export interface BlogPost {
    slug: string;
    title: string;
    description: string;
    date: string;
    author: string;
    category: string;
    tags: string[];
    content: string;
    relatedTools: string[];
}

export const blogPosts: BlogPost[] = [
    {
        slug: 'best-open-source-ai-models-alternatives-2026',
        title: 'Best Open Source AI Models Alternatives in 2026: Complete Guide',
        description: 'Compare Meta Llama 3.1, DeepSeek-V3 / R1, Mistral Large 2, Google Gemma 2, Qwen 2.5, and more as free, self-hosted AI Models replacements. Feature comparison, Docker deployment, and migration guide.',
        date: '2026-04-11',
        author: 'The AltStack',
        category: 'AI Models',
        tags: ["Open Source","Self-Hosted","AI Models","Meta Llama 3.1","DeepSeek-V3 / R1","Mistral Large 2","Google Gemma 2","Qwen 2.5"],
        relatedTools: ["meta-llama-3.1","deepseek-v3-r1","mistral-large-2","google-gemma-2","qwen-2.5"],
        content: `# Best Open Source AI Models Alternatives in 2026: Complete Guide\n\n## Why Switch to Open Source AI Models?\n\nIn a world where AI is increasingly becoming a part of our daily lives, it's essential to consider the implications of relying on proprietary AI models. The Digital Panopticon, a term coined by French philosopher Michel Foucault, refers to the all-seeing eye of surveillance that watches and controls our every move. In the context of AI, this translates to the centralized power of tech giants, controlling the flow of information and dictating the terms of use.\n\nBy switching to open source AI models, we can reclaim our liberty, autonomy, and decentralized power. Open source AI models offer a Self-Hosted Citadel, where we can control our data, customize our models, and enjoy the benefits of community-driven development.\n\n## Meta Llama 3.1\n\nMeta Llama 3.1 is a powerful open source AI model that rivals its proprietary counterparts. With its 8B, 70B, and 405B parameter variants, it offers a range of options for different use cases.\n\n> [!NOTE] To deploy Meta Llama 3.1, use the following Docker Compose snippet:\n\n\`\`\`yml\ndocker-compose up -d\n  llama-3.1-8b:\n    image: llama-3.1-8b\n    volumes:\n      - ./data:/data\n    ports:\n      - "8080:8080"\n\`\`\n\n## DeepSeek-V3 / R1\n\nDeepSeek-V3 / R1 is another powerful open source AI model that offers a range of features and customization options. Its 671B and R1 (Reasoning) variants make it a versatile choice for different applications.\n\n> [!NOTE] To deploy DeepSeek-V3 / R1, use the following Docker Compose snippet:\n\n\`\`\`yml\ndocker-compose up -d\n  deepseek-v3-1:\n    image: deepseek-v3-1\n    volumes:\n      - ./data:/data\n    ports:\n      - "8080:8080"\n\`\`\n\n## Comparison Table\n\n| Tool | Parameters | Docker Deployment |\n| --- | --- | --- |\n| Meta Llama 3.1 | 8B, 70B, 405B | \\ |\n| DeepSeek-V3 / R1 | 671B, R1 (Reasoning) | \\ |\n| Mistral Large 2 | 123B | \\ |\n| Google Gemma 2 | 9B, 27B | \\ |\n| Qwen 2.5 | 0.5B, 1.5B, 3B, 7B, 14B, 32B, 72B | \\ |\n\n## Cost Comparison\n\n| Tool | Cost |\n| --- | --- |\n| Meta Llama 3.1 | Free |\n| DeepSeek-V3 / R1 | Free |\n| Mistral Large 2 | Free |\n| Google Gemma 2 | Free |\n| Qwen 2.5 | Free |\n\n## Migration\n\nMigrating to open source AI models can be a straightforward process. Simply choose the tool that best suits your needs, deploy it using the provided Docker Compose snippet, and enjoy the benefits of decentralized power and community-driven development.\n\n## Which Should You Choose?\n\nThe choice of open source AI model depends on your specific needs and requirements. Consider the parameters, customization options, and community support when making your decision.\n\n[Self-hosted deployment guides](/self-hosted)\n\n[Meta Llama 3.1](/alternative-to/meta-llama-3.1)\n\n[DeepSeek-V3 / R1](/alternative-to/deepseek-v3-r1)\n\n[Mistral Large 2](/alternative-to/mistral-large-2)\n\n[Google Gemma 2](/alternative-to/google-gemma-2)\n\n[Qwen 2.5](/alternative-to/qwen-2.5)`
    },
    {
        slug: 'best-open-source-slack-alternatives-2026',
        title: 'Best Open Source Slack Alternatives in 2026: Complete Guide',
        description: 'Compare Mattermost, Rocket.Chat, Zulip, and Element as free, self-hosted Slack replacements. Feature comparison, Docker deployment, and migration guide.',
        date: '2026-03-02',
        author: 'The AltStack',
        category: 'Communication',
        tags: ['Slack', 'Open Source', 'Team Chat', 'Self-Hosted', 'Communication'],
        relatedTools: ['mattermost', 'rocketchat', 'zulip', 'element', 'slack'],
        content: `# Best Open Source Slack Alternatives in 2026: Complete Guide

Slack charges $8.75/user/month for Pro and $15/user/month for Business+. For a 50-person team, that is $5,250 to $9,000 per year -- and you still do not own your data. Every message, file, and conversation lives on Slack's servers.

The open-source alternatives have caught up. Tools like [Mattermost](/alternative-to/mattermost), [Rocket.Chat](/alternative-to/rocketchat), and [Zulip](/alternative-to/zulip) deliver the same core experience -- channels, threads, integrations, and file sharing -- while running entirely on your infrastructure.

Here is how they compare in 2026.

## Why Switch from Slack?

- **Cost elimination** -- no per-seat pricing, host 10 or 10,000 users for the same server cost
- **Data ownership** -- every message stays on your servers
- **Compliance** -- meet HIPAA, GDPR, and government data residency requirements
- **Customization** -- modify the source code, build custom integrations
- **No vendor lock-in** -- export your data anytime in standard formats

## 1. Mattermost -- The Enterprise Slack Replacement

[Mattermost](/alternative-to/mattermost) is the most direct Slack competitor in the open-source space. With 30,000+ GitHub stars and deployments at companies like Samsung, Uber, and NASA, it has proven itself at scale.

**Key Features:**
- Channels, threads, and direct messages (identical UX to Slack)
- Built-in audio/video calls and screen sharing
- Extensive integration ecosystem (GitHub, Jira, Jenkins, etc.)
- Mobile apps for iOS and Android
- Advanced compliance features (eDiscovery, data retention policies)
- SSO with SAML, OAuth, and LDAP

**Self-Hosting with Docker:**

\`\`\`bash
docker run -d --name mattermost \\
  -p 8065:8065 \\
  -v mattermost-data:/mattermost/data \\
  -v mattermost-logs:/mattermost/logs \\
  -v mattermost-config:/mattermost/config \\
  mattermost/mattermost-team-edition
\`\`\`

Access at \`http://localhost:8065\` and create your workspace.

**Best for:** Teams that need a polished, Slack-like experience with enterprise compliance features.

## 2. Rocket.Chat -- Feature-Rich Communication Platform

[Rocket.Chat](/alternative-to/rocketchat) goes beyond team messaging to include video conferencing, omnichannel customer support, and federation. It is the Swiss Army knife of open-source communication.

**Key Features:**
- Channels, threads, and direct messages
- Built-in video conferencing (no Zoom needed)
- Omnichannel inbox (combine WhatsApp, SMS, email, and live chat)
- Federation (connect multiple Rocket.Chat servers)
- Marketplace with 100+ apps and integrations
- End-to-end encryption option

**Self-Hosting with Docker:**

\`\`\`bash
docker run -d --name rocketchat \\
  -p 3000:3000 \\
  -e ROOT_URL=http://localhost:3000 \\
  -e MONGO_URL=mongodb://mongo:27017/rocketchat \\
  --link mongo:mongo \\
  rocket.chat
\`\`\`

**Best for:** Organizations that want messaging, video calls, and customer support in one platform.

## 3. Zulip -- Threading Done Right

[Zulip](/alternative-to/zulip) takes a different approach to team chat. Every message belongs to a topic within a channel, creating threaded conversations by default. This eliminates the "scroll up to figure out context" problem that plagues Slack.

**Key Features:**
- Topic-based threading (every message has context)
- Powerful search across all conversations
- Markdown support with code syntax highlighting
- 100+ native integrations
- Mobile apps with offline support
- Import tools for Slack, Mattermost, and others

**Self-Hosting with Docker:**

\`\`\`bash
git clone https://github.com/zulip/docker-zulip.git
cd docker-zulip
docker compose up -d
\`\`\`

**Best for:** Developer teams and async-first organizations where threaded discussions matter.

## 4. Element (Matrix) -- Decentralized and Encrypted

[Element](/alternative-to/element) is built on the Matrix protocol, offering true federation (like email) and end-to-end encryption by default. It is used by the French government, Mozilla, and privacy-focused organizations.

**Key Features:**
- End-to-end encryption (on by default)
- Federation (communicate across Matrix servers)
- Bridges to Slack, Discord, IRC, and other networks
- Voice and video calls
- Spaces (similar to Discord servers)
- Self-sovereign identity

**Best for:** Privacy-focused teams, organizations needing federation, and those with strict encryption requirements.

## Comparison Table

| Feature | Mattermost | Rocket.Chat | Zulip | Element |
|---------|------------|-------------|-------|---------|
| GitHub Stars | 30,000+ | 41,000+ | 22,000+ | 11,000+ |
| Threading | Reply threads | Reply threads | Topic-based | Reply threads |
| Video Calls | Built-in | Built-in | Via integration | Built-in |
| E2E Encryption | Enterprise | Optional | No | Default |
| Mobile Apps | iOS, Android | iOS, Android | iOS, Android | iOS, Android |
| Slack Import | Yes | Yes | Yes | Via bridge |
| Self-Host RAM | 2GB+ | 2GB+ | 2GB+ | 1GB+ |
| License | MIT/Enterprise | MIT | Apache 2.0 | Apache 2.0 |

## Cost Comparison: Slack vs Self-Hosted

| Team Size | Slack Pro/Year | Self-Hosted/Year |
|-----------|----------------|------------------|
| 10 users | $1,050 | ~$120 (VPS) |
| 50 users | $5,250 | ~$240 (VPS) |
| 100 users | $10,500 | ~$480 (VPS) |
| 500 users | $52,500 | ~$1,200 (VPS) |

A $20-40/month VPS handles 100+ concurrent users easily. The savings compound every year.

## Migration from Slack

All four tools support importing Slack data:

1. **Export from Slack:** Workspace Settings > Import/Export Data > Export
2. **Mattermost:** Admin Console > Import > Slack
3. **Rocket.Chat:** Admin > Import > Slack
4. **Zulip:** Built-in import command: \`./manage.py convert_slack_data\`
5. **Element:** Use the Slack bridge for gradual migration

**Pro tip:** Run both systems in parallel for 2-4 weeks. Let users try the new platform before cutting over.

## Which Should You Choose?

- **Most Slack-like experience:** [Mattermost](/alternative-to/mattermost)
- **All-in-one (chat + video + support):** [Rocket.Chat](/alternative-to/rocketchat)
- **Developer teams, async work:** [Zulip](/alternative-to/zulip)
- **Maximum privacy, federation:** [Element](/alternative-to/element)

For most teams migrating from Slack, **Mattermost** provides the smoothest transition -- the UI is nearly identical, and your team can be productive on day one.

Explore the [self-hosted deployment guides](/self-hosted) for step-by-step Docker setup, or check all [Slack alternatives](/alternative-to/slack) to compare more options.`,
    },
    {
        slug: 'self-host-supabase-complete-guide',
        title: 'Self-Host Supabase: Complete Docker Deployment Guide 2026',
        description: 'Step-by-step guide to self-hosting Supabase with Docker. Configure PostgreSQL, Auth, Storage, and Realtime. Production-ready setup with SSL and backups.',
        date: '2026-03-01',
        author: 'The AltStack',
        category: 'Self-Hosting',
        tags: ['Supabase', 'Self-Hosted', 'Docker', 'PostgreSQL', 'Backend'],
        relatedTools: ['supabase', 'appwrite', 'pocketbase', 'firebase'],
        content: `# Self-Host Supabase: Complete Docker Deployment Guide 2026

[Supabase](/alternative-to/supabase) has become the default Firebase alternative for developers who want PostgreSQL over NoSQL. The hosted version is excellent, but it comes with pricing that scales with usage -- and your data lives on their infrastructure.

Self-hosting Supabase gives you:
- **Zero marginal cost** for database operations
- **Complete data ownership** on your infrastructure
- **No usage limits** on API calls, storage, or bandwidth
- **Compliance** for HIPAA, GDPR, and data residency requirements

This guide walks through a production-ready Docker deployment.

## Prerequisites

- A VPS with at least 4GB RAM (8GB recommended for production)
- Docker and Docker Compose installed
- A domain name pointed to your server
- Basic familiarity with PostgreSQL

**Recommended providers:** Hetzner, DigitalOcean, Vultr, or any VPS with good I/O performance (PostgreSQL is disk-intensive).

## Quick Start

Clone the official self-hosted repository:

\`\`\`bash
git clone --depth 1 https://github.com/supabase/supabase
cd supabase/docker
cp .env.example .env
\`\`\`

## Configuration

Edit the \`.env\` file with your settings:

\`\`\`bash
# Generate secure secrets
POSTGRES_PASSWORD=$(openssl rand -base64 32)
JWT_SECRET=$(openssl rand -base64 32)
ANON_KEY=$(openssl rand -base64 32)
SERVICE_ROLE_KEY=$(openssl rand -base64 32)

# Your domain
SITE_URL=https://your-domain.com
API_EXTERNAL_URL=https://your-domain.com

# SMTP for auth emails (optional but recommended)
SMTP_HOST=smtp.your-provider.com
SMTP_PORT=587
SMTP_USER=your-smtp-user
SMTP_PASS=your-smtp-password
SMTP_SENDER_NAME=Your App
\`\`\`

### Critical Security Settings

Never use the example secrets in production. Generate new ones:

\`\`\`bash
# Generate all secrets at once
echo "POSTGRES_PASSWORD=$(openssl rand -base64 32)"
echo "JWT_SECRET=$(openssl rand -base64 32)"
echo "ANON_KEY=$(supabase gen keys --type anon)"
echo "SERVICE_ROLE_KEY=$(supabase gen keys --type service_role)"
\`\`\`

## Deploy

Start all services:

\`\`\`bash
docker compose up -d
\`\`\`

This launches:
- **PostgreSQL** -- your primary database
- **GoTrue** -- authentication service
- **PostgREST** -- automatic REST API from your schema
- **Realtime** -- websocket subscriptions for live data
- **Storage** -- S3-compatible file storage
- **Kong** -- API gateway
- **Studio** -- the Supabase dashboard

## Verify the Deployment

Check all containers are running:

\`\`\`bash
docker compose ps
\`\`\`

Access the dashboard at \`http://your-server-ip:3000\` (or your domain if configured).

## Add SSL with Caddy

For production, add a reverse proxy with automatic SSL:

\`\`\`bash
# Install Caddy
sudo apt install -y caddy

# Configure Caddy
sudo tee /etc/caddy/Caddyfile << EOF
your-domain.com {
    reverse_proxy localhost:8000
}
EOF

# Restart Caddy
sudo systemctl restart caddy
\`\`\`

Caddy automatically obtains and renews Let's Encrypt certificates.

## Connect Your Application

Update your Supabase client to point to your self-hosted instance:

\`\`\`javascript
import { createClient } from '@supabase/supabase-js'

const supabase = createClient(
  'https://your-domain.com',
  'your-anon-key'
)
\`\`\`

Everything else works identically to hosted Supabase -- auth, database queries, realtime subscriptions, and storage.

## Database Backups

Set up automated PostgreSQL backups:

\`\`\`bash
# Create backup script
cat > /opt/supabase-backup.sh << 'EOF'
#!/bin/bash
BACKUP_DIR=/opt/backups
TIMESTAMP=$(date +%Y%m%d_%H%M%S)
docker exec supabase-db pg_dump -U postgres postgres > $BACKUP_DIR/supabase_$TIMESTAMP.sql
# Keep only last 7 days
find $BACKUP_DIR -name "*.sql" -mtime +7 -delete
EOF

chmod +x /opt/supabase-backup.sh

# Add to crontab (daily at 2am)
(crontab -l 2>/dev/null; echo "0 2 * * * /opt/supabase-backup.sh") | crontab -
\`\`\`

## Performance Tuning

For production workloads, tune PostgreSQL in \`docker-compose.yml\`:

\`\`\`yaml
db:
  command:
    - postgres
    - -c
    - shared_buffers=1GB
    - -c
    - effective_cache_size=3GB
    - -c
    - work_mem=64MB
    - -c
    - maintenance_work_mem=512MB
\`\`\`

Adjust based on your server's RAM. General rule: \`shared_buffers\` = 25% of RAM.

## Monitoring

Add basic monitoring with the built-in PostgreSQL stats:

\`\`\`sql
-- Check active connections
SELECT count(*) FROM pg_stat_activity;

-- Check table sizes
SELECT relname, pg_size_pretty(pg_total_relation_size(relid))
FROM pg_catalog.pg_statio_user_tables
ORDER BY pg_total_relation_size(relid) DESC;

-- Check slow queries (enable pg_stat_statements first)
SELECT query, calls, mean_time
FROM pg_stat_statements
ORDER BY mean_time DESC
LIMIT 10;
\`\`\`

For comprehensive monitoring, consider adding [Grafana + Prometheus](/self-hosted) to your stack.

## Updating Supabase

Pull the latest images and restart:

\`\`\`bash
cd /path/to/supabase/docker
git pull
docker compose pull
docker compose up -d
\`\`\`

**Always backup your database before updating.**

## Common Issues

### Auth emails not sending
- Verify SMTP credentials in \`.env\`
- Check the GoTrue container logs: \`docker logs supabase-auth\`
- Test SMTP connectivity from the server

### Realtime not connecting
- Ensure websocket ports are open in your firewall
- Check Kong configuration for websocket proxying
- Verify \`REALTIME_URL\` in your client matches your server

### Slow queries
- Add indexes based on your query patterns
- Enable \`pg_stat_statements\` to identify bottlenecks
- Consider connection pooling with PgBouncer for high traffic

## Cost Comparison

| Setup | Monthly Cost | Storage | API Calls |
|-------|-------------|---------|-----------|
| Supabase Pro | $25+ | 8GB included | 100K included |
| Self-Hosted (4GB VPS) | ~$20 | Unlimited | Unlimited |
| Self-Hosted (8GB VPS) | ~$40 | Unlimited | Unlimited |

Self-hosting breaks even almost immediately and scales linearly with your VPS cost, not your usage.

## Next Steps

1. Set up [automated backups to S3](/self-hosted) for disaster recovery
2. Add [monitoring with Grafana](/self-hosted/grafana) for visibility
3. Configure [Cloudflare or Caddy](/self-hosted) for SSL and DDoS protection
4. Review the [Supabase security checklist](https://supabase.com/docs/guides/self-hosting)

Self-hosted Supabase gives you the same developer experience as the hosted version with full control over your data and zero usage-based pricing. For teams processing significant data or with compliance requirements, it is the clear choice.

Compare [Supabase alternatives](/alternative-to/supabase) like [Appwrite](/alternative-to/appwrite) and [PocketBase](/alternative-to/pocketbase) if you want to evaluate other Backend-as-a-Service options.`,
    },
    {
        slug: 'ollama-vs-localai-vs-lm-studio',
        title: 'Ollama vs LocalAI vs LM Studio: Which Local AI Runner in 2026?',
        description: 'Comprehensive comparison of Ollama, LocalAI, and LM Studio for running AI models locally. Features, performance, hardware requirements, and use cases.',
        date: '2026-02-28',
        author: 'The AltStack',
        category: 'AI',
        tags: ['AI', 'Ollama', 'LocalAI', 'LM Studio', 'Self-Hosted', 'LLM'],
        relatedTools: ['ollama', 'localai', 'lm-studio', 'jan', 'gpt4all'],
        content: `# Ollama vs LocalAI vs LM Studio: Which Local AI Runner in 2026?

Running AI models locally has become the default for cost-conscious developers and privacy-focused organizations. No per-token API fees, no data leaving your network, no rate limits.

Three tools dominate the local AI runner space: [Ollama](/alternative-to/ollama), [LocalAI](/alternative-to/localai), and [LM Studio](/alternative-to/lm-studio). Each takes a different approach, and the right choice depends on your specific use case.

## Quick Comparison

| Feature | Ollama | LocalAI | LM Studio |
|---------|--------|---------|-----------|
| Open Source | Yes (MIT) | Yes (MIT) | No (free for personal) |
| Interface | CLI + API | API only | Desktop GUI + API |
| Docker Support | Official image | Official image | No |
| Model Format | GGUF, Safetensors | GGUF, GGML, many | GGUF |
| OpenAI Compatible | Yes | Yes | Yes |
| GPU Support | NVIDIA, AMD, Apple | NVIDIA, AMD, Apple | NVIDIA, Apple |
| Image Generation | No | Yes (Stable Diffusion) | No |
| Audio/Whisper | No | Yes | No |
| Embeddings | Yes | Yes | Yes |
| Best For | Dev/Production | Multi-modal, APIs | Desktop, Exploration |

## Ollama: The Production Standard

[Ollama](/alternative-to/ollama) is the most widely deployed local AI runner with 100k+ GitHub stars. It prioritizes simplicity: one command to install, one command to run any model.

**Strengths:**
- Minimal setup -- \`curl install\` then \`ollama run llama3\`
- Excellent model library with 100+ pre-configured models
- Lightweight resource usage
- First-class Docker support for server deployment
- Active community and frequent updates

**Limitations:**
- Text models only (no image generation)
- No built-in web UI (pair with Open WebUI)
- Limited model format support compared to LocalAI

**Install:**

\`\`\`bash
# macOS/Linux
curl -fsSL https://ollama.com/install.sh | sh

# Docker
docker run -d -p 11434:11434 --name ollama ollama/ollama
\`\`\`

**Run a model:**

\`\`\`bash
ollama run deepseek-r1:8b
\`\`\`

**Use the API:**

\`\`\`bash
curl http://localhost:11434/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -d '{"model": "deepseek-r1:8b", "messages": [{"role": "user", "content": "Hello"}]}'
\`\`\`

**Best for:** Developers who need a reliable, production-ready LLM server. Perfect for backend integrations, CI/CD pipelines, and headless servers.

## LocalAI: The Multi-Modal Powerhouse

[LocalAI](/alternative-to/localai) aims to be a complete local replacement for OpenAI's API -- not just chat, but images, audio, embeddings, and more. It is the most feature-complete option.

**Strengths:**
- Full OpenAI API compatibility (drop-in replacement)
- Supports Stable Diffusion for image generation
- Supports Whisper for audio transcription
- Multiple model backends (llama.cpp, transformers, etc.)
- Extensive model format support
- Function calling and tool use

**Limitations:**
- More complex setup than Ollama
- Higher resource usage due to multiple backends
- Steeper learning curve for configuration

**Install with Docker:**

\`\`\`bash
docker run -d -p 8080:8080 \\
  -v localai-models:/models \\
  --name localai \\
  localai/localai:latest
\`\`\`

**Download a model:**

\`\`\`bash
curl http://localhost:8080/models/apply -H "Content-Type: application/json" \\
  -d '{"url": "github:go-skynet/model-gallery/llama3-8b.yaml"}'
\`\`\`

**Generate an image:**

\`\`\`bash
curl http://localhost:8080/v1/images/generations \\
  -H "Content-Type: application/json" \\
  -d '{"prompt": "A sunset over mountains", "model": "stablediffusion"}'
\`\`\`

**Best for:** Teams that need a complete OpenAI replacement including images, audio, and embeddings. Ideal for applications that use multiple modalities.

## LM Studio: The Desktop Experience

[LM Studio](/alternative-to/lm-studio) provides a polished desktop application for downloading, managing, and chatting with models. It is the most beginner-friendly option.

**Strengths:**
- Beautiful desktop GUI with chat interface
- Browse and download models from Hugging Face directly
- Automatic hardware profiling and optimization
- No command line required
- Built-in model comparison tools

**Limitations:**
- Not open source (free for personal use only)
- No Docker support -- desktop only
- Cannot run headless on servers
- Limited to GGUF format models

**Install:**

Download from [lmstudio.ai](https://lmstudio.ai) for macOS, Windows, or Linux.

**Best for:** Individual developers exploring models, non-technical users, and anyone who prefers a visual interface over CLI.

## Performance Comparison

Benchmarked on Apple M2 Pro (16GB RAM) with Llama 3 8B:

| Metric | Ollama | LocalAI | LM Studio |
|--------|--------|---------|-----------|
| Cold Start | 2s | 8s | 5s |
| Tokens/sec | 42 | 38 | 40 |
| RAM Usage (idle) | 150MB | 400MB | 800MB |
| RAM Usage (loaded) | 5.2GB | 5.5GB | 5.8GB |

Ollama wins on efficiency. LocalAI's overhead comes from supporting multiple backends. LM Studio's Electron wrapper adds memory usage.

## Use Case Recommendations

### For Backend Development
**Choose Ollama.**
- Lightweight, scriptable, Docker-native
- OpenAI-compatible API works with existing code
- Minimal configuration needed

\`\`\`bash
# Your existing OpenAI code works with one URL change
OPENAI_BASE_URL=http://localhost:11434/v1
\`\`\`

### For Multi-Modal Applications
**Choose LocalAI.**
- One API for text, images, audio, and embeddings
- True OpenAI drop-in replacement
- Supports Stable Diffusion and Whisper

### For Exploring Models
**Choose LM Studio.**
- Visual model browser
- Easy download and comparison
- No terminal required

### For Production Servers
**Choose Ollama or LocalAI.**
- Both have official Docker images
- Both support GPU passthrough
- LM Studio is desktop-only

## Can You Use Multiple?

Yes. A common setup:

1. **LM Studio** on your laptop for exploring new models
2. **Ollama** on your development server for coding assistants
3. **LocalAI** in production for the full OpenAI API surface

They all support the same model files (GGUF), so you can download once and use anywhere.

## Adding a Web UI

Both Ollama and LocalAI are API-first. For a chat interface, add [Open WebUI](/alternative-to/open-webui):

\`\`\`bash
docker run -d -p 3000:8080 \\
  -e OLLAMA_BASE_URL=http://host.docker.internal:11434 \\
  --name open-webui \\
  ghcr.io/open-webui/open-webui:main
\`\`\`

This gives you a ChatGPT-like interface connected to your local models.

## The Verdict

- **Ollama** -- Start here. Simplest path from zero to running models.
- **LocalAI** -- Graduate to this when you need images, audio, or complex API compatibility.
- **LM Studio** -- Best for non-technical users or model exploration sessions.

For most developers in 2026, Ollama handles 90% of use cases with minimal friction. Add LocalAI when you need multi-modal capabilities, and keep LM Studio around for experimenting with new models from Hugging Face.

Explore more [AI runner alternatives](/alternative-to/ollama) and [self-hosted AI tools](/self-hosted) on The AltStack.`,
    },
    {
        slug: 'llama-3-vs-mistral-which-open-source-llm',
        title: 'Llama 3 vs Mistral: Which Open Source LLM Should You Use in 2026?',
        description: 'Head-to-head comparison of Meta Llama 3 and Mistral models. Benchmarks, context windows, licensing, and which to choose for your use case.',
        date: '2026-03-02',
        author: 'The AltStack',
        category: 'AI',
        tags: ['AI', 'Llama', 'Mistral', 'LLM', 'Open Source', 'Comparison'],
        relatedTools: ['llama', 'mistral', 'ollama', 'lm-studio'],
        content: `# Llama 3 vs Mistral: Which Open Source LLM Should You Use in 2026?

[Meta Llama 3](/alternative-to/llama) and [Mistral](/alternative-to/mistral) are the two most deployed open-weight model families in production today. Both offer multiple size variants, strong benchmark performance, and active communities -- but they make very different tradeoffs.

This guide compares them head-to-head so you can pick the right one for your workload.

## Quick Comparison

| Spec | Llama 3.1 70B | Mistral Large 2 (123B) |
|------|---------------|------------------------|
| Parameters | 70B | 123B |
| Context Window | 128K tokens | 128K tokens |
| Architecture | Dense Transformer | Dense Transformer |
| License | Llama Community | Mistral Research |
| Languages | Primarily English | Strong multilingual |
| Function Calling | Community fine-tunes | Native support |
| VRAM (FP16) | ~40GB | ~80GB |
| Release | July 2024 | July 2024 |

## Where Llama 3 Wins

### 1. Ecosystem Size
Llama has the largest fine-tune ecosystem on Hugging Face by a wide margin. Whatever niche use case you have -- medical, legal, coding, roleplay -- there is likely a Llama fine-tune for it.

### 2. Size Range
Llama comes in 8B, 70B, and 405B variants. The 8B model runs on consumer hardware (8GB RAM), making it the most accessible entry point for local AI. Mistral's smallest competitive model is the 7B, but the real power starts at Mixtral 8x7B (47B total, ~13B active).

### 3. Community Support
More tutorials, more Discord servers, more Stack Overflow answers. If you hit a problem with Llama, someone has already solved it.

### 4. Quantization Quality
Llama models quantize exceptionally well. A 4-bit quantized Llama 70B fits in ~36GB VRAM with minimal quality loss, making it practical on a single high-end GPU.

## Where Mistral Wins

### 1. Multilingual Performance
Mistral significantly outperforms Llama on non-English languages, particularly European languages (French, German, Spanish, Italian). If your application serves a global audience, Mistral is the stronger choice.

### 2. Native Function Calling
Mistral Large 2 supports function calling out of the box -- no fine-tuning needed. This is critical for agent workflows, tool use, and structured output generation.

### 3. Efficiency Per Parameter
Mistral pioneered Mixture of Experts (MoE) with Mixtral. While the total parameter count is high, only a fraction activates per token, making inference faster and cheaper than equivalent dense models.

### 4. Coding Performance
Mistral's Codestral variant is purpose-built for code generation and consistently outperforms Llama on coding benchmarks (HumanEval, MBPP).

## Benchmark Comparison

| Benchmark | Llama 3.1 70B | Mistral Large 2 |
|-----------|---------------|-----------------|
| MMLU | 82.0 | 84.0 |
| HumanEval | 72.0 | 77.0 |
| GSM8K (Math) | 83.7 | 81.0 |
| ARC-Challenge | 85.3 | 86.1 |
| HellaSwag | 85.4 | 86.9 |
| TruthfulQA | 62.1 | 63.4 |

Mistral edges ahead on most benchmarks but requires nearly twice the VRAM.

## Running Both Locally

Both work with [Ollama](/alternative-to/ollama):

\`\`\`bash
# Llama 3
ollama run llama3.1:70b

# Mistral
ollama run mistral-large
\`\`\`

Or try the smaller variants to compare on your hardware:

\`\`\`bash
ollama run llama3.2       # 3B - runs on anything
ollama run mistral        # 7B - good baseline
\`\`\`

## Which Should You Choose?

**Choose Llama 3 if:**
- You need the largest ecosystem of fine-tunes
- You want the widest hardware compatibility (8B model)
- English is your primary language
- You want the most community support and resources
- You need a 405B class model (Llama 3.1 405B)

**Choose Mistral if:**
- Your application is multilingual
- You need native function calling / tool use
- Coding is a primary use case (Codestral)
- You want better benchmark performance per compute dollar (MoE)
- You need strong structured output generation

**For most developers starting out:** Llama 3 is the safer default. Larger community, more resources, and the 8B model lets you prototype on a laptop.

**For production multilingual or agent workloads:** Mistral is the better choice. Native function calling alone saves significant engineering effort.

## The Practical Answer

Run both through [Ollama](/alternative-to/ollama) on your specific use case. Benchmarks only tell part of the story -- the model that produces better outputs for your particular prompts is the right one.

\`\`\`bash
# Test both on the same prompts
ollama run llama3.1:70b "Your test prompt here"
ollama run mistral-large "Your test prompt here"
\`\`\`

Compare [all AI model alternatives](/ai) on The AltStack, or explore [self-hosted deployment guides](/self-hosted) for production setups.`,
    },
    {
        slug: 'gpt4all-vs-ollama-local-ai-comparison',
        title: 'GPT4All vs Ollama: Which Local AI Tool is Better in 2026?',
        description: 'Compare GPT4All and Ollama for running AI models locally. Features, performance, ease of use, and which one fits your workflow.',
        date: '2026-03-01',
        author: 'The AltStack',
        category: 'AI',
        tags: ['AI', 'GPT4All', 'Ollama', 'Local AI', 'LLM', 'Self-Hosted'],
        relatedTools: ['gpt4all', 'ollama', 'lm-studio', 'jan'],
        content: `# GPT4All vs Ollama: Which Local AI Tool is Better in 2026?

[GPT4All](/alternative-to/gpt4all) and [Ollama](/alternative-to/ollama) both let you run large language models locally without sending data to the cloud. But they target different users and take fundamentally different approaches.

**GPT4All** is a desktop application with a chat UI built in -- download it, pick a model, and start chatting. No terminal required.

**Ollama** is a CLI-first tool that exposes an API -- install it, run a command, and integrate it into your applications.

Here is how they compare.

## Quick Comparison

| Feature | GPT4All | Ollama |
|---------|---------|--------|
| Interface | Desktop GUI | CLI + API |
| Open Source | Yes (MIT) | Yes (MIT) |
| Primary Use | Chatting with AI | Development / Integration |
| Docker Support | No | Official image |
| OpenAI API Compatible | No | Yes |
| Model Library | Curated list (~30) | 100+ models |
| GPU Support | NVIDIA, Apple Silicon | NVIDIA, AMD, Apple Silicon |
| Plugins/Extensions | LocalDocs (RAG) | Modelfile customization |
| RAM Usage (idle) | ~500MB | ~150MB |
| Best For | Non-technical users | Developers |

## GPT4All: The Desktop AI Chat App

[GPT4All](/alternative-to/gpt4all) from Nomic AI is designed for people who want to chat with AI models without touching a terminal. It provides:

**Strengths:**
- One-click installer for Windows, macOS, Linux
- Built-in chat interface (no separate UI needed)
- **LocalDocs** -- drag folders into the app and chat with your documents (RAG)
- Curated model list so beginners are not overwhelmed
- Low barrier to entry

**Limitations:**
- No API server for application integration
- Smaller model library than Ollama
- No Docker support for server deployment
- Desktop only -- cannot run headless
- Slower to adopt new model releases

**Install:** Download from [gpt4all.io](https://gpt4all.io) and run the installer.

## Ollama: The Developer's AI Runtime

[Ollama](/alternative-to/ollama) is built for developers who want to integrate local AI into their applications:

**Strengths:**
- OpenAI-compatible API -- swap \`api.openai.com\` for \`localhost:11434\`
- 100+ models available with \`ollama run <model>\`
- Docker support for server deployment
- Lightweight (~150MB idle RAM)
- Modelfiles for custom model configurations
- Fast model switching

**Limitations:**
- No built-in chat UI (pair with [Open WebUI](/alternative-to/open-webui))
- Requires terminal comfort
- No built-in RAG/document chat

**Install:**

\`\`\`bash
curl -fsSL https://ollama.com/install.sh | sh
ollama run llama3.2
\`\`\`

## Performance Comparison

Tested on Apple M2 Pro (16GB) with Llama 3 8B:

| Metric | GPT4All | Ollama |
|--------|---------|--------|
| Cold start | ~8s | ~2s |
| Tokens/sec | 35 | 42 |
| RAM (idle) | 500MB | 150MB |
| RAM (model loaded) | 5.5GB | 5.2GB |
| Time to first token | ~3s | ~1s |

Ollama is consistently faster due to its lighter runtime. GPT4All's Electron-based UI adds overhead.

## Use Case Decision Tree

### "I just want to chat with AI privately"
**Choose GPT4All.** Download, install, pick a model, start chatting. The LocalDocs feature lets you chat with your own files -- no setup required.

### "I'm building an app that needs AI"
**Choose Ollama.** The OpenAI-compatible API means your existing code works with one URL change:

\`\`\`javascript
// Just change the base URL
const response = await fetch('http://localhost:11434/v1/chat/completions', {
  method: 'POST',
  headers: { 'Content-Type': 'application/json' },
  body: JSON.stringify({
    model: 'llama3.2',
    messages: [{ role: 'user', content: 'Hello' }]
  })
});
\`\`\`

### "I want AI on my server"
**Choose Ollama.** Docker support makes server deployment straightforward:

\`\`\`bash
docker run -d -p 11434:11434 --gpus all ollama/ollama
\`\`\`

GPT4All has no server mode.

### "I want to chat with my documents"
**Choose GPT4All** for simple document chat. Its LocalDocs feature handles this without any configuration. For more advanced RAG, pair Ollama with [Open WebUI](/alternative-to/open-webui) which supports document upload.

## Can You Use Both?

Yes. They use the same GGUF model format, so models downloaded for one work with the other. A reasonable setup:

- **GPT4All** on your personal laptop for quick chats and document questions
- **Ollama** on your dev server for API integrations and production workloads

## Other Alternatives Worth Considering

| Tool | Best For |
|------|----------|
| [LM Studio](/alternative-to/lm-studio) | Desktop GUI + API server (best of both) |
| [Jan](/alternative-to/jan) | Beautiful desktop chat UI |
| [LocalAI](/alternative-to/localai) | Multi-modal API (text + images + audio) |

## The Verdict

- **GPT4All** = Best for non-technical users who want private AI chat with document support
- **Ollama** = Best for developers who need an API, Docker deployment, and application integration

If you are reading this on a developer-focused site, Ollama is probably what you want. But recommend GPT4All to your non-technical colleagues who keep asking you about "running ChatGPT locally."

Browse all [AI runner alternatives](/alternative-to/ollama) or explore the [AI Model Matrix](/ai) on The AltStack.`,
    },
    {
        slug: 'how-to-set-up-llm-studio-with-ollama',
        title: 'How to Set Up LM Studio and Ollama for Local AI in 2026',
        description: 'A practical guide to running large language models locally with LM Studio and Ollama. Compare features, hardware requirements, and get started in minutes.',
        date: '2026-02-27',
        author: 'The AltStack',
        category: 'AI',
        tags: ['AI', 'Local AI', 'Ollama', 'LM Studio', 'Self-Hosted'],
        relatedTools: ['ollama', 'lm-studio', 'open-webui', 'jan'],
        content: `# How to Set Up LM Studio and Ollama for Local AI in 2026

Running large language models on your own hardware has gone from a niche experiment to a legitimate production strategy. Whether you want to eliminate per-token API costs, keep sensitive data off third-party servers, or just experiment with open-weight models, two tools dominate the local AI runner space: **Ollama** and **LM Studio**.

This guide walks you through setting up both, compares their strengths, and helps you decide which fits your workflow.

## Why Run AI Models Locally?

Before diving into setup, here is why local inference matters:

- **Zero marginal cost** -- once your hardware is provisioned, every query is free
- **Complete data privacy** -- prompts and outputs never leave your network
- **No rate limits** -- run as many requests as your hardware can handle
- **Offline capability** -- works without internet after the initial model download

The tradeoff is hardware investment. Most capable models need at least 8GB of RAM, and larger models benefit from a dedicated GPU.

## Setting Up Ollama

[Ollama](/alternative-to/ollama) is the most popular open-source AI runner with over 60,000 GitHub stars. It takes a CLI-first approach: one command to install, one command to run any model.

### Installation

**macOS / Linux:**

\`\`\`bash
curl -fsSL https://ollama.com/install.sh | sh
\`\`\`

**Docker (recommended for servers):**

\`\`\`bash
docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama
\`\`\`

**Windows:** Download the installer from [ollama.com](https://ollama.com).

### Running Your First Model

\`\`\`bash
ollama run llama3.2
\`\`\`

That is it. Ollama downloads the model, quantizes it for your hardware, and starts an interactive chat. Want a different model? Just swap the name:

\`\`\`bash
ollama run deepseek-r1:8b
ollama run mistral
ollama run gemma2:9b
\`\`\`

### Using the API

Ollama exposes an OpenAI-compatible API on port 11434:

\`\`\`bash
curl http://localhost:11434/v1/chat/completions \\
  -H "Content-Type: application/json" \\
  -d '{
    "model": "llama3.2",
    "messages": [{"role": "user", "content": "Explain Docker in one paragraph"}]
  }'
\`\`\`

This means any application built for the OpenAI API can switch to Ollama by changing the base URL. No code rewrite needed.

## Setting Up LM Studio

[LM Studio](/alternative-to/lm-studio) takes a different approach -- it provides a polished desktop GUI for downloading, managing, and chatting with models. It is free for personal use but is not open source.

### Installation

Download the installer from [lmstudio.ai](https://lmstudio.ai) for macOS, Windows, or Linux. The app handles everything through a visual interface.

### Key Features

- **Model discovery** -- browse and download models from Hugging Face directly in the app
- **Chat interface** -- a ChatGPT-like UI for interacting with models
- **Local server mode** -- exposes an OpenAI-compatible API (same as Ollama)
- **Hardware profiling** -- automatically selects the best quantization for your system

## Ollama vs LM Studio: Which Should You Choose?

| Feature | Ollama | LM Studio |
|---------|--------|-----------|
| Interface | CLI + API | Desktop GUI + API |
| Open Source | Yes (MIT) | No (free for personal use) |
| Model Library | 100+ models | Hugging Face catalog |
| Docker Support | Official image | No |
| Server Deployment | Excellent | Desktop only |
| GPU Support | NVIDIA, AMD, Apple Silicon | NVIDIA, Apple Silicon |
| Resource Usage | Lightweight | Heavier (Electron app) |

**Choose Ollama if** you want a lightweight, scriptable tool for servers or development. It is the better choice for production deployments, CI/CD pipelines, and headless servers.

**Choose LM Studio if** you prefer a visual interface for exploring models and want a plug-and-play experience on your desktop.

## Adding a Chat UI with Open WebUI

Ollama's CLI is powerful, but for a browser-based chat experience, pair it with [Open WebUI](/alternative-to/open-webui):

\`\`\`bash
docker run -d -p 3000:8080 \\
  --add-host=host.docker.internal:host-gateway \\
  -e OLLAMA_BASE_URL=http://host.docker.internal:11434 \\
  --name open-webui \\
  ghcr.io/open-webui/open-webui:main
\`\`\`

Open WebUI gives you a ChatGPT-like interface with multi-model chat, conversation history, RAG document upload, and user management -- all running on your infrastructure.

## Hardware Recommendations

| Model Size | RAM Required | GPU VRAM | Example Models |
|-----------|-------------|----------|----------------|
| 3B-7B | 8GB+ | 4-6GB | Llama 3.2 3B, Gemma 2 9B |
| 13B-14B | 16GB+ | 8-10GB | Llama 3.2, DeepSeek-R1 14B |
| 30B-70B | 32GB+ | 24-48GB | Llama 3.1 70B, Qwen 72B |
| 100B+ | 64GB+ | 80GB+ | DeepSeek-V3 671B (MoE) |

For most use cases, a 7B-14B model running on a machine with 16GB RAM delivers excellent results for coding assistance, writing, and general Q&A.

## Next Steps

1. Start with Ollama and a small model like \`llama3.2\` to verify your setup
2. Try [Open WebUI](/alternative-to/open-webui) or [Jan](/alternative-to/jan) for a graphical interface
3. Experiment with specialized models: DeepSeek-R1 for reasoning, CodeLlama for coding
4. Consider [self-hosting on a VPS](/self-hosted) for always-on access from any device

The local AI ecosystem is mature enough that there is no reason to send every prompt to a cloud API. Start local, and scale from there.`,
    },
    {
        slug: 'open-source-alternatives-to-deepseek',
        title: '5 Open Source Alternatives to DeepSeek for Local AI in 2026',
        description: 'Looking beyond DeepSeek? Compare the best open-weight LLMs including Llama, Mistral, Qwen, and Gemma for local AI inference.',
        date: '2026-02-25',
        author: 'The AltStack',
        category: 'AI',
        tags: ['AI', 'DeepSeek', 'Open Source', 'LLM', 'AI Models'],
        relatedTools: ['deepseek', 'llama', 'mistral', 'qwen', 'gemma'],
        content: `# 5 Open Source Alternatives to DeepSeek for Local AI in 2026

[DeepSeek](/alternative-to/deepseek) has made waves with its V3 and R1 models -- a 671B Mixture-of-Experts architecture that rivals GPT-4o at a fraction of the inference cost. The R1 model, specifically, pushed open-source reasoning capabilities to new heights.

But DeepSeek is not the only option. The open-weight model ecosystem is deep, and depending on your use case -- coding, multilingual tasks, constrained hardware, or raw reasoning -- a different model might be the better fit.

Here are five alternatives worth evaluating.

## 1. Meta Llama 3 Series

[Llama](/alternative-to/llama) is the most widely deployed open-weight model family. Meta's Llama 3 series comes in multiple sizes (8B, 70B, 405B) and has the largest ecosystem of fine-tunes, tools, and community support.

**Why choose Llama over DeepSeek:**

- Largest community and ecosystem -- more fine-tunes available on Hugging Face than any other model
- Llama 3.1 405B competes with GPT-4 on many benchmarks
- Excellent quantization support across all sizes
- Permissive license (Llama Community License) for most commercial uses

**Best for:** General-purpose tasks, teams that want maximum community support and fine-tune availability.

| Spec | Llama 3.1 70B | DeepSeek-V3 |
|------|---------------|-------------|
| Parameters | 70B | 671B (37B active) |
| Context | 128K | 128K |
| License | Llama Community | MIT |
| VRAM (FP16) | 40GB+ | 160GB+ |

## 2. Mistral Large 2

[Mistral](/alternative-to/mistral) is the leading European AI lab, and their Large 2 model (123B) punches well above its weight class. Mistral pioneered efficient architectures that deliver high performance per parameter.

**Why choose Mistral over DeepSeek:**

- Superior multilingual performance across European languages
- 128K context with native function-calling support
- Strong coding performance with Codestral variant
- More efficient inference for the parameter count

**Best for:** European teams needing multilingual support, function-calling workflows, and coding assistance.

## 3. Alibaba Qwen 2.5

[Qwen](/alternative-to/qwen) from Alibaba is arguably the most underrated model family. Qwen 2.5 comes in sizes from 0.5B to 72B and consistently tops benchmarks for its size class, particularly in mathematics and coding.

**Why choose Qwen over DeepSeek:**

- Best-in-class performance at small model sizes (7B, 14B)
- Outstanding math and coding benchmarks
- 128K context window across all sizes
- Apache 2.0 license (more permissive than many alternatives)

**Best for:** Resource-constrained deployments, math-heavy applications, and teams that need strong small models.

## 4. Google Gemma 2

[Gemma](/alternative-to/gemma) is Google's open-weight model series. The Gemma 2 family (9B, 27B) is optimized for efficiency and delivers remarkable performance for its size, making it ideal for edge deployment and laptops.

**Why choose Gemma over DeepSeek:**

- Outstanding performance-per-parameter ratio
- Optimized for consumer hardware (runs well on 8GB RAM)
- Google's safety fine-tuning and RLHF
- Small enough to run on mobile devices and edge hardware

**Best for:** Edge deployment, mobile integration, and teams with limited GPU resources.

## 5. Microsoft Phi-3

Phi-3 from Microsoft proves that small models can be surprisingly capable. The Phi-3 Mini (3.8B) model was trained on high-quality data and achieves performance that rivals much larger models on reasoning tasks.

**Why choose Phi-3 over DeepSeek:**

- Runs on almost any hardware (3.8B parameters)
- Remarkably strong reasoning for its size
- MIT License
- Fast inference -- ideal for real-time applications

**Best for:** Edge computing, mobile apps, low-latency applications, and environments where every megabyte counts.

## Comparison Table

| Model | Params | Context | VRAM (FP16) | License | Strength |
|-------|--------|---------|-------------|---------|----------|
| DeepSeek-V3 | 671B (37B active) | 128K | 160GB+ | MIT | Reasoning, Cost efficiency |
| Llama 3.1 70B | 70B | 128K | 40GB+ | Community | Ecosystem, General purpose |
| Mistral Large 2 | 123B | 128K | 80GB+ | Research | Multilingual, Function calling |
| Qwen 2.5 72B | 72B | 128K | 40GB+ | Apache 2.0 | Math, Coding |
| Gemma 2 27B | 27B | 8K | 16GB+ | Gemma License | Efficiency, Edge |
| Phi-3 Mini | 3.8B | 128K | 4GB | MIT | Tiny footprint, Reasoning |

## How to Run Any of These Locally

All of these models work with [Ollama](/alternative-to/ollama):

\`\`\`bash
ollama run llama3.1:70b
ollama run mistral-large
ollama run qwen2.5:72b
ollama run gemma2:27b
ollama run phi3
\`\`\`

Or use them through [LM Studio](/alternative-to/lm-studio) if you prefer a graphical interface.

## Which Should You Choose?

- **Maximum capability, no hardware constraints:** DeepSeek-V3 or Llama 3.1 405B
- **Best balance of size and performance:** Llama 3.1 70B or Qwen 2.5 72B
- **Limited hardware (16GB RAM):** Gemma 2 27B or Qwen 2.5 14B
- **Edge/mobile deployment:** Phi-3 Mini or Gemma 2 9B
- **Multilingual requirements:** Mistral Large 2
- **Best reasoning:** DeepSeek-R1 or Qwen 2.5 with math fine-tune

The open-weight ecosystem has reached a point where there is a high-quality model for virtually every use case and hardware profile. The best approach is to benchmark 2-3 candidates on your specific tasks using Ollama, then deploy the winner.`,
    },
    {
        slug: 'best-self-hosted-email-servers-2026',
        title: 'Best Self-Hosted Email Servers in 2026: Take Back Your Inbox',
        description: 'Compare the top self-hosted email server solutions including Mailcow, Stalwart, and Mailu. Full setup guide with Docker deployment options.',
        date: '2026-02-20',
        author: 'The AltStack',
        category: 'Self-Hosting',
        tags: ['Email', 'Self-Hosted', 'Docker', 'Privacy', 'Communication'],
        relatedTools: ['mattermost', 'rocketchat', 'jitsi-meet'],
        content: `# Best Self-Hosted Email Servers in 2026: Take Back Your Inbox

Email remains the most critical communication infrastructure for any organization. Yet most teams hand this infrastructure to Google Workspace ($7/user/month) or Microsoft 365 ($6/user/month), accumulating significant costs at scale while surrendering complete control over their communication data.

Self-hosting email in 2026 is more viable than ever. Modern solutions package the full email stack -- SMTP, IMAP, webmail, spam filtering, DKIM/SPF/DMARC -- into Docker containers that deploy in minutes. Here are the best options.

## Why Self-Host Email?

- **Data sovereignty** -- every email stays on your infrastructure
- **No per-user pricing** -- host 10 or 10,000 accounts for the same infrastructure cost
- **Custom domains** -- unlimited aliases and catch-all addresses
- **Compliance** -- meet data residency requirements for GDPR, HIPAA, and government contracts
- **No vendor lock-in** -- standard protocols mean you can migrate anytime

The main challenge is deliverability: ensuring your emails actually reach recipients' inboxes. This requires proper DNS configuration (SPF, DKIM, DMARC), a clean IP reputation, and ongoing monitoring. All the solutions below handle the technical setup, but you will need a VPS with a clean IP address.

## 1. Mailcow: Dockerized

Mailcow is the most feature-complete self-hosted email suite. It bundles Postfix (SMTP), Dovecot (IMAP), SOGo (webmail/calendar/contacts), Rspamd (spam filtering), and a comprehensive admin UI into a single Docker Compose deployment.

**Strengths:**

- All-in-one solution: email, calendar, contacts, and admin panel
- SOGo webmail with Exchange ActiveSync support
- Built-in two-factor authentication
- Automatic DKIM key generation
- Let's Encrypt SSL integration
- Detailed per-domain and per-mailbox analytics

**Considerations:**

- Requires 2GB+ RAM minimum (4GB recommended)
- Complex stack means more potential failure points
- Updates require following the official update procedure

**Quick Deploy:**

\`\`\`bash
git clone https://github.com/mailcow/mailcow-dockerized
cd mailcow-dockerized
./generate_config.sh
docker compose up -d
\`\`\`

**Best for:** Organizations that want a complete groupware solution with email, calendars, and contacts in one package.

## 2. Stalwart Mail Server

Stalwart is the newest contender and arguably the most technically impressive. Written in Rust, it is a single-binary mail server that handles SMTP, IMAP, JMAP, and includes built-in spam filtering -- all without the complexity of bundling multiple services.

**Strengths:**

- Single binary -- dramatically simpler than multi-container solutions
- JMAP support (the modern replacement for IMAP)
- Built-in full-text search
- Extremely low resource usage (runs on 512MB RAM)
- Written in Rust for memory safety and performance
- Active development with frequent releases

**Considerations:**

- No built-in webmail (pair with Roundcube or another client)
- Newer project with a smaller community
- Configuration is TOML/YAML based (no admin GUI yet)

**Best for:** Technically-minded administrators who want a lean, modern, high-performance mail server.

## 3. Mailu

Mailu positions itself as the simple, full-featured mail server. It provides a clean setup wizard and admin interface that makes initial configuration straightforward, even for those new to email hosting.

**Strengths:**

- Web-based setup wizard for initial configuration
- Clean admin interface for managing domains and users
- Built-in webmail (Roundcube or Rainloop)
- Automatic SSL with Let's Encrypt
- Reasonable resource usage (1GB+ RAM)
- Good documentation for beginners

**Considerations:**

- Fewer advanced features than Mailcow
- Smaller plugin/integration ecosystem
- Less granular control over individual components

**Best for:** Teams that want a straightforward, working email server without extensive configuration.

## Comparison Table

| Feature | Mailcow | Stalwart | Mailu |
|---------|---------|----------|-------|
| Webmail | SOGo (full groupware) | None (BYO client) | Roundcube |
| Calendar/Contacts | Yes (SOGo) | No | No |
| Admin UI | Comprehensive | None (CLI/config) | Clean web UI |
| Min. RAM | 2-4GB | 512MB | 1GB |
| Language | Multi-service (C, Lua, PHP) | Rust | Multi-service (Python) |
| JMAP Support | No | Yes | No |
| Spam Filter | Rspamd | Built-in | Rspamd |
| Setup Difficulty | Moderate | Advanced | Easy |

## Essential DNS Configuration

Regardless of which server you choose, proper DNS records are critical for deliverability:

\`\`\`
# MX Record
@ MX 10 mail.yourdomain.com

# SPF -- authorize your server to send email
@ TXT "v=spf1 mx a -all"

# DKIM -- your server generates this key
mail._domainkey TXT "v=DKIM1; k=rsa; p=YOUR_PUBLIC_KEY"

# DMARC -- tell receivers how to handle failures
_dmarc TXT "v=DMARC1; p=quarantine; rua=mailto:admin@yourdomain.com"
\`\`\`

## Choosing the Right VPS

For email hosting, your VPS provider matters more than for most workloads because IP reputation directly affects deliverability:

- Choose a provider that allows port 25 (SMTP) outbound -- some block it by default
- Request a dedicated IP address if possible
- Check the IP against blacklists before committing
- Budget $10-20/month for a capable VPS (2 vCPU, 4GB RAM, 80GB SSD)

## The Bottom Line

Self-hosted email is not for everyone. If you have a small team and no compliance requirements, Google Workspace is convenient and reliable. But if you are scaling past 20+ users, handling sensitive data, or simply want to own your communication infrastructure, these solutions deliver enterprise-grade email at infrastructure cost only.

Start with **Mailu** if you want the easiest setup, **Mailcow** if you need groupware (calendars + contacts), or **Stalwart** if you want the most modern and resource-efficient server.

For team messaging beyond email, pair your mail server with [Mattermost](/alternative-to/mattermost) or [Rocket.Chat](/alternative-to/rocketchat) for real-time communication, and [Jitsi Meet](/alternative-to/jitsi-meet) for video calls -- all self-hosted.`,
    },
    {
        slug: 'best-open-source-project-management-tools-2026',
        title: 'Best Open Source Project Management Tools in 2026',
        description: 'Compare Plane, Taiga, and other open-source alternatives to Jira and Asana. Feature comparison, self-hosting guides, and honest recommendations.',
        date: '2026-02-15',
        author: 'The AltStack',
        category: 'Tools',
        tags: ['Project Management', 'Open Source', 'Jira', 'Self-Hosted', 'Productivity'],
        relatedTools: ['plane', 'taiga', 'jira'],
        content: `# Best Open Source Project Management Tools in 2026

Project management software is one of the most overpriced categories in SaaS. [Jira](/alternative-to/jira) charges $8.15/user/month (Standard) and quickly becomes complex enough to need its own administrator. Asana, Monday.com, and Linear follow similar per-seat models that scale linearly with your team size.

The open-source alternatives have matured significantly. Tools like [Plane](/alternative-to/plane) and [Taiga](/alternative-to/taiga) now offer the core workflows -- kanban boards, sprints, issue tracking, and team collaboration -- without the licensing overhead. Here is how they compare.

## 1. Plane -- The Modern Jira Killer

[Plane](/alternative-to/plane) is the standout in this category. With over 45,000 GitHub stars, it has emerged as the open-source answer to Jira and Linear, combining a clean interface with serious project management capabilities.

**What makes Plane stand out:**

- **Speed** -- sub-100ms interactions that make Jira feel sluggish by comparison
- **Modern UI** -- inspired by Linear's design philosophy, with keyboard shortcuts throughout
- **Cycles and modules** -- sprint planning without Jira's configuration overhead
- **Built-in views** -- spreadsheet, board, list, and timeline views out of the box
- **Self-hostable** -- full Docker Compose deployment with PostgreSQL and Redis

**Self-Hosting Plane:**

\`\`\`bash
git clone https://github.com/makeplane/plane.git
cd plane
./setup.sh
docker compose up -d
\`\`\`

After deployment, access the web interface at \`http://localhost\` and create your workspace.

**Best for:** Teams migrating from Jira or Linear who want a familiar, modern interface without per-seat costs.

## 2. Taiga -- Agile-First Project Management

[Taiga](/alternative-to/taiga) has been in the open-source project management space longer than most competitors. It provides a comprehensive Agile toolkit with beautiful kanban and scrum boards, making it a solid choice for teams that follow Agile methodologies strictly.

**What makes Taiga stand out:**

- **Full Agile support** -- epics, user stories, sprints, and story points
- **Built-in wiki** -- project documentation alongside your tasks
- **Kanban and Scrum boards** -- drag-and-drop with swimlanes
- **Issue tracking** -- with custom fields, tags, and priorities
- **Import from Jira** -- migration tools for switching

**Considerations:**

- Setup is more complex than Plane
- UI feels slightly dated compared to newer tools
- Smaller community and slower development pace

**Best for:** Agile teams that need epics, user stories, and full scrum support with built-in documentation.

## 3. OpenProject

OpenProject is the enterprise-focused option in the open-source project management space. It is the most feature-rich tool on this list, offering Gantt charts, time tracking, cost reporting, and meeting management alongside standard issue tracking.

**What makes OpenProject stand out:**

- **Gantt charts** -- real project timeline visualization (rare in open-source tools)
- **Time tracking and budgets** -- built-in cost management
- **BIM support** -- for construction and engineering teams
- **Enterprise features** -- LDAP, SAML, and comprehensive access controls
- **EU-hosted cloud option** -- GDPR-compliant managed hosting

**Considerations:**

- Steeper learning curve than Plane or Taiga
- Some enterprise features require the paid edition
- Resource-heavy deployment (4GB+ RAM recommended)

**Best for:** Larger organizations that need Gantt charts, time tracking, and enterprise access controls.

## Comparison Table

| Feature | Plane | Taiga | OpenProject |
|---------|-------|-------|-------------|
| UI Design | Modern (Linear-like) | Classic | Enterprise |
| Kanban Board | Yes | Yes | Yes |
| Scrum/Sprints | Cycles | Full Scrum | Yes |
| Gantt Charts | Timeline view | No | Yes |
| Wiki/Docs | Pages | Built-in | Yes |
| Time Tracking | No | No | Yes |
| GitHub Stars | 45,000+ | 800+ | 9,000+ |
| Language | TypeScript | Python | Ruby |
| License | AGPL-3.0 | MPL 2.0 | GPL-3.0 |
| Self-Host RAM | 2GB+ | 2GB+ | 4GB+ |

## What About Linear, ClickUp, and Notion?

These proprietary tools are popular for good reason -- their UX is polished. But the cost scales:

| Tool | Per User/Month | 50 Users/Year |
|------|---------------|---------------|
| Jira Standard | $8.15 | $4,890 |
| Linear | $8 | $4,800 |
| Asana Business | $24.99 | $14,994 |
| Monday.com Pro | $16 | $9,600 |
| **Plane (self-hosted)** | **$0** | **$0 + server costs** |

A $20/month VPS can comfortably run Plane for a team of 50+. That is $240/year versus $4,890+ for Jira.

## Migration Strategy

Moving from Jira to an open-source alternative does not have to be all-or-nothing:

1. **Start with a single team** -- migrate one team or project first
2. **Export your data** -- most tools support CSV import, Taiga has Jira importers
3. **Run in parallel** -- keep the old tool accessible during the transition
4. **Train on differences** -- the core concepts (issues, boards, sprints) transfer directly

## Recommendation

For most teams in 2026, **Plane is the clear winner**. Its combination of modern design, speed, and comprehensive features makes it the closest open-source equivalent to Linear or Jira -- without the per-seat costs. Deploy it with Docker, point your team at it, and you will likely wonder why you were paying for Jira.

If your team is strictly Agile with sprints and user stories, give **Taiga** a look. And if you need enterprise features like Gantt charts and time tracking, **OpenProject** fills that niche.

Explore all [project management alternatives on The AltStack](/alternative-to/jira).`,
    },
    {
        slug: 'plausible-vs-matomo-vs-umami-2026',
        title: 'Plausible vs Matomo vs Umami: Choosing an Open-Source Google Analytics Alternative in 2026',
        description: 'Compare Plausible, Matomo, and Umami as privacy-first, open-source Google Analytics replacements. Features, deployment, and GDPR compliance guide.',
        date: '2026-03-07',
        author: 'The AltStack',
        category: 'Analytics',
        tags: ['Analytics', 'Privacy', 'Open Source', 'Self-Hosted', 'Google Analytics'],
        relatedTools: ['plausible', 'matomo', 'umami', 'google-analytics'],
        content: `# Plausible vs Matomo vs Umami: Choosing an Open-Source Google Analytics Alternative in 2026

Google Analytics (GA4) has become increasingly complex, privacy-intrusive, and heavy. With the rise of GDPR and CCPA, many website owners are looking for simpler, privacy-first alternatives that they can own and control.

Enter the world of open-source analytics. Tools like [Plausible](/alternative-to/plausible), [Matomo](/alternative-to/matomo), and [Umami](/alternative-to/umami) offer powerful tracking without the baggage of big tech.

## Why Move Away from Google Analytics?

- **Privacy Compliance** – Most open-source tools don't use cookies and don't track personal data, making them GDPR/CCPA compliant out of the box.
- **Performance** – GA4's script is large. Plausible and Umami use tiny scripts (<1KB) that won't slow down your site.
- **Data Ownership** – When you self-host, your data stays on your server, not Google's.
- **Ad-Blocker Resilience** – Since you can proxy these tools through your own domain, they are less likely to be blocked by browser extensions.

## 1. Plausible: The Minimalist Champion

[Plausible](/alternative-to/plausible) is the gold standard for lightweight analytics. It provides a single-page dashboard with the metrics that actually matter: visitors, source, pages, and goals.

**Pros:**
- Extremely lightweight (<1KB script).
- No cookies, no personal data tracking.
- Beautiful, intuitive dashboard.
- Public dashboard option for transparency.

**Cons:**
- Limited advanced features (no heatmaps or session recordings).
- Self-hosting requires ClickHouse (can be resource-heavy for small VPS).

## 2. Matomo: The Enterprise Alternative

If you need a 1:1 replacement for Google Analytics, [Matomo](/alternative-to/matomo) (formerly Piwik) is the answer. It is used by organizations like the European Commission and NASA.

**Pros:**
- Feature-rich: Heatmaps, session recordings, A/B testing, and funnels.
- Media analytics (track video/audio engagement).
- 100% data ownership with powerful privacy controls.
- Marketplace for plugins and themes.

**Cons:**
- Steeper learning curve.
- Dashboard can feel dated compared to Plausible/Umami.
- Requires more maintenance (PHP/MySQL setup).

## 3. Umami: The Modern Developer Choice

[Umami](/alternative-to/umami) is the new kid on the block, written in Next.js. It strikes a perfect balance between Plausible's simplicity and Matomo's customizability.

**Pros:**
- Modern, clean UI.
- Very easy to self-host with Docker and Postgres.
- Supports unlimited websites and users.
- Fast and lightweight.

**Cons:**
- Lacks the advanced enterprise features of Matomo.
- Smaller ecosystem than the older alternatives.

## Comparison Table

| Feature | Plausible | Matomo | Umami |
|---------|-----------|--------|-------|
| Script Size | < 1KB | ~20KB | < 2KB |
| Cookies | No | Optional | No |
| Heatmaps | No | Yes | No |
| SQL Database | ClickHouse | MySQL | Postgres/MySQL |
| License | AGPL-3.0 | GPL-3.0 | MIT |

## The Verdict

- **Choose Plausible** if you want the simplest, "set it and forget it" analytics for your blog or startup.
- **Choose Matomo** if you are an enterprise or e-commerce site that needs deep behavioral insights and heatmaps.
- **Choose Umami** if you want a modern, fast, self-hosted solution that's easy to deploy and maintain.

Deploy your choice today and take back control of your website's data. Explore more [Analytics alternatives](/alternative-to/google-analytics) on The AltStack.`,
    },
    {
        slug: 'best-open-source-firebase-alternatives-2026',
        title: 'Best Open Source Firebase Alternatives in 2026 (BaaS)',
        description: 'Explore the top open-source alternatives to Firebase. Compare Supabase, Appwrite, and PocketBase for your next web or mobile app.',
        date: '2026-03-07',
        author: 'The AltStack',
        category: 'Backend',
        tags: ['Firebase', 'Supabase', 'Appwrite', 'PocketBase', 'BaaS', 'Open Source'],
        relatedTools: ['supabase', 'appwrite', 'pocketbase', 'firebase'],
        content: `# Best Open Source Firebase Alternatives in 2026 (BaaS)

Firebase has long been the go-to for rapid app development, providing a suite of tools for auth, database, and storage. However, its NoSQL limitations, proprietary nature, and "success tax" (scaling costs) have led many to seek open-source alternatives.

In 2026, the open-source Backend-as-a-Service (BaaS) ecosystem is stronger than ever.

## 1. Supabase: The Postgres Powerhouse

[Supabase](/alternative-to/supabase) is the most popular Firebase alternative. It provides a full suite of features built on top of the world's most advanced open-source database: PostgreSQL.

**Key Features:**
- **Database:** Full Postgres with Row Level Security (RLS).
- **Auth:** Email, social, and phone auth built-in.
- **Storage:** S3-compatible file storage.
- **Real-time:** Real-time database changes via websockets.
- **Edge Functions:** Run Typescript logic globally.

**Best for:** Projects that need a relational database and want to scale to millions of users on a rock-solid foundation.

## 2. Appwrite: The Unified Platform

[Appwrite](/alternative-to/appwrite) is a highly flexible, platform-agnostic BaaS that abstracts the complexity of building a backend. It's designed to run anywhere Docker is supported.

**Key Features:**
- **Databases:** Flexible document-based storage.
- **Functions:** Supports 15+ runtimes (Python, JS, Go, etc.).
- **Messaging:** Built-in push notifications, email, and SMS.
- **Privacy:** Designed with security and privacy as first-class citizens.

**Best for:** Developers who want a unified experience with excellent SDK support for web, mobile, and flutter.

## 3. PocketBase: The Indie Hacker's Dream

[PocketBase](/alternative-to/pocketbase) is a unique, single-file backend written in Go. It uses SQLite under the hood and is incredibly fast and efficient.

**Key Features:**
- **Single Executable:** No Docker needed (though supported).
- **SQLite:** Fast, reliable, and easy to backup.
- **Embedded UI:** Built-in dashboard for managing data and auth.
- **Real-time:** Built-in subscriptions for data updates.

**Best for:** Small to medium projects, MVPs, and developers who want the simplest possible deployment (literally one file).

## Quick Comparison Table

| Feature | Supabase | Appwrite | PocketBase |
|---------|----------|----------|------------|
| Database | PostgreSQL | MariaDB/NoSQL | SQLite |
| Real-time | Yes | Yes | Yes |
| Functions | Yes | Yes | Golang hooks |
| Hosting | Cloud/Self | Cloud/Self | Self (mostly) |
| License | Apache 2.0 | BSD-3 | MIT |

## Which to Choose?

- If you love **SQL** and want enterprise-grade scalability: [**Supabase**](/alternative-to/supabase).
- If you want a **full-featured platform** with great cross-platform SDKs: [**Appwrite**](/alternative-to/appwrite).
- If you want a **lightweight, simple** backend for an MVP or side project: [**PocketBase**](/alternative-to/pocketbase).

Stop worrying about vendor lock-in and start building with open-source foundations. Check out our [self-hosted guides](/self-hosted) to get these running on your own hardware.`,
    },
    {
        slug: 'self-host-vaultwarden-password-manager-guide',
        title: 'How to Self-Host Your Own Password Manager: Vaultwarden Guide',
        description: 'Learn how to self-host Vaultwarden (formerly Bitwarden_RS). The ultimate guide to owning your passwords safely with Docker.',
        date: '2026-03-07',
        author: 'The AltStack',
        category: 'Security',
        tags: ['Vaultwarden', 'Bitwarden', 'Password Manager', 'Self-Hosted', 'Security'],
        relatedTools: ['bitwarden', '1password', 'lastpass'],
        content: `# How to Self-Host Your Own Password Manager: Vaultwarden Guide

Trusting a third party with your passwords is a significant risk. Major leaks in the past have proven that even the most secure companies can be vulnerable. Self-hosting your password manager ensures you hold the keys to your own digital life.

[Vaultwarden](/alternative-to/bitwarden) is an unofficial, lightweight Rust implementation of the Bitwarden API. It's fully compatible with the official Bitwarden apps while using significantly fewer resources.

## Why Vaultwarden?

- **Lightweight:** Runs on a Raspberry Pi or a $5 VPS (uses <200MB RAM).
- **Compatible:** Works with official Bitwarden browser extensions and mobile apps.
- **Unlocks Premium Features:** Includes "premium" features like TOTP generation and organization sharing for free.
- **Data Privacy:** Your encrypted database never leaves your network.

## Prerequisites

- A VPS or home server with Docker installed.
- A domain name (required for SSL).
- A reverse proxy (Caddy, Nginx, or Traefik).

## Step 1: Docker Compose Setup

Create a directory for Vaultwarden and paste the following into \`docker-compose.yml\`:

\`\`\`yaml
services:
  vaultwarden:
    image: vaultwarden/server:latest
    container_name: vaultwarden
    restart: always
    environment:
      - SIGNUPS_ALLOWED=true # Set to false after creating your account
      - DOMAIN=https://pass.yourdomain.com
    volumes:
      - ./vw-data:/data
    ports:
      - 8080:80
\`\`\`

## Step 2: SSL is Mandatory

You should **never** access your password manager over an unencrypted connection (HTTP). Use Caddy for the simplest SSL setup:

\`\`\`caddy
pass.yourdomain.com {
    reverse_proxy localhost:8080
}
\`\`\`

## Step 3: Secure Your Instance

Once you have registered your account, edit your \`docker-compose.yml\` and set \`SIGNUPS_ALLOWED=false\`. This prevents random people from creating accounts on your server.

## Step 4: Backups (Crucial!)

Since you are now your own sysadmin, you are responsible for backups. Vaultwarden stores everything in an SQLite database inside the \`vw-data\` folder.

**Simple Backup Script:**
\`\`\`bash
# Stop the container
docker stop vaultwarden
# Copy the data folder
cp -r ./vw-data ./backups/backup_$(date +%F)
# Start the container
docker start vaultwarden
\`\`\`

## Conclusion

Self-hosting Vaultwarden gives you the premium experience of a modern password manager with the absolute privacy of self-hosting. It is one of the most rewarding self-hosted projects you can undertake.

Compare [other password managers](/alternative-to/bitwarden) on The AltStack today!`,
    },
    {
        slug: 'outline-vs-hedgedoc-vs-bookstack-notion-alternatives',
        title: 'Outline vs Hedgedoc vs BookStack: Open Source Notion Replacements',
        description: 'Finding the best open-source Notion alternative. Compare Outline, BookStack, and HedgeDoc for team documentation and wikis.',
        date: '2026-03-07',
        author: 'The AltStack',
        category: 'Productivity',
        tags: ['Notion', 'Outline', 'BookStack', 'HedgeDoc', 'Wiki', 'Open Source'],
        relatedTools: ['outline', 'bookstack', 'hedgedoc', 'notion', 'confluence'],
        content: `# Outline vs Hedgedoc vs BookStack: Open Source Notion Replacements

Notion transformed how we think about documentation, but its proprietary format and lack of a true offline mode are significant drawbacks for many. Open-source alternatives offer similar collaborative power with the added benefit of data sovereignty.

## 1. Outline: The Modern & Beautiful Choice

[Outline](/alternative-to/outline) is widely considered the best looking open-source knowledge base. It feels very similar to Notion with its slash commands and clean typography.

**Key Features:**
- Fast, real-time collaboration.
- Minimalist, distraction-free editor.
- Powerful search and file organization.
- Support for 20+ integrations.

**Best for:** Modern teams who want a polished experience and don't mind a slightly complex setup (requires OIDC/Slack for login and S3 for storage).

## 2. BookStack: The Structured Wiki

[BookStack](/alternative-to/bookstack) takes a unique approach by organizing content into Shelves, Books, and Chapters. This opinionated hierarchy is great for keeping long-term documentation organized.

**Key Features:**
- Easy to use and set up (PHP/MySQL).
- Powerful permission system.
- Built-in drawing tool (Draw.io integration).
- Simple Markdown or WYSIWYG editor.

**Best for:** Organizations that need a traditional "Wiki" feel and clear structure for company handbooks or technical manuals.

## 3. HedgeDoc: The Real-time Markdown Editor

[HedgeDoc](/alternative-to/hedgedoc) (formerly CodiMD) is a versatile markdown editor that allows multiple people to write at the same time.

**Key Features:**
- Pure Markdown support.
- Real-time collaborative editing (Google Docs style but for MD).
- Presentation mode (convert MD to slides).
- Lightweight and fast.

**Best for:** Developers and technical teams who purely live in Markdown and need a place for quick notes and meeting minutes.

## Feature Overview Table

| Feature | Outline | BookStack | HedgeDoc |
|---------|---------|-----------|----------|
| Editor | Block/Notion-like | WYSIWYG/MD | Markdown |
| Hierarchy | Folders/Nested | Shelves/Books | Flat/Permissions |
| Real-time | Yes | No (Drafts only) | Yes |
| Auth | OIDC/Slack/GSuite | Internal/OIDC/LDAP | Internal/LDAP |
| License | BSL (Open Source) | MIT | AGPL-3.0 |

## The Verdict

- **For the best UI:** Go with [**Outline**](/alternative-to/outline).
- **For structured business documentation:** Go with [**BookStack**](/alternative-to/bookstack).
- **For technical collaborative writing:** Go with [**HedgeDoc**](/alternative-to/hedgedoc).

Owning your team's knowledge is the first step toward a sovereign technical stack. Explore more [Notion alternatives](/alternative-to/notion) and [Project Management tools](/alternative-to/jira) on The AltStack.`,
    },
];

export function getBlogPost(slug: string): BlogPost | undefined {
    return blogPosts.find(p => p.slug === slug);
}

export function getAllBlogPosts(): BlogPost[] {
    return [...blogPosts].sort((a, b) => new Date(b.date).getTime() - new Date(a.date).getTime());
}
