A free OCR model that reads whole PDFs in one pass, plus 3 tools worth your time

The Lens

Unlimited-OCR is Baidu's open model for turning images and PDFs into text. Point it at a scanned contract, a multi-page report, or a screenshot full of text and it reads the whole thing in one pass, even very long documents. It handles single images, batches, and full PDFs, with streaming output so you see results as they parse. The weights are on Hugging Face and ModelScope under the MIT license, which means free to use, including commercially, with no strings. Running it yourself is where the cost shows up. This needs an NVIDIA GPU, CUDA 12.9, Python 3.12, and a recent PyTorch and transformers stack. You can run it through plain transformers or through SGLang for faster batch serving. It builds on DeepSeek-OCR, so the lineage is solid, but you are still standing up GPU infrastructure and a model server. There is no hosted API here, no dashboard, no support line. You bring the hardware. For solo work, the Hugging Face demo is free to try and you can run it on a rented GPU when you have a real job. Small teams that already process documents will want a dedicated GPU box or a cloud GPU. Larger teams treating OCR as a pipeline should put SGLang behind a queue. If you need OCR as a managed service with an SLA, look at a cloud vendor instead. The catch: MIT covers the code and weights, but accuracy on messy real-world scans is the thing you have to test on your own documents before trusting it. No benchmark replaces running it on your actual files.

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Cloudflare's Security Audit Skill turns a coding agent into a vulnerability auditor. A skill is a module you add to an AI coding agent to give it a specific job. This one runs a six-phase audit: it maps your app, sends parallel agents to attack from different angles, then has separate agents try to disprove each finding before it gets reported. The philosophy is blunt, only report what you can actually exploit, with a concrete attack scenario, not a checklist of maybes. MIT licensed and free. There is no service to run. You install it into your agent with one command, npx skills add, and ask it to audit a codebase. The only real dependency is a coding agent that supports tool use and parallel sub-agents, plus Node for schema validation. The cost you do pay is model tokens, since the multi-phase, multi-agent design burns through a lot of them on a real codebase. The independent verification pass exists to cut false positives, which is the usual failure mode of AI security scanners. For solo developers and small teams without a security budget, this is a strong first pass and it is free. Larger teams should treat it as one input, not a replacement for a real pentest or a human reviewer. The catch: it is only as good as the agent running it and the tokens you feed it. It finds plausible issues and verifies them, but it does not replace someone who actually understands your threat model.

The Lens

vLLM is the fastest way to serve open-weight LLMs on your own hardware. It takes a model like Llama or Mistral and puts an OpenAI-compatible API in front of it, squeezing maximum throughput out of your GPUs. What's free: everything. Apache 2.0 license. The entire inference engine, all optimizations (PagedAttention, continuous batching, tensor parallelism), the OpenAI-compatible API server. All free. vLLM's key innovation is PagedAttention, which manages GPU memory the way operating systems manage RAM, in pages instead of contiguous blocks. The result: 2-4x more throughput than naive inference. It's become the default serving engine for self-hosted LLMs. The catch: you need serious GPUs. Running a 70B parameter model requires 2-4 A100 GPUs ($1-2/hr on cloud, or $10K+ each to buy). Even a 7B model needs a decent GPU with 16GB+ VRAM. vLLM is free but the hardware is emphatically not. And it's optimized for NVIDIA GPUs. AMD ROCm support exists but is second-class.

The Lens

Oh My OpenAgent (OMO) is an enhancement layer for coding agents like Claude Code, OpenCode, and Cursor. Instead of one agent doing everything, it splits work across specialized sub-agents, a planner, a builder, an orchestrator, that run in parallel and hand off to each other. One command turns the whole crew loose on a task until it's done. It's free and open source. The pitch is that orchestrating several models beats betting on one. It adds parallel team mode with tmux panes so you can watch agents work, content-anchored edits to avoid stale-line mistakes, and built-in web search and docs lookup. Setup is light: it's config files dropped into your project, not a service to run. It works best if you already live in an agent harness and want more horsepower. Solo developers and small teams get the most out of it. Larger teams should test it on throwaway work first, because autonomous multi-agent runs can rack up API spend and make sweeping changes fast. The catch: this is young, opinionated software under a custom license, not a battle-tested standard. The marketing leans hard on grand claims. Treat it as a promising experiment, not infrastructure, and read what it's doing before you let it run unattended on code you care about.