Your own feed, built from several platforms
OpenBiliClaw takes a more ambitious route than a feed reader. Its browser extension observes signals you authorize on supported sites, sends normalized events to a backend you run, and uses those events to build an evolving profile. The backend then searches Bilibili, Xiaohongshu, Douyin, YouTube, X, Zhihu, Reddit, Linux.do, Bangumi, V2EX, Weibo, and the open web. Recommendations arrive with an explanation, and likes, dislikes, saves, watch-later actions, and chat feedback influence later results.
The idea addresses a real weakness in platform feeds: each site knows only the behavior it sees and optimizes for its own goals. OpenBiliClaw can connect interests across sites and intentionally test adjacent topics. It also lets the user edit the profile, choose model providers, change source settings, and keep a common recommendation history. Desktop and mobile web interfaces use the same backend, while an optional Flutter client and DeepSeek Harness plugin live in separate repositories.
Local-first is meaningful, not absolute
The core database, configuration, profile, credentials, cache, and conversation history live on the machine running OpenBiliClaw. The project says its extension has no developer-operated analytics, advertising, or telemetry endpoint. By default, extension data goes to localhost. Remote extension access requires an optional host permission and a device-key flow that is disabled until configured. This is materially better than creating another hosted account that owns the profile.
The privacy boundary still includes services you choose. If a cloud LLM or embedding provider is configured, the backend may send content needed for summaries, profile generation, semantic processing, or recommendation explanations to that provider. Using your own API key controls the account and billing relationship; it does not make a remote model local. Users who want data to remain on one machine need both local embeddings and a local chat model, not merely the installer labeled “with embedding.”
The extension can process sensitive material. Its policy lists platform identity, cookies or session fields, URLs, titles, visit times, search, scrolling, viewing duration, likes, follows, saved items, visible content metadata, chat messages, and successfully submitted comments or Bilibili danmaku. The exact set depends on enabled sources and features, and some platform adapters deliberately return only normalized fields. Still, this is a high-trust extension attached to logged-in accounts. Review permissions and enable sources gradually.
Backup handling deserves its own warning. The portable .obcbackup can include model and source API keys, cookies, SQLite data, the profile, history, and caches. It excludes some server authentication secrets, but the file itself is not encrypted. Treat it like a password export: move it only through trusted storage and remove stray copies.
Installation has several moving parts
The friendliest route is a browser-store extension plus a desktop backend installer. Release 0.3.206 offers lean and embedding-bundled Windows packages and Apple Silicon macOS disk images. The lean build obtains bge-m3 on first launch; bundled installers exceed a gigabyte because they carry the model. The latest macOS application is ad-hoc signed and not notarized, so first launch may require overriding Gatekeeper warnings. That will be unacceptable on some managed devices.
After installation, you still configure a chat model, connect the extension to a logged-in source or choose public discovery, and run initialization. The installer verifies chat and embedding services before building the first profile. Initial model use can be noticeable: issue 137 records a user surprised by token consumption, and the maintainer replied that initialization uses more tokens as the number of events grows, followed by later optimization work. Set a provider budget before importing years of activity.
Source and Docker paths require Python 3.11 or newer, Git, dependency installation, configuration, and health checks. The scripted installer binds to 0.0.0.0:8420 so phones on the LAN can connect. The documentation correctly notes that this exposes the interface to other local devices and provides an optional password gate. Public access should use the documented HTTPS overlay, password authentication, and extension device keys, never a bare backend port.
Browser support is uneven. Chromium has the easiest store path. Firefox 140 or newer is supported, but release 0.3.206 has no signed XPI, only a temporary debugging package that must be loaded again after restart. Safari support is still an open request.
Recommendation quality is personal and difficult to prove
OpenBiliClaw describes a five-layer “soul profile” that infers MBTI, cognitive style, traits, interests, and deeper needs. This can make recommendation explanations engaging, but the labels are model interpretations of incomplete behavior. They are not clinical findings or stable facts. A person may watch something for work, share an account, or behave differently on each platform. Users should correct the profile and judge the actual recommendations, not admire the confidence of the prose.
The system also depends on platform interfaces it does not control. Cookies expire, site markup changes, anti-automation rules shift, and APIs return different fields over time. The detailed source adapters and platform-specific safeguards show that maintainers understand this. They also create a large continuing compatibility burden.
Exceptionally active, still young
Release 0.3.206 and the last push both landed August 15, 2026. Recent issues and pull requests include Windows installer fixes, bounded embedding-cache growth, mobile clients, platform synchronization, and new deployment options. The open count was 27 issues and pull requests. More important than the number, maintainers were closing concrete user reports within hours or days.
The documentation is extensive, bilingual, and unusually candid about remote model flow, plaintext backups, cookies, unsigned apps, LAN exposure, and platform-specific behavior. It is also extremely long, repetitive, and full of product language that can obscure the security decisions.
OpenBiliClaw is ready for a curious self-hoster to trial with a limited set of sources and a capped model account. It is not ready to install thoughtlessly across every logged-in profile. Start with public sources, inspect the generated profile, add one account at a time, and keep the backend private. The result may be a genuinely better discovery feed, provided you remain the system’s operator rather than merely its subject.