ollama-llmwatch is a free, MIT-licensed Python terminal monitor that tails Ollama’s own server log and turns it into a live prefill board: tok/s sparklines, prompt-cache reuse percentage, time-to-first-token, a progress bar with ETA, and plain-language diagnostic hints. It never calls the Ollama API and adds zero inference overhead. What Is ollama-llmwatch and Why Does Ollama Model Monitoring Need It? Most Ollama monitoring tools watch the wrong layer. They scrape an API response, count tokens after the fact, or read a Prometheus endpoint that may not exist. None of them can tell you what is happening during prefill — the window where your prompt is being read and nothing at all is being emitted. ...

An agent token usage meter is the component that reads the provider’s usage object on every model response, keeps input, output, and cache-read tokens separate, and converts those counts into dollars at log time using a versioned price table. It meters a run, not a request, because one agent task fans out into dozens of model calls. That is the short answer. The rest of this guide separates the four quantities people keep collapsing into one number, shows where the ground truth actually lives, explains the OpenTelemetry contract the industry is converging on, and states plainly where the current tooling still fails. ...

Coasty is a hosted computer use agent API: you send a screenshot and a plain-language instruction, and it returns typed mouse and keyboard actions, backed by managed cloud desktops, retries, schedules, and an audit trail. It sells the harness around a vision model, not the model itself — and its own benchmark numbers verify unevenly. That second sentence is the entire review. Coasty is a YC S26 company that launched on Hacker News in July 2026 as “an API for computer-use agents,” and the product it actually ships is not a smarter vision model. It is the operational layer that every team building on raw computer-use tool calls eventually has to write themselves: VM lifecycle, the screenshot-action loop, action execution, retries, human approval gates, scheduling, and a per-action evidence trail. ...

A2A agent routing is the runtime decision of who should execute a task after the handshake: keep it with the incumbent (SELF), keep the incumbent as owner and recruit peers for uncovered requirements (COLLABORATE), or transfer ownership entirely (HANDOFF). The A2A protocol standardises discovery and transport; a router supplies the missing policy layer. That is the short answer. The rest of this guide is the part that actually decides whether the router helps or hurts: what a state-aware utility function prices, why the honest benchmark signal is wasted work rather than the utility column, and the mode mix behind those numbers — SELF rising from 20% to 42% of decisions while HANDOFF falls from 66% to 44% — which shows that checkpoint awareness mainly teaches a router to leave a task where it is. ...

An mcp server browser automation setup usually fails the same way: the agent launches a fresh, cookie-less Chromium, hits a login wall, and stalls. NeoBrowser is a Rust MCP server that takes the opposite approach — it drives your installed Google Chrome over CDP and can start already authenticated from your real profile. That is the pitch, and the engineering behind it is real. Whether you should install it today is a harder question, and the answer involves a 404, a disputed differentiator, and four security questions the project’s own launch thread left open. ...

An mcp rest api bridge works by reading your API’s OpenAPI spec at runtime and translating each operation into an MCP tool. You keep the REST API exactly as it is, add a proxy or gateway in front of it, and point your agent at the MCP endpoint. Nothing in the backend is rewritten or redeployed. That is the short answer, and it is genuinely accurate — with one important caveat that the marketing pages bury. “No code changes” describes the API, not the project. You still own a configuration surface, an authentication decision, and a curation judgment call. The teams that get burned are the ones who treat auto-conversion as the finished deliverable instead of step one. ...

The best mobile on-device agent SDK answer in 2026 is a pure-Rust engine that runs entirely inside your app process. PhoneBuddySDK, open-sourced by APUS-AI-Lab under Apache-2.0, ships a planner, tool loop, sandboxed file system, virtual shell and JavaScript interpreter with C FFI, Swift and Kotlin bindings — and no child processes anywhere. What PhoneBuddy SDK Actually Is (and What It Is Not) PhoneBuddySDK is a Rust core crate compiled to a C ABI (phone_buddy.h) with first-class Swift (PhoneBuddy.swift) and Kotlin (NativeAgent.kt plus JNI) wrappers layered on top. It landed on GitHub on 2026-08-18 under Apache-2.0. As of 2026-10-01 the repository shows 36 stars, 13 forks, one open issue, and a language breakdown of roughly 1.36 MB of Rust against 36 KB of C — a ratio that tells you where the engineering lives. The C layer is a boundary, not an implementation. ...

NorthCinder is a local-first MCP server that turns a general AI assistant into a buyer-side shopping researcher: it queries only the stores you configure, ranks offers against your own brief, labels sponsored placements beneath every organic result, and requires a signed, single-use approval before any checkout. That is a genuinely unusual set of promises for 2026. Most agentic commerce tooling asks you to trust a marketplace, a hosted catalog, or a vendor’s own ranking service. NorthCinder asks you to trust code you can read on your own laptop — and then, unusually, publishes a document explaining exactly where that trust stops being verifiable. This review installs it, probes it over MCP, reads the ranking and mandate internals, and then separates the two questions that get conflated in every vendor announcement: does it work, and should you depend on it. ...

Working with AI coding agents feels like leadership because it is. The unit of work changed from writing code to decomposing it, delegating it, and verifying the result. That shift is measurable: a 2026 NBER working paper found that a leader’s performance managing AI agents correlates at rho = 0.81 with their performance managing human teams — and still 0.69 after controlling for hard technical skills. That single finding reframes everything else in this guide. If managing AI coding agents were a disguised coding skill, the correlation with human-team leadership would collapse once you controlled for task-specific ability. It does not. What the AI task is actually measuring is the soft, transferable part of leadership: how you set expectations, calibrate trust, and run a review loop. ...

Build a native coding-agent GUI in Rust by pairing Zed’s GPU-accelerated GPUI framework with Longbridge’s gpui-component kit, keeping both on the same dependency channel, and driving every update as an event rather than a polling timer. Why Build a Native GUI for Coding Agents in Rust and GPUI? Because the interesting part of an agent app is not the chat bubble — it is the input path, the process boundary, and the frame budget. GPUI hands you all three as explicit primitives instead of hiding them behind a webview. The counter-argument is equally real: GPUI is pre-1.0, its dependency channels are genuinely confusing, and its first build compiles a graphics stack from source. This guide is the build path that currently does not exist as a single document — the popular GPUI tutorials stop at a static Linear-lookalike dashboard, and the best agent-specific write-up (Paneflow’s post-mortem) is an architecture essay rather than a reproducible how-to. ...