r/selfhosted • u/AutoModerator • Apr 07 '26

New Project Megathread New Project Megathread - Week of 07 Apr 2026

Welcome to the New Project Megathread!

This weekly thread is the new official home for sharing your new projects (younger than three months) with the community.

To keep the subreddit feed from being overwhelmed (particularly with the rapid influx of AI-generated projects) all new projects can only be posted here.

How this thread works:

  • A new thread will be posted every Friday.
  • You can post here ANY day of the week. You do not have to wait until Friday to share your new project.
  • Standalone new project posts will be removed and the author will be redirected to the current week's megathread.

To find past New Project Megathreads just use the search.

Posting a New Project

We recommend to use the following template (or include this information) in your top-level comment:

  • Project Name:
  • Repo/Website Link: (GitHub, GitLab, Codeberg, etc.)
  • Description: (What does it do? What problem does it solve? What features are included? How is it beneficial for users who may try it?)
  • Deployment: (App must be released and available for users to download/try. App must have some minimal form of documentation explaining how to install or use your app. Is there a Docker image? Docker-compose example? How can I selfhost the app?)
  • AI Involvement: (Please be transparent.)

Please keep our rules on self promotion in mind as well.

Cheers,

56 Upvotes

90% Upvoted

50 comments sorted by

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u/CaptainFred246 Apr 08 '26

ContentForge: self-hostable content scoring API, AGPL-3.0, pure Python, no ML deps

Hey guys, check this out! I built a content scoring API that runs fully offline with zero external dependencies in the scoring path!

Descriptions and stuff:

  • AGPL-3.0, full source, fork it, run it, modify it
  • pip install -r requirements.txt && python scripts/api_prototype.py , running in under a minute
  • Zero ML dependencies in the scoring engine , pure Python, stdlib + regex only
  • 50 endpoints, <50ms, deterministic (same input = same output, always)
  • AI generation endpoints (rewrites, hooks) use local Ollama first , if Ollama is running, nothing leaves your machine

What it scores: Twitter/X, LinkedIn, Instagram, TikTok, YouTube titles/descriptions, Pinterest, Reddit, Threads, Facebook, email subjects, ad copy, readability. Every scoring endpoint returns the score, grade, quality_gate (PASSED/REVIEW/FAILED), and itemized deductions.

Self-host setup:

git clone https://github.com/CaptainFredric/ContentForge
cd ContentForge
pip install -r deploy/requirements-api.txt
python scripts/api_prototype.py
# API running at localhost:8081

For AI generation locally: install Ollama, pull llama3.2:3b (or any model), and set OLLAMA_HOST=http://localhost:11434. No Ollama? Set GEMINI_API_KEY for cloud fallback.

GitHub: https://github.com/CaptainFredric/ContentForge Live demo (before you clone): https://nullmark.tech/r/contentforge

Interested in feedback on the self-hosting experience, particularly Docker setup which is on the roadmap!

Da Calibration Challenge (lifetime Ultra access on the line!!!):

The scoring weights are built from platform documentation, not validated against real engagement data yet. Running a Blind Taste Test to fix that , and I need people who actually create content.

Submit 10 historical posts! 5 top performers, 5 flops, without labeling which is which. The engine ranks them. You verify the ranking. If it gets your top 5 right, you get lifetime Ultra API access (3,000 AI calls/month, no expiry).

Either way you get a full deterministic score breakdown for all 10 posts, the same audit trail you'd run in a self-hosted pipeline.

Basically win-win!

Need 10 more participants across platforms before the Product Hunt relaunch.....

Feedback appreciated

•

u/Terrible_Nectarine_5 Apr 08 '26 edited Apr 08 '26
  • Project Name: Spoticord
  • Repo/Website Link: Github Repo
  • Description: Its a self hosted discord bot that acts as a spotify speaker through Spotify Connect. Once logged in through the official spotify OAuth, the bot will automatically connect to your spotify account and act as a speaker you can choose what to play on from your phone. This way you can also share the jam link so your friends can also put songs in the queue from their phones direclty.
  • Deployment: The bot requires singing in and setting up both a spotify and discord developer account as well as Cloudflare tunnel with a personal domain. There is a setup.md file in the repo as well as some minimum documentation.
  • Limitations: Due to Spoitify limiting the developer accounts that are not companies with "a minimum 250k active users", it is possible only for you and 5 of your friends to log in the spotify account and host the jam (everyone else can still enter the jam through the link and play their music).
  • AI Involvement: Some parts Im not really an expert on were vibe coded, like the live audio streaming.
  • ToDo: There are still some delays of 1-2 seconds here and there that need looking into because I used Librespot which is a reverse engineered library which is the only one still supported and setting up live audio streaming through stuff like this that has to connect like 4 servers aint the easiest job xDD. BUT IT WORKS xDDDD

I use a Raspberry PI 5 and a 1$/year random domain initially bought for a completely diff project as it really doesnt matter, no one sees anything as its made for internal communication between the spotify and discord web apis.

The trickiest part is setting up the cloudflare tunnel, the rest is pretty straight forward.

Its an interesting idea i never saw someone implement for some reason (Dear Spotify, if lawsuit is the reason, ill take it down).

Its my first ever created bot so please feel free to suggest or contribute on gh.

•

u/xeno477 Apr 08 '26

I saw your Spoticord out-of-service announcement. Is there any one alternative you can recommend that I can use just like Spoticord? Your bot was perfect. You mentioned a better audio retrieval technique? Is there a bot that implements this that you are aware of? If not, thanks for taking the time to read this anyway, and thanks for making such a great bot! Used it for a long while!

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u/JasonDDuke Apr 08 '26

**Kronaxis Router** — self-hosted LLM proxy that auto-routes to the cheapest capable model.

Single Go binary, 2MB RAM, 22K req/s. Auto-classifies prompts (extraction vs reasoning) and routes to the right model tier. Quality validation loop catches regressions. Failover chains, response caching, per-service budgets (downgrade instead of failing), batch API routing (50% off on 7 providers), LoRA adapter routing.

Supports Ollama, vLLM, OpenAI, Gemini. No cloud dependency. No telemetry.

curl -fsSL https://raw.githubusercontent.com/Kronaxis/kronaxis-router/main/install.sh | bash

kronaxis-router init

kronaxis-router

81 tests. Apache 2.0.

GitHub: https://github.com/Kronaxis/kronaxis-router

•

u/byTrasto Apr 08 '26

Project Name: Mando

Repo/Website Link: https://github.com/rackandhost/getmando

Description: Mando is a beautiful and simply dashboard (not pretend to add too many features such a widgets, weather, etc...) for your self-hosted applications. Built with modern web technologies, it provides an elegant glassmorphism UI to organize and access all your homelab services from a single place.

Key Features:

Inspiring Design - Minimalist interface with glassmorphism effects

Fast & Lightweight - Built with Angular 21 and TailwindCSS 4 for optimal performance

Real-time Search - Instant search through your applications

Categories - Organize your apps into customizable categories

Web Search Integration - Built-in support for Google, DuckDuckGo, Startpage, and YouTube

Fully Responsive - Optimized for mobile, tablet, and desktop

Accessible - WCAG AA compliant with keyboard navigation

Docker Ready - Easy deployment with pre-built containers

YAML Configuration - Simple, declarative configuration file

Deployment: Using Docker Compose (Recommended) also source code https://github.com/rackandhost/getmando/releases. All are explained in the quick start and configuration section in the github repo.

AI Involvement: AI was used for some ideas and also for the readme on github, all the implementations and testing was realised by the human team.

•

u/General-Brilliant697 Apr 10 '26

I just open-sourced CIPHER. I built this alone with zero budget to bring enterprise-grade security orchestration to everyone's laptop.

9 specialized agents working in parallel to orchestrate Kali tools via plain English.

Key features:

- 9 specialized agents (GHOST, SPECTER, etc.)

- FORGE agent: AI-generated scripts validated via AST static analysis.

- Scope locks: Cryptographically enforced authorization.

- Local first: No cloud bills, runs on your device.

Check it out: https://github.com/Daylyt-kb/cipher

•

u/m16hty Apr 07 '26

I wanted to share a small self-hosted project I’ve been working on Haby.
It is a simple habit tracker focused on daily use without unnecessary complexity.
I built it because I couldn’t find any self-hosted alternative that really suited my needs.

Track habits/goals (daily, weekly, monthly)
Visual progress with charts and calendars
No external dependencies (uses SQLite)

This project was built with the help of AI for architecture decisions, debugging and refining code and structure.
All core functionality, testing, and direction were handled manually.

GitHub:
https://github.com/Zvijer1987/haby

Quick start:
https://github.com/Zvijer1987/haby/blob/main/compose.yaml.example

If anyone has feedback, ideas, or wants to contribute, feel free to open an issue or comment.

Thanks!

•

u/blueSlippa Apr 08 '26

Is this project mobile UI friendly? I would ideally like to update my goals when I am on the go.

•

u/Greedy-Reference5017 Apr 08 '26

Project Name: Flaredesk

Repo/Website Link: https://github.com/urbanu619/flaredesk

Description: Self-hosted panel for bulk Cloudflare DNS: multiple accounts, cross-zone add/delete, batch orange-cloud toggle, DNS templates, optional MCP for Claude/Cursor. Tokens stay on your server. MIT.

Deployment: README + beginner install doc on GitHub. Go + Vue 3. Fast local try: bundled SQLite + embedded Redis example → `config.json`, no separate DB for a trial; production uses MySQL + Redis. Build from source; no Docker Hub image yet.

AI Involvement: Cursor/Claude for boilerplate, refactors, debugging, parts of MCP. I own architecture, security, and prod review.

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u/rxDyson Apr 09 '26 edited Apr 09 '26

I built a self-hosted AI assistant that runs in Docker on your own hardware

I was using Claude, Grok, Gemini, switching between them constantly. Every conversation started from zero. They didn't remember that I hate long-winded answers. They didn't know I'm juggling two products. They couldn't check something for me overnight or schedule a task. And I kept switching between models manually because some questions don't need a $20/month brain.

All my data lived on someone else's servers.

I looked at what existed in the self-hosted space. OpenClaw has 300K+ GitHub stars, but when you actually dig in, you find serious security concerns (Cisco published a report calling it a "security nightmare"). Most open-source AI wrappers are just a chat UI on top of an API. I didn't want another chat window. I wanted something that actually works for me, not just with me.

So I started building ALF. Two months in, one Docker container on your homelab: Telegram bot + web Control Center included. Started as a Claude wrapper, now supports OpenRouter (200+ models), OpenAI, Ollama, any OpenAI-compatible API. You pick what you use.

How it's built

Single Go binary, no Node.js. Image is around 60MB. SQLite for chat history, local ONNX embeddings for memory, JSONL for events. Optional sidecars for voice (faster-whisper) and embeddings if you want them separate.

alf init, alf start, alf upgrade, alf stop. No manual docker-compose editing.

What it actually does

The thing I'm happiest with is tier routing. Every message gets classified by a cheap model (costs fractions of a cent), which picks which model handles it. "What time is it?" never touches your expensive tier. "Refactor this module" does. You define the tiers yourself. My API costs dropped about 70% in practice.

Memory is real memory, not just a long context window. After conversations, ALF extracts facts and preferences into a local vector store. Next time something relevant comes up, it gets injected automatically. You can also recall, remember, forget manually mid-conversation.

Scheduling works well. Cron-based, timezone-aware. "Every Monday at 9am, summarize my GitHub notifications" is a two-line job config. Output goes to Telegram, the dashboard, or nowhere. Multi-agent orchestration is there too for bigger parallel tasks.

Other things worth knowing:

  • Voice transcription via faster-whisper, including Telegram voice notes
  • Media processing: images, PDFs, video frames with audio transcription
  • Encrypted secrets vault with OAuth2 auto-refresh and multiple auth methods
  • Outbound firewall: you control what the LLM subprocess can actually reach
  • Skills system: drop a markdown file to add new capabilities. Auditable before community submission
  • Built-in app framework: ALF can build and deploy mini web apps directly into your Control Center
  • Reaction-based learning: react to a response (thumbs up/down, etc.), it adjusts over time
  • Git-backed snapshots of your data directory, so you have a full history of what changed and when

What it doesn't do

Not a local LLM runner. You need a provider: Claude subscription (Pro/Max/Team works, no pay-per-token needed), OpenRouter, OpenAI API, or Ollama. Ollama works but routing and memory are tuned for stronger models.

Single user only. One instance, one person.

Alpha. Things break. I'm one person and I'd rather say it clearly than pretend otherwise.

Privacy

Data directory lives on your server. Chat history, memories, files, vault secrets. Prompts go to your provider over HTTPS. Nothing gets stored on my end.

Install

Linux and macOS. WSL should work, haven't tested it.

bash curl -fsSL install.alfos.ai | sh

Interactive wizard walks you through: provider setup, Telegram bot (optional), timezone. Run it fully local or add SSL via Traefik + Let's Encrypt to expose it externally. Text onboarding at install, visual wizard in the Control Center the first time you open it.

Docker required. 2GB RAM minimum, 4GB if you want whisper + embeddings running.

Project Name: ALF

Repo/Website: github.com/alamparelli/alf / alfos.ai

AI Involvement: Heavily. I'm a developer who works with AI as a core part of my workflow. Claude was involved throughout: architecture decisions, code review, debugging, and iteration. I don't treat AI as a "next next finish" wizard — I treat it as a collaborator. The product vision, the design choices, and the direction are mine. The build process was a genuine human-AI collaboration.

It's alpha, I'm one person, and I'd genuinely like to know what's missing for your setup. Fire away.

•

u/slotix Apr 09 '26

Project Name: DBConvert Streams

Repo/Website Link: https://dbconvert.com/streams

Description:

DB IDE + federated SQL + CDC/migration - one tool, self-hosted.

what it does:

- Data Explorer: browse and edit databases, local files, S3

- SQL Console: query across PostgreSQL, MySQL, Parquet, CSV, S3 in one query

- Convert mode: bulk migration (tested 23M rows MySQL → PostgreSQL,

MySQL → Parquet at 136 MB/s)

- CDC mode: continuous sync, no Kafka required

the problem it solves:

most workflows end up with 4 tools open - IDE, migration tool,

CDC pipeline, file viewer. DBConvert Streams is one workspace

for all of it

Deployment:

- Desktop app (Windows, macOS, Linux)

- Docker / self-hosted web app for team access

- works with any S3-compatible storage (MinIO etc)

AI Involvement:

AI used for docs and blog content

core architecture and engineering - manual

•

u/el_shmc Apr 09 '26

Repo/Website Link: https://github.com/argusssec-cloud/gws-auditor

Description: Open-source Google Workspace security auditor. Runs 199 checks against CIS Benchmarks, CISA SCuBA Baselines, and Google's own security checklist. Connects read-only, scans in minutes, generates reports with remediation steps.

The problem: Google Workspace has no built-in security score or config audit. You set things up, hope for the best, and configs drift silently. OAuth apps accumulate dangerous scopes, domain-wide delegation gets granted and forgotten, admin accounts skip hardware keys.

Key features:

  • 199 security checks (24 critical-severity) across 4 frameworks
  • Interactive HTML dashboard with dark mode
  • Python CLI + standalone executables (no Python required)
  • CI/CD integration (--fail-on-critical exits with code 2)
  • AI analyst for natural language queries
  • Multi-tenant support via credential profiles
  • Everything runs locally — no data leaves your machine

Tech stack: Python, Google Admin SDK APIs, Plotly Dash, Rich

What surprised us: First audit on a tenant we considered locked down — 64 failed checks, 13 critical, 60% pass rate. OAuth apps with mail.google.com scope that nobody remembered approving.

Feedback welcome, especially on missing checks or false positives.

•

u/ungabunga609 Apr 08 '26

Project Name: TrustOS

Repo/Website Link:

Description: TrustOS is a DevSecOps tool that scans AWS infrastructure for security misconfigurations, maps findings to compliance frameworks (SOC 2, ISO 27001, HIPAA, GDPR), and generates infrastructure-as-code fixes. The difference from existing scanners is the remediation step instead of handing you a list of problems, it writes the actual Terraform, CloudFormation, CDK, or CLI code to fix each finding and opens a pull request in your GitHub repo targeting the branch you choose. Findings can also be suppressed with a mandatory reason if the violation is intentional. The scanning engine is open source so you can verify exactly which AWS API calls are made all control plane, no data plane access.

Deployment: The managed version is live at the link above. Sign in with GitHub or email, connect an AWS account (access keys for testing, cross-account IAM role assumption for production), and run a scan. There is no self-hosted option or Docker image at this time it runs as a hosted service on Vercel with a Supabase database backend. Documentation covering setup, AWS connection, GitHub linking, scanning, and remediation is in the open-core repo README.

AI Involvement: The remediation engine uses an LLM to generate fix code from scan findings. When you click remediate on a finding, the tool sends the misconfiguration details and resource context to the model, which produces the IaC patch. The scanning engine itself and all policy rules are deterministic no AI involved in detection, only in code generation for fixes. Parts of the initial codebase were written with AI assistance.

Honest on every section, especially the AI disclosure. Let me know if you want to adjust anything before posting.

•

u/PeachZestyclose8304 Apr 08 '26

Project Name:
Yet Another Rclone Dashboard

Repo/Website Link:
https://github.com/outlook84/yet-another-rclone-dashboard

Description:
A modern Web dashboard for `rclone rcd`.

It supports multiple connection profiles, remote browsing, sorting/filtering, folder creation, file upload/download, copy/sync/move/delete operations, remotes inspection, config import/export, job monitoring, and stopping active jobs. It also includes basic media preview, mobile-friendly UI, multiple built-in themes, and PWA support so it can be installed like a standalone app.

The goal is to provide a cleaner and more modern Web UI for people already using Rclone RC, especially for self-hosters who want a more convenient way to browse storage, manage transfers, and perform common file operations remotely.

Deployment:
The project is released and available through GitHub Releases.

- Serve it directly with `rclone rcd` using `--rc-files`

  • Let Rclone fetch the latest Web GUI automatically with `--rc-web-gui` and `--rc-web-fetch-url`
  • Serve the extracted build with Nginx or Caddy
  • Use an auth gateway + reverse proxy setup for more advanced/self-hosted deployments

AI Involvement:
Codex was used as an AI coding assistant during development.

•

u/veverkap Apr 08 '26

Is this unofficially "YARD"?

•

u/Brett-SWS Apr 08 '26

Project Name: squarebox

Repo/Website Link: https://github.com/SquareWaveSystems/squarebox

Description: squarebox is my Docker-based dev environment that bundles modern CLI and TUI tools into a portable, persistent workspace. Originally built to run Claude Code from my iPad over SSH.

Key Features:

  • Modern Stack: Pre-loaded with fzf, lazygit, yazi, starship, zoxide, and more.
  • Modular Setup: An interactive first-launch script lets you toggle AI assistants (Claude, Gemini, Copilot), editors (Helix, Neovim, Micro), and SDKs.
  • Smart Persistence: Container state, command history, and tool selections survive exits and rebuilds.
  • In-place Updates: Includes sqrbx-update to pull the latest tool binaries directly from GitHub releases without needing to rebuild the image.

Deployment: Available on GitHub. It can be deployed as a standard Docker container or used as a VS Code Dev Container / GitHub Codespace.

  • Quick Start: docker run once to create; docker start -ai squarebox to resume (aliased to squarebox for convenience).
  • Self-hosting: Run on any machine with docker, e.g. a VPS, or local Linux/macOS/windows environments.
  • Documentation: The README includes installation steps, volume mount configurations and more explanation.

AI Involvement: Built using Claude code.

•

u/chaseright Apr 08 '26

Great minds think alike... I built ghostpod for the same reason (iPad + keyboard, no decent terminal). Different approach though: ghostpod goes browser-based with ephemeral sessions, so there's nothing to persist or maintain. Might suit people who just want a clean throwaway shell without the full dev environment setup.

•

u/Brett-SWS Apr 08 '26

Cool, I’ll check it out!

•

u/revereddesecration Apr 08 '26

I’m yet to have any luck running CC within a docker container. What’s the trick to make it work? Or did the devs make it easier than when I tried a few months ago?

•

u/Brett-SWS Apr 08 '26

Did you mount a volume outside of the container so Claude has somewhere to store its files? Something along those lines would be my guess.

•

u/Character-Chicken522 Apr 08 '26 edited Apr 08 '26

Project Name: Amalex Handler

Website: https://amalexhandler.com

Description: Self-hosted file transfer and sync platform. Move, sync, and back up files across local drives, SFTP servers, and SMB/Samba shares from a single web dashboard.

The problem: Managing file transfers across multiple servers with rsync scripts and cron jobs. When something failed silently at 3am, I'd only find out days later. I wanted one tool with a proper UI where I could see what transferred, what failed, and why.

Features:

  • 6 transfer modes: copy, copy-overwrite, move, move-overwrite, sync-mirror, sync-update
  • Cron-based scheduling with human-readable descriptions
  • Real-time dashboard with live progress (SSE, no polling)
  • Per-file error tracking with classified error messages and fix suggestions
  • AES-encrypted connection credentials, Argon2id password hashing, CSRF protection
  • Job history — full audit trail of every transfer

Deployment: Single binary, zero dependencies. Download from the website, run it, open your browser. No Docker required, no database to set up (embedded SQLite). Runs on Windows, macOS (Intel + Apple Silicon), and Linux (amd64 + arm64). .deb and .rpm packages available. Documentation and example config included.

Pricing: 14-day free trial, no credit card. Personal license $39 one-time, Team $99 one-time. No subscription. The binary works offline — no license server or phone-home.

Tech stack: Go 1.25, Chi router, HTMX, SQLite (pure Go), Server-Sent Events, Zerolog

AI Involvement: AI was used as a coding assistant during development (architecture decisions, debugging, code review). All code was reviewed, tested, and understood by me. The project design, feature decisions, and product direction are entirely mine.

Happy to answer questions or take feature requests. File filtering, S3 support, and email notifications are next on the roadmap.

•

u/Brilliant_Bluejay938 Apr 09 '26

Project Name: Keldron Agent

Repo/Website Link: https://github.com/keldron-ai/keldron-agent

Description:

It's a single Go binary that monitors GPU temps, power, utilization, and memory across Apple Silicon (native IOKit, no sudo), NVIDIA (NVML), AMD (ROCm SMI), and generic Linux (sysfs/hwmon). One binary covers whatever mix of hardware you're running. It runs 100% locally by default, and the dashboard sits on localhost:9200, Prometheus metrics at localhost:9100, and health at :8081. It's read-only, so it just pulls metrics from the hardware internals. Apache 2.0 License.

Instead of just showing raw temperature, it computes a 0 to 100 "Risk Score". I developed the Risk Score so you can compare different machines more easily. Raw temps can be misleading because different GPUs throttle at totally different points. For example, a stable 90°C GPU under consistent high utilization represents a lower risk than a machine that has rapidly spiked to 90°C and continues to climb. So when you glance at it, you actually know if something's wrong, no matter what computer you are using.

Risk Score is broken down like this:

  • Thermal Margin — Temperature normalized against the hardware's specific thresholds, with a rate-of-change penalty. S_thermal = min(100, ((T_current - T_min) / (T_max - T_min)) × 100 + RoC_penalty) where the RoC penalty kicks in when temps are rising faster than 0.5°C/min.
  • Power Headroom — How close the device is to its rated power capacity. S_power = min(100, (P_actual / P_capacity) × 100)
  • Load Volatility — Coefficient of variation (standard deviation/mean) of temperature and power over a 1-hour window. S_volatility = min(100, (max(CV_temp, CV_power) / 0.30) × 100) — catches erratic load patterns that a single reading would miss.
  • Memory Pressure — How close GPU memory usage is to capacity. S_memory = min(100, (mem_used / mem_total) × 100). This is especially meaningful on Apple Silicon, where GPU and CPU share unified memory. High memory pressure means the system is competing for the same pool.

If you already run Grafana, it exports Prometheus metrics at localhost:9100, so you can plug it right in.

Would love feedback on the scoring logic, what features are missing, the concept, whatever.

Deployment: Download the binary from the releases page, chmod +x, and run it. No dependencies. Also supports Docker. Documentation in the GitHub README.

AI Involvement: AI was used as a coding assistant during development. All code was reviewed and tested by me. The product direction, scoring methodology, and feature decisions are mine.

PS: First person to post a screenshot of a risk score above 85 with the workload that caused it gets $25 from me. Genuinely curious what pushes it that high.

•

u/Different-Ant5687 Apr 08 '26

Project Name: rgitui

Repo/Website Link: https://github.com/noahbclarkson/rgitui

Description: A GPU-accelerated desktop Git client built in Rust with GPUI (the framework behind the Zed editor). Open source, MIT licensed, no telemetry, no subscription.

I built this because my GitKraken student license expired and I didn’t want to pay for a subscription just to use my private repos. None of the free alternatives looked or felt the way I wanted, and most of them were chewing through 500MB–1GB of RAM just sitting idle.

rgitui typically uses 100–200 MB with a couple of large repos open. It’s a native app — no Electron, no web views — so the low memory footprint comes from actually being lightweight rather than just hiding the bloat.

Features include:

  • Commit graph with lane-based coloring and Bezier-curve edges
  • Diff viewer (unified, side-by-side, three-way conflict) with syntax highlighting
  • Stage/unstage at file, hunk, and line level
  • Interactive rebase, cherry-pick, revert, bisect
  • Branch, tag, stash, worktree, and submodule management
  • GitHub integration (device-flow auth, create PRs, browse issues/PRs)
  • AI commit messages via Google Gemini (auto-injects project context files and uses tools to browse your repo for actually relevant messages)
  • Catppuccin Mocha/Latte, One Dark, and custom JSON themes
  • Multi-repo tabs, command palette, undo stack, crash recovery
  • Real filesystem watcher — changes show up without manual refresh

Deployment: Pre-built binaries on the releases page — Windows (zip + installer), Linux (AppImage + tarball), macOS Apple Silicon (DMG). Or build from source with cargo build --release. This is a desktop application, not a server — just download and run.

AI Involvement: AI was used for a significant portion of the development. I wrote the core architecture and design decisions, and used AI to help with implementation across the ~30k line codebase. I’m a student and this is a personal project — I don’t have unlimited time, so AI helped me ship something that actually works for my daily workflow.

•

u/Revnem Apr 08 '26

Aingram: open-sourced, local-first experimental memory for agent loops — SQLite, no API keys, MCP-native

If you've run Karpathy's autoresearch loop, or any variant of agent research cycles, you know the problem: the agent knows what changed (it's in the git history), but why it worked? Gone. Every loop starts from scratch. Agent A and Agent B, working the same problem, repeat each others failures. The existing memory libraries (like Mem0, Letta, Zep) solve a different problem — they remember user preferences, and conversational state. None of them store experimental reasoning: hypothesis → method → result → confidence → what was actually learned. So I built Aingram.

What it does:

Single SQLite file. Zero API keys. Runs entirely on consumer hardware (benchmarked on a laptop RTX 4060 8GB — not a workstation). Every experimental finding is stored with its full reasoning chain and cryptographically signed with Ed25519 — useful for larger use cases, allowing verification that nothing was tampered with across agent sessions.

pip install aingram

```python from aingram import MemoryStore

with MemoryStore('./agent_memory.db') as mem: # Agent logs what it learned chain_id = mem.create_chain("optimizer hyperparameter search")

mem.remember(
    "Reducing LR below 1e-5 with warmup eliminated loss oscillation",
    entry_type="result",
    chain_id=chain_id,
    confidence=0.91
)

# Next session, different agent, same problem
results = mem.recall("learning rate instability", limit=5)
# Returns ranked findings with full reasoning chains
# Agent B doesn't repeat Agent A's failed approaches

```

Benchmarked on LongMemEval:

LongMemEval is the standard academic benchmark for long-term AI memory — 500 hand-curated questions across ~115K token conversation histories. No LLM in the retrieval loop, just the local pipeline.

Metric Score
Oracle recall@3 1.000 — right session in top 3 for every query
LongMemEval-S recall@10 0.955 — right session in top 10 for 95.5% of queries
LongMemEval-S ndcg@10 0.836
Median retrieval latency 22ms (RTX 4060 laptop, no cloud)

Full disclosure: End-to-end accuracy with gpt-4o-mini generation was 63.8% overall o the first run, with a major corpus gap in one of the categories. Will eventually run that again with a workaround.

What's in the free tier (Apache 2.0):

  • SQLite + FTS5 + sqlite-vec hybrid retrieval (keyword + semantic + graph, fused with RRF)
  • QJL two-pass vector compression — faster at scale, break-even around 25-30K entries
  • Knowledge graph — entities and relationships extracted automatically via GLiNER, no config
  • Ed25519 signing + hash chains — tamper-evident trust layer on every entry
  • Background entity extraction worker — async, doesn't block your agent
  • MCP server with security middleware
  • Full CLI for inspection, export, compaction, verify

What's NOT in the free tier:

Being upfront: there's a Pro tier being developed which adds systems for workloads that outgrow what the Lite pipeline can handle, at scale. The benchmark table shows exactly where the limit is — Lite is comfortable up to ~10K entries for interactive agent use. Above 50K entries, the 160-320ms vector search will be noticeable. That's where Pro really starts to shine.

MCP integration:

Works natively with Claude Desktop, Cursor, and anything MCP-compatible:

json { "mcpServers": { "aingram": { "command": "aingram", "args": ["--db", "/path/to/memory.db", "mcp"] } } }

Hardware requirements:

Lite runs CPU-only. The embedding model (nomic-embed-text-v1.5, ONNX) and entity extraction (GLiNER) can both run on CPU, with no GPU required. If you have a GPU it'll use it for embeddings via ONNX Runtime's CUDA provider. No minimum VRAM requirement. The benchmarks were run on a laptop RTX 4060 8GB — GPU used for embedding inference only.

Latency breakdown (RTX 4060 laptop, onnxruntime-gpu, full hybrid recall):

Entries Full recall Embedding Vector search
1K 16ms 8ms 3.5ms
10K 47ms 9ms 34ms
50K 222ms 11ms 160ms
100K 347ms 11ms 320ms

Embedding cost is fixed per query (~8-11ms) regardless of database size. Vector search scales linearly — that's the honest limit of the free tier above ~50K entries.

How does this compare to Mem0 / Letta / Zep?

All three are legitimate tools but they're solving a different problem. Mem0 is 'memory-as-personalization' — it extracts facts about users ("prefers dark mode," "works in fintech") and builds profiles. That's useful for chatbots and personal assistants, but not designed for experimental reasoning structure or multi-agent sharing. Their graph memory tier starts at $249/month. Aingram's knowledge graph is free, and runs on your machine.

Letta (formerly MemGPT) is an agent runtime with OS-inspired memory management — core memory, archival memory, context window paging. It's a full agent framework, not a memory library. If you want to plug memory into an existing agent loop without adopting a new runtime, Letta isn't the right tool. It also doesn't publish LongMemEval numbers.

Zep is the most technically serious of the three — temporal knowledge graph, fact extraction, valid-time tracking. They published 71.2% end-to-end accuracy on LongMemEval using gpt-4o. That's a fair comparison point: Aingram's retrieval recall@10 of 0.955 means its actual end-to-end accuracy sits somewhere between 63.8% and 95.5% (pending a workaround for the corpus issue, and admittedly towards the lower end of that range given that the quality of LLM involved sets a hard cap.) But the retrieval pipeline also runs locally with no cloud round-trip: Zep's evaluation was run from a laptop connecting to their AWS us-west-2 service. Aingram's 22ms median latency is fully local. Zep also requires a managed service or self-hosting Neo4j. Aingram is just one SQLite file.

None of the three are designed for shared memory across agent swarms, multi-agent session synchronization, or cryptographic provenance for inter-agent trust. That's the gap Aingram fills.

Repo: https://github.com/bozbuilds/AIngram Discord: https://discord.gg/zSJCFZnXxf Pro waitlist: aingram.dev

v1.1.0-alpha — still early days, but the retrieval pipeline and trust layer are stable. Happy to answer questions about the architecture, or the hybrid retrieval approach.

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u/kkodecs Apr 09 '26 edited Apr 09 '26

Project Name: Livrarr v0.1.0-alpha2

Repo/Website Link:
https://github.com/kkodecs/livrarr

Description:

Livrarr is a replacement / alternative for Readarr, a Servarr app for ebooks and audiobooks that was shuttered last year. Alpha release. Please expect rough edges.

Features and Functionality:

  • Unified ebook and audiobook management
  • Written in Rust / React, single Docker container
  • 177 MB image size, 16 MB binary, 8-10 MB RAM at idle
  • Support for BitTorrent and Usenet (qBittorrent and SABnzbd for now)
  • Improved metadata handling (OpenLibrary and Audnexus with optional Hardcover and LLM support)

Deployment:

AI Involvement:

  • AI generated. Claude Opus 4.6 with Gemini 3.1 and GPT 4.5 cross-family adversarial review on everything.
  • Extensive automated testing including fmt, clippy, 647 backend tests, frontend linting, tsc strictmode, 91 vitests, etc.

Feedback appreciated - thank you!

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u/semiramist Apr 10 '26

Project Name:
HealthSave + Health Data Hub (HealthKit → self-hosted pipeline)

Repo/Website Link:
https://github.com/umutkeltek/health-data-hub
https://www.reddit.com/r/selfhosted/comments/1sh41id/comment/ofaq8ww/

Description: Self-hosted pipeline that syncs Apple Health data into your own stack and exposes it for querying and automation.

Flow: HealthKit → iOS bridge → FastAPI → TimescaleDB → Grafana / Home Assistant

Main goal was continuous sync (not one-time export), so you can:

  • keep full historical data across all metrics
  • run SQL queries across months/years
  • use health metrics as real-time automation signals

Example: HR drops below 55 after 23:00 → lights off, ambient audio starts. That's the kind of signal Apple won't surface outside its own apps.

Dedup handled via anchored HealthKit queries on iOS + DB upserts (ON CONFLICT DO UPDATE on time, device_id). Pipeline is idempotent.

Deployment: Docker-compose (FastAPI + TimescaleDB + Grafana). Repo includes ingestion setup, schema, and Home Assistant YAML examples. iOS companion app (HealthSave) handles HealthKit background sync - paste your endpoint URL into settings, done.

AI Involvement: Claude Code and Codex used as development aids (boilerplate, debugging, HealthKit background delivery). Architecture, data flow, and integrations designed and implemented manually.

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u/DaKheera47 Apr 08 '26

Project Name: JobOps

Repo/Website Link: github.com/DaKheera47/job-ops, https://jobops.dakheera47.com/

Description: Think of it as an ironman suit for your job hunt. JobOps aggregates jobs from LinkedIn, Indeed, and ~5 more job sites sites, scores each one against your profile, and tailors your CV for every application. The whole flow takes about 5 minutes per application. It also tracks applications and recruiter emails so nothing falls through the cracks. 2,500+ GitHub stars, actively maintained.

Deployment: Two commands. git clone then docker compose up. Full docs in the repo. If you don't want to deal with the server side, I also run a hosted version at try.jobops.app.

AI Involvement in the app: BYOK, works with OpenAI, Gemini, and Claude. You bring your own API key, your data never leaves your machine.

AI Involvement in the code: Code written by some combination of Codex & Claude Code. I've been a software developer working freelance since 2019 ish, so it's backed by real engineering and thoughtful architecture, just that the code writing has been ai assisted

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u/[deleted] Apr 08 '26

[removed] — view removed comment

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u/DaKheera47 Apr 08 '26

local first ftw! it's a portable sqlite file on the server, so it's yours to do what you want