Nik Kale
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nik-kale.bsky.social
Nik Kale
@nik-kale.bsky.social
Building AI systems that don’t break
Principal Engineer @ Cisco
Agentic automation · AI security · In-product AI systems
Patents · Industry awards · Judging
TechCrunch has the full breakdown of Discord's rollout and how the verification works: techcrunch.com/2026/02/09/...
Discord to roll out age verification next month for full access to its platform | TechCrunch
All users will be put into a "teen-appropriate experience" by default unless they prove that they are adults.
techcrunch.com
February 11, 2026 at 5:00 AM
And the real risk isn't the check itself. It's what persists afterward. A breached password can be reset. A breached faceprint is compromised forever. You can't rotate your face.

The critical design question isn't whether to verify. It's where the verification happens and what survives it.
February 11, 2026 at 5:00 AM
Biometric age verification gives platforms plausible deniability more than actual certainty. It lets them say "we checked" without guaranteeing "we know."
February 11, 2026 at 5:00 AM
The plan: containment plus a clean slate going forward. Don't try to fix legacy. Assume it's compromised and treat it as hostile territory.
February 10, 2026 at 8:00 PM
Enterprise teams will need both patterns. The question nobody's asking: who governs the agent when it can finish the ticket without you?
February 10, 2026 at 1:00 AM
That's not a competition. That's two different theories of what "AI engineer" means.

One reads the whole repo before touching anything. The other starts running commands and fixes things in motion.
February 10, 2026 at 1:00 AM
Most orgs are already undercounting by 2-3x because machine identities are scattered across cloud consoles, repos, config files, and secrets managers nobody's aggregating.
February 7, 2026 at 1:00 AM
The engineers who develop this instinct early will be the ones trusted with autonomous systems. The ones who don't will be supervised by the ones who do.
February 6, 2026 at 8:00 PM
Knowing when to trust an output. When to verify. When to override. Understanding what audit trail you're creating. Recognizing when a model is operating outside its training distribution.

This isn't a course you take. It's judgment you build from operating real systems and watching them fail.
February 6, 2026 at 8:00 PM