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CUA-S1 is not a computer-use agent. It is trycua’s family of small “System One” models that score a closed set of (element, action) options in a single forward pass, returning a probability per option instead of generating action text. Planning, ordering, and execution stay in your application code. That distinction is the whole review. Everything CUA-S1 does well, and everything it fails at, follows from the decision interface it chose. ...

Ordewell coding agent task planning works in five stages: a read-only planner explores your repository and asks about anything vague, emits an ordered task list with dependencies, each task runs in its own git worktree on a runner and model you assign, a task passes only when its completion marker appears in the output, and nothing reaches your branch until you approve the handoff. That is the whole product in one sentence. The interesting parts are the two decisions inside it — the plan is a file you edit rather than a prompt you typed, and the pass/fail verdict comes from evidence rather than from the model’s opinion of its own work. ...

Skillsync portable agent chat sessions work by deterministic translation, not summarization: it reads a session transcript from one coding agent, converts it into the target agent’s native format, and opens it so you continue where you left off. Tool calls and reasoning traces ride along. It is a YC W26 product built on an open-source Rust engine, txcript. What does Skillsync actually do (and what is it not)? Skillsync is a macOS desktop app plus a CLI (skl) plus an MCP server. Its one-line pitch, as listed on the Y Combinator company page, is “Notion for AI chats” — a searchable, portable store of every session you have ever run, across every harness you use. The marketing page frames it as: “Your AI sessions made portable across every agent and teammate.” ...

Pizza Bot is a self-hosted, local-first inbox for background AI agents: you give an agent work, close the window, and it comes back with a result — or pauses in an Action queue when it needs your decision. It is a finished desktop application built on DeepAgents and LangGraph, Apache-2.0 licensed, not an SDK. The design brief behind it is the whole argument: the interface assumes you are not watching. ...

A local LLM rig cost payback runs from about 4.8 months to infinity, and the machine is not what decides it. The same $3,500 RTX 5090 box pays back in ~4.8 months at 20 h/day against a $2/M API tier, stretches to ~39 months at 24/7 against a $0.50/M hosted open-weight model, and never pays back at all at 12 h/day — because a $133/month API bill undercuts a $137/month local rig. Payback is a property of your queue depth, not your GPU. ...

No. A chat LLM does not replicate human reasoning — on cognitive-bias tests, GPT-4 scored 96% where humans scored 38%. What LLMs do replicate is the mechanism of a psychic’s con: statistically generic statements, a subjective-validation loop, and an RLHF training signal that rewards agreement over truth. That distinction is the whole article. The phrase “chat LLM replicate human reasoning study” fuses two claims that the evidence treats very differently, and most coverage collapses them. The first claim — that models reason like people, with our shortcuts and our biases — is contested and, on the standard instruments, largely contradicted. The second claim — that models reproduce the social apparatus that makes reasoning seem present, whether or not it is — is well measured, replicated, and quantified in numbers strong enough to cite in a product review. ...

Laya as an offline OS for Jev on Mac M4 Core ML is a headline stitched from three separate sources, and only one of them survives inspection. The Core ML port is real, Apache-2.0 licensed, and genuinely runs without a network. The M4 label comes from a four-line install gist title, not a benchmark. The 45 decisions per second figure is an uncapped average measured on an M3 Max, rounded down from 49.66/s — and when the same port was asked to hold a 50/s deadline, more than half the ticks missed. ...

Wake is a free, MIT-licensed macOS app that indexes the local sessions of 22 coding agents — Claude Code, Codex, Cursor, Gemini CLI and 18 more — into one read-only SQLite index searchable in under a millisecond. It also ships wake-mcp and wake-cli, so your other agents can query that archive themselves. Why does a coding agent session manager exist at all in 2026? Because almost nobody runs one agent any more, and the reasoning behind your code is not in git. ...

herdrm is a native macOS SwiftUI client for herdr, the Y Combinator-backed agent runtime. It puts every space, coding agent, and terminal from your local Mac and any SSH device into a single sidebar, and clicking a row attaches the real agent PTY. It is a console, not an engine: it owns no terminals, and it does nothing without a herdr server behind it. Is herdrm a coding agent console on macOS, or just a window onto one? The single most important thing to understand about herdrm is that it does not run agents. It renders them. ...

Autoprompt is a coding agent prompt skill that injects an orchestration procedure — plan, delegate, implement, verify — into 11 host agents, including Claude Code, Codex, and OpenCode. Its headline claim of 45% fewer failures comes from a single 89-task Terminal-Bench 2.1 run where failures fell from 29 to 16. That result is real arithmetic but version 1 evidence, and it costs roughly 3x wall-clock time and 2x tokens. What the 45% Figure Actually Measures The number everyone quotes is not a percentage of tasks solved and it is not a percentage reduction in tokens. It is a reduction in failures on one benchmark, measured once. ...