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1/ Been trying out more AI coding Agents - here's a quick breakdown of my experience with the AI coding agent space and what I use each agent for
Aman Khan retweeted
Such a high signal event. Many of the top people in the AI benchmark/eval field, both on stage and off stage.
@kaggle Bench Summit
This is worth following!
Bench Summit by Kaggle has officially kicked-off!
Trust in benchmarks is eroding. The path forward starts here.
We started strong with @janiejgu sharing key insights on the problems with benchmarks today and Kaggle’s mission to build trust in evals and help the world discover what works and doesn’t work in AI.
During the session, she shared four ways benchmarks fail today:
1. Relevance: Many public benchmarks don’t resemble what your agents do in production.
2. Saturation: Evals go stale within a release cycle.
3. Verification: Everyone has their own implementation of the same benchmarks, meaning scores vary wildly due to infrastructure, not model quality.
4. Infrastructure: Evals infra is hard to build correctly, and everyone rolls their own.
To get numbers you can actually trust, Janie highlighted four essentials:
1. Real workflows as the foundation for benchmarks
2. Continuous benchmarks from crowdsourced tasks, arenas, and production data
3. Rigorous and transparent methodology audited by the community experts
4. Eval infrastructure that everyone can use out of the box
Here’s a sneak peak of the early moments:
Aman Khan retweeted
Introducing Gemini 4 Carbon, it's a big jump over Argon which we released just 2 weeks ago.
Gemini 4 Carbon is SOTA on cyber, long horizon tasks, ranks #1 on Artificial Analysis V5, significantly improves on usability in domains like coding, and is more aligned.
btw this is why you need an internal eval benchmark flywheel - not just a one and done set of tasks
evals will become a core skillet (still early) for every company that wants to use AI and the more “up to date” they are, the higher impact new models will have on that business.
OpenAI, Anthropic and other leading AI labs face a growing challenge: Better models aren't necessarily attracting more usage. A 10-point intelligence advantage now earns just 20% more developer traffic, down from nearly 69% in late 2025.
Full story: thein.fo/4hGJ9jY
Aman Khan retweeted
We are hosting our first "Bench Summit" to bring together people excited about pushing the frontier of benchmarking, model evals, and more.
Great benchmarks are a gating function for model progress, lots to do here!
Join us in SF: rsvp.withgoogle.com/events/b…
token based pricing is honestly very strange
- maps roughly to compute time pricing, except because LLMs are non deterministic this is basically unpredictable
- no direct hop to value or capacity
- incentives don’t exist to lower token spend w/o competition (see: billable hours)
extreme outcome distributions are seemingly becoming the norm even more
- in using AI products (and how to get value from them)
- in fraud, abuse and unexpected outcomes (below)
- in perception of AI systems (they will cure everything vs kill us all)
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the most stressful part of the job
now handled by agents
curious what friends at @datadoghq @cleric_io @sentry @pagerduty @arizeai @resolveai think!
















