Introducing Merge’s Universal Context Layer. It substantially decreases token cost while increasing accuracy. Every company uses AI wrong: Say your designer makes a deck using Claude. They teach Claude your design systems and typography. Now a Marketing hire makes a deck. None of the information your designer taught Claude gets passed over… why? Merge fixes this: All knowledge and memory across your entire company gets stored inside our context layer. When someone on the org teaches Al, that skill lives across the entire organization instead of staying locked in their session. Your AI runs faster, cheaper, and with more accuracy. You don’t have to switch systems, incur more costs, or retrain anyone on your team, Merge runs invisibly in the background. Book a demo here: merge.dev/merge-for-workforc…

We just cut AI spend by 75% across an entire workforce in one click. Introducing: Merge for Workforce. Without Merge, your employees are left choosing one of two options: 1. Spend 100x the cost they should be from using frontier models for everything 2. Deliver subpar work from overly restrictive model access Instead with Merge, IT can connect any identity provider and set model routing policies by team in one click. Merge pushes them to every machine through a desktop client, overriding the model configs across your team’s AI assistants and coding tools. Now every task gets the right model for the job. The result? 75x fewer tokens with faster and better output.

Bumped into @merge_api CTO @GilFeig on the street and asked him to show me his office… here’s how it went

New @ThePeelPod with @shensi Most founders get one founding story. Shensi thinks @merge_api has three. Shensi and @GilFeig started Merge in 2020, watched their first customers die, then rebuilt themselves twice for the AI era. Not many founders talk this honestly about tearing up their company. A lot of lessons for others trying to do the same. We get into why integrations turned out to be so important in AI, the playbook behind their night-shift 5-11pm AI transformation, almost hiring a foreign spy, why founders with an EA are moving too slow, the Embarrassment Framework, vibe coding a dinner bot that 10x'd their customer events, and why 6% of Merge employees get married. Full episode here + links below! Timestamps: 0:00 Something broke every year since 2020 2:22 How Merge went all-in on AI on nights and weekends 5:40 New launches got faster 8:32 Building connective infrastructure for AI 10:51 The dinner where Merge started 13:11 Six months of research before a line of code 19:00 Almost hiring a foreign spy 20:48 Merge’s 6% marriage rate 22:50 Hiring enthusiastic, nice, smart people 28:02 Early stage founders don’t need an EA 31:27 Shortcuts are a mentality 33:01 The AI tool that 10x'd their customer dinners 38:33 Marketing became an engineering function 40:51 Starting with SMB and climbing the logo ladder 42:07 How the product went cross-category 44:16 Launching Agent Handler and a new pricing model 46:47 Every product should be multi-model 49:58 Why most MCP servers don’t work 53:31 Startups should go all-in on enterprise 57:00 The hardest things are most defensible 1:00:26 Say "psycho shit" to be memorable 1:05:47 The Embarrassment Framework 1:10:23 Frank Slootman and being okay with being disliked 1:12:48 Giving feedback got scarier at 100 people 1:14:01 What only the CEO can do 1:19:44 The best marketing is not doing what everyone else does

high quality connectors cut an agent's work in half. using the same model and the same task, an agent running on @merge_api's connectors finished in 5 turns where public MCP servers took 10. connectors are our specialty, so every one is continuously maintained and regression tested against the live API. merge.dev/merge-agent-handle…