u/elnino2023

Post : https://openai.com/careers/power-trading-lead-us-remote/

I've been chasing the question of what algorithms a transformer can actually express -- separate from what it can learn. So I built a compiler: define a computation graph in ordinary Python, and it produces the weights of a transformer that executes the graph. The result is a standard Phi-3-architecture checkpoint that vanilla huggingface loads with no custom code and no trust_remote_code. Zero training in the pipeline.

Write-up (origin + how the constructions work): https://ood.dev/posts/torchwright-intro/

Repo (twelve runnable examples): https://github.com/physicsrob/torchwright

Hand-built transformer weights aren't a new idea. RASP defines a language whose primitives map onto transformer sublayers, and Tracr compiles RASP programs into actual weights. I wanted two things they don't aim for: expressing a computation graph in ordinary Python, and targeting a stock architecture, so the output loads in vanilla huggingface with no custom code.

Agent Cursors: many background agents can now each own a cursor, scoped to a non-overlapping action space.

What makes it work: each agent opens a named session tied to one window, and its cursor only touches that window, so they don't collide. Inputs run through both accessibility tree and vision, and an agent clicks and types in a background window without raising it.

Take a look here : https://github.com/trycua/cua

Watch me control my computer with just my voice. This is the future of operating systems.

No hands.

GPT-Realtime 2.0 is very, very underrated.

Made using : https://github.com/trycua/cua

Take a look at : https://www.heyclicky.com/

The most capable computer-use model for the web.

Pareto-domination: accuracy, latency, cost

• SoTA across all benchmarks

• +5-10% over GPT 5.5, Opus 4.7, n1

• +25% over Gemini

• 2x faster, significantly cheaper

Expanded action space

• UI actions (like n1)

+ JavaScript generation & execution

Asked to extract all available product sizes and colors:

• n1 clicked 22 buttons

• n1.5 finished in 5 steps

Take a look here : https://yutori.com/delegate

Cua Driver - the new macOS driver that lets any agent (Claude Code, Codex, your own loop) drive any app in the background, with true multi-player and multi-cursor built-in.

Take a look at delegated demo capture. Agent drives the app, cua-driver record and renders zoom-on-click at export. Imagine just delegating an entire product demo to an agent, background computer-use now unlocks this.

GitHub : https://github.com/trycua/cua

Computer Use rocks!

Opus 4.7 with 78 % on computer use - human level performance at 72 %.

Saw this on X.

I too am struggling with the term post agentic ai just posting here for further discussion.