Solo developer builds cross-platform desktop AI agent with mobile remote control in 3 weeks, ships to 40+ countries
Remember the kid who fixed everyone's dial-up and HDMI cables? 30 years later, he's back — this time shipping a desktop AI agent with a mobile remote control, solo, in 3 weeks. The project is called Skales, and it's already gathering steam.
What it is
Skales is a native desktop agent (macOS signed, Windows, Linux) with 139+ tools. Bring your own API keys. Local-first. Free. You use it directly on your desktop, or pair it with a mobile companion app (Android Closed Testing on Play Store, iOS on TestFlight) via QR through an encrypted relay. From your couch, you can command your desktop in another room to read files, send emails, run code — whatever you want. The mobile app can also run standalone and local with Gemma.
Key details from the build
- Timeline: Started rebuilding a killed project from December 25. Previous version was a Laravel SaaS — "bloated, wrong shape." This time: native desktop agent. Three weeks from start to shipping.
- Tech stack: Desktop agent is native (not Electron, not a web wrapper). Signed for macOS. Also runs on Windows and Linux.
- Tools: 139+ tools available. BYOK (bring your own keys). Local-first design means no vendor lock-in for LLM access.
- Mobile pairing: QR code + encrypted relay. Phone pairs with desktop. Can also be used standalone with Gemma locally.
- Reach: Active users from 40+ countries and growing.
- Testing hiccup: At 00:30, Play Console flagged "12,522 devices no longer supported." Fixed quickly with help from Claude Opus and Sonnet. Green light for closed testing.
How Claude helped
The developer credits Claude for cutting development time by roughly 3x. Without it, the project would have taken three times as long. In particular, Opus and Sonnet helped quickly resolve the Play Store compatibility issue late at night.
Who it's for
Developers who want a free, local-first desktop AI agent they can control remotely from their phone, with full control over their own API keys and models.
Check out the project on GitHub: github.com/skalesapp/skales
📖 Read the full source: r/ClaudeAI
👀 See Also
Claude AI Session Compaction Issues and Workarounds
Default compaction in Claude AI sessions can degrade retrieval accuracy from ~9.75/10 to ~5/10, causing hallucinations. The user tested with 418K tokens and found manual compaction using Opus maintains accuracy while default compaction fails.
Cowork vs. Claude Chat: Document Extraction Accuracy Comparison
A developer tested Claude.ai chat and Cowork on extracting data from 140+ page financial PDFs using identical prompts. Chat produced institutional-grade results with self-correction and zero errors across 150+ data points, while Cowork fabricated reconciling line items, reversed unit counts, and had prior-year column contamination.
Scaling Karpathy's Autoresearch with 16 GPUs: Results and Methods
The SkyPilot team gave Claude Code access to 16 GPUs on a Kubernetes cluster to run Karpathy's Autoresearch project. Over 8 hours, the agent submitted ~910 experiments, reduced validation bits per byte from 1.003 to 0.974 (2.87% improvement), and reached the best validation loss 9x faster than sequential execution.
Developer shares solution for Claude AI ignoring rules beyond 50-count threshold
A developer reports Claude Code started silently dropping rules once their shared rule set exceeded approximately 50 items, particularly during frontend-heavy tasks. They built a hook that scans prompts and loads only 2-3 relevant rules based on keyword matching.