New Hotness
Fall conference season arrived, and with it the annual firehose of product announcements designed to make you feel like everything is changing all at once. Some of it is. Most of it is a slide deck with a waitlist attached. September's job is to separate the two — what
New Hotness
August is the month where AI companies start positioning for fall conference season. The press releases get louder, the beta invites multiply, and the gap between "announced" and "usable" stretches to its annual maximum. Meanwhile, a few tools that spent the summer quietly iterating crossed the
New Hotness
The "summer slowdown" is a persistent myth in tech. The theory: teams go on vacation, shipping slows, nothing interesting happens between June and September. The reality: July is when teams ship without the conference spotlight, product managers are on PTO so engineers build what they actually want, and
New Hotness
June is the mid-year checkpoint. Six months into 2026, we have enough data to say what actually changed versus what January promised would change. The short version: the useful stuff got more useful, the hype stuff stayed hype, and a surprising number of tools that seemed invincible in January are
New Hotness
May is the month where spring conference season meets reality. Every AI company held a keynote, dev day, or "special event" between mid-April and late May. The demos were impressive. The shipping was not. Here's what actually survived the demo-to-delivery gap, what didn't, and
New Hotness
This is the last one. Twelve months of monthly drops, starting with April 2026 and ending here. March 2027 gets its own changelog, and then we do the thing we've been building toward all year — a full retrospective on what twelve months of honest tracking actually reveals about
New Hotness
The holidays are over. CES is a fading memory. The teams are back at full capacity, and February is the first month where we see what 2027 is actually going to look like — not what the roadmaps promised, but what developers sitting at desks decided to ship. It's
New Hotness
New year, new roadmaps, new funding announcements, new promises. January is when every AI company publishes a blog post about what they're going to ship this year, and approximately 60% of those things will never materialize. CES happened. Predictions were made. Some of them are already aging badly.
New Hotness
Welcome to the first Monthly Drop. The premise is simple: every month, we look at what actually shipped in AI tools, what quietly died, what got leapfrogged, and what the AI itself was confidently lying about. Less newsletter, more honest changelog. No hype scores. No "top 10 AI tools
Mcp Plumbing
Every AI product ships with a marketing page that says "integrates with 1,000+ tools." The number is always large, always round, and always misleading. What it actually means: a Zapier connector exists, or someone wrote an MCP server six months ago, or there's a REST
Mcp Plumbing
This article is not about data engineering. It's not about Spark clusters, Kafka streams, or petabyte-scale ETL. It's about what happens when a solo developer or small team needs to process a few hundred to a few thousand items through an LLM — classify them, extract structured
Mcp Plumbing
Before you promise a client — or yourself — that "the AI can connect to X," check this. The marketing pages say "integrates with 1,000+ tools." The reality is that integration quality exists on a spectrum from production-grade to vaporware, and the number on the marketing page