Hackney – Compare Uber, Lyft, Waymo, and Robotaxi Prices
I created an app that compares real-time prices and wait times across Uber, Lyft, Waymo, Tesla Robotaxi, Curb, and Empower. It shows you all ride options in one list, then once you’re ready to book, it deeplinks you to the provider’s app with the route pre-filled.Edit: Here's a demo video: https://www.youtube.com/watch?v=VV8PEAjxwQII reverse-engineered ride-hailing mobile apps to understand how they fetch prices from their servers. You sign in to my app with your ride-hailing accounts, and then my app requests live prices from the same APIs that ride-hailing apps use. Importantly, my app is built using an on-device approach: the app on your phone stores authentication tokens locally and sends network requests directly to each ride-hailing company’s servers. This keeps your accounts private. I wrote a blog post showing network requests sent by my app, which you can verify yourself: https://blog.hackney.app/p/how-hackney-worksThis seems like an obvious app. Why doesn’t it already exist? That’s because most ride-hailing companies don’t offer public APIs for prices and wait times. Uber does offer one, but they prohibit using it for price comparison. When someone built a comparison app using the official API, Uber terminated their API access (https://www.benedelman.org/news-053116). There are apps today that don’t use official APIs, but they run your account tokens through their servers and send price requests server-side.To integrate a ride-hailing provider, my app sends network requests for sign-in, token refresh, ride prices, and ride history (to power a feature that shows you unified ride history across apps and how much you’ve saved on each ride). Some ride-hailing apps implement certificate pinning to prevent you from viewing their network requests, and some communicate with their server using Protobuf, a data format that doesn’t include the original field names. Building an app using this approach is technical
FL score
out of 100
Verdict
low confidence
Competition
No competitor data yet
Trend
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A price comparison app for ride-hailing that relies on reverse-engineering APIs that companies actively block, with no defensible moat and high legal/technical fragility.
The pain
The gap
Build angle
Strengths
- Solves a real user problem, comparing prices across platforms is genuinely tedious
- Founder has proven technical execution ability and transparency (blog post showing network requests)
- On-device token storage is a legitimate privacy advantage over server-side competitors
- Early traction suggests product-market fit exists at some level
Risks
- Legal exposure: Terms of Service violations across all integrated platforms. Ride-hailing companies have legal teams and will send cease-and-desist letters
- Technical fragility: Each app update, certificate pinning change, or API modification breaks the integration. Maintenance burden scales with number of providers
- No defensible moat: Any competitor can reverse-engineer the same APIs. The only barrier is willingness to violate ToS
- Revenue model unclear: Can't charge users (they'll use free alternatives). Can't charge ride-hailing companies (they're adversaries). Affiliate commissions are possible but tiny
- Precedent against you: Uber already killed a comparison app. They'll do it again, faster next time
- Regulatory risk: As robotaxi services scale, regulators may view API scraping as unfair competition or data theft
- Unit economics unknown: What's the cost to acquire a user? How many actually switch rides based on the app? How much do they spend?
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
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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