Is Hormuz open yet?
I built this because I was interested in the data. Didn't fully get it to what I wanted, but thought I'd share it nonetheless. Maybe someone has better data sources they could share!Turns out live ship tracking APIs are expensive so I manually just copied the json from https://www.marinetraffic.com/en/ais/home/centerx:57.4/cente... I'll probably have an ai agent do the same thing on some cron interval, if this gets any fanfare.To actually know if the port is open without live ship tracking I found https://portwatch.imf.org/pages/cb5856222a5b4105adc6ee7e880a... which was perfect, except it has 4 day lag!I also thought of adding news feed parsing or prediction market data to get a more definitive answer on if it's open right when you load it, but I spent a few hours and am gonna move on for now.
FL score
out of 100
Verdict
high confidence
Competition
23
competitors found, emerging market, funded players
Trend
No signal yet
A hyper-niche tool to provide real-time status of the Strait of Hormuz for maritime and energy sectors, facing significant data acquisition hurdles.
The pain
The gap
Build angle
Strengths
- Addresses an extremely specific, high-stakes problem.
- High pain point for a targeted, paying audience (maritime, energy, finance).
- Clear value proposition (real-time clarity reduces immense risk).
- Existing solutions are either too slow or too expensive for this specific query.
Risks
- Reliable, real-time data acquisition is a major technical and financial hurdle.
- Monetizing a single binary answer might be difficult against free news or broader enterprise tools.
- The market, while high-value per user, is extremely niche and event-driven.
- Legal implications of scraping and potential for data source changes/blockages.
- Low compound value for future projects.
Next steps
- Deeply research sustainable and legal real-time data sources beyond manual scraping (e.g., specific API partnerships or novel data aggregation techniques).
- Conduct interviews with potential users (maritime logistics, oil traders) to confirm willingness to pay for this specific solution at various price points.
- Explore potential partnerships with news agencies or maritime intelligence providers for data access.
- Build a proof-of-concept that demonstrates reliable, low-latency data aggregation and validation, before fully committing to the product.
Fly Labs Method
Is the pain real, is there a gap, is it the right time, can one person build it.
- Problem clarity
- 29
- Solution gap
- 22
- Willingness to pay
- 10
- Buildability
- 7
The idea addresses a real, severe, and specific problem in a potentially underserved niche, but faces significant challenges in reliable data acquisition and monetizing a very narrow offering.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
Niche product with high individual value but limited broader market and monetization potential without proprietary data sources.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
Clear problem statement for a niche audience, but challenges in data acquisition, monetization, and broader market appeal for a solo builder.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
Clear value proposition for a niche but faces significant risks in data acquisition and validating real-time reliability.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Addresses a deeply specific and painful problem, but faces challenges in acquiring reliable real-time data and scaling as a standalone solution.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Deeply research sustainable and legal real-time data sources beyond manual scraping (e.g., specific API partnerships or novel data aggregation techniques).
- Conduct interviews with potential users (maritime logistics, oil traders) to confirm willingness to pay for this specific solution at various price points.
- Explore potential partnerships with news agencies or maritime intelligence providers for data access.
- Build a proof-of-concept that demonstrates reliable, low-latency data aggregation and validation, before fully committing to the product.
Who is already there, emerging market
- HORMUZ AIVisit
HORMUZ AI provides end-to-end intelligent monitoring and management for maritime security, energy and trade flow optimization, environmental protection, and geopolitical analysis, utilizing AI-powered monitoring, predictive models, and simulations.
Pricing: Not available publicly.
Julius AI is a collaborative AI data analysis tool that uses natural language queries, shared notebooks, and visual insights to help teams explore data together.
Pricing: $29.16/month (billed annually)
Databricks offers a unified analytics platform for data engineering, machine learning, and analytics, supporting enterprise-scale collaboration with AI-assisted analytics.
Pricing: Pay as you go
Power BI connects data across Microsoft Excel, Teams, and Azure to create visual dashboards and supports AI-driven insights through Copilot.
Pricing: $14/user/month
Tableau provides visual AI insights with auto-generated narrative summaries from Salesforce Einstein for collaborative data platforms.
Pricing: $75/user/month for the Creator license
Qlik Sense offers augmented team analytics with an associative engine for dynamic, interactive data exploration.
Pricing: $200/month for 10 users
- camelAIVisit
camelAI is a conversational analytics tool that allows teams to create dashboards and summaries using natural language prompts without code.
Pricing: Not available publicly.
- FastioVisit
Fastio is a cloud storage platform built for AI agents and human-agent collaboration, providing persistent file storage and memory for agentic workflows.
Pricing: 50GB of free storage for AI agents
Google Vertex AI is a unified platform for building, deploying, and scaling ML models, offering a complete set of tools for MLOps teams.
Pricing: Usage-based (Google Cloud pricing)
Glean is an AI-driven enterprise search and knowledge discovery platform that connects and surfaces information from over 100 applications using generative AI.
Pricing: Not available publicly, tiered pricing based on user count, feature access, or usage volume
Asana is a comprehensive task management platform enhanced with AI capabilities, particularly effective for marketing and operations teams seeking clearer project visibility and accountability.
Pricing: Free for up to 15 users, paid plans starting at £9.25 per user per month
ClickUp is a comprehensive workflow and integration hub with AI enhancements for task automation and resource management.
Pricing: Not available publicly, offers a free tier and various paid plans.
What they charge
Recent news
- Intel, Google Deepen Collaboration to Advance AI Infrastructure
Intel, April 09 2026
- AI is becoming more widespread in collaboration tools
App Developer Magazine, April 09 2026
- Inner AI Raises R$30M Seed Round
The SaaS News, April 10 2026
- The Hormuz Crisis Exposes AI's Hidden Inequality Problem
Medium, April 04 2026
- Hormuz Is The Hidden Risk To The AI Economy
NDTV, March 06 2026
Market signals
The market for AI-powered data collaboration and communication tools is experiencing significant growth, with the AI in Enterprise Communications and Collaboration Market valued at $32.2 billion in 2023 and projected to reach $130.3 billion by 2033, growing at a CAGR of 15.0%. Similarly, the Human AI Collaboration Market is predicted to reach $1063.71 billion by 2035 with a staggering 39.60% CAGR. Key drivers include the increasing need for efficient communication, cloud-based AI solutions, and the growing use of AI for natural language processing and machine learning in enterprise applications. Recent funding rounds in the broader AI space are substantial, with OpenAI raising a record $110 billion in February and an additional $12 billion in March 2026. The focus on data sharing and data quality, along with the rise of data mesh architectures and the crucial role of LLMs in enhancing data engineering, are also significant trends. Many companies are rushing to democratize data and AI to empower employees and overcome data silos.
What frustrates people
- Traditional collaboration tools like Google Drive or Dropbox were not built for the scale of AI development, leading to challenges in versioning terabyte-scale datasets, tracking thousands of model experiments, and managing autonomous agent output.
- Teams struggle to accelerate GenAI projects when information is trapped in silos and data management is split across many different tools.
- The need for better ways to assess talent beyond resumes and Zoom calls for remote hiring is a significant challenge.
- Data quality issues often stem from operational discrepancies, such as changes made by individuals or inaccuracies in data received from providers.
- Many data catalogs require complex setups with multiple components like Elasticsearch, Kafka, and graph databases, making them difficult to run without a dedicated platform team.
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
Questions about this idea?
FlyBot reads the scoring and gives you a second opinion on “Is Hormuz open yet?”.