Do you want a router that has DD-WRT pre-installed?
I sometimes run across comments where someone says they want an open-source router. I wonder if this is a viable idea for someone to start as a small company. What makes me hesitant is that, first, it looks like this already exists in several wireless and non-wireless routers (like the GL.iNet Flint or OpenWrt One), and second, I assume hackers would just make their own.I have been flashing DD-WRT to my routers for many years. My current setup is an Archer C9 that connects via cable to an Archer C8, which then wirelessly bridges to an R7000. All of them run DD-WRT. If I had "fun money," I would buy a low-power SFF PC with dual NICs, run OPNsense or OpenWrt, and use the wireless routers strictly as access points. Since I don't have that much to spend, I look for older DD-WRT friendly routers, usually under $30, on Craigslist or Facebook Marketplace, and flash them myself.I see that Ubiquiti and MikroTik make quality routers with proprietary OSes for around $65 new. Protectli and Netgate offer pfSense routers starting at $259.00. The GL.iNet Flint 2 is $169.00, and the OpenWrt One is $89.According to ResellBot (because eBay now requires a login to see sold listings with prices), the sold "DD-WRT Routers" generally go for under $160. These include the WRT3200ACM, Netgear AC1750, Linksys EA9300, and Linksys R7350.When I asked an AI how I would go about judging interest, building, raising funds, and manufacturing, it seems doable but expensive (said $125,000 minimum in total made up of $30,000 for regulatory approval, engineering and protoyping $10,000+) and time-consuming just to get to a "golden prototype" for demos.So if I were a "hacker" and wanted an open-source router, depending on my funds, I would either:1. Flash DD-WRT to an existing router (used or new).2. Buy one with it already installed, like the OpenWrt One or a Protectli device.3. Get an SFF PC and build my own.I remember when some comments about DropBox said it
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
A router with DD-WRT pre-installed targets a niche group of tech-savvy users but faces strong competition and high costs to enter.
The pain
The gap
Build angle
Strengths
- Clear niche demand from tech-savvy users
- Open-source firmware is proven and popular
- Potential to bundle hardware and software for convenience
Risks
- Market already served by existing products and DIY solutions
- High upfront costs and regulatory hurdles
- Limited willingness to pay premium over cheaper used routers
- Competition from established brands and open-source communities
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 60
- Solution gap
- 40
- Willingness to pay
- 45
- Buildability
- 45
The pain of wanting pre-flashed open-source routers is somewhat clear but niche, the market already has options so the gap is small, willingness to pay is moderate but limited by cheaper alternatives, and building a new product is costly and complex.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The value is moderate given existing products and DIY options, the price point is constrained by cheaper used routers, and the market size is limited to enthusiasts and hackers, reducing overall attractiveness.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The idea addresses a real but small segment, with competition from existing products and DIY solutions, making it hard to differentiate and scale profitably.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The idea lacks a strong unique advantage, faces competition from established brands and open-source communities, and requires significant upfront investment, lowering feasibility and potential returns.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The market is niche and somewhat saturated, the product is not clearly differentiated, and the founder would face challenges in customer acquisition and scaling given the technical and cost barriers.
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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