翻訳待ち：HashCortX – AI workspace app, Now works on pre-2013 CPU windows devices

AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。

Notifications You must be signed in to change notification settings Fork 13 Star 113 BranchesTags Open more actions menu Latest commit History 270 Commits 270 Commits Folders and files NameName Last commit message Last commit date .githooks .githooks .github .github docs docs logosss logosss scripts scripts src-tauri src-tauri src src .gitignore .gitignore CHANGELOG.md CHANGELOG.md CODE_OF_CONDUCT.md CODE_OF_CONDUCT.md CONTRIBUTING.md CONTRIBUTING.md LICENSE LICENSE MODES_GUIDE.txt MODES_GUIDE.txt README.md README.md package-lock.json package-lock.json package.json package.json Repository files navigation A local-first AI workspace — multi-provider chat, an autonomous coding agent, and multi-agent swarms in one native desktop app. No backend. No telemetry. No account. MIT-licensed. What this is Eight workspaces — chat, an autonomous coding agent, multi-agent swarms, financial document analysis, a security scanner, a business-app builder, 3D planning and a virtual project desktop — behind one window, with nine specialist agents and a real Python sandbox working inside them. Every AI request goes straight from your machine to the provider whose key you entered. Nothing passes through HashCortx infrastructure, because there is no HashCortx infrastructure. Point it at Ollama and it runs with the network off — everything except the Python sandbox, which fetches its runtime on first use. Type Native desktop app (Tauri v2) Runs on macOS Apple Silicon — built and used daily. Windows — tested on Windows 10, with an installer in the release. Linux compiles and passes its tests in CI, but nobody has run the app there yet License MIT Latest release v2.5.0 (17 August 2026) — 43 MB DMG for Apple Silicon, 80 MB installed. 33 MB of that is the bundled embedding model, and most of the rest is the runtime that executes it AI providers 11 cloud (Groq, Gemini, OpenAI, Anthropic, Moonshot, DeepSeek, Mistral, Cerebras, SambaNova, OpenRouter, NVIDIA NIM) + Ollama Stack Rust · vanilla JavaScript · no bundler · no framework · ~35,700 lines JS, ~4,530 Rust Tests 93 Rust tests, run by CI on Linux, macOS and Windows · 1,384 source checks, every one of them run by CI on every push Telemetry · backend · accounts None · None · None v2.5.0 is what this page describes. It carries 138 commits since v2.0.0 — the offline knowledge base, the security work, Windows and Linux support, and a long list of features that were advertised and did not run. What changed, including what is still open. Why you might want it Nothing phones home. No analytics, no crash reporting, no update pings. The only outbound connections are to providers you configured yourself. Your keys, your models. Eleven cloud providers and Ollama, configured at once, switched freely, mixed inside a single swarm run. The agent asks before it acts. File and shell calls hit a Rust permission gate and a compiled denylist that no prompt can talk its way past. Search that understands meaning. Ask about "stopping a runaway command" and your notes about killing a process on timeout come back — from a model that ships inside the app and never sends anything anywhere. You can audit it. MIT, no build step, no minified application code. Read it, fork it, ship your own. The eight workspaces Workspace What it does 01 Chats Multi-provider chat with projects, attachments, slash commands, full history 02 Coder The coding agent: file tree, real file edits, shell access, browser panel 03 3D Forge Describe a part, get a dimensioned solid you can print. Fuses to one watertight body, cuts real holes, exports in millimetres 04 Finance Statements, CSV, PDF and XLSX into KPIs and charts. Never invents a number 05 Sandbox Agents scanning untrusted code for malware, prompt injection, suspicious logic 06 ERP Describe a workflow, get a working interactive prototype 07 Agent Swarm Chain mode, vote mode, automatic provider failover mid-run 08 Virtual OS A simulated project desktop an agent works inside Two more surfaces are not workspaces and have no tab of their own: Agents — the nine built-in specialists and the builder for your own — opens as a menu over the message you are writing, and Split — one prompt, two models, streamed side by side — is a toggle inside chat. The nine agents, the Python sandbox and every workspace in detail: MODES_GUIDE.txt · Wiki → Features Coder The agent reads your real files, edits them, runs commands, and shows every change as a diff you can expand. It does not get to do any of that quietly. Every filesystem and shell call passes through HC.guard.request() and lands in Rust, where a compiled denylist refuses anything touching ~/.ssh, ~/.aws, ~/.gnupg, the system directories, or HashCortx's own stored keys — whether the path arrives as a file operation or inside a shell command. Inside the folder you opened, the agent works without interrupting you. Outside it, everything asks first, including reads, because an agent that reads a file is an agent that can send it to a provider. Every command is bounded: a five-minute timeout, closed stdin, a 512 KB output cap. Full detail, and the honest limits: SECURITY.md. Agent Swarm Chain mode hands each agent's output to the next. Vote mode runs one prompt across several models and has a judge score the answers. If a provider rate-limits or dies mid-run, the swarm swaps to another one you configured and carries on with the same context. The knowledge base Anything you ingest becomes searchable by meaning, not just by matching words — bge-small-en-v1.5 (MIT) ships inside the app and runs natively in Rust. It is inference-only: a sentence encoder, not a language model. On an x86-64 machine without AVX2 the app is built without it and searches by keyword instead; see Older processors. Nothing is fetched. No first-run download, no cache to warm. It works offline on first launch. Nothing is sent. What you index never crosses a network boundary. 34 MB of the download. That is the price of the two lines above, paid once. Results are ranked by meaning and by keyword at once, then fused — so a rare error code still finds its exact match while a paraphrased question still finds the right passage. Install Download the DMG from the latest release, open it, drag HashCortx to /Applications. The DMG is built for Apple Silicon. On an Intel Mac, build from source. On Windows there is an installer in the latest release. It is the build made without local embeddings, so it starts on a processor of any age and searches the knowledge base by keyword rather than by meaning; for search by meaning on an AVX2 processor, build with the default features (below). On Linux there is no prebuilt download — build from source (below). Check your processor first: the default build needs AVX2 and BMI2, and there is a build that does not. See Older processors. The build is unsigned and not notarised, so on first launch right-click the app and choose Open, then Open again. If macOS still refuses: xattr -dr com.apple.quarantine /Applications/HashCortx.app Then open Settings → API keys, add a key, press Test. Or skip keys entirely and run a model on your own machine — Settings → Local model walks you through it and checks each step for you. Build from source cd ~ # start in your own folder, not wherever the shell opened git clone https://github.com/Hash-7777/HashCortX.git cd HashCortX npm install npm run tauri dev # live-reload development npm run tauri build # DMG in src-tauri/target/release/bundle/dmg/ Node 18+ and a Rust toolchain via rustup, plus macOS: Xcode Command Line Tools · Linux: Ubuntu 24.04+ and the Tauri v2 system libraries (glibc 2.38+ is required to link the bundled ONNX Runtime) · Windows: MSVC build tools and WebView2. On Windows, run cd ~ before cloning — the line above is not decoration. PowerShell opened as an administrator starts in C:\Windows\System32, so a pasted git clone lands inside Windows' own system folder. The prerequisite installers want an administrator window; the build itself does not, and a normal PowerShell already starts somewhere sensible. A checkout under System32 fails in a way that points nowhere near the cause. The WiX tools Tauri bundles with are 32-bit, and a 32-bit process reading that path is redirected by Windows to SysWOW64, where the checkout does not exist — so the Rust build succeeds and bundling then fails saying it cannot find a file that is plainly there. The folder also inherits System32's permissions, so getting rid of it afterwards needs an administrator: robocopy C:\Windows\System32\HashCortX $HOME\HashCortX /E /XD target Remove-Item -LiteralPath C:\Windows\System32\HashCortX -Recurse -Force Older processors (without AVX2) The default build links a prebuilt ONNX Runtime for the embedding model. It is compiled for x86-64 processors with AVX2 and BMI2 — Intel Haswell (2013), AMD Excavator (2015) and newer — and it is linked statically, so its start-up code runs before main(). On an older processor it executes an instruction the CPU does not have and the process is killed while it is still loading: no window, no error, nothing on screen. Build without it, and the app starts on any x86-64 machine: npx tauri build -- --no-default-features The -- matters: the Tauri CLI has no such flag of its own and passes everything after it to cargo. Through an npm script it takes two, because npm eats the first one — npm run tauri build -- -- --no-default-features. That build has no embedding model in it. The knowledge base still works and still searches, by keyword rather than by meaning; embed_available reports false so the interface can say so rather than quietly returning worse results. The binary is also far smaller — about 20 MB against about 80 MB, measured on Apple Silicon — since neither the model nor the runtime is compiled in. Apple Silicon is unaffected either way. Before pushing, run what CI runs: npm run check # 2,968 checks over the real source cargo test --manifest-path src-tauri/Cargo.toml # 97 tests Under the hood Layer Technology Shell Tauri v2 — Rust core, the system webview, no Chromium Backend Rust: filesystem, shell, audit log, usage log, embeddings, Keychain migration Security Compiled denylist in security/denylist.rs, permission prompt via HC.guard.request() Frontend Vanilla JavaScript. No React, no TypeScript, no bundler, no build step Embeddings bge-small-en-v1.5 (MIT) compiled into the binary, run via ONNX Runtime Python Pyodide (CPython on WebAssembly) with pandas, numpy, matplotlib, python-docx, openpyxl, reportlab Vendored libs marked, highlight.js, DOMPurify, mermaid, pdf.js, jsPDF, three.js — all local, no CDN No bundler is a deliberate constraint. The interface is about 2.5 MB of source that ships as written, so any reader can follow a feature from the button that triggers it to the Rust function that performs it, without a source map. What makes the download large is the embedding model and the runtime that executes it, not the app. ARCHITECTURE.md · SECURITY.md · CONTRIBUTING.md · CHANGELOG.md Privacy and security No backend, no telemetry, no accounts, no auto-updater. The binary makes no network call except to the provider endpoints you set up. A permission gate in Rust. Sensitive paths are denied unconditionally, whether they arrive as a file operation or inside a shell command. Every guarded action is logged to ~/.hashcortx/audit.log. Keys are not encrypted. They sit in an app-scoped local directory protected by your user account, not by Keychain encryption — because a Keychain item's access list is bound to the code signature, and an unsigned build would re-prompt for every key on every update. Code signing is on the roadmap; the reasoning is written out in full in SECURITY.md. Measured usage, not guessed. One [truncated for AI cost control]