I've been working on a social platform called Polaris and it's finally at a point where it actually runs on my laptop.

The core idea is: what if a social network was designed to be popular but positive? No outrage amplification, no influencer hierarchy, no shadowy moderation. Instead:

• Trust-based moderation — you earn the ability to moderate through positive contributions. Permanent bans require a 90% jury consensus. A random, balanced panel reviews every ban proposal. Every moderation action is public in an append-only log.

• Collaborative tagging (booru-style) — any trusted user can tag any post. Tags aren't controlled by the author. If the community disagrees on a tag, a vote is triggered among experienced taggers. The community curates the taxonomy together, wiki-style.

• Auto-generated circles — the system groups nearby people who share your interests. "5 people near you also like astrophotography" — you accept or dismiss.

• Community Notes (Twitter/X-style consensus warnings) — weighted by trust score, with a diversity requirement to prevent coordinated voting. Authors can also add correction notes that always appear with a green banner.

• Algorithmic feed you can fully override — the default feed blends recency, engagement velocity, tag affinity, and trust network signals. But you can build your own custom feeds from any mix of tags, users, and keywords with include/exclude rules. RSS export for every feed, tag, collection, and community.

• Communities with slow-boot, curators, and forking — topic-based public spaces. New members can be required to go through a probation period. Curators enforce scope tags but can't ban anyone. If the community disagrees with the direction, members can fork it — creating a new community with the same tag scope and member list.

• Credit economy — daily rewards with streak bonuses, giftable credits between users, bounty system for questions. Fully auditable ledger using a hash chain in Postgres. Not a real blockchain — no mining, no gas, no wallet.

• Achievements and multi-period leaderboards — daily, weekly, monthly, yearly, and all-time rankings for posting, tagging, gifts, trust network, and achievement points. Achievements focus on positive behaviors like curation, quality contributions, and community service.

• Advanced search — full boolean operators (AND, OR, NOT), field search (title, author, tag, date range), exact phrase matching. Saved searches with new-result notifications.

• Collections with four visibility layers — private, public read, public edit, and anybody edit. Perfect for collaborative link collections, reading lists, or community wikis.

• Per-user content filters — hide, blur, or deprioritize content matching keywords, regex patterns, domains, or specific users. Processed both server-side (feed ranking) and client-side (blur overlay).

• Trending topic detection from content, not hashtags — the system automatically detects rising terms by analyzing post text and comparing frequency against baseline. No hashtag spam gaming the algorithm.

• Onboarding wizard — new users pick 5+ tags of interest, follow suggested users, and optionally make their first post before they see the main feed. The algorithm needs data to work with.

• Content warnings and post scheduling — authors can add content warnings (blurred until clicked). Posts can be drafted, scheduled for future publication, or published immediately.

• Shared blocklist for federation — domains, IP prefixes, and fingerprint-based blocks can be exported as JSON for other instances to consume.

• ActivityPub-ready — every user has an ed25519 keypair and an actor_id. The data model supports federation without migration. Public keys are already stored for HTTP Signatures.

Tech stack is Go + Gin + Svelte + PostgreSQL + Redis. Single lightweight binary.

Would love feedback on it.