I built a real-time OSINT dashboard pulling 15 live global feeds
Sup HN,So I got tired of bouncing between Flightradar, MarineTraffic, and Twitter every time something kicked off globally, so I wrote a dashboard to aggregate it all locally. It’s called Shadowbroker.I’ll admit I leaned way too hard into the "movie hacker" aesthetic for the UI, but the actual pipeline underneath is real. It pulls commercial/military ADS-B, the AIS WebSocket stream (about 25,000+ ships), N2YO satellite telemetry, and GDELT conflict data into a single MapLibre instance.Getting this to run without melting my browser was the hardest part. I'm running this on a laptop with an i5 and an RTX 3050, and initially, dumping 30k+ moving GeoJSON features onto the map just crashed everything. I ended up having to write pretty aggressive viewport culling, debounce the state updates, and compress the FastAPI payloads by like 90% just to make it usable.My favorite part is the signal layer—it actually calculates live GPS jamming zones by aggregating the real-time navigation degradation (NAC-P) of commercial flights overhead.It’s Next.js and Python. I threw a quick-start script in the releases if you just want to spin it up, but the repo is open if you want to dig into the backend.Let me know if my MapLibre implementation is terrible, I'm always looking for ways to optimize the rendering.
FL score
out of 100
Verdict
high confidence
Competition
6
competitors found, emerging market, funded players
Trend
4 community mentions
A real-time, locally deployed OSINT dashboard for independent analysts, offering high performance and unique features like GPS jamming detection, as a cost-effective alternative to expensive enterprise tools.
The pain
The gap
Build angle
Strengths
- Problem is highly specific and severe for target users.
- Solution is already built and proven performant on commodity hardware.
- Unique GPS jamming detection feature is a strong differentiator.
- Addresses major user frustrations of high cost, complexity, and non-transparent pricing of incumbents.
- High market growth potential for OSINT solutions.
Risks
- Monetizing an open-source project can be challenging.
- Market is crowded with well-funded competitors; direct competition on features may be difficult without a clear niche.
- Data licensing costs for some commercial feeds might impact profitability.
- Maintaining and updating diverse real-time global feeds requires ongoing effort and reliability.
Next steps
- Conduct user interviews with independent OSINT analysts and journalists to validate willingness to pay for a hosted/premium version and identify their most desired paid features.
- Explore potential business models: freemium with core open-source, paid hosted service, premium data feeds, or API access.
- Investigate data licensing costs for commercial feeds to ensure a viable pricing model.
- Build a landing page to capture interest and gauge demand for a commercial offering, detailing pricing tiers and premium features.
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
- 13
This idea solves a very real and specific pain for a niche audience with an already-built, performant solution. The market is competitive but the local, customizable, open-source approach carves out a defensible niche, especially with its unique features.
Value Equation
Dream outcome and how likely it feels, against the time and effort it costs.
This idea has strong market viability with high value, excellent market timing, and a unique differentiator, making it highly attractive from a profit perspective, assuming a clear monetization path for the open-source base.
One-Person Business
Curiosity pull, identity fit, and a path from free value to paid for a solo creator.
This idea demonstrates excellent problem and creator fit, with a clear, specific niche and high potential for building a focused community around its unique features, though monetization needs to be formalized.
Viral Frameworks
Hook strength, shareability, and how cheaply it can be tested.
This micro-SaaS has a clear target audience, strong value proposition, and established distribution channels for an open-source product. The key challenge will be defining and validating a sustainable business model to monetize the existing solution.
Builder Lens
Evidence the problem exists, timing, defensibility, and a model that fits on a napkin.
Shadowbroker solves a critical, growing problem for a specific user with a functional product that demonstrates strong insights and a clear path to initial adoption, positioning it well for future relevance.
Why this verdict
Five lenses, one composite. How scoring works
The angle
This weekend
- Conduct user interviews with independent OSINT analysts and journalists to validate willingness to pay for a hosted/premium version and identify their most desired paid features.
- Explore potential business models: freemium with core open-source, paid hosted service, premium data feeds, or API access.
- Investigate data licensing costs for commercial feeds to ensure a viable pricing model.
- Build a landing page to capture interest and gauge demand for a commercial offering, detailing pricing tiers and premium features.
Who is already there, emerging market
An AI-enabled data solution for global defense, international intelligence, and law enforcement organizations that integrates and analyzes near-real-time data.
Pricing: Starts from $68.00/month. Perpetual software licenses may be priced by CPU core, with Palantir Gotham at $141K per core. Pilot software licenses are priced on a term basis, and organization licenses are priced per annum. Usage-based pricing and various support levels are also available.
An incident detection solution that helps businesses analyze and mitigate high-impact global events and risks using AI technology to process multi-format data.
Pricing: Not publicly disclosed. One user subscription for Dataminr First Alert is $30,000.00 for a 12-month contract.
A media intelligence and social listening platform that tracks brand mentions across news and social media.
Pricing: Not publicly disclosed; custom pricing based on goals, modules, and scale. Basic 1-user subscriptions start around $7,000 per year, with average subscriptions between $15,000-$20,000/year, and enterprise plans over $40,000/year. Annual contracts are standard.
Provides a maritime AI platform for risk management, combining AI with maritime expertise.
Pricing: Windward Core Flex is $605.00 (On Premise), Windward Core Pro is $547.00 (On Premise), Windward Hub Standard is $19.00 (Cloud), Windward Hub Premium is $49.00 (Cloud), and Windward Hub Enterprise offers custom pricing (Cloud). Annual payments receive a 15% discount, and quarterly payments a 10% discount for Windward Core. Custom quotes are provided based on business needs.
Deploys unmanned surface vehicles (USVs) for ocean data collection, maritime domain awareness, and national security efforts.
Pricing: Saildrones cost ~$2,500/day for data collection on a mission-as-a-service basis, with multi-year subscriptions available for defense customers. A 3% discount is offered on orders of 10+ USVs for 90 Mission Days or more.
- Hunt IntelligenceVisit
A social media search tool specializing in location-based data from social media posts for corporate security, public safety, media, journalism, and marketing teams.
Pricing: Not publicly disclosed. Requires contact for pricing.
What they charge
What people say, 4 mentions
We just launched InsForge 2.0: an open source backend built for AI coding agents
r/SaaS
I got 9 paying subs in 4 days, then lost my entire distribution channel overnight. Here's what I'm doing about it.
r/SaaS
I kept forgetting birthdays and anniversaries, so I built an Android app to fix it — here's what I've learned so far
r/SaaS
🚀 congviec.me Just Leveled Up: Agentic AI Now Auto-Scores Projects, Tasks & Costs!
r/SaaS
Recent news
Shadowbroker: dashboard OSINT en tiempo real
El Ecosistema Startup, March 08 2026
ShadowBroker: How Open-Source Intelligence Layers Turn Raw Signals Into Analyst Findings
Medium (by Alborz Nazari), March 20 2026
ShadowBroker is a real-time, multi-domain OSINT dashboard that aggregates live data from dozens of open-source intelligence feeds and renders them on a unified dark-ops map interface.
Reddit, March 12 2026
Show HN: Real-time OSINT Dashboard with 15 Live Global Feeds
AIToolly, March 08 2026
GitHub - BigBodyCobain/Shadowbroker: Open-source...
daily.dev, March 08 2026
Market signals
The Open Source Intelligence (OSINT) market is a large and rapidly growing market. It was valued at approximately $11.65 billion in 2025 and is projected to reach $99.32 billion by 2035, growing at a CAGR of 23.9% from 2026-2035. The growth is driven by increasing cybersecurity threats, the proliferation of public digital data sources, and the rising demand for real-time intelligence insights. AI and machine learning are increasingly integrated into OSINT solutions for faster analysis and proactive threat detection. North America is currently the largest market, with Asia-Pacific expected to be the fastest-growing region.
What frustrates people
- High cost and complexity of tools like Palantir Gotham and Dataminr.
- Lack of transparent pricing and forced engagement with sales teams for platforms like Meltwater, Dataminr, and Talkwalker.
- Slow search capabilities in some established platforms like Meltwater.
- Difficult navigation and limited customization options in platforms like Meltwater.
- Auto-renewal of contracts without user's explicit intent (reported for Meltwater).
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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