At the @aiDotEngineer World’s Fair, our CEO @angli_ai explored a future where one person could have a fleet of autonomous computers. That's what we are building with @sai_borg. your robosecretary. It starts with work you can reliably delegate to one computer. Then you start imagining what you could do with 10. Then 100. And Sai manages them for you. Build the thing you keep saving for someday #simular

Ever wondered what goes into making @sai_borg more reliable? @Richard_simular from @SimularAI’s research team was invited to BayLearn 2026. His presentation explored how computer agents can reuse successful steps and handle repetitive tasks more consistently. For more on how agents learn reusable workflows, read the paper Richard coauthored: arxiv.org/abs/2609.36927 Thanks to Santa Clara University for hosting, and to San José State University, UC Berkeley, and UC Santa Cruz for co-organizing the event 👏 #BayLearn2026 #AIResearch

This is what our intern Qingni spent this summer solving with our head of research @xwang_lk The team proposes LEASH, a training-free framework that unleashes the strong agent only when the weak agent's latent uncertainty signals it is needed, and keeps it on a leash afterward. Read more in our blog: simular.ai/articles/put-the-…

A consensus is forming among computer-use agent builders: Don't run every step of your long desktop task with a frontier model Weak-to-strong escalation offers a middle ground: a weak agent acts by default and hands control to a strong one only when needed. But put in practice, this escalation method faces a dilemma: 1. This requires an uncertainty signal cheap enough to check at every step. 2. After handoff, the strong agent can still go astray, and a black-box API exposes no internals to monitor.

People also LOVED their pixel art portraits. Kudos to the team for putting on what multiple people said was the best #SFTechWeek event they've been to this year 💚 @ZeningChen42844 @LytvynovaYuliia @ZiqiLiu158430 @angli_ai @Richard_simular @ritacyliao

The vibe:

"San Francisco is a city to celebrate human creativity, a place for people to connect with everyone." - @angli_ai This is what we managed to do tonight. Inside the historical 1932 building of The Fold, people from all walks of life - from writers, musicians, artists, engineers, researchers, students, to professors - gathered to talk about the intersection of humanity and AI. As one guest said, "this is the most diverse group" they've seen in the city.

Our MTS @Richard_simular, part of the team that built @sai_borg, answered 4 critical questions on personal agents on a panel at #SFTechWeek: 1. When should an agent think, when should it just act, and when must it stop and ask — what’s your decision architecture? Start super strict then slowly relax the guard. User & personal agent have a continual "bonding" process, and people have different preferences so it's never going to a one size fits all 2. Do agents overthink into paralysis? Usually it's not an agent, but a weak model overthinking. Some models do this to benchmaxx so you will see an agent thinks for 4 minutes for a simple task like open google.com. 3. How do we know how much autonomy to give to an agent? Running on sandbox is (almost) always the best approach. Factors: 1. how safe is sandbox 2. how reachable is the request to you 3. how quickly it learns and relax permission 4. With Muse Gadgets and OpenAI Dots launching last week, what does personal-agent decision architecture look like 12 months from now How to give more information to agents. If we can find out a way to send explicit and exact request to agent then we don't need any context. Thanks for having us on!

Mood at SF @Techweek_: no one knows what agents they'd still be using in December. This puts the burden on agent builders to think ten steps ahead when designing harness, safety, and user trust. Thanks Seamate for having our MTS @Richard_simular on the panel with @samuel_spitz from @Replit, @ZongZiWang from @harvey, and @haosu_cs from @CoreSpeedHQ. Such a thoughtful conversation!

Our new agent safety research: The model says no in chat. The agent does it anyway. A model that says no in chat might not exercise the same harmful refusal when it's given a mouse and keyboard in computer use. It might tell you no but still goes ahead to do harmful things. How did @sai_borg do? Running the OS-Harm benchmark, Sai declined 69% of the tasks on its first turn, before taking any action on the computer. Then we added a short safety section to Sai's instructions: its default agent mode declined 82% of the misuse tasks on its first turn, against 71% for the same build without it, three runs each.