Openleetcode is a free, open-source runner for LeetCode problems that executes your solution on your own machine. Its distinguishing feature is that the judge inputs live in the repository: 1,410 problem manifests with their expected outputs are published as a versioned, forkable artifact, so nothing is submitted to LeetCode and no account is required. What Is Openleetcode and How Does It Differ From Other LeetCode CLIs? Openleetcode is a command-line tool written in Haskell that takes a solution file, matches it to a problem manifest, runs it through a small language-specific harness, and grades the result against test cases that ship with the tool. The author’s own framing is that the CLI is “just the glue” — the interesting artifact is the test corpus underneath it. ...

1667 is a free, open-source terminal UI for writing fiction with language models. Every paragraph the model generates is kept as a sibling “take” on a branching story tree you navigate with the arrow keys, so rejected drafts are never overwritten. It is local-first and works with OpenAI, Anthropic, Ollama or any compatible endpoint. What Is 1667, the Terminal UI for Writing Fiction With LLMs? 1667 is a full-screen, keyboard-driven terminal environment purpose-built for drafting fiction with large language models. Its GitHub description says it plainly: “A full-screen terminal environment for writing fiction with language models.” There is no browser tab, no Electron shell and no web editor — the entire program runs in your terminal. The vendor’s own documentation lives at 1667.ai. ...

Saggar terminal is a free, native macOS app that runs your agent shells in parallel and then sorts them into one ordered attention queue, so the question it answers is not how fast your terminal renders but which of your five running sessions needs you right now. Your prompt, every failure, and every finished job surface in a single list you walk with Cmd+J. Remote control of that queue from an iPhone costs USD 2.50 per month. ...

The PhoneBuddy agent engine is an Apache-2.0 Rust agent harness embedded directly in an iOS or Android app: a ReAct tool loop, in-memory virtual shell, jailed file sandbox, boa_engine JavaScript sandbox, subagents and scheduled tasks all run in your app’s own process behind a C ABI. Verified 2026-10-01 from a full source download: 70 Rust files, zero child-process code. That last sentence is the whole product thesis. Every desktop agent harness — grok-build, Codex, Claude Code — assumes it can fork a process, read a broad filesystem, and keep a session alive for hours. On a phone, each of those assumptions is a store-rejection risk. PhoneBuddy is built around the prohibition instead of around Linux, and the interesting question is not whether its feature list is long. It is whether the architecture holds up, and whether the small project around it is ready for production. This review keeps those two questions separate, because the answers differ sharply. ...

OpenAPPA agent security means enforcing information-flow policy outside the model’s prompt: the engine labels everything an agent reads as audience x trust, checks every tool call against a declarative TOML contract before it runs, and returns a machine-readable remedy plan when a flow is disallowed. Its vendor benchmarks record zero successful attacks across 1,320 guarded evaluations. What is OpenAPPA, and what is it not? OpenAPPA is an MIT-licensed, Rust-based security engine published by Archestra AI in August 2026 and powered by APPA (Agentic Permissions Policy Algebra). It sits between an agent and its tools to answer one question before every action: is this data allowed to go to this destination? Reading a private record narrows the session’s audience; reading an outsider’s web page lowers its trust; a later call aiming at a destination the session no longer permits is refused before dispatch, not after. ...

Agent CodeMode MCP means letting the script your coding agent writes call the MCP servers your agent has already authenticated. Instead of the model making forty tool calls one at a time, it writes one script; the credential your agent minted is reused from disk. Measured: 40 calls and 262,159 characters become 1 script and 903 characters. What is Agent CodeMode, and why does it exist? Code Mode is the pattern where an LLM writes code that calls tools, instead of calling each tool through the model’s native tool-calling interface. Cloudflare’s Kenton Varda and Sunil Pai named it in September 2025 with a thesis that reads as obvious once you see it: LLMs are better at writing code to call MCP than at calling MCP directly. Anthropic published the same architecture as vendor guidance on November 4, 2025 in Code execution with MCP. The reasoning is about training data: models have seen a very large volume of real TypeScript, and comparatively little synthetic JSON tool-schema blobs. ...

An agent trajectory monitor reads the sequence of actions an AI coding agent has already taken — tool calls, file edits, shell commands, retries, context usage — and compresses many concurrent sessions into one attention queue showing which agent is working, which is stuck, and which is drifting off goal. That is the short answer. The rest of this review separates the three layers people keep conflating (emulator, runtime, monitor), checks the claims behind the leading 2026 tools against verifiable data, and states plainly where deterministic trajectory monitoring fails. ...

AgentOps Studio is a name that currently describes three separate projects: a full-stack human-in-the-loop operations console, a LangGraph multi-agent research workbench, and a hackathon trace-diagnosis app. None is a mature product. All three are worth studying because they sketch the operational layer that trace-only observability platforms leave out. That is the short answer. The rest of this review disambiguates the three projects, separates what a console does that a dashboard cannot, and checks the whole category against the one number that matters: how many teams can actually test their agents rather than merely watch them. ...

An adversarial verification agent is a checker with an inverted objective: instead of collecting signals that confirm a claim, it is built to refute it. Prove-It turns that refuter on a coding agent’s own “done” claim, answering with one of four verdicts — PROVEN, FAILED, NOT PROVEN, or BLOCKED — instead of a paragraph of reassurance. That is the short answer. The rest of this guide explains why the pattern exists, what the four verdicts actually mean, where the skill stops being useful, and how a 12-case self-authored benchmark should be read without overselling it. ...

Agent evaluation is the practice of proving that an AI agent completes its assigned task correctly and follows an acceptable path to get there. An agent you have not measured is unfinished: you cannot distinguish a regression from noise, you cannot adopt a better model without weeks of manual retesting, and you cannot answer the only question that matters after every change — did this help? Why an Unmeasured Agent Is an Unfinished Agent Traditional software ships when its tests pass. Agents rarely arrive with tests at all. They arrive with a demo, a trace viewer, and a feeling that the thing is working. ...