WallHop – 12ft.io is gone, so I built a replacement
Hi HN, I'm Josh. WallHop is a free paywall bypass: paste an article URL (or prefix any URL with wallhop.io/) and you get the readable article without the paywall popup, ads or clutter. No signup, no extension.I built it after 12ft.io went away. The News Media Alliance took it down in July 2025 and I missed having something I could hit from my phone.How it works: it started as a fork of everywall/ladder (GPL-3.0) and I've modified it heavily since. Instead of relying on one trick, it chains several bypass methods per request and falls back to archive.is when they all fail, so one site blocking one technique doesn't break it.Things that might be interesting:- iOS Shortcut: on iPhone, tap Share > WallHop from any article in Safari or another app. This is how I use it 95% of the time. - It works on Medium member-only posts, which 12ft never handled. - Bookmarklet for desktop, plus /raw/<url> and /api/<url> endpoints if you want to script it.I've tested it against most major news outlets and a bunch of local papers. It won't work everywhere. There are SOME server-side paywalls that are out of reach, and I'd rather say that up front. If you find a site that breaks, I'd love the link, and I can likely figure out a way to bypass it!
FL score
out of 100
Verdict
high confidence
Competition
No competitor data yet
Trend
No signal yet
WallHop is a free tool that bypasses paywalls to provide clean article access without signup or extensions.
The pain
The gap
Build angle
Strengths
- Clear and widespread user pain
- No signup or extension needed, lowering friction
- Works on sites like Medium that competitors miss
- Open source foundation with room for improvement
Risks
- Legal challenges from news organizations
- Low willingness to pay limits monetization
- Some paywalls are technically impossible to bypass
- Potential for sites to block or counteract the tool
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 85
- Solution gap
- 80
- Willingness to pay
- 50
- Buildability
- 85
The pain of paywalls is clear and real, there is a gap after 12ft.io shut down, but willingness to pay is low and the technical build is feasible for one person.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
The idea solves a clear pain with a simple solution, but monetization and defensibility are weak, limiting overall value.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
The product addresses a niche but real user need with a straightforward approach, though market size and revenue potential are limited.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
The concept is technically sound and user-friendly, but legal risks and unclear monetization reduce its long-term viability.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
WallHop tackles a known problem with a practical tool and good user experience, but faces challenges in scaling and monetization.
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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