Will my flight have Starlink?
Hey HN, If you’ve been lucky enough to be on a flight with Starlink, you understand the hype. It actually works!However, its availability on flights is patchy and hard to predict. So we built a database of all airlines that have rolled out Starlink (beyond just a trial), and a flight search tool to predict it. Plug in a flight number and date, and we'll estimate the likelihood of Starlink on-board based on aircraft type and tail number.If you don’t have any trips coming up, you can also look up specific routes to see what flights offer Starlink. You can find it here: https://stardrift.ai/starlink .-I wanted to add a few notes on how this works too. There are three things we check, in order, when we answer a query:- Does this airline have Starlink?- Does this aircraft body have Starlink?- Does this specific aircraft have Starlink?Only a few airlines at all have Starlink right now: United, Hawaiian, Alaskan, Air France, Qatar, JSX, and a handful of others. So if an aircraft is operated by any other airline, we can issue a blanket no immediately.Then, we check the actual body that's flying on the plane. Airlines usually publish equipment assignments in advance, and they're also rolling out Starlink body-by-body. So we know, for instance, that all JSX E145s have Starlink and that none of Air France's A320s have Starlink. (You can see a summary of our data at https://stardrift.ai/starlink/fleet-summary, though the live logic has a few rules not encoded there.)If there's a complete match at the body type level, we can confidently tell you your flight will have Starlink. However, in most cases, the airline has only rolled out a partial upgrade to that aircraft type. In that case, we need to drill down a little more and figure out exactly which plane is flying on your route.We can do this by looking up the 'tail number' (think of it as a license plate for the plane). Unfortunately, the tail number is usua
FL score
out of 100
Verdict
high confidence
Competition
5
competitors found, emerging market, funded players
Trend
No signal yet
A flight prediction tool for Starlink availability that addresses real user frustration but faces strong competition from free alternatives and significant data complexity.
The pain
The gap
Build angle
Strengths
- Addresses a real, specific, and annoying pain point for modern travelers.
- The underlying market for in-flight Starlink is growing rapidly.
- The service could provide significant value if it delivers consistently higher accuracy than competitors.
- Good potential for organic traffic via SEO for flight-related queries.
Risks
- A direct, free competitor (StarlinkFlights.com) already exists, making monetization very challenging.
- High technical complexity for a solo builder in acquiring and maintaining accurate, real-time tail number and Starlink equipment data.
- Willingness to pay for a *prediction service* is unproven, especially against free alternatives.
- The long-term need for a standalone prediction tool may diminish as Starlink becomes ubiquitous or airlines integrate this information.
- Big Tech players (e.g., FlightAware) could easily integrate this feature.
Next steps
- Conduct user interviews with frequent flyers who use existing prediction tools to understand specific frustrations with those tools and their willingness to pay for enhanced accuracy or features.
- Explore alternative monetization strategies beyond direct subscription, such as affiliate partnerships with airlines offering Starlink or premium features (e.g., alerts).
- Research the feasibility and cost of reliably acquiring tail number data and other critical information, potentially through APIs or data partnerships.
- Focus on a hyper-niche audience (e.g., specific airline's elite status members, or high-end business travelers) to test a premium offering with explicit value.
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
- 11
- Willingness to pay
- 15
- Buildability
- 8
A clear, annoying problem for travelers exists, but a direct free competitor is already in the market, making monetization challenging. The technical difficulty of reliably acquiring and maintaining tail number data for prediction is high for a solo builder.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
While the underlying market for in-flight Starlink is growing, the viability of a paid prediction service with clear differentiation and strong pricing power is weak, especially given existing free tools and high data complexity.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Clear problem for a broad audience, but significant complexity for a solo builder in data acquisition and a challenging monetization path against free competitors.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
While the audience is clear and the value proposition straightforward, the business model is highly challenged by free alternatives and the risk of commoditization or integration by incumbents.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
The demand for Starlink information is real, and the current alternatives are imperfect, but the long-term viability of a dedicated prediction tool is questionable as Starlink becomes ubiquitous.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Conduct user interviews with frequent flyers who use existing prediction tools to understand specific frustrations with those tools and their willingness to pay for enhanced accuracy or features.
- Explore alternative monetization strategies beyond direct subscription, such as affiliate partnerships with airlines offering Starlink or premium features (e.g., alerts).
- Research the feasibility and cost of reliably acquiring tail number data and other critical information, potentially through APIs or data partnerships.
- Focus on a hyper-niche audience (e.g., specific airline's elite status members, or high-end business travelers) to test a premium offering with explicit value.
Who is already there, emerging market
- StarlinkFlights.comVisit
StarlinkFlights.com provides real-time predictions for Starlink WiFi availability across 202 airlines and thousands of routes.
Pricing: Free
- Stardrift.ai/starlinkVisit
A flight search tool that predicts the likelihood of Starlink on board a specific flight based on aircraft type and tail number.
Pricing: Not explicitly stated, but the 'Show HN' post implies free access.
FlightAware is an all-purpose flight tracker that provides live flight status, tracking, and airport information.
Pricing: Free to download (ad-supported) with premium subscriptions available.
- FlightRadar24Visit
FlightRadar24 offers global coverage for live flight tracking with a polished interface and various filtering options.
Pricing: Not explicitly stated, but typically offers free basic tracking with paid premium features.
- satellitemap.spaceVisit
This tool tracks live satellite positions from Starlink, GPS, and other constellations with an interactive 3D globe visualization.
Pricing: Free
What they charge
Recent news
British Airways Begins Starlink Wi-Fi Rollout with Boeing 787 Fleet
Drive Tesla, March 19 2026
British Expat Builds Free Global Starlink Flight Tracker After Frustrating WiFi Experiences
vertexaisearch.cloud.google.com, March 06 2026
Show HN: Will my flight have Starlink?
Hacker News, March 20 2026
The Sky-High Internet Revolution: How to Check If Your Flight Has Starlink (And Why It Matters for Developers)
DEV Community, March 19 2026
British Airways Launches Free Starlink Wi-Fi, First in UK Skies
Businessday NG, March 20 2026
Market signals
The market for in-flight Starlink availability prediction is a growing niche within the larger in-flight connectivity (IFC) market. Starlink is rapidly expanding its presence in commercial and business aviation, with projections suggesting it could connect over 10,000 aircraft by the end of 2034, acquiring a 39% market share in commercial aviation alone. This growth is fueled by increasing demand for high-speed, low-latency internet on flights, driven by passengers who expect seamless connectivity for work and leisure. Airlines are increasingly adopting Starlink, and some are even offering it for free to passengers, a significant shift from traditional paid models.
What frustrates people
- Airlines announce Starlink partnerships but never specify which individual planes are equipped, leading to inconsistent experiences.
- The availability of Starlink on flights is patchy and hard to predict.
- Traditional in-flight Wi-Fi is often slow, intermittent, and expensive.
- Tail numbers, crucial for accurate Starlink prediction, are usually only assigned a few days before a flight, making advance planning difficult.
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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