Travel Hacking Toolkit – Points search and trip planning with AI
I use points and miles for most of my travel. Every booking comes down to the same decision: use points or pay cash? To answer that, you need award availability across multiple programs, cash prices, your current balances, transfer partner ratios, and the math to compare them. I got tired of doing it manually across a dozen tabs.This toolkit teaches Claude Code and OpenCode how to do it. 7 skills (markdown files with API docs and curl examples) and 6 MCP servers (real-time tools the AI calls directly).It searches award flights across 25+ mileage programs (Seats.aero), compares cash prices (Google Flights, Skiplagged, Kiwi.com, Duffel), pulls your loyalty balances (AwardWallet), searches hotels (Trivago, LiteAPI, Airbnb, Booking.com), finds ferry routes across 33 countries, and looks up weird hidden gems near your destination (Atlas Obscura).Reference data is included: transfer partner ratios for Chase UR, Amex MR, Bilt, Capital One, and Citi TY. Point valuations sourced from TPG, Upgraded Points, OMAAT, and View From The Wing. Alliance membership, sweet spot redemptions, booking windows, hotel chain brand lookups.5 of the 6 MCP servers need zero API keys. Clone, run setup.sh, start searching.Skills are, as usual, plain markdown. They work in OpenCode and Claude Code automatically (I added a tiny setup script), and they'll work in anything else that supports skills.PRs welcome! Help me expand the toolkit! :)https://github.com/borski/travel-hacking-toolkit
FL score
out of 100
Verdict
high confidence
Competition
8
competitors found, emerging market, funded players
Trend
No signal yet
An open-source AI toolkit automating complex travel points/cash comparisons for advanced travel hackers, offering deep integration but needing a clear monetization path.
The pain
The gap
Build angle
Strengths
- Addresses a real, specific, and severe pain for a high-value audience (advanced travel hackers).
- Unique 'AI toolkit' approach creates significant whitespace and future-proofing.
- High willingness to pay demonstrated by existing paid competitors.
- Leverages AI and open-source principles for a robust foundation.
- Creator has strong domain expertise and has already built a functional core.
Risks
- Monetization strategy is not inherent to the open-source toolkit and needs to be defined.
- High ongoing maintenance burden to keep integrations (25+ programs, cash sources, valuations) up-to-date for a solo builder.
- Market is crowded at the end-user application level, requiring clear differentiation beyond the 'toolkit' aspect for a SaaS offering.
- Potential complexity for non-developer users to set up and utilize the AI toolkit locally.
Next steps
- Define a clear monetization strategy: Will it be a hosted SaaS, a premium API, consulting, or paid modules for the toolkit itself?
- Interview advanced travel hackers to understand their specific willingness to pay for a hosted AI solution vs. self-hosting/integrating the open-source toolkit.
- Prioritize integrations based on user demand and pain points to manage the ongoing maintenance burden.
- Develop a simple, user-friendly wrapper/interface for the toolkit to lower the barrier to entry for non-technical users, if a SaaS model is pursued.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 26
- Solution gap
- 20
- Willingness to pay
- 19
- Buildability
- 11
This idea addresses a real and severe pain for advanced travel hackers, offering a novel 'AI toolkit' approach that differentiates it from existing web-based solutions. While there's a clear willingness to pay for such a solution, the ongoing maintenance and complexity of integrating so many data sources could challenge a solo builder's long-term sustainability.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
This idea has strong market viability and a compelling value proposition, leveraging AI for a growing, complex problem. The unique toolkit approach provides differentiation, but ongoing resource needs could be a challenge.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
A clear problem solved by a passionate creator, leveraging a niche AI toolkit approach. Monetization is a primary concern for this open-source project.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
A viable micro-SaaS foundation with clear value for a niche, but needs a defined business model and ongoing validation of its AI toolkit approach for the broader travel hacking community.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
A strong idea with clear demand from a desperate, specific user, offering a novel AI-driven approach to a complex problem that will become more essential over time.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Define a clear monetization strategy: Will it be a hosted SaaS, a premium API, consulting, or paid modules for the toolkit itself?
- Interview advanced travel hackers to understand their specific willingness to pay for a hosted AI solution vs. self-hosting/integrating the open-source toolkit.
- Prioritize integrations based on user demand and pain points to manage the ongoing maintenance burden.
- Develop a simple, user-friendly wrapper/interface for the toolkit to lower the barrier to entry for non-technical users, if a SaaS model is pursued.
Who is already there, emerging market
Searches for award flight availability across many airline loyalty programs and provides step-by-step booking instructions.
Pricing: $5 for a 48-hour pass (first 3 searches free); $129/year; $260/year for concierge service.
- Roame.travelVisit
An award flight search engine that helps users find and maximize points and miles for flights across major airline loyalty programs.
Pricing: Free tier with live search; Pro plan at $110/year.
- PointsYeahVisit
A free all-in-one tool that helps you search for flights and hotels you can book using points and miles, scanning real-time award availability.
Pricing: Free plan with unlimited searches and basic features; Premium Plan $11.99/month or $99.99/year.
- Seats.aeroVisit
A fast search engine for award travel that helps users discover award availability on popular airlines and mileage programs.
Pricing: Free for 60-day rolling availability with unlimited alerts; Pro account $9.99/month for full year availability, advanced filters, SMS alerts, and live search.
- AwardFaresVisit
A search engine that shows live, bookable award seats, prices, and alerts across multiple frequent flyer programs.
Pricing: Free trial; Premium and Diamond plans (specific pricing not found, but noted Diamond plan offers unlimited alerts).
- AwardHackerVisit
A free website that estimates theoretical miles needed for rewards travel based on old award charts.
Pricing: Free
- AwardTravelFinderVisit
Shows real-time award seat availability across premium airlines.
Pricing: Free for basic searches; $9.99/month for Pro access.
- Points PathVisit
A Chrome plug-in that integrates point prices next to cash prices in Google Flights search results.
Pricing: Free version; Pro Pricing: $7.99/month or $79.99 per year.
What they charge
Recent news
- 10 Hyatt hotels to book with points now — before major award chart changes this May.
The Points Guy, April 03, 2026
- Deal alert: Points and miles travel deals for April 2026.
The Points Guy, April 03, 2026
- Amex Platinum Is Losing Benefits & A Controller Asked A Landing Plane 'Where Are You?'
Miles & Points Daily, March 31, 2026
- Singapore Slashes Award Prices 30% & Alaska's Award Chart Just Did Something Weird.
Miles & Points Daily, March 31, 2026
- Delta's Record Bonus Is Ending, ITA Airways Joins Star Alliance & TSA Lines Are Breaking Records.
Miles & Points Daily, March 25, 2026
Market signals
The market for points and miles search and trip planning is growing and increasingly competitive, driven by the desire of travelers to maximize value from loyalty programs. Recent news indicates dynamic pricing is becoming the norm for airlines, and there's a continuous stream of promotions and changes in loyalty programs. The market sees a mix of established players and newer startups leveraging AI to offer real-time availability and enhanced search capabilities, catering to both beginner and advanced travel hackers.
What frustrates people
- AwardHacker is outdated and doesn't show real-time availability, leading to 'phantom' awards.
- Point.me can be slow, taking several minutes per search, and its free/partner versions lack critical features like alerts and multi-day searches.
- Seats.aero's interface can be less beginner-friendly and uses a data pivot table format that requires specific filtering knowledge.
- PointsYeah includes heavy ad placement in its free version and can sometimes be glitchy with less common airline programs.
- Many tools have limited airline loyalty program support, missing key international airlines or making it difficult to search across alliances.
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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