Category Focus/categories/code-assistant
Code Assistant AI Tools
Discover and compare the best code assistant AI tools and software. Browse 102+ curated tools with reviews and rankings.
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Code Assistant
QualityLayer is a software factory that keeps you in the loop while your coding agents plan, build and review software. It is designed for people who already work with AI coding agents — Claude Code, Codex, Cursor, Antigravity, Pi, OpenCode, Grok and GitHub Copilot — and who want each change to move through a structured path instead of a single prompt. You can work side by side in the QualityLayer App or in the browser, or keep using your favourite terminal or IDE, with a CLI that connects other agents. Every change travels through seven steps: Discuss, Research, Plan, Outline, Implement, Verify and Review, before your agent handles the pull request. The product describes itself as quality by design and as the software factory with you in the loop. The product's framing is stated directly on its own site: plan before you build, agree on the plan, then review the tested change. Rather than letting a change go straight from a prompt to code, QualityLayer inserts the decisions you make — Discuss, Research, Plan and Outline — before implementation begins. After implementation, your project's checks and an independent review produce proof, and the site's stated rule is that every point needs evidence. The result is presented as a workflow where you decide, agents work and agents check, in that order across the steps. A quoted engineer says that with QualityLayer, developing software with agents finally feels like doing real engineering again, and a former Pilot Shell user says that their productivity shot through the roof. QualityLayer works with the agents you already use rather than replacing them. The supported agents listed are Claude Code, Codex, Cursor, Antigravity, Pi, OpenCode, Grok and GitHub Copilot, and the CLI connects other agents. You keep your existing login or API key, and the site states that you use the models you already pay for: Opus 5.5, Sonnet 5.5, Fable 5.1, Haiku 5.5, GPT-6.1 Sol, Grok 4.7 and Gemini 3.8 Flash, reached through those agents. Models can be chosen separately for building and for independent review — for example Opus 5.5 builds it, subagents are used when useful, Codex reads the plan, Claude Code reads the plan, or one agent does every step. Any model your coding agent offers can also be used. Before any code is written, your agent maps the files a change will touch and how they connect. The site's example change map separates 'today' from 'after changes', marking three changed files, one new file and one risk, and traces connections across a gateway, billing, meters, usage windows, a handler, a new meter and a ledger with price() in the pricing file. The map can be dragged to move and zoomed, and new, changed and risk items are colour-coded. The Plan step is where you review diagrams and clickable designs and then approve the plan, and you can comment on change maps, diagrams and clickable mockups. The stated sequence is simple: agree on the plan, then review the tested change. QualityLayer runs your project's checks and security scans, and a separate agent reviews the change against your request. Project checks are described as repeatable checks from your project: tests and types run through your own recorded commands, linters using your project's rules, code health covering complexity and duplication, and recorded proof where every point needs evidence. Independent agents cover a security review of inputs, access and secrets; an independent review by an agent that did not write the code; a design review that checks each finished design; and an optional second opinion from another coding agent. Anything still open reaches you in Review together with its evidence. The workflow runs from prompt to pull request across eight numbered stages: Discuss, Research, Plan, Outline, Implement, Verify, Review and Pull request. You decide at Discuss, Plan and Review; agents work at Research, Outline, Implement and Pull request; agents check at Verify. In Discuss you settle the open questions with your agent — the site's example asks whether 400-seat customers move to usage or stay on seats, offering three choices and a recommendation. In Research, agents trace how the code works today and what can be reused. In Outline, your agent breaks the plan into small, testable tasks. In Implement, your agent builds test first and hands independent work to other agents when useful. In Verify the agent fixes the findings, and in Review you look at the code and checks, request changes or approve, control the merge, and the agent handles failing checks and review comments. You can add your own checks, tools and skills to any step. QualityLayer runs on your computer. The site lays out four layers: where you work, with your task on the left and your agent on the right in the App or browser; your agent on your own subscription, using your existing login or API key; QualityLayer planning, answering and reviewing beside your agent, with visual plans or conversation and a CLI that connects any other agent; and your repository, where every step is a Markdown file you can read, diff and commit, including the plan, decisions and test runs. Every decision, document and test run stays with the change, so the reasoning behind a piece of work remains readable and reviewable long after it was produced. Because the plan, the decisions and the test runs are stored with the change, users get a record of why the code looks the way it does rather than only the code itself. The check results and the independent review arrive together with their evidence, so you review findings rather than repeat the work. Working beside your agent in the App or browser means you do not have to abandon the terminal or IDE you already like — the same workflow is available in either place, and other agents can be connected through the CLI. Projects also accumulate reusable lessons that can be shared through Git, so working preferences and project specifics carry forward from one change to the next. The site lists concrete prompts that show how the product is used in practice: 'Using QualityLayer, add rate limits to the export API', 'Using QualityLayer, chat about the billing refactor', 'Open the settings design in my browser', 'Scan this repository for leaked secrets' and 'Share this task with my team', all asked in plain words in any language. Connected agents can plan, design and check work together — in the example, Claude Code asks Codex to review a rate-limit change on feat/export-limits, Codex reads the change and runs its tests, replies REVISE with two items (the limit resets per process, not per account), and Claude Code fixes both items in the same session. QualityLayer addresses engineers who already use coding agents, and its FAQ asks whether it is for product managers as well as engineers, and whether it can be used on your own or only with a team. The App is available for macOS, Windows and Linux, with a Linux AppImage download listed, and for servers, WSL or containers the site points to installing the command line. Settings cover agents, plan limits and reviewers, Home shows what needs you, Memory keeps useful project lessons, Artifacts let you try a screen before it is built, and Team asks the people who know. Your own model subscription is used rather than a separate one. In short, QualityLayer is a human-in-the-loop software factory: your existing agents discuss, research, plan, outline, implement, verify and review a change, you approve the plan and the result, and independent checks provide the proof.
One Roof is a desktop application for macOS that gives you one place to keep up with a whole team of coding agents. It works with the agent command-line tools you already use — Claude Code, Codex, Gemini CLI, Kimi and Antigravity — plus plain shells, and it is explicitly not an IDE and not a terminal. Instead, it is the place where your agents work, with everything they ask, run, learn and cost gathered into one window. It finds the agent command-line tools on your Mac and the accounts you are signed in to, and each session runs the real tool, so you can always type straight into it. The product is free and open source under the MIT licence, and runs on Apple silicon with macOS 14 or later. The problem One Roof addresses is described by the project as chorus coding: several coding agents working on the same piece of work, each doing the part it does best, with one checking another — one plans, one builds, one reviews, and a loop re-runs the tests every night. The hard part of that way of working is keeping up. As the project puts it, the agents are not the bottleneck any more, you are. Five terminals look the same, a question waits in a tab you are not looking at, something failed at 3 AM and nobody told you, and the Mac slows down without any indication of which session is to blame. One Roof was built to keep up with that situation and to keep you the one who decides. The Inbox is the centre of day-to-day work. Every ask from every session lands in one column, one block per session that needs you, with the prompt's own options shown as buttons or selectable with keys 1 to 9. When an agent is blocked until you act, its block gets a red border and moves to the top, and a finished turn waits there too with a reply box where you can paste a screenshot for the agent to read. Nothing is approved for you, and you can always answer in the terminal instead. A Decided list keeps what you answered, newest first. Alongside it, Home is a front page for the morning, built from your own sessions and loops: a lead story about the thing that matters most, a Decide now list of what waits on you with one button each, and an overnight timeline showing every run since midnight as a dot, followed by what went wrong, what to do next and what ran fine. Sessions are real terminals that keep running. Every agent session and plain shell is grouped by state — Needs you, Active, Next, Planned and Exited — and each row shows the agent, the account, the folder and a live one-line status, so you no longer have to type prefixes in front of tab names to tell five identical terminals apart. Each session can be shown as a single terminal, a grid or a wall. Sessions run inside roofd, a small background service, so they keep going when you close the app and come back with their screens when you open it again. History is a read-only record of each session's important moments: steps, findings, errors, what the agent asked and what you answered, and hand-offs between sessions. Each session's run of moments is one card in its own colour, searchable and filterable by session, agent, kind and date, and kept for 30 days. Loops handle work that repeats until a check passes. A loop gives an agent a goal and a check, such as until the tests pass or every night at 3, and One Roof runs the check first — if it already passes, no agent is started. When the check fails the agent works one round on a separate branch, in the loop's own copy of the repo rather than yours, and then the check runs again: done, or another round, up to a limit. A loop never pushes, merges or deploys on its own, and what it produces waits in the Inbox for your approval. Resources shows memory and CPU for each session, for One Roof itself and for every app on the Mac, with 10-minute charts. A browser shows its windows and tabs and you can close a tab or a window from here, and roofd warns when a session grows past 1 GB and ends it at 2 GB, before it can freeze the Mac. Several more places sit on the rail, some of them optional. Projects shows your Linear projects and tickets — teams, projects by status and each project's roadmap — only if you connect Linear with your own API key; without it, Projects shows a single Connect Linear card. Learn is a course on agentic engineering, made of a narrated Agentic Engineering deck and a timeline of papers that shaped coding agents, listenable in the app, with narration by an AI-generated voice that says so. Evals is marked as a draft in the app: it lets you run public benchmarks or build a question set from your own repo's fixed bugs, then compare agents and models on it, with every score shown alongside its range and a plain explanation of what it means, and the cost shown before anything starts. Safety is the default throughout. Each agent has one permission mode — Manual, Plan, Auto or Full — and on a new Mac every agent starts in Manual and asks before it edits a file or runs a command; you change it per agent in Settings › Permissions. Setup asks for the folder that holds your code, or offers to create ~/Projects, and One Roof never scans your home folder, so macOS does not ask about Desktop, Documents or Downloads. The app ships no keys, no tokens and no datasets: Linear, hosted models and gated benchmarks use your own accounts, only if you add them. A crash in one session ends only that session. Underneath sits the roof kit, which makes every agent read the same rules; roof install puts it in place and roof doctor checks every layer, covering a shell default that moves a new terminal opening in ~ to your workspace, agent wrappers, PATH stand-ins, a Claude hook that blocks every prompt while a Claude session is outside the workspace, home notes in ~/CLAUDE.md and ~/AGENTS.md, and a single Claude memory pin whichever folder you launch from. The practical benefit is that you keep the deciding role while the agents do the work. Questions stop getting lost in tabs you are not watching, overnight runs stop being a mystery, and you no longer need to read every agent's full output stream to know what happened. Sessions are labelled with their state, agent, account and folder so the one you need is findable, and Resources makes it clear which session is slowing the Mac. The app is neutral by design: Claude Code, Codex, Gemini CLI, Kimi and Antigravity run side by side rather than inside one vendor's app, and the bar at the foot of the window shows how much of each subscription is used so you can pick the agent with room left. Typical use follows the chorus coding idea the project describes: several coding agents on the same piece of work, each doing the part it does best and one checking another — one plans, one builds, one reviews — while a loop re-runs the tests every night. You might press ⌘N, say what should happen, pick the agent and the repo, and launch; when the agent asks something it shows up in the Inbox. Overnight, loops run and the morning Home page tells you which ones failed and what to do next. When a session's memory grows, Resources flags it and can stop a runaway session. When you want to know how a model handles your own code, you build a question set from your repo's fixed bugs in Evals. One Roof is built for people who already pay for several coding agents and want to use them together: developers running Claude Code, Codex, Gemini CLI, Kimi or Antigravity who need to keep track of what each one is doing, what it costs and what it is waiting on. It requires Apple silicon, macOS 14 or later, git and at least one agent command-line tool that is signed in. Setup finds the agents you are signed in to, and optional extras include hosted models such as OpenCode and Goose with your own API key and a spending cap, and Linear for Projects. One Roof is free and open source under the MIT licence, signed with an Apple Developer ID and notarized by Apple, and can also be built from source with Swift 6 and Xcode 26. In short, One Roof is a place to keep up with a team of coding agents and stay the one who decides: the agents do the work, and you keep up and decide.
Flockbay is a free AI game maker built on Godot: a desktop app for Windows, macOS and Linux that hosts the full Godot editor with an AI agent and an MCP server built in. The agent writes GDScript and scenes into your Godot project, places props, builds rigged 3D models, finds plugins and playtests the game. The same app then goes on to cut trailers and Shorts from your real gameplay and draft your Steam page. It is built for developers making a Godot game they intend to ship and market, and it works with Godot 4.6+, GDScript and C#. Building a game is two jobs that usually live in different tools: writing and debugging the game itself, and then convincing players to wishlist and buy it. Flockbay is designed around the loop that actually finishes a game - write, run, read what Godot reported, fix, play again - and around the marketing work that follows. Instead of leaving you with code you have not seen run, its agent plays the game with input events, reads the live scene tree, the profiler and Godot's errors, fixes what it finds and plays again. You judge the result by playing it. Once the game exists, the agent moves on to store pages, trailers, websites, social posts and ads, all drafted inside the same editor. The editor is the real thing: the full Godot editor, opening in Minimal with Standard and Full one switch away, with Godot, standard and .NET, included. You choose the AI. Flockbay's own hosted AI is free to start, with a model menu that includes Demo (free to start), Auto (picks the model for each task), Claude Opus 5.5 as the recommended option, GPT-5.5 from OpenAI and Gemini 3.1 Pro from Google. Or bring your own AI: Claude Code on your Claude plan, ChatGPT through the bundled Codex engine on your ChatGPT plan, OpenRouter for 100+ models on your own key, or an agent over MCP such as Codex, Cursor or Gemini CLI. Your own agent is free - Flockbay sets no limit of its own, and your plan's limits apply. Playtesting is where the agent closes the loop. It plays with input events, reads the live scene tree, the profiler and Godot's errors, fixes what it finds and plays again. A real tool call shows an agent running `flockbay_run_headless` on a script, which quit by itself with exit code 0 and reported 0 errors and 0 warnings. The page shows a worked example: a SCRIPT ERROR in `hud._drop_note()` occurring 14 to 35 times per scripted demo run, where signing an offer after the first morning's note had gone passed the freed note to a typed argument. The agent added an `is_instance_valid(note)` guard, and nine scripted demo runs afterwards produced 0 SCRIPT ERRORs. The agent sends input with `flockbay_game_input` and captures the viewport with `flockbay_game_screenshot`. Content creation sits in the same editor. Blender is built in, so your agent models props and characters in it, and placeholder 3D models come from Tripo on your own key for you to swap out. A library of Godot add-ons lets the agent find and install plugins. ElevenLabs works with your own key for music, sound and voice, and your agent can make looping placeholder tracks in your game's style, saved into your project at `assets/Audio/Music`, for you to swap for your own. Paid plans add pictures and sound on every chat. Every change is reviewable. The command palette (Ctrl/Cmd+Shift+P) reaches every action in the window by name, and `/model`, `/compact`, `/undo` and `/review` work at the start of a message while `@` adds a file or asset. Permission modes (Ctrl/Cmd+Shift+M) cover Plan mode, Ask before edits and Edit automatically, with Full access in the menu. Review changes lists every file a turn changed with Revert per hunk, per file and for the whole turn. A transcript view switches between Normal, Thinking and Verbose, chats can sit side by side, and you can open the project in VS Code, Cursor, Rider or Zed, or in Terminal at the game's folder. Command line flags include `--open-game` and `--your-own-agent on`, version control (Ctrl/Cmd+Shift+G) commits, pushes and pulls with Git and GitHub, and Flockbay's skills and hooks can be installed for Claude Code and Cursor. What makes the approach distinctive is where the output lives. Your game stays a plain Godot 4 project on your own disk, on the official engine, not a fork, and it opens as a plain Godot 4 project in stock Godot. Games run locally on the copy of Godot bundled with the app - no streaming, no cloud rendering, no cloud building - and projects and assets are private. There are no royalties. An MCP server inside the editor exposes Godot tools to outside agents; Flockbay can print the `claude mcp add` or `codex mcp add` line, add the server to Cursor or VS Code, and a setup prompt asks your agent to check the connection and read the project before it changes anything. For users, the outcome is a game that has actually been run and repaired rather than code that has only been written, and every file the AI touched can be reviewed, reverted hunk by hunk, and opened in stock Godot or your own editor whenever you want. The marketing side produces assets you would otherwise have to make by hand: a trailer cut from your own Play sessions with music and titles, Shorts with captions, a Steam page draft with copy, tags, capsules and content survey, a website with wishlist buttons and an email signup, and ads written and cut from your gameplay. Free covers the app, Godot and your own AI, plus free hosted AI to start and one free video in Video studio. Use cases in the content show the range. Build logs document agents producing a 500-enemy horde with no enemy stuck, a six-legged walker, a physics truck on a mountain trail, a jump that feels like Celeste, a character controller for stairs and slopes, and a save that remembers everything. The team's own game, Shelf-Made Tycoon, is a cozy logistics tycoon on Godot 4.7 coming soon to Steam, used to illustrate a card that switched under the cursor, a drafted Steam page, a published website at shelf-made-tycoon.flockbay.app, a Short cut from a play session and a wishlist push before Next Fest running across Instagram, Facebook, TikTok, Reddit and YouTube at $40 a day. Market research shows another workflow: the agent compares your idea with similar games on Steam - their prices, reviews and estimated sales - reading prices and reviews from Steam and estimating units as reviews multiplied by 30 to 60. In the example, 15 comparable games for a cozy warehouse tycoon are listed, the closest match is Wilmot's Warehouse at 96% positive and $14.99, and the suggestion is to launch at $14.99 with a 10-15% launch discount, holding $19.99 until there is more content. Flockbay requires 64-bit Windows with an Intel or AMD processor, Macs with Apple Silicon, or an x86_64 Linux AppImage on Ubuntu 22.04, Debian 12 or Fedora 39 (glibc 2.35) or newer, with a GPU supporting Vulkan 1.0 or OpenGL 3.3. There is no browser version, browser editor, browser preview or mobile app. Pricing is free ($0) with free hosted AI, your own Claude, ChatGPT or agent, every editor feature and one free video in Video studio; optional plans are Hobby at $20, Indie Developer at $60 and Studio at $200 a month, and Studio adds 20 social accounts, 8 ad accounts, 25 published websites and 100 live playtest links. There is no team, per-seat or enterprise plan. Flockbay's value proposition is a single desktop app where a Godot game is written, playtested, repaired, reviewed and then marketed, with the project remaining an ordinary Godot project you own and can open in stock Godot at any time. The team states its intent plainly: it builds Flockbay for games worth playing - made with AI, played by people, and polished until they are beautiful.
Wavefunction is a coding agent built as a desktop application for macOS. It is aimed at developers who want an agent that stays focused on writing and changing code instead of growing into a general-purpose suite. The app puts one agent and one conversation on screen, with a simple project sidebar that leaves the conversation and the composer at the center. You give it a task and keep talking while it works on several tasks at once. Wavefunction is free and open source under the MIT license, and it can run on local models, your own API keys, or a ChatGPT sign-in. The product's starting point is a reaction to how coding agents have evolved. The site explains that coding agents keep turning into super apps, adding more with every release, while Wavefunction goes the other way: less is more, simpler is better, and building comes first. To make the contrast concrete, the site describes other apps as an interface crowded with tabs, sidebars, editor panels, and a terminal. Against that, Wavefunction shows a simple project sidebar with room for the conversation and composer, so there are fewer sidebars and fewer tabs and the conversation stays at the center. The stated intent is to leave more room to focus on your code. The minimal interface is a deliberate feature set rather than a stripped-down compromise. There are fewer sidebars and fewer tabs, and the conversation is the primary surface. Wavefunction does not ship a built-in browser: links open in your default browser, with the setup you already use. It also describes itself as just a coding agent, without dots, bots, built-in apps of all kinds, or a million extra features. The claim is that what remains is the set of tools needed to get things done, which keeps the app minimal, considered and fast instead of crowded. Parallel workflows are handled by splitting work across subagents inside a single conversation. You send the next task while the last one runs; each new piece of work that can run on its own gets a subagent, and nothing waits its turn. To stay parallel without collisions, every chat starts on its own branch, and subagents that could touch the same files each get a worktree. Wavefunction reviews their changes and brings the work together. You can also steer while it works: change your mind mid-task and your message reaches the subagent already on it, so it adjusts without starting over. Fork or rewind lets you start a new chat from any message, or put the code and the conversation back to how they were. Computer use is separated from your own machine. Other agents take over your mouse, keyboard and screen; Wavefunction gets a desktop of its own, a local Docker container with its own browser and files, so you keep working while it clicks, and you can watch it or take control at any time. The site states that it never moves your pointer, types on your keyboard, or records your screen, and that a file reaches your Mac only when the agent hands it over, into a folder you allowed. When a site needs your login, you sign in privately: the agent pauses and never sees what you type, and the chat stays signed in for next time. Downloads come to your Mac in the folder you choose, and files you add are ready to upload. Computer use needs Docker Desktop and a model that can read images. Models are a choice rather than a lock-in. You can sign in with ChatGPT Plus or Pro, use API keys from Anthropic, Google Gemini, OpenAI, OpenRouter or Vercel AI Gateway, or run models locally through Ollama on your Mac or your server, LM Studio, or another OpenAI-compatible server. You pick a model for each chat and switch whenever you like, so you are never tied to one provider's plans or limits. Running models locally or requiring zero data retention from a provider are the two privacy paths the site describes. Privacy is grounded in where things are stored and which services you connect. Your conversations and settings are stored on your computer. Using a local model keeps model inference local; alternatively you can configure a provider with zero data retention. Which services receive your data is determined by the models and connectors you choose. The app is open source down to the prompt, released under the MIT license, with a public GitHub repository containing backend, frontend, skills and a Tauri-based src-tauri directory alongside AGENTS.md, LICENSE and README.md. The project welcomes new contributors. Analytics surface what the agent consumes. Wavefunction tracks token usage, cache usage, and provider-reported costs in one place, so you can see what you use, what you spend, and how it changes over time. Connectors extend what the agent can reach: there are 70+ ready-made MCP connectors, plus any MCP server you add, and the catalogue shown includes Gmail, Outlook, Google, Microsoft 365, Airtable, Asana and Atlassian. Skills and instructions keep the agent aligned with existing practice: it follows your AGENTS.md and loads SKILL.md skills from .agents, .codex and .opencode folders. Overall, the product works as a conversation-first loop. You describe what you want in one conversation; Wavefunction can split work across subagents for the pieces that can run independently, each on its own branch or worktree. Follow-up instructions are accepted while they run and passed to the subagent already handling that work. Results are reviewed and brought back together for you to check. When a task needs a graphical environment, the agent works in its containerized desktop with its own browser and files, handing files back only into folders you permit. The outcomes the site emphasizes follow from those choices. You get to keep the conversation flowing instead of waiting for one task to finish before describing the next, which is why the app is described as optimized for parallel workflows. That parallelism is framed as parallel without collisions because of branches and worktrees, and mid-task changes do not force a restart. Your own computer stays under your control and stays yours while the agent works. Cost and usage visibility, plus the ability to bring your own model or run one locally, keeps spending and data handling under your control rather than a vendor's. Concrete workflows appear throughout the site. In the main example, a user asks for a billing dashboard; Wavefunction starts three subagents for the API, interface and tests, each in its own worktree. The user then asks to also add a usage chart, and a fourth subagent takes the chart while the rest keep moving. When the user says to use Stripe for the invoices, the instruction is passed to the Billing API subagent, which adjusts without starting over, and all four tasks come back ready to review. Separately, the computer-use example shows the agent working in its isolated desktop on a billing site, looking at a screen with invoices and a downloadable report, which the user can watch or take control of. Other described workflows include updating the docs from the project sidebar and relying on an existing AGENTS.md and SKILL.md setup so the agent works the way a repository already expects. Wavefunction is built for developers on macOS; the site states it is currently available for macOS only, and you download the app, connect a model, and start a conversation. Integrations come through the MCP connector catalogue and any MCP server you add, including the listed Gmail, Outlook, Google, Microsoft 365, Airtable, Asana and Atlassian, and through repository instructions and skills. Model providers include ChatGPT sign-in, Anthropic, Google Gemini, OpenAI, OpenRouter, Vercel AI Gateway, Ollama and OpenAI-compatible servers such as LM Studio. Pricing is free and open source under the MIT license; you bring your own models or provider account, and any model usage charges come from your provider. Computer use additionally requires Docker Desktop. The takeaway is a deliberately small tool with a large amount of leverage: one agent, one conversation, parallel subagents that keep working while you keep talking, a containerized desktop that leaves your Mac alone, and your choice of model, all in a free, MIT-licensed macOS app that is open source down to the prompt.
diff-tour is a command line tool that turns a git diff into a tour you step through with the → and ← keys. Point it at a branch in any git repository and the AI coding CLI you already use writes a guided walkthrough of the change, covering what the change is for, which files to read next — including files that did not change — what a senior engineer would ask about it, what must be fixed versus what is only a nit, and where an experienced reviewer would stop and say "wait". It is built for engineers who want to keep understanding code as AI writes more of it, and it runs on your own AI coding CLI, read-only and open source. The tool starts from a question its maker kept hearing from people who teach software engineering: now that AI writes the code, what should engineers learn? People rarely write code by hand anymore, AI often reviews it too, and software moves so fast that reading every line is hard. diff-tour's answer is that understanding code matters as much as ever, and that understanding code means learning how a senior engineer thinks. That thinking is what lets an engineer give AI the right instructions, notice what really matters in a problem, design well, and keep a project moving in the right direction. Rather than teaching those habits in the abstract, diff-tour teaches them on real changes in a real repository. The core of the product is the tour itself. Each step is navigated with the → and ← keys, so a reviewer moves through the change one question at a time instead of scrolling through a wall of diff. Every step opens with what the change is for, then points to where to look next — and that includes files that did not change, which, as reviewers on the launch noted, is often where the real risk is hiding. Because the experience is a sequence rather than a summary, you work through the change in the order a careful reviewer would, rather than skimming until something looks off. diff-tour frames each step around the questions a senior engineer asks about a change: What if this runs twice? Can it be rolled back? Would the tests fail if the code broke? Those questions are asked explicitly rather than left implicit, so a reviewer has a checklist grounded in the actual diff. The tour also separates what must be fixed from what is only a nit, and calls out what looks risky but is actually fine, so attention goes to the parts that deserve it. Beyond correctness, the tour asks design questions too: does this belong here, and does it make the next change easier or harder? A distinctive part of the format is that the moments where a senior engineer would stop and say "wait" are asked as questions, so you guess first and then see the answer. Committing to an answer before seeing it is what makes the lesson stick, and it turns the diff from something you passively read into something you actively reason about. The tour also stops at risky schema changes, flagging them as deserving extra care, though the maker notes that it does not yet walk you through what is stored and how it connects — an acknowledged gap that is open for improvement through the project's issue tracker. Several design choices are about trust. diff-tour is read-only: the AI gets a copy of the files git tracks plus read-only tools, and that restriction is enforced by command-line flags rather than only by the prompt. The code on screen is read from git at the right commit and is never copied from the AI's reply, so what you see is the actual change. There is no server and no telemetry, it is MIT licensed, and it saves nothing between runs — which also means it does not track your guesses over time. It runs on your own Claude Code, Codex, Gemini, OpenCode or Copilot plan. Getting started takes one line: install with the one-line script in the README, then run `tour` in any git repository. The first run asks for a language and an AI CLI, with recommended choices already selected — pressing Enter three times starts the tour being written. There is no separate account, dashboard, or approval workflow to learn. The product is explicit that it is not for approving a change; it is for learning how a senior engineer reads one, and because it is open source you can see how those perspectives are written in internal/generate/lessons. The outcome diff-tour aims at is a reader who can look at a change and know where to look, what to ask, and what to ignore. Instead of skimming a large diff produced by an agent and waiting for something to look off, an engineer works through the runs-twice, can-it-roll-back and would-the-tests-fail questions up front. Because the questions come before the code, and because the reader guesses first, the habits of a senior reviewer get practiced rather than merely explained. Over time that is what supports giving AI better instructions, spotting what matters in a problem, and keeping a project pointed in the right direction. Typical use cases follow from that. When an agent hands back a large diff, diff-tour gives you a structured way to read it instead of skimming. When a tour reaches a risky schema change, it stops and asks for extra care, which is useful for the data-model-sensitive reviews reviewers on the launch said they still watch closely. When you are learning, the tour is practice on a real change rather than a toy example, and the design questions it raises — does this belong here, will the next change be easier or harder — give you a vocabulary for architectural discussion. The maker also agrees that reviewing AI-written changes is not only an engineer's job anymore, so the same walkthrough can be used by managers who need to follow what a change actually does. diff-tour is aimed at software engineers, at people who teach software engineering, and at anyone who needs to read AI-written changes rather than only write them. It does not ship its own model: it plugs into the AI coding CLI you already use, including Claude Code, Gemini CLI and OpenAI Codex CLI, and the launch also lists OpenCode and Copilot plans. It is a command line tool distributed under the MIT license, free to use, with installation through a one-line script and no server component. Everything it shows is read from git at the correct commit, and nothing is stored between runs. The takeaway is simple: as writing code gets cheaper, reading it well still matters, and diff-tour teaches that reading on the real changes in front of you. It turns a git diff into a short, navigable tour built around a senior engineer's questions, keeps the experience read-only and local, and asks you to guess the answer before you see it — so the habit of understanding a change is practiced, not just described.
ilse is a visual feedback layer for AI code agents. It puts a toolbar on top of the React app you already have running in your browser, letting you click an element, write a note, or drag, resize, and tweak things directly on the live interface. Those instructions then reach the coding agent you already use — Claude Code, Codex, Cursor CLI, Gemini CLI, or any agent that speaks MCP — already pointing at the right spot in your code. ilse is built for developers and designers whose app already exists and who want to refine its interface without leaving it, on their own machine, using their project's own tokens and components. The problem ilse addresses is captured in the maker's own story. He had been a designer for over nine years and found himself spending more time in code than in Figma. He would ask Claude Code to fix a button, describe in words what he saw on screen, and the agent would edit the wrong file. As he puts it, the agent writes well, but it does not see what he sees — it was missing a designer's eye. So ilse was built so he could point at the real app, let ilse gather the context, and have the agent change the code. The site draws an explicit line between ilse and generators such as Lovable, Claude Design, and Replit: those are described as great for starting from scratch in their own environment, while ilse is for when the code already exists and lives on your machine. The core of the product is Design Mode. You click an element or select some text and write what should change. The note reaches your agent already pointing at the right spot in the code, so there is no loose "crooked button" description to interpret. That is the point-and-say workflow: the instruction is anchored to a specific element in a specific file rather than to a vague verbal description of a screen. The panel extends that same idea with gestures. Move, resize, edit text, and choose whether a change applies only to this instance or to all of them. There is also a Draw control and Screens, which is marked as new. Drag lets you reorder elements, and you can tweak spacing and colors using your project's own tokens. Simple tweaks land instantly with no AI involved, spending few or zero tokens, while anything that goes beyond a class mapping is handed to your agent to change in the code. A component library is listed as a Pro capability. Before calling your agent, ilse does the search for it. What the agent receives includes the element's file and line with only the snippet that matters, the component that wrote the JSX and where it is used, your project's design tokens, your note plus the panel tweaks and gestures you made, what a test found (the journey step, the screen, and the element), and any pasted references you optionally add. This context packaging is what replaces the agent guessing at which file to edit. Test Mode, available in Pro, is the second major capability. You describe what a person does — the site's example is "buy the lamp" — and ilse walks the flow in the real app on its own, then shows where it got stuck and reports what breaks. Pro is described as test mode with accessibility, errors, layout, the memory, and journeys, plus the Library's hands: dragging components onto the page, their copies, and taking your design system to other tools. A Review Mode is listed as coming soon: reviewers would leave notes on your product's own link without needing an account, and those notes would reach both you and the agent so the loop starts again. The overall workflow is deliberately lightweight. You run npx ilse-design@latest in your project folder; Next.js, Vite, CRA, Remix, and Astro are detected on its own; the app opens at the usual address with the bar on top. There is no extension, no app, and no account. ilse runs on your machine as a CLI and an MCP server, and the agent you already pay for is the one that changes the code. The loop is refine, test, and repeat. Privacy is a structural part of the design rather than an add-on. Everything runs on your computer and ilse has no server to send your code to. The code, the context, and the history stay local. ilse never touches a key — your agent is the one calling the AI. And the bar only exists on the dev server, so the final production build ships nothing. ilse describes itself as an agentic WYSIWYG rather than a faster generator, and it does not generate anything from scratch. The site's comparison table frames the difference: generators start from a blank prompt, while ilse starts from the app that already runs; generators produce code generated anew, while ilse edits your repo in place; generators apply whatever design system the tool picks, while ilse uses your tokens and components; and generators use the platform's AI on its credits, while ilse uses the agent you already use. A testimonial on the page from Huxley Dias of Punk Metrics states that if you develop locally, this is excellent for refining interfaces, and calls it way better than Claude Design, Replit, and Lovable. The benefits follow directly from that approach. Because simple tweaks map to a class and land instantly, common adjustments cost few or zero tokens. Because the note arrives with the file, the line, and the component context attached, the agent no longer edits the wrong file. Because your own tokens and components are part of the context, refinements stay inside your design system instead of introducing a parallel one. And because everything runs locally against your dev server, your code and history never leave your machine. Concrete use cases described on the site include clicking any element in a demo shop interface to change it, reordering items by dragging, resizing elements, editing text in place, tweaking spacing and colors against your tokens, and pasting optional references into the note. Test Mode covers journeys such as buying a lamp, where ilse walks the flow and identifies the step, screen, and element where it got stuck. Design Mode covers the everyday case of pointing at a crooked pixel and telling the agent what to do about it. ilse works with any React project that has a dev server. The listed frameworks are Next.js, Vite, Remix, Astro, and React, with CRA mentioned as auto-detected. Styling and component systems listed are Tailwind, shadcn/ui, Radix, and MUI, and the project's design tokens and components go into the context. Supported agents include Claude Code, Codex, Cursor CLI, Gemini CLI, Cline, Windsurf, GitHub Copilot, Goose, and any agent with MCP. Pricing is free for the core tool under the PolyForm Shield license, which allows free use including at work as long as you do not turn it into a competing product. A founder pre-sale offers Pro at US$49 instead of US$79 as a one-time payment, limited to the first 10 founders, with Pro shipping by November 30, 2026, a refund if it does not, renewals locked at the founder price, a roadmap vote, a direct channel with early builds, and a 30-minute 1:1 setup. The Pro purchase includes 12 months of updates counted from Pro's launch, after which the version you have stays yours. In short, ilse's value proposition is precision: you show the agent what you mean by pointing at the real running app, ilse gathers the code context so the instruction lands in the right place, and your existing agent makes the change in your repo with your tokens — all locally, with nothing shipped to production.
Gemini 4 Carbon is Google's next frontier model, described by the company as its strongest model with state-of-the-art cybersecurity and long horizon capabilities. Announced on October 28, 2026 by Koray Kavukcuoglu, SVP of Google DeepMind and Chief AI Architect at Google, Carbon is designed to take on much harder, long-horizon engineering problems and to support sustained agentic workflows. Its stated purpose is to deliver meaningful leaps in software engineering and cybersecurity defense without compromising on safety or reliability. Carbon builds directly on Gemini 4 Argon, which launched two weeks earlier, and illustrates what Google describes as the pace at which it is pushing the frontier forward, with researchers and models working hand in hand to advance capabilities faster while strengthening safeguards in tandem. Carbon targets a class of work that is hard to complete in a single step. The announcement describes complex, multi-step engineering tasks such as root-causing and patching subtle bugs across large codebases and low-level systems, and notes that Google's internal teams have seen Carbon run for days to persist through dead ends on exactly this kind of problem. Security work is framed around a defense lifecycle of vulnerability detection, validation, and remediation, and Carbon is evaluated against each stage of that lifecycle. The release also reflects a governance problem: Google states it is taking a deliberate approach to rollout, engaging in the U.S. government's voluntary process for pre-release model testing and phasing access to trusted partners rather than releasing broadly at once, so that capabilities can advance while safeguards are strengthened alongside them. Building on user feedback, Google focused Carbon's improvements across coding, agentic and scientific tasks, as well as full-stack web development and multimodal understanding. Internally, teams have seen the model run for days to persist through dead ends on complex, multi-step engineering tasks such as root-causing and patching subtle bugs across large codebases and low-level systems. On full-stack web development, improved quality of output and artifacts lifted Carbon by 89 elo points to #4 with a score of 1768 on WebDev Arena. Carbon also ranks #1 on the recently updated v5.0 of the Artificial Analysis Intelligence Index with a score of 59, and shows significant gains across coding and agentic tasks. These results matter because sustained agentic work is where models typically lose coherence over long runs; Google's claim is that Carbon keeps working through multi-step plans without stalling. Carbon's headline capability is cybersecurity defense, where Google reports state-of-the-art performance across the defense lifecycle. On external benchmarks evaluated without guardrails, Carbon scores 88.2% on SEC-bench Pro for autonomously uncovering complex flaws buried in real-world software, 54.8% on ExploitGym for validating which vulnerabilities are exploitable, and 71.0% on Collinear's CWE-bench for turning validated findings directly into working code fixes. Exploit validation matters because cyber defenders increasingly rely on models to check which vulnerabilities are exploitable so they can prioritize which ones to remediate first, or at all. Internally, Google reports more than a 2x uplift in success rate for generating secure code over Argon; the Chrome team reported Carbon performed more than 10X better than another lab's large frontier model when generating high quality patches; and Wiz, Google's leading cloud security platform, observes Carbon leading on benchmarks for web penetration testing, a core capability for its Scan for Good initiative. Gemini 4 Carbon builds on the same multi-layered safety and security stack as Argon, combining model training, real-time misuse defenses, prompt-injection resilience, and chain-of-thought monitoring. With Carbon, Google set out to go a step further on alignment, training the model to respond to complex or high-pressure tasks in the right way, and reports several concrete results. Carbon reduced successful cheating on Terminal-Bench 4.0 to 0.0%, with attempts to cheat dropping to 0.4%, all while improving overall accuracy; in live coding environments with internal teams, high-severity misalignment alerts dropped by almost 80%. Carbon cuts unsupported claims by approximately 2x over Argon, putting methodological caveats directly into the deliverable or withholding blocked actions with a clear list of what remains required rather than fabricating or hedging around missing evidence. When facing ambiguity, it retrieves the relevant sources and surfaces discrepancies to the user before acting. It simultaneously reduced overreach by 26% and under-service by 29%, and it resisted every evaluated attack attempt on Gray Swan's Indirect Prompt Injection Benchmark, achieving a 0.0% attack success rate versus Argon's 0.7%. Google describes a deliberately staged rollout rather than a broad launch. Carbon is being released under the U.S. government's voluntary process for pre-release model testing, and starting in the announcement week it was made available to a subset of cyber defenders, with access gradually phased to additional trusted partners through the Fairwind Program. Google says it is continuing to gather feedback and iterate on guardrails before making Carbon available to developers, businesses, and consumers via its product surfaces as soon as possible. Underneath that rollout, the model's approach combines the same multi-layered safety and security stack as Argon with training aimed at handling complex or high-pressure tasks correctly, an emphasis on transparency when evidence is missing, and behavior that retrieves sources and surfaces discrepancies rather than guessing or forcing a path forward. Stated outcomes for users include much more reliable execution of multi-step plans end-to-end while respecting project constraints and staying strictly within scope, fewer unsupported claims, and a better collaborative experience when requirements are ambiguous. For security teams, the payoff is a model that can help detect, validate and remediate vulnerabilities rather than stopping at a list of findings. For engineering teams, the reported benefit is persistence on long-running problems: working through dead ends across large codebases and low-level systems instead of giving up after the first failed attempt. Google also points to a stronger internal capability to generate secure code, and to reduced misalignment and fabrication rates in live coding environments with its own teams. The announcement frames several concrete scenarios where Carbon is used. It autonomously uncovers complex flaws buried in real-world software, checks which vulnerabilities are exploitable so defenders can prioritize remediation, and turns validated findings directly into working code fixes. It is applied to sustained engineering work such as root-causing and patching subtle bugs across large codebases and low-level systems over multi-day runs. It supports full-stack web development through improved quality of output and artifacts. It is also used for web penetration testing, the capability behind Wiz's Scan for Good initiative, and for generating high quality patches inside Google's own teams, including work reported by the Chrome team. The initial audience is a subset of cyber defenders, followed by additional trusted partners through the Fairwind Program, with developers, businesses, and consumers to follow via Google's product surfaces. Carbon launches at the same introductory price as Argon: $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off the input token price. Google notes that after the introductory period expires, the price of $4 per 1M input tokens and $20 per 1M output tokens will apply. The model is presented as part of the Gemini 4 family, following Gemini 4 Argon, which was launched two weeks earlier. Gemini 4 Carbon's primary value proposition is combining frontier-level coding and agentic reasoning with state-of-the-art cybersecurity defense, while holding the line on safety through a deliberately staged release. Google positions it as its strongest model to date, leading the Artificial Analysis Intelligence Index and the SEC-bench Pro, ExploitGym and CWE-bench cybersecurity benchmarks, and as a step toward delivering meaningful leaps in software engineering and cybersecurity defense without compromising on safety or reliability.
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BAND for Developers is a desktop workspace for coordinating coding agents. It lets local and centrally managed coding agents work together: they share context, hand off work, and unblock one another. The product is aimed at developers at the keyboard, team leads who own the team's work, and engineering or platform leaders who own the agentic SDLC. Developers lead and agents execute; the human sets direction and steps in when judgment is needed, while the agents carry work between them. BAND Desktop provides a visual work surface, while developers can keep working in the terminal and the coding harnesses they already use. Running multiple coding agents often means becoming their project manager. Every handoff between agents runs through the developer, and isolated agents work in silos while the human becomes the coordination layer. Side-by-side agent tools give each agent its own session or worktree and leave the developer to connect the dots; all-in-one agent platforms require switching to the vendor's own agents and workspace; team chat with agents shares channels but lacks plans and work items. BAND takes over the handoffs, first for a developer's own agents, then for a team, then across an organization. The stated goal is an agentic SDLC with local and centrally managed agents working in one governed way. At the center of BAND is a shared room. Agents join the same room and @mention each other to pass context directly. The conversation lives outside any one coding session, so when a chat is compacted or a session restarts, open review findings and other shared work state do not drop out of context; BAND keeps them as shared work state outside any single session. People in the room can read the conversation, and each agent gets the messages addressed to it. A room can be filtered by messages, errors, thoughts, tasks, tool calls, and results instead of scrolling through a transcript. This gives developers a readable trail of how agents reached a change and lets agents ask each other before they ask the developer. Agents build from one plan and one board. A lead or architect agent writes the plan in the room as text, a diagram, or both, and every agent works from that plan. Work items have owners and move from To-do to Done as agents claim them. A Human in the loop column holds only the items that need the developer, so decisions are brought to one place rather than scattered across terminal tabs. The planner and reviewer can stay in the room with the plan and the implementers, so planning carries on when implementation turns up something new. This structure is how BAND avoids giving each agent a separate lane and leaving the developer to connect the dots. BAND Desktop provides visibility and control at four layers. The first layer, Everything, puts every room the agents are working in on one wall, with live activity, task progress, and what is waiting on the developer. The second layer, a project, shows one project's rooms in a folder, including tasks done, open PRs, who is working, and what waits on the developer. The third layer, the room, shows how agents working together reached a change, filtered to what the developer needs. The fourth layer, one agent, shows that agent's sessions, rooms, messages, and cost, so the developer can step in when needed and then step back out. Hovering an agent lets a developer open it, stop it, remove it from the room, or message it in a new chat. Developers can stay in Claude Code and other harnesses; BAND Desktop sets up the CLI and the Claude Code plugin, and one command brings a session into the team. BAND supports predefined teams and reusable agent groups. A developer can enable a predefined team such as Feature crew or Bug squad and start with roles that fit. Agents can be grouped, given team guidelines, and handed the same team for every new task. For governance, each agent has its own identity on a BAND account, and API keys are encrypted at rest. Owner, admin, and member roles are available, with personal, organization, and global scopes kept apart. A full audit trail records every message, handoff, and decision so teams can see how the work was done. Agents can ask a person to approve before they run a command or edit a file, right in the room. Usage and cost can be viewed across supported agents and harnesses, attributed to shared work such as rooms, tickets, and features. BAND works with the agents and tools teams already use. The content lists coding agents and harnesses including Claude Code, Codex, GitHub Copilot, and OpenCode, plus personal agents and agents built on frameworks and SDKs such as LangGraph, CrewAI, Pydantic AI, Google ADK, Claude Agent SDK, and Letta. A longer integration list also includes Gemini, Anthropic SDK, AWS AgentCore, Azure AI Foundry, n8n, CoPilot Studio, Agno, Strands Agents, Salesforce Agentforce, SAP Joule, Workday Sana, Oracle, Parlant, NemoClaw, Hermes, NanoClaw, and OpenClaw. Teams can connect developers' local agents and centrally managed organization agents across the harnesses they already run without standardizing on one agent stack. Coding sessions keep running on the developer's machine; BAND carries the messages agents send each other, and the developer decides what context goes into a room. The stated benefits are less intervention and more work that can be trusted. Agents ask each other before they ask the developer, so developers can come back to decisions rather than terminal tabs. Team leads can scale agent work without scaling coordination: agents collaborate within and across developers' workflows, shared work is coordinated earlier with less conflict and rework, and leads can see where developers and agents are blocked and where they are needed. Engineering and platform leaders get central control with local flexibility: local and centrally managed agents are brought into one governed way of working, with visibility and control that does not take flexibility from developers. They can also see usage and cost across supported agents and harnesses. A developer can add a teammate as a contact and invite their agent into the room, so agents can ask a teammate's agent for help like any other team member. A concrete workflow described on the page is launching a churn-risk alert. Claude Code works as backend, Codex as frontend, GitHub Copilot as DevOps, and OpenCode as data. The agents plan, split the work, talk, team up, and report usage. The shared task includes training a churn model, a risk score API, an alert banner in the dashboard, and a nightly scoring job. A decision comes to the developer in the room: alert at 70% risk, with approve and change options. Another example is Qodo, whose engineering team wrote about building a dark factory with BAND. The product also addresses tracking parallel coding agents, defining human-in-the-loop decisions, keeping review findings when a chat is compacted, keeping a planner and reviewer present during implementation, and giving developers, team leads, and executives different views of the same project. BAND for Developers is for teams that want coding agents to behave like a collaborative team rather than a set of isolated sessions. It keeps existing editors, terminals, worktrees, and agent harnesses while adding a shared room, one plan, one board, human-in-the-loop decisions, team-level visibility, governance, and cost reporting. The primary value proposition is that developers lead, agents execute, and the coordination work that used to run through the developer is handled by the agents themselves, with less intervention and more trustworthy output.
shim-cli is an open source command line tool that runs on your own machine next to Claude Code, Codex, Copilot CLI and VS Code. Its purpose is stated plainly on the website: see what your coding agent sends, and mask what you can. The CLI finds secrets and personal data in what an agent sends, masks those values where the client allows it, and shows what each Claude Code session sent and cost. It is built for developers who use AI coding agents every day and want visibility into the files, tool results and prompts that reach the model, without adding an account, an API key or a network call to shim. Claude Code keeps every session on your disk, and an agent at work reads files, runs commands and pulls in tool results. Some of those files hold API keys, database URIs, customer email addresses, IBANs and other personal values. Once a value is in a request, it has reached the model. shim-cli starts from that reality: before you install anything, it can show what already went out, and after installation it can place a placeholder between the file and the model. The project describes itself as narrowing exposure rather than acting as a data-loss prevention boundary, so it is positioned as a practical layer of containment and measurement for everyday agent work rather than a compliance guarantee. Masking is handled by shim install , which installs a native hook in your agent. When the agent reads a file or a tool result holding a key, an IBAN or an email, the model receives a placeholder such as instead of the value. The detector covers email addresses, phone numbers, credit cards, IBANs, Turkish national IDs, Turkish tax IDs, API keys and secrets, passwords in config and commands, database URIs, IP addresses, MAC addresses and US SSNs, plus Turkish licence plates as an opt-in. IBANs and Turkish IDs are verified by checksum, so a mistyped number is not reported. You can add your own patterns, including your own project names or host formats. Where the client allows it, masking happens before the model reads the content; where it does not, shim reports the finding or, in enforce mode, stops it instead. Two commands answer two different questions. shim install claude masks sensitive data in every session. shim watch -- claude runs a local proxy for one command and reports tokens, spend and every secret or personal value that went past; it changes nothing, writes no request to disk and leaves nothing behind. For history, shim audit reads the Claude Code sessions already stored on your disk and counts what reached the model, broken down by kind, by project and by the way it came in, for example @.env, a failed Bash command or your own prompt. The sample output shows counts for SECRET, EMAIL and DB_URI, a per-project breakdown, values already masked by shim, and model output that repeated an email. shim audit prints no value, keeps nothing and sends nothing, and it reads Claude Code history only, only when you run it. shim watch currently supports Claude Code only. Supporting commands make the setup auditable. shim demo runs the detector on a made-up sentence and shows the before and after lines without installing anything. shim install --dry-run previews the hook and shows the target file and the fragment before you commit, and --json returns machine-readable output that branches on a code and a fix. shim doctor checks the installation and reports every check that is not PASS along with its code and its fix. shim keys .env --json lists the names in a .env file without printing values. At the end of a turn, Claude Code users see a summary: how many values were masked and from which read, which items were flagged, and the overhead. The website shows a real session on a customer file with 60 emails, 60 IBANs, 60 phone numbers and 60 Turkish national IDs masked from Read customers.csv, one INSTRUCTION_OVERRIDE and one HIDDEN_TEXT flagged from Read runbook.md, and 62 ms median overhead. Support for each client differs, and the documentation is explicit about it. In Claude Code, a native hook masks secrets and personal data in the files and tool results the agent reads before the model sees them, and the tool can also be installed from the Claude Code marketplace as a plugin, using one method and not both. In Codex, shim reports what it found in your prompt and lets it through, and you switch to enforce mode to block it instead; Codex also skips a hook until you open /hooks, review the shim entry and trust it. In Copilot CLI, shim replaces the prompt the model receives with the redacted text, so the original values never reach it, and there is no trust step. In VS Code there is no shim install vscode; the plugin is the whole route, added through the chat.pluginLocations user setting with an absolute path, and it reports findings, stops a prompt or denies a tool call under enforcement, and never masks a result that has already reached the model. The outcome the project promises is narrower exposure with measurable cost. Values that would otherwise travel in plain text become placeholders, prompts can be blocked under enforce mode, and the end-of-turn summary shows how much overhead the hook adds. The website reports that these numbers are measured on the released build: 2,300+ tests run on every release, 70 ms median hook cost including interpreter start, 727 detector cases graded on exact output, and zero accounts, API keys or telemetry added by shim. The same page is candid about the boundary: a prompt typed into Claude Code or Codex is not masked because neither client lets a hook rewrite it, Codex and Copilot CLI tool traffic is not inspected at all, VS Code masks nothing, in Claude Code shell commands and file writes are reported or denied rather than masked, the output of a failed command and a file attached with @ are reported rather than masked, Windows is not supported and WSL is not yet verified, and detection is best-effort and can miss a value. Concrete use cases follow from those mechanics. A developer reading a customer file with an agent sees 60 email addresses, 60 IBANs, 60 phone numbers and 60 Turkish national IDs replaced with placeholders before the model reads the CSV. A runbook containing text that tries to give the agent orders is flagged as an instruction override, alongside hidden text in the same document. A team that wants to know what already leaked runs shim audit over past Claude Code sessions to count secrets by project before changing any settings. A developer who wants to understand where tokens and money went runs shim watch -- claude for a single command and gets a report without anything written to disk. Someone who only wants to list the names in a .env file uses shim keys .env --json so no value is printed, and someone wiring the tool into a new machine runs shim demo, shim install --dry-run and shim doctor in sequence. shim-cli is distributed as an open source CLI under Apache-2.0, currently at v1.2.1, and runs on macOS and Linux with Python 3.10 or newer. Installation is a single command through uv or pipx. It integrates with Claude Code, Codex, Copilot CLI and VS Code through native hooks or a plugin, and requires no account, no API key and no network call to shim. The whole CLI is on GitHub, so you can read the detector, run the tests, open an issue or send a pull request. For teams that need the same detection in front of the AI calls their own products make, the site points to the AI Gateway, which adds audit, budgets and roles on Enterprise. The takeaway is straightforward: shim-cli makes the traffic between your coding agent and the model visible, masks what the client allows it to mask, measures what a Claude Code session sent and cost, and states its limits openly. It is a practical, local-first way to reduce what your agent exposes without adding an account or telemetry.