740 job listings, one agent pipeline: plus a runtime where everything is a plugin

The Lens

DeepSeek Harness is DeepSeek's own agent runtime, the layer that sits between a model and your machine and gives it tools, memory, skills, and an interface. The architecture is one idea taken all the way: everything is a plugin, built on top of Cordis, so pieces get swapped rather than forked. MIT licensed and free. Running it is one command, npx @deepseek-ai/dsh web, which needs Node and serves a web UI on 127.0.0.1 port 3080. Building from source is a pnpm install and build. There is no server to operate: it runs on your machine and calls out to whichever provider you configure. A plugin ecosystem formed within days of launch, including native desktop wrappers and a terminal UI, all discoverable through the dsh-plugin topic on GitHub. The harness costs nothing and the model calls are the only bill. DeepSeek's own API is cheap by frontier standards, and it charges roughly half rate outside its weekday peak window. Solo developers: free, and worth an evening if composable systems appeal to you. Small teams: free, but pin your version and mean it. Large teams: watch it rather than standardize on it. The catch, and DeepSeek puts it in capital letters in their own README: this is a developer preview and there will be compatibility-breaking changes. The plugin API you build against this month may not exist next month. The popularity is real and so is the churn, so treat anything you write on top of it as disposable until the interfaces settle.

The Lens

OpenViking is a context database built specifically for AI agents. Instead of the usual pile of vector embeddings, it organizes an agent's memory, retrieved documents, and skills the way a filesystem organizes files: directories you can navigate, tag, and search recursively. It is open source under AGPL-3.0. Setup is heavier than a typical pip install. You will need Python 3.10+, a Rust toolchain for building the RAGFS component, and a C/C++ compiler for the core extensions, on top of wiring up a vision-language model and an embedding model (Volcengine Doubao, OpenAI, or others) before it does anything useful. Once running, it compresses conversation history automatically and shows you the retrieval path it took to answer a query, so debugging why an agent recalled the wrong thing is actually possible. There is a hosted demo, OpenViking Studio, if you want to try the retrieval and memory features before committing to the build. Solo devs experimenting with agent memory: worth the setup time if you are hitting the limits of flat vector search. Teams building production agents: the tiered loading, it only pulls in the context tier it actually needs, is the real selling point for cutting token costs at scale. It is AGPL-3.0, not MIT or Apache. If you build a hosted product on top of OpenViking and offer it as a network service, the license requires you to open source your modifications too. That is a real constraint for anyone planning to build a commercial product on this, not just an internal tool.

The Lens

Career-Ops turns your AI coding CLI into a full job search command center. Paste a job URL and get back a structured report broken into blocks A through H, with a single 1 to 5 score reached by holistic judgement across five dimensions. It generates tailored, ATS-optimized PDF resumes per application. One developer used it to evaluate 740+ offers and land a Head of Applied AI role. The system gets smarter over time. It builds an interview story bank in STAR format, ships a scanner preloaded with 100+ companies and 45+ search queries, and can batch-evaluate offers in parallel using sub-agents. The dashboard is a Go TUI built on Bubble Tea, so you browse the entire pipeline from the terminal, and an experimental web UI is available as an opt-in alpha. Running it needs Node.js and Playwright for the PDF generation and portal scanning. Solo job seekers running any AI coding CLI: this is a force multiplier. It used to be Claude Code only, and now it also works with Gemini, OpenCode, Qwen, Grok, Codex, and Kimi, several of which have free tiers. It does not replace your judgment, it structures it. The human in the loop design means the AI evaluates and you decide. The catch: the tool is free, but it leans on an AI CLI underneath, so your real cost depends on which provider you pick and how hard you run it. The preloaded company list skews heavily toward AI and tech companies. And the first evaluations are rough until you feed it enough context about yourself.

The Lens

Plane is the open source alternative to Jira, covering sprints, kanban boards, and roadmaps without the Jira prices or the Linear waitlist. It's what Jira should have been: clean UI, fast, and you can self-host it. AGPL v3, TypeScript. Features include issues with custom properties, cycles (sprints), modules, pages for docs, views, and a roadmap timeline. The UI is noticeably snappier than Jira, and GitHub and GitLab integrations sync issues with your code. Free cloud tier: up to 12 members with unlimited issues and projects. Self-hosting the Community Edition is free. Pro is $6/user/month for custom workflows, bigger uploads, and priority support. Business is $13/user/month for SSO, audit logs, and advanced analytics. Every tier now includes a per-seat monthly allowance of AI credits, which is how Plane meters its AI features. Solo: the free cloud tier handles personal project management. Small teams under 12: free cloud or self-host, both work. Growing teams: $6/user Pro when custom workflows matter. Larger orgs: $13/user Business when SSO stops being optional. The catch: the AGPL license means if you modify Plane and host it for others, you must open source your changes. The ecosystem is younger than Jira's: fewer integrations, fewer plugins, and some features like time tracking and advanced reporting are still maturing. If your team has Jira muscle memory, the migration cost is real.