AI PM @google on evals, benchmarks | ex @arizeai views my own

Joined December 2011
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
29
137
10
962
129,444
I promise I’m not just saying this because I work at @Google But Gemini generated analysis and tokens are just more pleasant to read than other LLM-speak (I.e “claudese”)
80
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
1
1
1
12
703
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:
2
195
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.
1,071
1,011
784
13,816
862,563
superhuman intelligence is literally VC subsidized what an insane time to be alive
1
2
341
Aman Khan retweeted
Abundant is partnering with @baseten to empower customers to own their capabilities. Excited to leverage our experience working with every frontier lab to build environments tailored to the customers' unique needs.
3
13
5
63
6,895
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
2
328
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…
131
38
8
839
111,620
Should everyone on your team have the same personality?
I don’t want to manage a agents with different personas. If intelligence is truly super there should be no need for roles. Smart generalists rule the world. If you’re making agents with personas, are you actually finding value from it?
1
1
3
1,009
pacing the frontier is a marathon not a sprint
227
what if every query had its own harness
3
5
761
agree
Auditing RL environments and reward models is one of the highest-leverage tasks evaluators can do in service of building aligned models (embedded or otherwise).
3
320
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)
1
13
817
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)
This quoted post is unavailable.
1
118
could you imagine a world where inference just turned off for the weekend
2
6
382
somehow, for the last few years, this is always the time of year when things get really interesting
1
3
441
the most stressful part of the job now handled by agents curious what friends at @datadoghq @cleric_io @sentry @pagerduty @arizeai @resolveai think!
Agents write application code, but you still get paged at 2am when it breaks. We wanted to know whether AI could handle that part of the job too. Introducing Incident Arena: a benchmark that puts coding agents on call! Check out our paper & full dataset release below!
2
10
1,593
the energy in the personal agents space is kind of electric. can’t wait for the LinkedIn takes once someone actually builds a good one for enterprise. 👀
10
437
today’s a good day to work on evals
1
9
958
does anyone else spend their Sunday after a big model release week just updating their workflows and code?
3
329