Radius – A Meetup.com Alternative
Hello HN!I'm relaunching Radius [1] - far too long after the original Show HN [2] (I'll blame the day job for that).Radius started out from my frustration with not knowing what was going on around me. The initial plan was to "show all and every type of event" from big to small, but over time I narrowed the focus to communities, groups, events, and helping people connect through them.The initial Show HN gave me 150+ comments (surprising, looking back, given how terrible the plain Bootstrap UI was...) with a huge amount of useful feedback and feature requests. I've spent the time since then working through that, along with additional requests from groups using Radius, and it finally feels ready for a proper relaunch.I've also recently added "Activities" [3] - which are lightweight events, independent of groups, intended to help people find others to do things with - for example:> John is going for a cycle and a coffee in New York at 10amPeople can join and vote for a time that works for them.I've got a bunch of ideas for where to go next with this, including things like open sourcing and federation, but those are for another day!Any feedback would be greatly appreciated!P.S. it's built with Ruby on Rails, for those interested. I highly suspect it would have taken me 2 more years to get to this point without Rails![1] https://radius.to/[2] https://news.ycombinator.com/item?id=40717398[3] https://radius.to/documentation/activities/post-an-activity
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
A Meetup.com alternative focused on lightweight activities and community discovery that works technically but faces an uphill battle against an entrenched competitor with stronger network effects.
The pain
The gap
Build angle
Strengths
- Founder has demonstrated shipping ability and iterated based on feedback from 150+ comments on first launch
- Activities feature is a genuine innovation that Meetup does not offer, lowering friction for spontaneous group plans
- Built with Rails which enabled faster iteration than other frameworks
- Product is live and usable with real groups already using it
Risks
- Meetup.com is a direct competitor with 50+ million users, strong brand recognition, and entrenched organizer relationships
- Network effects mean the first mover advantage is massive. Users go where events are listed, organizers list where users are
- No traction metrics shared. Unknown if Radius has paying customers, monthly active users, or growth rate
- Monetization strategy not articulated. Meetup charges organizers. How will Radius sustain itself
- Geographic fragmentation means success in New York does not transfer to success in Denver. Must build network effects in every city separately
- Solo founder attempting to compete in a market that requires simultaneous supply and demand growth
- Open sourcing and federation mentioned as future ideas but these do not solve the core network effects problem
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 72
- Solution gap
- 58
- Willingness to pay
- 55
- Buildability
- 63
The problem of discovering local events and communities is real but Meetup.com already solves it adequately, making the solution gap narrow and user willingness to switch unclear.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The market exists but is owned by an entrenched player with network effects, the value creation is incremental rather than transformative, and monetization strategy is not articulated.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The founder has shipping discipline and technical execution ability but lacks evidence of product-market fit traction, paying customers, or a clear unfair advantage over Meetup.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The idea targets a real problem but the competitive moat is weak, the addressable market is fragmented by geography, and growth will depend entirely on local network effects that are expensive to build.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
A competent solo founder with a working product in a crowded space, but without clear differentiation, traction metrics, or a path to venture-scale returns.
Five lenses, one composite. How scoring works
The angle
No market research recorded for this idea yet.
Semble – Code search for agents that uses 98% fewer tokens than grep
Hey HN! We (Stephan and Thomas) recently open-sourced Semble. We kept running into the same problem while using Claude Code on large codebases: when the agent can't find something directly, it falls back to grep, reading full files or launching subagents. This uses a lot of tokens, and often still misses the relevant code. There are existing tools for this, but they were either too slow to index on demand, needed API keys, or had poor retrieval quality.Semble is our solution for this. It combines static Model2Vec embeddings (using our latest static model: potion-code-16M) with BM25, fused via RRF and reranked with code-aware signals. Everything runs on CPU since there's no transformers involved. On our benchmark of ~1250 query/document pairs across 63 repos and 19 languages, it uses 98% fewer tokens than grep+read and reaches 99% of the retrieval quality of a 137M-parameter code-trained transformer, while being ~200x faster.Main features:- Token-efficient: 98% fewer tokens than grep+read- Fast: ~250ms to index a typical repo on our benchmark, ~1.5ms per query on CPU (very large repos may take longer)- Accurate: 0.854 NDCG@10, 99% of the best transformer setup we tested- MCP server: drop-in for Claude Code, Cursor, Codex, OpenCode- Zero config: no API keys, no GPU, no external servicesInstall in Claude Code with: claude mcp add semble -s user -- uvx --from "semble[mcp]" sembleOr check our README for other installation instructions, benchmarks, and methodology:Semble: https://github.com/MinishLab/sembleBenchmarks: https://github.com/MinishLab/semble/tree/main/benchmarksModel: https://huggingface.co/minishlab/potion-code-16MLet us know if you have any feedback or questions!
AI
Postgres extension for BM25 relevance-ranked full-text search
Last summer we faced a conundrum at my company, Tiger Data, a Postgres cloud vendor whose main business is in timeseries data. We were trying to grow our business towards emerging AI-centric workloads and wanted to provide a state-of-the-art hybrid search stack in Postgres. We'd already built pgvectorscale in house with the goal of scaling semantic search beyond pgvector's main memory limitations. We just needed a scalable ranked keyword search solution too.The problem: core Postgres doesn't provide this; the leading Postgres BM25 extension, ParadeDB, is guarded behind AGPL; developing our own extension appeared daunting. We'd need a small team of sharp engineers and 6-12 months, I figured. And we'd probably still fall short of the performance of a mature system like Parade/Tantivy.Or would we? I'd be experimenting long enough with AI-boosted development at that point to realize that with the latest tools (Claude Code + Opus) and an experienced hand (I've been working in database systems internals for 25 years now), the old time estimates pretty much go out the window.I told our CTO I thought I could solo the project in one quarter. This raised some eyebrows.It did take a little more time than that (two quarters), and we got some real help from the community (amazing!) after open-sourcing the pre-release. But I'm thrilled/exhausted today to share that pg_textsearch v1.0 is freely available via open source (Postgres license), on Tiger Data cloud, and hopefully soon, a hyperscalar near you:https://github.com/timescale/pg_textsearchIn the blog post accompanying the release, I overview the architecture and present benchmark results using MS-MARCO. To my surprise, we were not only able to meet Parade/Tantivy's query performance, but exceed it substantially, measuring a 4.7x advantage on query throughput at scale:https://www.tigerdata.com/blog/pg-textsearch-bm25-fu
AI
GlycemicGPT – Open-source AI-powered diabetes management
I'm a Type 1 diabetic and software engineer. Last year I went months between endocrinologists with no clinician reviewing my data. I'm an engineer, so I built the tool I needed — and now I'm open sourcing it. GlycemicGPT is a self-hosted platform that connects continuous glucose monitors, insulin pumps, and existing Nightscout instances to an AI analysis layer running on your own infrastructure. Data sources:Dexcom G7 (cloud API) Tandem t:slim X2 and Mobi pumps (direct BLE) Nightscout (point it at your existing instance and you're running in minutes)What the AI layer does:Daily briefs summarizing overnight and 24-hour patterns Meal response analysis Conversational chat with RAG-backed clinical knowledge Predictive alerting with configurable thresholds and caregiver escalationImportant: this is monitoring and analysis only. GlycemicGPT does not deliver insulin, does not control your pump, and is not a closed-loop system. It reads your data and gives you insight on top of it. Your clinical decisions stay between you and your care team. Architecture:Self-hosted via Docker or K8S — the GlycemicGPT stack runs entirely on your hardware BYOAI — bring your own AI provider. Use Ollama for fully local operation (no data leaves your hardware), or point it at Claude, OpenAI, or any OpenAI-compatible endpoint if you prefer a hosted model. Data flows directly from your instance to the provider you choose; nothing is routed through any centralized service operated by the project. GPL-3.0, no subscriptions, no vendor lock-inStack:Backend API: FastAPI, Python 3.12, PostgreSQL 16, Redis 7 Web Dashboard: Next.js 15, React 19, Tailwind CSS, shadcn/ui AI Sidecar: TypeScript, Express, multi-provider proxy Android App: Kotlin, Jetpack Compose, BLE Wear OS: Kotlin, Wear Compose, Watch Face Push API Plugin SDK: Kotlin interfaces, capability-based, sandboxedLooking for contributors — especially folks with BLE/Android experience or anyone in the diabetes tech spa
AI
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