Three of this week's four tools run on hardware you already own

The Lens

Unsloth is the free way to fine-tune open models on your own hardware: teaching an existing model like Qwen, Gemma or gpt-oss your data, about 2x faster on 70% less VRAM, per the project. It now comes three ways: Unsloth Desktop (a native app, in beta), Unsloth Studio (the same tool as a web UI) and Unsloth Core (the Python library). Desktop installs like any app on Mac, Windows or Linux and runs models too: GGUF and MLX chat, image and video generation, an OpenAI-compatible API, and unsloth start claude to point Claude Code at a local model. Training works on NVIDIA, AMD, Intel and Mac. CPU-only machines get chat and dataset building, not training. All of that is free. Unsloth also sells Pro and Enterprise builds for multi-GPU and multi-node speedups, contact sales only, on a pricing page that still calls multi-GPU 'coming soon'. Solo and small teams: free. Large training clusters: talk to sales. The catch is licensing. The pip package says Apache 2.0, but Studio's UI, which Desktop wraps, is AGPL-3.0, and so are the mixture-of-experts kernels inside Core. Training your own models is unaffected. Shipping a product on top of it is not.

The Lens

ToolJet gives you a drag-and-drop builder for internal tools (admin panels, dashboards, CRUD apps) that connects to your databases, APIs, and SaaS tools. It is the open source, self-hostable take on Retool. Connect Postgres, MySQL, MongoDB, REST, GraphQL, Google Sheets, and dozens more, drag components onto a canvas, wire them to queries, and you have a working tool. AGPL-3.0. Self-hosting runs on Docker or Kubernetes, and ToolJet says a first deploy usually takes under 30 minutes. Read the fine print on what free means. The pricing page's free self-hosted plan lists 2 builders, 50 end users, and 2 apps, and the README quickstart pulls the enterprise image. A Community Edition image exists under AGPL, but we could not find ToolJet stating in writing whether those caps apply to it. Cloud pricing, billed annually: Basic $23 per builder, Pro $79, Team $199. Month to month, Pro is $99 and Team is $249. Pro caps at 50 end users and Team at 100. Past that, the only plan is Enterprise, from $3,000/mo. OIDC, LDAP, and SAML single sign-on, audit logs, and white-labeling start at Team. Solo and small: free tier, or Pro past 2 apps. Medium: Team. Large: price out appsmithorg/appsmith and Budibase/budibase first. The catch: AGPL means modified versions served to outside users must be open sourced. Team audit logs only keep 14 days. And two builders who need SAML are paying $400 to $500 a month.

The Lens

H3.c generates video with synchronized audio locally on a Mac. It's a from-scratch C inference engine for MiniMax's H3 model, written by the creator of Redis, with an interactive REPL that previews output right in your terminal. The code is MIT and a pleasure to read; local, private video generation with no cloud API is the pitch. The hardware is the gate. You want Apple Silicon with serious unified memory: peak usage runs into the tens of gigabytes depending on canvas size, plus about 37 GB of disk for the checkpoint. An SSD-streaming mode drops memory use to around 2 GB, but at a steep speed penalty at small canvas sizes. Build is a simple make with FFmpeg on PATH. This is for Mac owners with serious unified memory who want local generation, and for anyone who wants to read a modern diffusion pipeline in C. ComfyUI is the mainstream local workbench; Runway and Pika are the hosted alternatives. The catch: the MIT badge covers only the code. The model weights ship under MiniMax's custom community license, non-OSI, with an application process for US, EU, UK, and South Korea users, and the engine is useless without them. The author also frames the repo as working vertical slices, not a finished product, and canvas sizes and frame counts have sharp constraints.

The Lens

oMLX runs large language models on your Mac and manages them from the menu bar. You get a local API that speaks both the OpenAI and Anthropic formats, so Claude Code, Codex or Cursor can point at your own machine instead of a paid cloud model. Apache 2.0, no account, no paid tier. The reason to pick it over Ollama is the cache. Coding agents keep resending the same long prompt, and oMLX saves that work to your SSD, so a returning context loads from disk instead of being recomputed, even after a restart. The signed DMG auto-updates and ships the fast native kernels prebuilt. Solo developers running a local coding assistant get the most from it. Small teams can share one big Mac over the API; it refuses network binds without an API key. Larger teams belong on vLLM and real GPUs. The catch: Apple Silicon, macOS 15 or newer, and the project calls 16GB of RAM the minimum with 64GB recommended. Homebrew and source installs without full Xcode skip the native kernels, and GLM-5.2 prefill then runs roughly 30x slower. Take the DMG. It's also one person's after-hours project.