The knowledge infrastructure agents build on
San Francisco
- Tweets 1,291
- Following 49
- Followers 20,024
- Likes 3,907
We're excited to launch a newly redesigned mintlify.com!
The site reflects our greater direction, building knowledge infrastructure for agents.
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Open roles:
Marketing:
→ Product Engineer
→ Senior Product Engineer
→ Product Marketing Manager
→ Field Marketing Manager
→ Mid-Market Account Executive
→ Enterprise Solutions Engineer, Pre-Sales
→ Solutions Engineer, Pre-Sales
→ People Operations
→ Recruiter
→ Support Engineer
→ Head of Customer Success
→ Solutions Engineer, Post-Sales
→ Enterprise Customer Success Manager
& more.
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Mintlify retweeted
Join us for a happy hour to kick off the YC S26 batch this Friday!
Drinks, tacos, and swag provided. Alumni welcome too!
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Mintlify retweeted
we've upgraded hot reloads performance in the mintlify CLI
large repo (~4,000 files)
- mdx page edits: 9s → 0.05s (150x)
- snippets edits: 25s → 0.1s (250x)
- docs.json edits: 80s → < 1s (80x)
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Mintlify retweeted
interesting finding across all @mintlify hosted sites
web traffic used to follow people. now it follows data centers.
at data center states like virginia, oregon, and california, 98%+ of the traffic from the state are coming from agents
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Mintlify retweeted
Since our Series B, we’ve been growing faster than ever.
It's crazy how many new faces I see around the office every week.
&& we're hiring all across engineering, marketing, and ops!
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Mintlify retweeted
In SF for Config next week?
Come hangout with me, @hahnbeelee, and our lovely @Replit x @mintlify community 💕
We'll yap about designing Agents, building knowledge bases, and more!
Come through:luma.com/azsxz7vp
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We're hosting a happy hour + fireside chat with @reductoai on June 29th, the first night of AI Engineer World's Fair.
@hahnbeelee + @raunakdoesdev speaking on what it's like to build for agents as the primary user.
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Companies are pulling back on AI spend for the first time.
We ran a controlled experiment to answer one question:
Does structured documentation actually make coding agents better?
We worked with a company running Claude Code on million-line monorepos daily, with a full team dedicated to one thing: making their agents more efficient.
We tested their environment against two conditions:
1. No docs, just the codebase
2. Docs + the codebase
When you provide agents with structured information, you get
→ 64% more precise answers
→ 39% better discoverability
→ ~50% fewer tokens per task
→ 1.5x faster completion
Better models don't address the issues on cost or efficiency. Working from raw code, they still relearn the whole codebase every session, burning context just to orient themselves.
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