Sidhartha Mani retweeted
Bots are going to violate ToS. Is that your problem as well? Talk to us! We will enable legality of the bots.
1/ Today we're introducing Cua-S1-4B-0.2, the first multimodal decision model trained with RLOO on live computer-use tasks, using task-completion rewards.
Text and multimodal adapters are available under Apache-2.0: github.com/trycua/cua
huggingface.co/cua-ai/cua-s1…
Sidhartha Mani retweeted
Cua is trending on github!
we've all been a bit Jev-crazy these last few days, putting a few other shiny objects on hold to hack on decision models for computer use
out of that came CUA-S1 and jev-use. thanks for trying them and sharing feedback!
github.com/trycua/cua
2/ Cua Driver + Jev playing 2048.
In this measured 2048 run, Jev was 5x faster and about 1,000x cheaper than Astra. That result is specific to this run, not a general claim about either system.
Video attachment: 30 seconds, silent, 1920 x 1080. The final frame shows Jev at 44.9 seconds and about $0.00108 API-equivalent cost, versus Astra at 294.9 seconds and about $1.45.
Demo by @injaneity
1/ Fast Computer Use is now solved with @typesafeai Jev + Cua Driver.
Available in development preview for macOS, Windows, and Linux. We call it jev-use.
Draft #3943: github.com/trycua/cua
Sidhartha Mani retweeted
Constal is live.
We built the runtime for agents that run for weeks to months!
Checkpoints every step.
Guardrails on every action.
Never again pay for the sandbox while your agent waits.
Runs your LangGraph, Mastra, or OpenAI SDK code. No rewrite.
Sidhartha Mani retweeted
Name one underrated/generous free tier service.
I’ll start: @Cloudflare
1/ OpenClaw 2.0 is out - we're excited to have worked with the @openclaw team to bring a first-class computer-use experience directly into OpenClaw.
Now you can put that experience to work: launch an OpenClaw desktop fleet on Cua Cloud Fleets, with native computer use built for multi-player agent workflows: run.cua.ai/?utm_source=x&utm…
Sidhartha Mani retweeted
fun one today with @thursdai_pod @altryne - windows recall vibes, background computer-use hacks, and a lot of speculation about what the next era of computer use looks like. to watch my segment 👇
thursdai.news/guests/franced…
Sidhartha Mani retweeted
every founder should internalize this
no one cares if you get 500k views on a random Tuesday and then they never see you ever again. if you actually care about 'attention' you have to stay top of mind
Always. Be. Shipping.
Replying to @levelsio @lulumeservey
Everyone overthinks marketing.
For every product launch we did at X, I wrote the announcement 20min before clicking post.
Better to be launching something every week instead of focusing on how to make a single-day traffic spike slightly larger.
Sidhartha Mani retweeted
Replying to @fintables
@fintables gives investors the financial analysis tools to make faster, better-informed decisions. When investors are moving quickly, every second counts.
Sidhartha Mani retweeted
Today, we raised a $13M series A to solve the GPU shortage
Trillions are being spent on GPU CapEx while 80% of it sits idle
The capacity already exists
Virtualizing GPUs unlocks it
Backed by Matrix, YC, and CEAS
Sidhartha Mani retweeted
there's a nice touch to shipping this from a plane over starlink. anyway, your agents just got a free 2x boost ✈️
Sidhartha Mani retweeted
It makes sense to optimize model routing at the harness layer instead of the gateway layer if you want to hillclimb on accuracy/cost for any e2e task.
Every task is solved by a combination of a model mixture and agent harness. Every task requires a different mixture of models (+harness logic) to be at the pareto frontier of accuracy and cost.
* If you only optimize the model mixture at the gateway layer, you lose the broader context encoded in the harness and only optimize at the LLM completion layer.
* If you optimize the model mixture at the harness layer, you can make the "optimal" model choices a priori and while in the agent loop
For any given task, the model and harness are probably co-optimized together, so that the model mixture can only exist with this specific harness shape and vice versa
Sidhartha Mani retweeted
1/ Offline computer-use models are here. Today, one running on our Mac made it all the way to hotel checkout.
We ran Qwen3.8-27B from @Alibaba_Qwen through Cua Driver. It also turned a flyer into a Calendar event and reconciled receipts.
Every model token stayed on the Mac 🧵
Sidhartha Mani retweeted
We’re launching Optima. Now anyone can create a custom benchmark for their use case, leveraging Artificial Analysis’ leading research and platform
Building and running benchmarks is difficult. We have distilled Artificial Analysis’ research and experience developing benchmarks into Optima, a new platform for benchmarking models on your own workloads and comparing performance, speed and cost efficiency. Optima allows you to find the best model for your task, or an equally performant alternative to your current setup at 10x lower cost or time per task.
We’ve integrated Artificial Analysis' research and experience in benchmarks across the Optima workflow:
➤ Build benchmarks based on your own data and use cases: There are three ways to build a benchmark with Optima. Upload an existing evaluation dataset from your own files or @huggingface, or import agent traces from platforms including @arizeai, @braintrust and @langfuse. Install the Optima skill to build a benchmark using context from your coding environment and previous sessions. Or simply describe your use case and provide example inputs and outputs, and Optima will build the benchmark for you
➤ Run across the latest models: Run the same benchmark across leading models in a single click, and keep your leaderboard up to date as soon as new models are released
➤ Bring Artificial Analysis grading to your own benchmark: Evaluate responses against objective rubric criteria or using the same pairwise judging approach used for Artificial Analysis benchmarks including GDPval-AA and AA-Briefcase. For pairwise judging, select your preferred responses from a sample and Optima uses those preferences to rank models across your test set
➤ Compare performance, cost and time efficiency: Optima measures more than model performance. Cost per Task and Time per Task are tracked alongside benchmark scores, with category-level results and support for custom metrics, allowing you to compare the tradeoffs between models for your specific use case
Ahead of launch, here are examples questions our beta testers answered with Optima:
➤ Which model can save me 10x the cost without a meaningful decrease in quality for my finance & accounting agent?
➤ Which model best matches the writing style of lawyers for my legal agent?
➤ Which model can best identify different elements in my custom image dataset?
Optima is available today. Build your own benchmark at artificialanalysis.ai/optima
Sidhartha Mani retweeted
I’ve claimed my Local AI username. Claim yours here:
local.ai/ronaldmannak/invite
Sidhartha Mani retweeted
After two packed meetups at WWDC in Cupertino, AiOS is coming to San Francisco. Hosted by @RayFernando1337, featuring top speakers on AI in the Apple ecosystem: Local AI, MLX, Foundation Models, CoreAI.
First one is this Tuesday with speakers from @Hailuo_AI and @wendylabsinc. Join us: luma.com/cboi9km9
























