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Designing for AI Uncertainty
Pretending AI is certain when it isn't is the root cause of most AI UX failures — here's how to communicate uncertainty without breaking user trust or making your product useless.
2026-05-01
ObservabilityHow Leading AI Companies Test Models
Inside the evaluation practices of companies shipping AI at scale — what their testing infrastructure looks like and what you can steal for your own team.
2026-05-01
StartupsThe Cheapest AI Tech Stack for Startups
The exact tech stack to build an AI startup for under $50/month — LLM API, frontend, backend, database, auth, payments, and more.
2026-04-29
InfrastructureEdge AI Deployment
When to run AI inference at the edge — browser, CDN, mobile, and on-prem — and when centralized is still the right call.
2026-04-28
ObservabilityLLM Metrics That Actually Matter
Most LLM metrics are vanity numbers. Here are the metrics that actually correlate with user satisfaction and business outcomes — and the ones you should ignore.
2026-04-27
InfrastructureScaling LLM Applications to Millions of Requests
LLM scaling is a cost and latency problem as much as a throughput problem — here's the infrastructure playbook that actually works.
2026-04-26
ProductDesigning Trust into AI Products
Trust in AI products isn't a UX polish problem — it's an architecture decision that determines whether users actually rely on your product when it matters.
2026-04-25
StartupsAI Startup Costs Explained
A CFO-level breakdown of every cost category in an AI startup — LLM APIs, inference, hosting, vector DBs, and hidden costs — with real numbers at every stage.
2026-04-24
InfrastructureBuilding AI Apps on AWS
A practical guide to the AWS services that actually matter for LLM-powered apps — Bedrock, SageMaker, Lambda, and when to use each.
2026-04-22
ObservabilityBuilding Trustworthy AI Products
Trust isn't a feature you add at the end. It's built into the evaluation, monitoring, and design choices from day one. Here's how.
2026-04-22
ObservabilityRed Teaming Your AI Application
How to systematically attack your own AI product to find failures before users do — a practical guide to AI red teaming.
2026-04-20
InfrastructureGPU Providers Compared
A practical comparison of AWS, GCP, Azure, Lambda Labs, CoreWeave, RunPod, Vast.ai, and Paperspace for AI training and inference workloads.
2026-04-18
ProductAI Workflows vs AI Chat
A decision framework for developers and PMs choosing between chat and workflow patterns when building AI-powered products.
2026-04-17
ProductProduct-Market Fit for AI Startups
AI PMF is harder to measure than traditional PMF because the technology creates false positives — everyone tries it once, few come back. Here's how to tell the difference.
2026-04-17
ProductChat Interfaces Are Not Enough
Chat became the default AI interface because of ChatGPT, not because it's the right choice — here's when to ditch it and what to build instead.
2026-04-16
StartupsHow to Get Your First 100 Customers
A tactical playbook for early-stage AI founders on how to get your first 100 paying customers without relying on SEO, word of mouth, or anything that requires scale to work.
2026-04-16
InfrastructureInference Providers Ranked
A technical ranking of managed LLM inference providers — OpenAI, Anthropic, Groq, Fireworks, Together, Mistral, and more — compared on speed, price, reliability, and fit for production.
2026-04-12
ProductAI Product Case Studies
Six deep dives into AI products that work — Cursor, Perplexity, Fin, Harvey, Replit, and Linear — with the specific decisions that made them succeed.
2026-04-07
ObservabilityRegression Testing for AI Products
How to know if your AI product is getting worse over time — building regression tests that catch model updates, prompt degradation, and pipeline drift.
2026-04-05
ProductAI Product Mistakes to Avoid
A sharp checklist of 13 traps that kill AI products — from shipping without evals to building chat when you needed a workflow.
2026-04-04
InfrastructureThe Cheapest Way to Serve Open Models
A cost-first breakdown of every practical strategy for running open-source LLMs in production — managed APIs, spot GPU instances, quantization, and CPU inference — with real numbers and a decision tree.
2026-04-04