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Founder Tools We Use Daily
The actual tools we use every day building an AI startup — honest takes on Cursor, PostHog, Railway, Stripe, and more.
2026-05-25
StartupsHow We Built an AI Startup in 30 Days
A week-by-week account of building and launching an AI product in 30 days — the stack, the cuts, the mistakes, and what actually worked.
2026-05-25
ProductAI Product Patterns That Work
Ten concrete AI product patterns — with real examples from Copilot, Cursor, Notion, and Linear — that consistently ship well in production.
2026-05-24
ObservabilityBenchmarking Open Source Models
How to fairly benchmark open source models against each other and against proprietary alternatives — and avoid the common traps that make benchmarks misleading.
2026-05-24
InfrastructureServerless AI Infrastructure
A practical guide to serverless AI infrastructure — where it genuinely wins for LLM workloads, where it fails, and how to pick the right platform.
2026-05-24
StartupsBuilding an AI MVP
An AI MVP is not about the AI — it's about validating the job-to-be-done as fast as possible before you over-engineer the solution.
2026-05-23
ProductHuman-in-the-Loop Design Patterns
A practitioner's guide to designing AI systems with the right level of human oversight — covering the full autonomy spectrum and seven concrete HITL patterns.
2026-05-23
ProductThe Best AI Features We've Seen This Month
A curated breakdown of the most interesting AI product launches from mid-2026 — what shipped, why it matters, and what it tells us about where AI UX is heading.
2026-05-21
StartupsDistribution Strategies for AI Products
Most AI founders figure out distribution too late. Here are the specific channels that work, what good looks like, and the honest tradeoffs for each.
2026-05-20
InfrastructureVercel AI SDK Deep Dive
A technical breakdown of the Vercel AI SDK — its core primitives, streaming model, tool use, structured output, and where it falls short for production AI apps.
2026-05-20
ObservabilityProduction Monitoring for LLM Applications
The practical monitoring setup you need before putting an LLM-powered feature in front of real users — metrics, traces, alerts, and dashboards.
2026-05-18
ObservabilityDetecting Hallucinations in Production
Hallucinations are unavoidable in LLM applications. Here's how to detect, measure, and mitigate them in production systems without blocking every output.
2026-05-17
ObservabilityEvaluating Agent Performance
Evaluating agents is harder than evaluating single-turn LLM calls. Here's how to measure whether your AI agent is actually doing its job.
2026-05-14
ObservabilityAI Reliability Engineering Explained
AI systems fail in new and interesting ways. Here's the emerging discipline of AI Reliability Engineering — what it borrows from SRE and what's entirely new.
2026-05-13
StartupsPricing AI Products
Most AI founders price too low, ignore their cost structure, and get killed by usage at scale — here's how to price an AI product that actually sustains a business.
2026-05-13
ObservabilityBuilding an Evaluation Pipeline
A step-by-step guide to building an automated evaluation pipeline for your AI application — from dataset creation to metrics that actually catch regressions.
2026-05-12
ObservabilityLangSmith vs Braintrust vs Arize vs MLflow: Which AI Observability Tool Is Right for You?
A hands-on comparison of four leading AI observability platforms — what each does well, where they fall short, and how to pick based on your actual needs.
2026-05-09
ObservabilityCreating Reliable Benchmarks
Public benchmarks lie to you. Here's how to build internal benchmarks that actually predict how your AI product will perform for real users.
2026-05-08
StartupsOpen Source vs Closed Models for Startups
A practical decision framework for founders choosing between closed APIs and open source LLMs — when to use OpenAI, when to self-host, and how to build a hybrid strategy.
2026-05-06
ObservabilityHuman Evaluation vs Automated Evaluation
When to use human evaluators, when to automate, and how to design a hybrid system that gives you reliable quality signals without breaking the bank.
2026-05-03
StartupsAI Startup Unit Economics
AI startup unit economics are harder than traditional SaaS because COGS scales with usage, not seat count — and most founders discover this too late.
2026-05-02