Local AI on Producthunt daily

Product Hunt Daily | 2026-04-02

Thu, 02 Apr 2026 08:13:50 +0000

1. Noiz Easter Voice

Tagline: Crack an Easter egg to generate an AI voice
Description: This Easter, turn your voice into something unexpected. On Noiz, crack a voice egg to unlock new AI voices, or create your own with a prompt and image. From playful characters to unique greetings, generate expressive voices in seconds.
Website: open
Product Hunt: View on Product Hunt

Keyword: AI voice generator, Easter egg, voice changer, custom voice, voice creation, voice prompt, playful voice, voice greeting, expressive AI, Noiz app
VotesCount: 🔺406
Featured: Yes
CreatedAt: 2026-04-01 07:01 AM (UTC)

2. Ollama v0.19

Tagline: Massive local model speedup on Apple Silicon with MLX
Description: Ollama v0.19 rebuilds Apple Silicon inference on top of MLX, bringing much faster local performance for coding and agent workflows. It also adds NVFP4 support and smarter cache reuse, snapshots, and eviction for more responsive sessions.
Website: open
Product Hunt: View on Product Hunt

Keyword: Ollama, local AI, Apple Silicon, MLX, inference speedup, coding assistant, agent workflows, NVFP4, model cache, performance optimization, Mac AI
VotesCount: 🔺364
Featured: Yes
CreatedAt: 2026-04-01 07:01 AM (UTC)

3. traceAI

Tagline: Open-source LLM tracing that speaks GenAI, not HTTP.
Description: traceAI is OTel-native LLM tracing that actually works with your existing observability stack. ✓ Captures prompts, completions, tokens, retrievals, agent decisions ✓ Follows GenAI semantic conventions correctly ✓ Routes to any OTel backend—Datadog, Grafana, Jaeger, anywhere ✓ Python, TypeScript, Java, C# with full parity ✓ 35+ frameworks: OpenAI, Anthropic, LangChain, CrewAI, DSPy, and more ✓ Two lines of code to instrument your entire app No new vendor. No new dashboard. Open source (MIT).
Website: open
Product Hunt: View on Product Hunt

Keyword: Open-source observability, LLM tracing, OpenTelemetry, GenAI monitoring, OTel-native, AI agent tracing, prompt tracking, multi-framework support, no vendor lock-in, APM integration, AI development tools, distributed tracing, LLM telemetry
VotesCount: 🔺261
Featured: Yes
CreatedAt: 2026-04-01 07:01 AM (UTC)

4. Zzzappy

Tagline: Science-backed breaks to protect your vision & prevent RSI
Description: Zzzappy is a native macOS health guardian that monitors BOTH your screen time AND arm input load, scientifically scheduling breaks to protect your vision and prevent RSI (Repetitive Strain Injury). Eye Guard👉🏻20-20-20 rule with customizable intervals, pre-break reminders with snooze Arm Guard👉🏻Real-time monitoring of 5 input dimensions: keystrokes, mouse clicks, trackpad travel, scroll distance, and continuous use duration Smart Pause、Immersive Breaks、Health Dashboard、100% Offline and more
Website: open
Product Hunt: View on Product Hunt

Keyword: macOS app, eye strain relief, RSI prevention, screen time monitor, 20-20-20 rule, break reminder, productivity tool, ergonomic software, health dashboard, offline privacy
VotesCount: 🔺233
Featured: Yes
CreatedAt: 2026-04-01 07:01 AM (UTC)

5. ClawMetry for NVIDIA NemoClaw

Tagline: Know what’s happening inside your NemoClaw sandboxes
Description: Full observability inside NVIDIA NemoClaw sandboxes. One command on the host, every sandbox gets covered. See every thought, tool call, and token cost in real time. Brain activity, flow visualization, memory monitoring. All E2E encrypted. 95K+ installs. 100+ countries. Open source (MIT). Cloud sync: $5/sandbox/month.
Website: open
Product Hunt: View on Product Hunt

Keyword: NVIDIA NemoClaw, sandbox observability, AI monitoring, real-time analytics, token cost, tool call tracking, open source, encrypted, cloud sync, developer tools
VotesCount: 🔺215
Featured: Yes
CreatedAt: 2026-04-01 07:01 AM (UTC)

6. Remodex

Tagline: Control Codex on your iPhone
Description: Remodex is an open-source iOS app that remotely controls Codex running on your Mac. From your iPhone: create threads, run subagents, push git commits, use skills and /commands and more Codex stays on your Mac. End-to-end encrypted, the relay never sees your prompts. Pair once with a QR code, reconnects automatically. Open source. Self-host the relay if you want full control.
Website: open
Product Hunt: View on Product Hunt

Keyword: iOS app, remote control, open-source, iPhone, Mac, Codex controller, end-to-end encrypted, self-hosted, QR code pairing, automation, developer tools, git commits, subagents, threads, /commands, skills, privacy focused, local AI, cross-device, workflow automation
VotesCount: 🔺172
Featured: Yes
CreatedAt: 2026-04-01 07:01 AM (UTC)

7. Prospecting by Clarify

Tagline: Source leads, send outbound, grow pipeline. All in your CRM.
Description: Prospecting is broken. We’ve fixed it. No more sourcing leads from one tool, enriching them in another, sending them sequences in a third. And then figuring out how to connect all the dots in your CRM. With Lead Finder and Campaigns in Clarify, you can find targeted leads with a single prompt, build personalized campaigns in a click, and watch your pipeline grow on autopilot. All in one place. All in your new (autonomous) CRM.
Website: open
Product Hunt: View on Product Hunt

Keyword: lead prospecting, CRM automation, outbound sales, lead generation, sales pipeline, autonomous CRM, sales automation, B2B prospecting, lead sourcing, sales campaigns
VotesCount: 🔺158
Featured: Yes
CreatedAt: 2026-04-01 07:01 AM (UTC)

8. Google Veo 3.1 Lite

Tagline: Google’s most cost-effective video generation model
Description: Veo 3.1 Lite is Google’s most cost-efficient video generation model on the Gemini API. It enables high-volume Text-to-Video and Image-to-Video creation at <50% cost of Fast, with 720p/1080p output, flexible ratios, and adjustable durations for scalable video apps.
Website: open
Product Hunt: View on Product Hunt

Keyword: Google Veo 3.1 Lite, video generation, AI video, text-to-video, image-to-video, cost-effective, affordable AI, Gemini API, scalable video, 720p, 1080p, video creation
VotesCount: 🔺156
Featured: Yes
CreatedAt: 2026-04-01 07:01 AM (UTC)

9. OpenBox

Tagline: See, verify, and govern every agent action.
Description: OpenBox provides a trust platform for agentic AI, delivering runtime governance, cryptographic verification, and enterprise-grade compliance. Integrates via a single SDK with LangChain, LangGraph, Temporal, n8n, Mastra, and more. Available to every organization with no usage limits.
Website: open
Product Hunt: View on Product Hunt

Keyword: AI governance, agent monitoring, runtime verification, compliance, enterprise AI, SDK integration, LangChain, LangGraph, cryptographic audit, agentic AI, trust platform, no limits
VotesCount: 🔺140
Featured: Yes
CreatedAt: 2026-04-01 07:01 AM (UTC)

10. Snapstick

Tagline: Turn your camera roll into group chat chaos
Description: Your selfies and photos are just sitting there. Give them new life with Snapstick. Turn your friends, foes, and frappés into shareable stickers, and level up your group chats. And when you make a good one? You can print it as a real sticker, as a mug, a tote bag, or even a t-shirt, with Snapstick’s Make It Real feature. Your best friend deserves a cool t-shirt with that inside joke you have. Snap. Style. Share. Wear. Create your own unique sticker packs with Snapstick. Available on iOS.
Website: open
Product Hunt: View on Product Hunt

Keyword: sticker maker, photo to sticker, group chat stickers, custom stickers, print on demand, personalized gifts, meme creator, iOS app, photo app, social sharing, custom merch, inside joke gifts, photo printing, Snapstick app
VotesCount: 🔺131
Featured: Yes
CreatedAt: 2026-04-01 07:01 AM (UTC)

local-ai-packaged

Mon, 02 Jun 2025 15:31:52 +0800

coleam00/local-ai-packaged

Self-hosted AI Package

Self-hosted AI Package is an open, docker compose template that quickly bootstraps a fully featured Local AI and Low Code development environment including Ollama for your local LLMs, Open WebUI for an interface to chat with your N8N agents, and Supabase for your database, vector store, and authentication.

This is Cole’s version with a couple of improvements and the addition of Supabase, Open WebUI, Flowise, Neo4j, Langfuse, SearXNG, and Caddy! Also, the local RAG AI Agent workflows from the video will be automatically in your n8n instance if you use this setup instead of the base one provided by n8n!

Important Links

Local AI community forum over in the oTTomator Think Tank
GitHub Kanban board for feature implementation and bug squashing.
Original Local AI Starter Kit by the n8n team
Download my N8N + OpenWebUI integration directly on the Open WebUI site. (more instructions below)

Curated by https://github.com/n8n-io and https://github.com/coleam00, it combines the self-hosted n8n platform with a curated list of compatible AI products and components to quickly get started with building self-hosted AI workflows.

What’s included

✅ Self-hosted n8n - Low-code platform with over 400 integrations and advanced AI components

✅ Supabase - Open source database as a service - most widely used database for AI agents

✅ Ollama - Cross-platform LLM platform to install and run the latest local LLMs

✅ Open WebUI - ChatGPT-like interface to privately interact with your local models and N8N agents

✅ Flowise - No/low code AI agent builder that pairs very well with n8n

✅ Qdrant - Open source, high performance vector store with an comprehensive API. Even though you can use Supabase for RAG, this was kept unlike Postgres since it’s faster than Supabase so sometimes is the better option.

✅ Neo4j - Knowledge graph engine that powers tools like GraphRAG, LightRAG, and Graphiti

✅ SearXNG - Open source, free internet metasearch engine which aggregates results from up to 229 search services. Users are neither tracked nor profiled, hence the fit with the local AI package.

✅ Caddy - Managed HTTPS/TLS for custom domains

✅ Langfuse - Open source LLM engineering platform for agent observability

Prerequisites

Before you begin, make sure you have the following software installed:

Python - Required to run the setup script
Git/GitHub Desktop - For easy repository management
Docker/Docker Desktop - Required to run all services

Installation

Clone the repository and navigate to the project directory:

1
2

git clone -b stable https://github.com/coleam00/local-ai-packaged.git
cd local-ai-packaged

Before running the services, you need to set up your environment variables for Supabase following their self-hosting guide.

Make a copy of .env.example and rename it to .env in the root directory of the project

Set the following required environment variables:

############
# N8N Configuration
############
N8N_ENCRYPTION_KEY=
N8N_USER_MANAGEMENT_JWT_SECRET=

############
# Supabase Secrets
############
POSTGRES_PASSWORD=
JWT_SECRET=
ANON_KEY=
SERVICE_ROLE_KEY=
DASHBOARD_USERNAME=
DASHBOARD_PASSWORD=
POOLER_TENANT_ID=

############
# Neo4j Secrets
############   
NEO4J_AUTH=

############
# Langfuse credentials
############

CLICKHOUSE_PASSWORD=
MINIO_ROOT_PASSWORD=
LANGFUSE_SALT=
NEXTAUTH_SECRET=
ENCRYPTION_KEY=

[!IMPORTANT] Make sure to generate secure random values for all secrets. Never use the example values in production.

Set the following environment variables if deploying to production, otherwise leave commented:

############
# Caddy Config
############

N8N_HOSTNAME=n8n.yourdomain.com
WEBUI_HOSTNAME=:openwebui.yourdomain.com
FLOWISE_HOSTNAME=:flowise.yourdomain.com
SUPABASE_HOSTNAME=:supabase.yourdomain.com
OLLAMA_HOSTNAME=:ollama.yourdomain.com
SEARXNG_HOSTNAME=searxng.yourdomain.com
NEO4J_HOSTNAME=neo4j.yourdomain.com
LETSENCRYPT_EMAIL=your-email-address

The project includes a start_services.py script that handles starting both the Supabase and local AI services. The script accepts a --profile flag to specify which GPU configuration to use.

For Nvidia GPU users

`1`	`python start_services.py --profile gpu-nvidia`

[!NOTE] If you have not used your Nvidia GPU with Docker before, please follow the Ollama Docker instructions.

For AMD GPU users on Linux

`1`	`python start_services.py --profile gpu-amd`

For Mac / Apple Silicon users

If you’re using a Mac with an M1 or newer processor, you can’t expose your GPU to the Docker instance, unfortunately. There are two options in this case:

Run the starter kit fully on CPU:

`1`	`python start_services.py --profile cpu`

Run Ollama on your Mac for faster inference, and connect to that from the n8n instance:
1

python start_services.py --profile none
If you want to run Ollama on your mac, check the Ollama homepage for installation instructions.

For Mac users running OLLAMA locally

If you’re running OLLAMA locally on your Mac (not in Docker), you need to modify the OLLAMA_HOST environment variable in the n8n service configuration. Update the x-n8n section in your Docker Compose file as follows:

x-n8n: &service-n8n
  # ... other configurations ...
  environment:
    # ... other environment variables ...
    - OLLAMA_HOST=host.docker.internal:11434

Additionally, after you see “Editor is now accessible via: http://localhost:5678/”:

Head to http://localhost:5678/home/credentials
Click on “Local Ollama service”
Change the base URL to “http://host.docker.internal:11434/"

For everyone else

`1`	`python start_services.py --profile cpu`

The environment argument

The start-services.py script offers the possibility to pass one of two options for the environment argument, private (default environment) and public:

private: you are deploying the stack in a safe environment, hence a lot of ports can be made accessible without having to worry about security
public: the stack is deployed in a public environment, which means the attack surface should be made as small as possible. All ports except for 80 and 443 are closed

The stack initialized with

`1`	`python start_services.py --profile gpu-nvidia --environment private`

equals the one initialized with

`1`	`python start_services.py --profile gpu-nvidia`

Deploying to the Cloud

Prerequisites for the below steps

Linux machine (preferably Unbuntu) with Nano, Git, and Docker installed

Extra steps

Before running the above commands to pull the repo and install everything:

Run the commands as root to open up the necessary ports:
- ufw enable
- ufw allow 80 && ufw allow 443
- ufw reload
WARNING

ufw does not shield ports published by docker, because the iptables rules configured by docker are analyzed before those configured by ufw. There is a solution to change this behavior, but that is out of scope for this project. Just make sure that all traffic runs through the caddy service via port 443. Port 80 should only be used to redirect to port 443.
Run the start-services.py script with the environment argument public to indicate you are going to run the package in a public environment. The script will make sure that all ports, except for 80 and 443, are closed down, e.g.

`1`	`python start_services.py --profile gpu-nvidia --environment public`

Set up A records for your DNS provider to point your subdomains you’ll set up in the .env file for Caddy to the IP address of your cloud instance.

For example, A record to point n8n to [cloud instance IP] for n8n.yourdomain.com

⚡️ Quick start and usage

The main component of the self-hosted AI starter kit is a docker compose file pre-configured with network and disk so there isn’t much else you need to install. After completing the installation steps above, follow the steps below to get started.

Open http://localhost:5678/ in your browser to set up n8n. You’ll only have to do this once. You are NOT creating an account with n8n in the setup here, it is only a local account for your instance!
Open the included workflow: http://localhost:5678/workflow/vTN9y2dLXqTiDfPT
Create credentials for every service:

Ollama URL: http://ollama:11434

Postgres (through Supabase): use DB, username, and password from .env. IMPORTANT: Host is ‘db’ Since that is the name of the service running Supabase

Qdrant URL: http://qdrant:6333 (API key can be whatever since this is running locally)

Google Drive: Follow this guide from n8n. Don’t use localhost for the redirect URI, just use another domain you have, it will still work! Alternatively, you can set up local file triggers.
Select Test workflow to start running the workflow.
If this is the first time you’re running the workflow, you may need to wait until Ollama finishes downloading Llama3.1. You can inspect the docker console logs to check on the progress.
Make sure to toggle the workflow as active and copy the “Production” webhook URL!
Open http://localhost:3000/ in your browser to set up Open WebUI. You’ll only have to do this once. You are NOT creating an account with Open WebUI in the setup here, it is only a local account for your instance!
Go to Workspace -> Functions -> Add Function -> Give name + description then paste in the code from n8n_pipe.py

The function is also published here on Open WebUI’s site.
Click on the gear icon and set the n8n_url to the production URL for the webhook you copied in a previous step.
Toggle the function on and now it will be available in your model dropdown in the top left!

To open n8n at any time, visit http://localhost:5678/ in your browser. To open Open WebUI at any time, visit http://localhost:3000/.

With your n8n instance, you’ll have access to over 400 integrations and a suite of basic and advanced AI nodes such as AI Agent, Text classifier, and Information Extractor nodes. To keep everything local, just remember to use the Ollama node for your language model and Qdrant as your vector store.

[!NOTE] This starter kit is designed to help you get started with self-hosted AI workflows. While it’s not fully optimized for production environments, it combines robust components that work well together for proof-of-concept projects. You can customize it to meet your specific needs

Upgrading

To update all containers to their latest versions (n8n, Open WebUI, etc.), run these commands:

# Stop all services
docker compose -p localai -f docker-compose.yml --profile <your-profile> down

# Pull latest versions of all containers
docker compose -p localai -f docker-compose.yml --profile <your-profile> pull

# Start services again with your desired profile
python start_services.py --profile <your-profile>

Replace <your-profile> with one of: cpu, gpu-nvidia, gpu-amd, or none.

Note: The start_services.py script itself does not update containers - it only restarts them or pulls them if you are downloading these containers for the first time. To get the latest versions, you must explicitly run the commands above.

Troubleshooting

Here are solutions to common issues you might encounter:

Supabase Issues

Supabase Pooler Restarting: If the supabase-pooler container keeps restarting itself, follow the instructions in this GitHub issue.
Supabase Analytics Startup Failure: If the supabase-analytics container fails to start after changing your Postgres password, delete the folder supabase/docker/volumes/db/data.
If using Docker Desktop: Go into the Docker settings and make sure “Expose daemon on tcp://localhost:2375 without TLS” is turned on
Supabase Service Unavailable - Make sure you don’t have an “@” character in your Postgres password! If the connection to the kong container is working (the container logs say it is receiving requests from n8n) but n8n says it cannot connect, this is generally the problem from what the community has shared. Other characters might not be allowed too, the @ symbol is just the one I know for sure!

GPU Support Issues

Windows GPU Support: If you’re having trouble running Ollama with GPU support on Windows with Docker Desktop:
1. Open Docker Desktop settings
2. Enable WSL 2 backend
3. See the Docker GPU documentation for more details
Linux GPU Support: If you’re having trouble running Ollama with GPU support on Linux, follow the Ollama Docker instructions.

👓 Recommended reading

n8n is full of useful content for getting started quickly with its AI concepts and nodes. If you run into an issue, go to support.

🎥 Video walkthrough

Cole’s Guide to the Local AI Starter Kit

🛍️ More AI templates

For more AI workflow ideas, visit the official n8n AI template gallery. From each workflow, select the Use workflow button to automatically import the workflow into your local n8n instance.

Learn AI key concepts

Local AI templates

Tips & tricks

Accessing local files

The self-hosted AI starter kit will create a shared folder (by default, located in the same directory) which is mounted to the n8n container and allows n8n to access files on disk. This folder within the n8n container is located at /data/shared – this is the path you’ll need to use in nodes that interact with the local filesystem.

Nodes that interact with the local filesystem

📜 License

This project (originally created by the n8n team, link at the top of the README) is licensed under the Apache License 2.0 - see the LICENSE file for details.