Introducing Handshake AI—the most ambitious chapter in our story. We leverage the scale of the largest early career network to source, train, and manage domain experts who test and challenge frontier models to failure for the top AI labs.
We came to Seoul for #ICML. We stayed for the tteokbokki!
Last night we hosted a group of researchers at Gwangjang Market, one of Seoul's oldest night markets, for a private food tour. We ate our way through the stalls, and yes, it lived up to every bit of the hype.
But the
Day 1 @icmlconf is officially in the books! 🇰🇷
The Handshake AI booth was buzzing all day long with great conversations, sharp questions, and even sharper minds. Thank you to everyone who stopped by!
Haven't made it by yet? We're just getting started. Swing by booth B600 on
We worked with parents and professionals in child-protection and clinical psychology to test 7 frontier AI models on child safety scenarios that go beyond the frequent focus on explicit content. The parents catch what standards evaluations don't, and the professionals bring real,
AI models pose serious child-safety risks. While many model developers evaluate for explicit abuse material, other child-safety failures begin upstream: when a model helps an adult manipulate, impersonate, profile, or isolate a minor; or when it deepens a child’s emotional
We’re in the middle of its biggest skills shift ever. Today, Handshake acquires Uplimit, the leading AI-native learning platform. Together, we're building toward the destination for AI-era talent development.
Read more: bit.ly/4ePWvaH
We built a better way to grade agentic work.
Gandalf is a reactive agent-as-judge that inspects files, tool state, and artifacts the same way a human expert would. On our banking benchmark, even the cheapest Gandalf config beat the next-best verifier at ~10x lower cost.
Grading agent rollouts in rubric-graded RL environments is itself a hard task.
Prior approaches pass serialized artifacts or agent trajectories to an LLM judge; this loses information / doesn't support sophisticated criteria.
In contrast, we built a reactive agentic judge.
Agent evals are becoming foundational infrastructure.
@jomulr joined @CAISconf’s RLEval workshop to share Handshake’s perspective on RL environments, evaluation, and why @harborframework is emerging as the framework.
Packed room to hear @alexgshaw and @ryanmart3n break down how @harborframework grew into *the* framework for RL environments.
In our RLEval workshop at @CAISconf today, attendees tackled big open challenges in RLEs & Agent Evals + I shared the approach we take at @joinHandshake
Kudos to @anishathalye and @jomulr for co-chairing the RL agentic benchmarks workshop track for the inaugural ACM CAIS conference this week.
We presented two separate Handshake AI Research papers in: (1) AI agentic systems - first evaluation of grader frameworks, and (2) AI
This spring, we worked with @OpenAI to launch the Codex Creator Challenge. More than 1,500 students built something on their own terms, driven by their own ideas. That kind of confidence and creative ownership is exactly what the most forward-thinking employers are hiring for.
Demo gods were on my side for this guest lecture on AI Agent Security at @MIT_CSAIL: I was able to show a prompt injection attack against @AnthropicAI's Opus 4.6 model. Agent security is still an unsolved problem!
The Handshake x @OpenAI Codex Creator Challenge winners are in 👇
🥇See why your code fails, line by line, with TraceCode by Obinna Nwachukwu
🥈Interactively explore America's power grid with InfraMap by Leonard Alsleben
🥉 Explore global dragon mythology with Where Dragons
This @joinHandshake event with @OpenAI was so energizing.
Not surprisingly, when you give young people powerful tools, their creativity and ambition run wild. The @UCBerkeley students were incredible.
With AI, your career will be more about showing than telling. Build something
Students are learning to build with Codex, and building to learn.
Here’s what @UCBerkeley students built at the Codex Creator Challenge with @joinHandshake.