short intro post before i make an actual one:
- i'm nik, 21 y/o
- always loved computers and psychology
- attended a ui/ux workshop and realised design is perfect mix of psy. and cs.
- did TONS of free design work, while gaining more and more visibility.
- started getting freelance gigs, with $5 for 7 screens.
- kept up with lots of volunteer work, earned a great word of mouth among the best engineers in the city.
- fast forward, i'm now the go-to designer in my city for community initiatives by google dev groups, figma and more.
- worked with 8+ startups to design products 0-1, designing some of the coolest products i could.
- now, kinda running a design studio, on the way to becoming design engineer and handle cooler projects.
We ran our recursive learning layer on Terminal-Bench 2.0. Same agent. Same model. Same harness. Same budget.
The result: Claude Sonnet went from 42.2% → 52.4%. A +10.2 percentage-point lift, significant at p < 0.05, with a 13:3 task-level win/loss ratio (internal).
The only variable was a learning layer.
We wrote a full technical breakdown on what changed, why it worked, and what this means for production AI agents.
Read it here 👇
https://t.co/VhVCgURuXv
.@BentoLabsAI is the monitoring and learning layer for long-running agents. Their learning layer gives agents model-jump gains: Sonnet 4.5 went 42.2%→52.4% on TB2 (Internal).
Congrats on the launch, @Abhinavv_soni & @kacppian!
https://t.co/LTy5sslfni
career update.
joined https://t.co/3y1uLlo74b (YC P26) as a product designer.
we're building recursive learning for ai agents to minimize failures in production.
;)
this is exactly why AI agents fail in production.
no regression detection. no drift alerts. just an agent confidently hallucinating "they must be cooking" while the restaurant is closed.
every failure like this is a silent regression that reached a real user.
that's the problem @BentoLabsAI solves.
cc @deepigoyal@zomato 👀
this is exactly why AI agents fail in production.
no regression detection. no drift alerts. just an agent confidently hallucinating "they must be cooking" while the restaurant is closed.
every failure like this is a silent regression that reached a real user.
that's the problem @BentoLabsAI solves.
cc @deepigoyal@zomato 👀