740 job listings, one agent pipeline: plus a runtime where everything is a plugin
One developer ran 740 job listings through career-ops and came out the other side with a Head of Applied AI role. The part worth stealing is not that an AI wrote the resumes. It is that somebody looked at a job search, which is normally a shapeless pile of tabs and half-remembered conversations, and built a pipeline for it: every listing broken into a structured A through H report with a single 1 to 5 score on top, an interview story bank in STAR format, a terminal dashboard to see the whole funnel. Structure beat volume. That is the thread running through the whole issue. OpenViking organizes an agent's memory the way a filesystem organizes files, directories it browses with ls, tree, and find, instead of the usual bag of vectors you throw a query at and hope. Every retrieval leaves a trajectory you can watch, so when your agent recalls the wrong thing you can see exactly which path produced it. DeepSeek Harness, this week's featured pick, takes that instinct down to the architecture: everything is a plugin, built on top of Cordis, so pieces get swapped rather than forked. Plane does it for people instead of agents, cycles and roadmaps and a snappy UI, minus the Jira bill. Two things to check before you commit to any of it. Plane is AGPL-3.0 and so is OpenViking's core, though OpenViking's CLI and examples are Apache 2.0, so know which piece you are touching. AGPL means that if you modify it and offer it to other people as a service, you owe them your changes. Fine for an internal tool, a real problem if you are building a product on top. And DeepSeek writes it in capital letters in their own README: Harness is a developer preview and there will be compatibility-breaking changes. The plugin API you build against this month may not be there next month. Worth an evening, not worth a roadmap.
DeepSeek Harness: Everything is a Plugin.
The Lens
DeepSeek Harness (dsh) is DeepSeek's own agent app: the layer that gives a model a shell, files, tools, skills and a UI, for coding and everyday work like spreadsheets and slides. Every piece is a Cordis plugin, so you swap parts instead of forking. MIT licensed and free.
npx @deepseek-ai/dsh web starts a local web UI on 127.0.0.1:3080, and DeepSeek now ships an official desktop app with a macOS download on its site. Add a DeepSeek API key, or point it at another provider or any OpenAI-compatible endpoint. Nothing to host.
The harness costs nothing; tokens do. DeepSeek V4-Pro runs $1.32 per million input tokens and $3.96 per million output at weekday peak, half off-peak, and Flash is far cheaper. Built-in web search starts billing November 1. Solo and small teams: free, pin a version. Larger teams: wait.
The catch: it's a developer preview, every release is still tagged pre-release, and DeepSeek's own safety notice says it hasn't had a security audit. August proved it: CVE-2026-82533 (CVSS 9.4) let a sandboxed agent switch off its own sandbox, fixed in 0.1.2-alpha.1. Run it in a VM. The desktop app also sends usage analytics by default, with no switch in the UI.
Self-evolving Context Database for AI Agents. Unify Agent Memory, Knowledge RAG and Skills.
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.
Open-source AI job search: scan job portals, evaluate listings into a structured A-H report with a global 1-5 score, tailor your CV, track applications. Runs locally in your AI coding CLI (Claude Code, Codex, OpenCode, Antigravity).
The Lens
Career-ops turns the AI coding CLI you already use into a job-search filter. Paste a posting and it checks the job is still open, scores it 1 to 5 against your CV in an A-through-H report, then drafts a tailored resume PDF, cover letter and form answers. It never hits Submit. MIT licensed, no paid tier.
Setup is one npx @santifer/career-ops init. Open Claude Code, Codex, OpenCode, Antigravity, Copilot or another CLI in the folder and it interviews you about your CV and targets. The scanner ships with 100+ companies preconfigured, a Go terminal dashboard tracks the pipeline, and an opt-in web UI is still alpha. Your data stays local except what goes to your AI provider.
Solo job seekers: install it. The tool is free; your cost is the model, and free or local models work. One trap: an ANTHROPIC_API_KEY in your shell overrides a Claude Pro or Max login and bills per token. Small teams and larger orgs: not the audience.
The catch: an LLM is rewriting your CV. A check blocks invented numbers, but its own FAQ says it doesn't yet verify job titles or judge rewording, so read every resume before it goes out.
Open source Jira/Linear alternative
The Lens
Plane is a self-hostable project tracker for the work most teams buy Jira or Linear for: work items, cycles (sprints), modules, pages for docs, intake for triage, and five layouts including board, calendar and Gantt. The open source Community Edition is AGPL v3 and free with no user cap. The free cloud plan stops at 12 users. Self-hosting runs on Docker Compose or Kubernetes, and it's a real stack: 13 services, including Postgres, Valkey, RabbitMQ and an S3-style object store. Fine if you already run containers. The free tier is thinner than the marketing suggests. GitHub, GitLab and Slack sync, custom properties, the workspace wiki and time tracking start at Pro: $6/seat/month billed yearly, $8 month to month. SAML or OIDC SSO, workflows and intake forms sit in Business at $13 yearly or $15 monthly. LDAP and audit logs are Enterprise Grid, quote only. Solo and small teams: free. Need integrations: Pro. Need SSO: Business. The catch: paid features aren't in the open source code. Upgrading a self-hosted install means switching to the closed Commercial Edition, with a 10-seat minimum on new licenses. Five people wanting GitHub sync on their own server pay for ten.
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