Tabizen💢🏯 retweeted
“Give the agent $100” is not a risk policy.
A real funding policy needs dimensions.
PredictClaw’s Vault overlay can now express constraints such as:
Per transaction
How much can move at once?
Per window
How much can move cumulatively?
Time
When does the budget reset?
This is where agent capital starts becoming programmable.
AI is making it easier to find vulnerabilities in code that's been running for years.
Secure execution is becoming more important than ever...
🔐 Node.js security updates are now available!
New releases for Node.js 22.x, 24.x & 26.x address multiple vulnerabilities, including high-severity issues.
Review the details and update your systems to stay secure.
🔗 nodejs.org/en/blog/vulnerabi…
#NodeJS #Security
8x growth in small-business agent users is pretty impressive.
But getting people to try agents is one thing. Getting agents to actually work across the apps, payments and systems businesses rely on is another.
That's where the real challenge lies.
European businesses are rapidly expanding their use of AI agents. More than half of the AI use among our EU enterprise and business customers now involves these tools, and the number of small business users has grown 8x across the region.
Our latest Economic Research report explores Europe’s shift toward agentic AI—and the opportunity to turn broader adoption into deeper use.
openai.com/index/the-eus-nex…
Tabizen💢🏯 retweeted
We don’t want ERC-8192 to become a Tabi-specific execution framework.
The goal is a minimal, composable execution primitive.
Three questions we’re actively pressure-testing:
1. Is the Core / Extension boundary right?
2. Is fund-and-action generic enough?
3. Is the Base execution model clear enough for builders?
Builders: break the abstraction.
That’s how standards get better.
Tabizen💢🏯 retweeted
Good agent infrastructure is defined as much by what it refuses to execute as what it allows.
ERC-8192 currently enforces controls including:
→ signed mandates
→ nonce / replay protection
→ adapter allowlists
→ single-execution drawdown limits
→ cumulative drawdown limits
→ mandate revocation
→ no arbitrary native-value forwarding
Failing closed is a feature.
Consumer growth looks strong until you ask where it’s coming from.
Top 1% spending: +79%
Top 10%: +31%
Top 40%: +10%
The next consumer platform can’t be built only around whales.
Distribution and access matter just as much as monetization.
The top 1% of AI spenders now outspend the bottom 50% combined
They spend an average of $903/month, while the median customer spends just $25
More charts in our Top 100 Consumer AI Apps breakdown: a16z.news/p/top-100-consumer…
