 min read
- SaaS Architecture and Scaling
Why Your SaaS Should Treat Its Database Like a Product
The database is the most durable thing you will build. Most SaaS teams treat it like implementation detail. The ones that treat it like a product build something that survives five years of feature growth without a rewrite.
12 min read
- SaaS Architecture and Scaling
The SaaS Refund Workflow: A Quiet Source of Engineering Debt
Refunds look simple from the outside. A few button clicks, money goes back, done. Inside the system, a refund touches billing state, subscription status, usage records, access controls, and audit trails. Most SaaS products handle them badly.
12 min read
- SaaS Architecture and Scaling
The Data Export Feature: Why Customers Always Ask and Founders Always Delay
Why data export is the most-requested SaaS feature that gets deprioritized the longest, and how to build a version that actually satisfies enterprise buyers.
12 min read
- SaaS Architecture and Scaling
The Reconciliation Job: A SaaS Pattern Founders Should Know
The reconciliation job is the background process that finds and fixes the gap between what your system thinks is true and what is actually true. Most SaaS products need one. Most founders do not know it until something goes wrong.
12 min read
- SaaS Architecture and Scaling
The Background Sync Problem: Patterns That Survive
The background sync patterns that keep SaaS data consistent without blocking the user or corrupting the state. What works, what breaks, and when each applies.
12 min read
- SaaS Architecture and Scaling
Transactional Email Architecture: Templates, Retries, Bounces
Transactional email is the part of the product that talks to the user when the user is not looking at the screen. Most teams treat it as a library call. The teams that treat it as infrastructure ship email that is reliable, readable, and maintainable.
11 min read
- SaaS Architecture and Scaling
The Email Sending Infrastructure: Postmark, Resend, SendGrid Compared
Which email sending provider to use for transactional email, when to switch, and what the operational differences actually feel like.
12 min read
More than blog posts.
The Labs folder has the experiments themselves. The Systems folder has the stack and architecture. The Journey folder has the timeline. Pick whichever pulls you.