Upscayl is a FOSS desktop application that upscales low-resolution images locally using Real-ESRGAN models. It wraps a C++ Vulkan engine in an Electron frontend, processing files entirely on your own hardware rather than routing them through a cloud API. Packages are provided for Linux, macOS, and Windows.
It is worth keeping around if you need to bulk-enhance graphics or UI assets locally without paying per-image subscription fees. The main catch is hardware dependency: it requires a dedicated Vulkan-compatible GPU, and integrated graphics chips often fail to run the models.
Hyprland is a dynamic tiling Wayland compositor written in C++ for Linux, built on its own rendering engine rather than depending on wlroots or libweston. It replaces traditional desktop environments or older tilers like Sway, adding fluid window animations, dynamic layouts, and a built-in plugin system. Configuration files reload instantly upon saving, and the compositor includes socket-based IPC along with explicit tearing support for gaming.
It is worth installing if you want a modern tiling desktop that looks polished out of the box without writing custom layout scripts. The catch is its rapid development pace, which leads to frequent updates and occasionally broken configuration syntax between releases. It is not suitable if you want a minimalist tiler like dwl or require rock-solid ABI stability.
SatpamAI (satpam is Indonesian for “security guard”) is a Claude Code skill that acts as a mandatory checkpoint before the AI runs any other skill. The premise: third-party AI skills are untrusted artifacts that can hide prompt injection, malware, hardcoded secrets, or supply-chain compromise — so inspect first, act last. Its motto: “No artifact earns trust. Trust is proved by evidence.”
It runs an 8-phase security gate (classify → threat-assess → inspect → decide → controlled execute → verify → audit) and a 10-layer malware analysis (provenance, static code analysis, secret scanning, dependency and permission auditing, integrity checks). A dedicated engine hunts for prompt injection — override attempts, authority impersonation, bypass requests, obfuscated multi-vector payloads — and everything gets a risk rating from PASS up to CRITICAL / UNKNOWN. Crucially it is static-only (“never execute untrusted code to test if it is malicious”) and it fails closed: uncertainty blocks.
One design choice worth noting: “prior session approval means nothing here” — each decision stands on its own evidence, with no grandfathered trust. Install via claude skill install <repo> (or /satpamai, or auto-load from CLAUDE.md). From the same author as SANITY; MIT licensed.
SANITY is a Claude Code “skill” — a drop-in instruction module — built to stop two of the most expensive failure modes of AI-assisted coding: confident hallucinations (made-up functions, APIs, file contents) and insecure code (SQL injection, hardcoded secrets, missing auth). Its motto is blunt: “a wrong answer delivered confidently causes more damage than ‘I don’t know.’”
It works on two axes at once — epistemic integrity (verify before you claim) and security integrity (OWASP Top 10 enforcement). There is a “FORTRESS” framework of mandatory checks (read-before-claim, threat modeling, security audits), a confidence matrix that rates each claim CONFIRMED / INFERRED / UNCERTAIN / BLOCKED, an anti-pattern blacklist, and a single SANITY score — security weighted 60%, epistemics 40%, because a breach hurts more than wasted time. The blocks are hard: OWASP violations and hardcoded secrets cannot be worked around.
Install it with claude skill install <repo>, copy sanity.md into ~/.claude/skills/, or auto-load it from your project’s CLAUDE.md. If you run Claude Code (or similar agents) against production code, it is worth a look — and the README is bilingual (EN/ID). MIT licensed.
