How did you convince family and friends to join your XMPP/Matrix server?
I love open-source software and open protocols, but my family and friends primarily communicate through SMS for exactly the reasons in https://xkcd.com/2365/Whenever XMPP or Matrix threads pop up on HN, several commenters casually mention their "friends and family XMPP (or Matrix) server."How did you convince friends and family to move away from SMS/WhatsApp and replace it with a niche messaging app?My family and friends aren't hardcore nerdy, but they're more tech-savvy than average. Their needs in messaging are mainly pragmatic: they want ease of use, reliability, and fun features. They don't care that much about privacy, decentralization, or extensibility (though I care about those things a lot).I've been experimenting with self-hosting XMPP and Matrix servers and clients in the past week, and the UX is even worse than I expected. It seems like all the clients either imitate Slack, imitate Discord, or imitate hot garbage. I haven't seen any that try to be a simple, friendly replacement for SMS. I'm a software developer who uses computers all day, and I find the UX for most of the clients incredibly hard to use.The only angle I could see to pitch XMPP/Matrix to people for non-philosophical reasons are that there's a more consistent experience across iOS/Android. The biggest shortcoming I think is visible to regular users is that there's feature degradation and missing information when iOS users have SMS conversations with Android users, especially in group chats.For people who have successfully won family and friends to XMPP or Matrix, what was your pitch? What do you recommend others do to make XMPP or Matrix an attractive experience for a regular person?
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
Improving the user experience of open-source messaging to convince tech-savvy but non-expert users to switch from SMS and mainstream apps.
The pain
The gap
Build angle
Strengths
- Clear pain point in UX and fragmentation
- Open protocols offer technical advantages
- Developer has domain knowledge and motivation
Risks
- Low willingness of average users to switch messaging platforms
- Strong network effects of SMS, WhatsApp, iMessage
- High effort to build polished, cross-platform client
- Limited monetization or growth potential
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 70
- Solution gap
- 60
- Willingness to pay
- 30
- Buildability
- 70
The pain of poor UX and fragmentation in open-source messaging is clear, but the willingness of average users to switch is low and the solution gap moderate.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The value proposition is weak because the target users prioritize ease and familiarity over open protocols, limiting growth and monetization potential.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The idea addresses a real user experience problem but lacks a compelling hook or clear market demand beyond niche enthusiasts.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The concept has moderate feasibility but faces significant adoption challenges due to entrenched competitors and user habits.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The idea is technically interesting but does not solve a pressing problem for a large market, making it unlikely to scale quickly.
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
Consus (YC F24) – LLM gateway for companies handling sensitive data
Hey Hacker News! I'm Eric, the founder of Consus. We route all AI requests to ITAR restricted and controlled unclassified information (CUI) compliant infrastructure. Consus itself satisfies ITAR restrictions, and we recently completed our FedRAMP equivalency audit against the 410 FedRAMP High controls. FedRAMP High is the U.S. government’s strictest security baseline for cloud services handling sensitive unclassified data. A breach of this data could cause severe or catastrophic harm. Achieving FedRAMP equivalency, or authorization, is required before processing CUI information.Demo: https://youtu.be/6MYxJe6P4oYAerospace and defense is experiencing rapid growth. Unfortunately, most companies are struggling to use AI because of necessary data protections & compliance risk. These companies can move even faster if they could use the AI tools that most other industries take for granted.I spent most of my career prior to Consus in aerospace & defense. I launched national security payloads at SpaceX, and was an early employee at Castelion. I’ve watched first hand how compliance red-tape makes it difficult for companies to adopt new technologies and trust vendors that touch their sensitive data. For example, I worked with a satellite manufacturer that doesn’t allow any AI tools, even for non-sensitive work.Even as AI has become extremely useful over the past 9-12 months, I was surprised by how many aerospace & defense companies refused to touch it. They could see how it could be helpful, but they were scared about model providers training on their data, violating an ITAR rule, or risk losing new business because of a failed cyber assessment. These are all rational fears.There were a lot of ways to solve some/all of these problems, but we focused on building a compliant AI inference layer that can "plug" into any AI tool or workflow. Just as OpenRouter integrates with most tools, Consus can connect to tools like ChatGPT or Clau
AI
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