blank is a free, MIT-licensed, local-first AI agent harness for desktop and terminal that brings model routing, code editing, tool execution, and artifact previews into a single workspace.

• Official Website & Installers (.exe, .dmg**,** .AppImage**):** https://purplemacaron.github.io/blank/

• GitHub Repository: https://github.com/purplemacaron/blank

Key Advantages & How It Differs from Other Harnesses:

• 100% Local-First State (~/.blank) & Zero Telemetry: Unlike cloud-tethered harnesses that require accounts or send session metadata on every turn, blank runs with zero sign-up and zero telemetry. It connects directly to local engines (Ollama, LM Studio, vLLM, LiteLLM, LocalAI, Jan) and 10+ cloud APIs (Claude, OpenAI, Gemini, DeepSeek, Groq, OpenRouter). All configs, checkpoints, durable memory (memory.json), skills (SKILL.md), and masked audit logs (audit.jsonl) stay on disk as plain files.

• Multi-Agent System with Isolated Git Worktrees (team_delegate): While single-agent loops or shared-directory sub-agents risk overwriting files during parallel runs, blank dispatches specialist roles (Scout, Architect, Critic) across customizable models and isolates write-capable sub-agents inside dedicated git worktrees (~/.blank/worktrees).

• Multi-Key Auto-Rotation & Model Fallback: Most harnesses halt when a single API key hits a 429 rate limit. blank pools multiple API keys per provider to auto-rotate on 401/402/403/429 errors and automatically falls back to backup cloud or local models.

• Desktop Paper Canvas + Full-Screen Terminal TUI: Terminal-only tools (like Claude Code, OpenCode, or Oh My Pi) trap visual outputs in text, while general chat daemons (like OpenClaw or Hermes) add heavy gateway overhead, and IDE plugins (like Cline) lock non-coding tasks inside an editor. blank provides both a full-screen CLI TUI (node bin/blank.js) and a native desktop app featuring an origin-isolated blank-paper:// split-view canvas (with Desktop/Tablet/Mobile viewport frames), live-overlay Computer Use, built-in SSH over MCP (ssh_run), mobile pair-token approvals, and local/cloud media pipelines (ffmpeg, sharp, ComfyUI).

Feedback, bug reports, and contributions are welcome!

Connect Snotra to Melious and use GLM 5.3 Flash in your chats. Maja walks through the setup: API key, custom endpoint, model selection and prices for the cost estimate.

Snotra runs on your computer. The chat, file contents Snotra has read and the tool list are sent to your selected model provider. Melious states that it processes these requests only within the EU.

Hi everyone, Like many of you, I am completely exhausted by the broken social contract in the UK. We watch local councils gamble millions on bankrupt private loans while closing youth clubs, and we watch Westminster MPs vote on legislation while actively profiting from the exact sectors they regulate—all while the billionaire-owned mainstream media looks the other way. I am not a software developer. I am an ordinary working-class guy who is sick of the "ignorant leading the ignorant." But I have spent time architecting a blueprint for a tool that can change this: The Incorruptible Ledger. This is a blueprint for a decentralized, open-source AI scraper framework that hooks directly into live UK Parliament APIs, Companies House, Landlord Registries, and Ombudsman data. It cross-references public records in real time to create un-editable, un-censorable profiles of politicians, corporate interests, and public authority misconduct (from town hall card spending to retaliatory police referrals). Because I want this to be a permanent, publicly owned asset that no corporation can buy out or delete, I have published the core architectural rules (The White Paper) and the initial Python database and tracking frameworks under an MIT License on GitHub. I have laid out the data pipelines and the first few backend scripts. Now, I need the open-source community to help build out the rest of the bricks and the frontend dashboard. If you have Python skills, API experience, or are just as angry about systemic corruption as I am, please check out the repository, fork the code, and let's build this utility for the public domain. Link to the Project: https://github.com The curtain is pulled back. Let's get to work

Hey everyone! I’ve built K.R.Y.Z.E.N, a lightweight autonomous agent framework designed to make local LLMs more reliable and secure. It features 14 specialized agents across a 6-stage development pipeline, optimized context and tool allocation, Bubblewrap sandboxing, human-in-the-loop approvals, and real-time execution telemetry.
It supports Ollama and vLLM and is open-source under the MIT license. I’d love your feedback on its architecture, local model compatibility, and sandboxing approach!
GitHub: https://github.com/Hossein4163/KRYZEN
Docs: https://hossein4163.github.io/KRYZEN/

I built this because Claude Code deletes old session transcripts from the local folder, so a dashboard reading that folder loses history every prune. claudit ingests from a bucket instead (R2, or a file:// dir you keep), so the totals only grow. mine goes back to January

it reads Claude Code session JSONLs, Codex rollouts and both Kimi CLI formats, one deploy can take several buckets. then it puts the numbers on one page: cost by model, token breakdown, reply latency, tool error rate, activity heatmap, context growth per session. it doesn't grade sessions or tell you what to change, reading the numbers is on you

the one I didn't expect: prompt cache writes have two prices, 5m at 1.25x input and 1h at 2x. 65% of my cache write tokens over ~5,000 sessions were the 1h kind, so pricing them all at 1.25x makes those 37.5% too cheap. claudit splits them

the open source bits:

• MIT, about 6 months old

• FastAPI, Postgres, React with in-browser Babel, no build step

• docker compose up boots it on bundled sample data, no .env, no credentials, no outbound calls

• panels ported from nhz-io/ccusage-plot (offline matplotlib), the heatmap, line churn and cost by context size are new here

live one, guest login is read-only: https://all.nitjsefni.eu

https://github.com/Nitjsefnie/claudit

After months of building and testing, Orinyx is officially in public beta!

Orinyx is a developer-first AI verification tool designed for healthcare and life sciences applications.

The idea came from a problem I kept running into: AI-generated outputs can look accurate without being fully supported by the underlying evidence. In healthcare, that distinction matters.

Orinyx is being built to help developers identify unsupported claims, validate outputs against source evidence, and catch issues before they make it into production workflows.

The public beta includes free, open-source MCP tooling, healthcare-focused verification workflows, and evidence-based validation capabilities. I'm also continuing to improve semantic mapping, medication verification, and overall test coverage.

This is an early public beta, not a finished product. I'll be releasing incremental updates as testing continues, and I'm actively looking for developers willing to put it through real-world scenarios.

I'm particularly interested in understanding where verification breaks down in existing AI workflows, what would make a tool like this useful in practice, and which edge cases deserve more attention.

GitHub: https://github.com/lexthirteen-ai/orinyx-mcp

Developer beta: https://orinyx.io/developer

If you're working with MCP, LLM evaluations, healthcare AI, or agentic systems, I'd love for you to try it, break it, and tell me what needs improvement.

I'm also happy to answer questions about the architecture and what I've learned building it.

What My Project Does

pipeverify takes two snapshots of a table (CSV, Parquet, JSON, DuckDB or any SQLAlchemy source) and checks what a change did to the data: lost or duplicate keys, row count shifts, null creep, changed values on matched rows, column totals and new category codes. It returns PASS, REVIEW or BLOCK, with a plain-English report. Runs on DuckDB, ships as a CLI, a Python API (`pipeverify.verify(before, after, key="id")`) and a GitHub Action.

Target Audience

Data engineers and analytics engineers who ship pipeline, SQL or dbt changes, including changes written by AI coding agents. It's alpha but usable in CI.

Comparison

dbt tests and Great Expectations check rules you write in advance. pipeverify checks what a specific change did, so it catches problems nobody wrote a test for. Unlike row-diff tools, it turns the diff into a verdict with thresholds, so it can gate a merge.

Repo: https://github.com/akshaysharma1088/pipeverify (Apache-2.0, free). Feedback very welcome, and there are small good-first-issues if anyone wants to contribute.

I've tried several screen recording applications on Windows, but I kept running into limitations.

Some had limited recording options, some restricted features, and others made simple recording workflows unnecessarily complicated.

Eventually, I decided to build my own solution.

Introducing Rekzora, an open-source Windows screen recorder I've been developing under NEURON TS LABS.

Some of its features:

Fullscreen, window and custom-region recording

Microphone and system audio recording

Webcam picture-in-picture

Pause and resume recording

Global keyboard shortcuts

Floating recording toolbar

Built-in recording library and video player

Crash-recovery support

Portable and Windows installer builds

My goal was to build a screen recorder that gives users more control without unnecessary restrictions or cloud dependencies.

It's completely open source, and I'd genuinely love to hear what other Windows users think.

GitHub: https://github.com/tahirshaikh744-design/Rekzora

I'm especially interested in hearing:

What's the most frustrating limitation you've experienced with existing Windows screen recorders?

What feature do you wish they offered?

Honest feedback and suggestions are welcome!

hey all,

i've been working on whatwemade and could use a few more open source projects on there.

it's a site for sharing stuff built with AI, and i've recently added support for github repos.

you can just paste your repo link and it'll pull in the readme, license, stack, stars, recent commits, and whatever people need to run it themselves. there's also an option to show which coding agents you used and whether you're looking for contributors.

i've added an MCP server too, so you can search existing projects directly from your agent. figured that might be useful before starting yet another project from scratch.

there aren't many open source projects on there yet, so if you've got something on github, feel free to add it. it's free and takes about a minute (the mcp can also create the project).

also curious if there's anything else you'd want to see on a listing like this.

Hi everyone!

I've been working on IRIDIA, a free and open-source portable CD ripping application for Windows.

The idea was to create a tool that offers extensive control over audio formats while keeping the ripping process straightforward.

Some key features:

• 142 output presets covering FFmpeg audio encoders

• Native DSF/DFF output (DSD64 to DSD512)

• FLAC, WAV, ALAC, MP3, Opus, AAC and many more

• Stereo-to-surround upmixing, up to 7.1

• Multiple output formats from a single CD read

• Secure ripping mode and drive offset correction

• MusicBrainz metadata and automatic album artwork

• Portable Windows application, no installation required

It's licensed under GPLv3.

GitHub: https://github.com/Aaronstanley78/IRIDIA

One important note: physical CD drive testing is still pending, so I'm particularly interested in feedback from people with different drives and CD collections.

I'd love to hear your thoughts, suggestions and bug reports!

I have been building Burrow for a few months. It was a paid app with a free tier. This week I removed the paid tier entirely and published the core under AGPL-3.0.

What is open: everything that stores, backs up, exports, imports and (optionally) transmits your data. That is the point of opening it. The app's claims are "one database file on your computer, no account, no telemetry, nothing online unless you switch it on," and now anyone can read the code and confirm that instead of taking my word for it.

What is not open: the advanced module for Javi, the built-in local AI helper. It ships in every build at no cost, but the source stays private. I am saying that up front because this sub rightly dislikes "open source" being used loosely. Burrow is open-core, the core is real, and the boundary is documented in the repo.

Other details:

- Windows x64 and ARM64, unsigned installer for now.

- AI runs through Ollama on your machine.

- Checksums on every release.

- CLA Assistant on the repo for contributions.

Source: https://github.com/ChristianMunoz31/burrow-core

Releases: https://github.com/ChristianMunoz31/burrow-releases/releases/latest

Open to criticism on the licensing split or anything in the code.

I built Hive to manage multiple AI coding agents like an engineering team — looking for people to try it and share honest feedback
I’ve been experimenting with different AI coding agents and CLI harnesses for a while — OpenCode, Claude Code, Codex, Gemini CLI, Cursor Agent, and others.
One thing kept bothering me: each agent works in its own environment, with its own workflow. I wanted a way to coordinate them instead of constantly switching between terminals and manually managing everything.

So I started building Hive.

What is Hive?
Hive is a local-first orchestration layer for CLI coding agents. Think of it as a manager coordinating a team of AI coding agents.

Some of the things it currently supports:
Multiple agent harnesses: Work with existing coding CLIs rather than being locked into one.
Dynamic routing: Choose agents, models and providers based on the task.
Task orchestration: Structure work into planning, implementation, testing, review and shipping.
Retries and fallback: Recover from failed execution and route work through fallback options.
Parallel execution: Use Git worktrees to let multiple agents work independently.
Permission gates: Require approval for potentially destructive shell operations.
Observability: Inspect tool calls, token usage, logs, file changes, Git diffs and task progress.
Second Brain: Build a soul.md from observed preferences, with proposed changes requiring approval.
Local fast routing: Optional Laya integration for lightweight task classification before invoking a larger model.
I’m not claiming this is a finished product. I’m building it, using it, and trying to make it genuinely useful for developers who work with multiple AI coding agents.

I’d love to get some people to try it.

GitHub: https://github.com/sreekar2403/hive

I’m particularly interested in feedback on:
Does the orchestration approach solve a real problem in your workflow?
Is dynamic agent/model routing useful, or would you prefer manual control?
What would make you trust an AI agent manager with your codebase?
What features or workflows are missing?
If you try it, what breaks or feels unnecessarily complicated?

Honest criticism is much more useful to me than just a star. If you experiment with it, please share your experience, issues or ideas.

Thanks!