u/Ill-Process-7232

1. Tools in every mode Designed a two-tier tool loading model (cheap/normal) so a shared core toolset stays available while optional capability surfaces load on demand per task.
2. Provider-native lanes Built per-provider request lanes (Anthropic, OpenAI-compatible, Antigravity, Cline, Kiro, local, gateway) that normalize messages to each endpoint's native protocol instead of a lowest-common-denominator shim.
3. Context optimization Engineered context assembly around prompt cache hit rate: stable system and tool bytes in a cacheable prefix, volatile session state behind a dynamic boundary, plus lane affinity to keep byte identity across repeated requests.
4. Diagnostics during edits Integrated optional LSP tooling (definitions, references, hover, symbols, call hierarchy, diagnostics) so the agent catches broken references mid-run instead of after context has moved on.
5. Signals from long logs Implemented log pruning for lint and exception output that preserves the failing error plus surrounding context, cutting token spend on repetitive noise without losing actionable signal.
6. Snapshots and session control Shipped project-local session management with resume and branch trees plus working-tree snapshots stored in a shadow git store, enabling save, list, diff, and restore without rewriting project history.
7. Self-learning loop Built a /learned feedback loop that proposes one reusable lesson per work session, with human approve, edit, or skip before it persists to project memory as reviewable, revocable entries.
8. Claude Code compatible extensions Added compatibility for Claude Code-shaped MCP servers, LSP integrations, agents, plugins, and skills, loadable per mode so extension surface matches project need.
9. Browser automation with vision Built vision-driven browser automation pairing navigate, click, type, scroll, and wait with screenshot capture routed to a multimodal model, so the agent verifies rendered UI, console errors, and layout regressions instead of inferring state from the DOM, with accessibility-tree fallback when selectors drift.

https://github.com/AbdoKnbGit/tau

Hey let me introduce my new AI assistant workflow across using mistral-ocr-4 and explain how it differs from other AI toolchains like OpenCode in terme of exploiting this capability ,I’ve been juggling research papers, parallel projects, and coding agents this year and kept running into a tradeoff between low cost and high-quality content extraction from PDF, document images, and other attachments. When Mistral OCR 4 was released I started using it heavily because its extraction quality is excellent and the cost justified it, so I plugged Mistral OCR into my agent pipeline. The challenge I faced is that many large models now include built-in extraction but they’re expensive, and for many routine attachments I’d rather use cheaper models specially when i don't required a high level thinking output . My solution was to integrate Mistral OCR 4 natively with those lower-cost models so they gain robust OCR capability and no longer return “sorry, I can’t read that” errors instead they deliver the extracted content. If you need a low-cost coding workflow with heavy attachment processing (PDFs, docs, images), this approach and the agent I built should suit you well.

Edit : Mistral models also supported for coding

https://github.com/AbdoKnbGit/tau

whats this problem fix : Web_fetch gives you errors when rendering shared-link conversations from your web AI sessions, and the Playwright MCP server is so slow and more expensive because it takes screenshots; it’s impossible for it to cover everything without destroying your usage with low-quality context, so you always end up massively copying content from your web session into your harness, which creates a big cost, or manually asking the web models to give you a concentrated context to copy-paste, and this can sacrifice your context trade-off and waste your tokens.

whats the fix : one MCP server where you give it the shared link, with effectively 0 token usage for ingestion; it parses all your session into a .md file with the usage metadata, and you only pay for the smart read tool, prebuilt by Opencode, that will read based on signal so you can have your 999-turn session and only keep reading what’s relevant for you without worrying about losing context.

why : I’ve been struggling a lot since I started using the web app heavily, and I need to re-explain things or accept a crazy cost for leaking my context into my builder, so I plan in the web app and I build in the builder.

The left image is without MCP, and the right one is with the MCP server.

https://github.com/AbdoKnbGit/shared_url_mcp

Tau is a native AI assistant built for more control, more features, and more cost optimization than other coding agents offer. It natively integrates 22+ AI providers, including cheap, free, and private subscription options, so you can use any of them without local installs, plus a reusable Claude Code ecosystem that runs any MCP, skill, agent, or plugin built for Claude Code. A built-in cheap mode strips all of that down to a legacy CLI experience with one command, and switches back just as easily, so you only pay for what you use. On cost, each turn gets cheaper as cache hits climb: new turns stay byte-identical to the last, with only the delta added as fresh context, respecting every major provider's caching format (OpenAI-compatible, Anthropic, Gemini). A lightweight command lets you skip the full self-learning loop and context injection while still saving lessons and pinned preferences. And built-in LSP tools trigger an automatic diagnostic on every edit or file write, catching bugs before they hide in the codebase and cutting the tokens spent hunting them down, alongside time-travel debugging, resource monitoring, and GitHub project management.

https://tau-site-ten.vercel.app

https://github.com/AbdoKnbGit/tau

Hey, as a Hugging Face ecosystem enthusiast, I always stay in touch with the latest news about tools, models, optimizations, and so on, and suddenly I saw a video on their YouTube channel introducing a coding agent named "tau" https://github.com/huggingface/tau , using the same keyword I use for example, tau = 2*pi. They started it a month ago, and it's been getting some ads in the last two weeks to make it famous, but three months ago I was working on my own coding agent harness called "tau," inspired by Claude Code style but also extending a lot of functionality from pi itself, and they used the same logo and the same description it's just a smaller Python version of mine https://github.com/AbdoKnbGit/tau . I don't know are they inspired by it, or is it just a coincidence?

So to be quick, TauCode is an agent that has a combination between Claude Code and OpenCode, while extending its optimization, having more access and control to your memory, your tools, more optimized for OpenAI-compatible models, hitting the maximum cache hit, and if you are a person that doesn't prefer subagents , skills , mcp servers , plugins ... TauCode have a cheap mode that can save your tokens. The philosophy is simple (native, not a proxy wrapper, so you don't need Cline CLI, or any extension of it to work with; its optimized for token by cache hit for every model and lazy tools calls ; + generic, you can use any skill/agent/MCP server built for Claude Code inside it; + controllable, you can toggle optional tools on demand). I would be happy to receive any feedback.

https://github.com/AbdoKnbGit/tau

Meet Tau, a native AI assistant that integrates over 22 AI providers and offers a reusable Claude Code ecosystem so you can run any MCP, skill, or agent built for Claude Code. Tau provides cheap or free provider options alongside support for private AI subscriptions, near-perfect token optimization and cache hits, and advanced tools for fast, context-aware queries and project awareness. It natively supports multiple AI providers so you can work with them without installing each provider locally. Built-in LSP tools and full integration reduce coding errors so every edit or file write triggers an automatic diagnostic that catches bugs early before they become hidden in a growing codebase. Tau also includes features like time-travel debugging, system resource monitoring, GitHub project management, and more to streamline development.

https://github.com/AbdoKnbGit/tau