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  <title>Maritime GPT — Insights</title>
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  <description>Research and analysis on maritime AI, autonomous ships, digital twins and the future of seafaring.</description>
  <language>en</language>
  <lastBuildDate>Sun, 25 Oct 2026 07:00:26 GMT</lastBuildDate>
  <item>
    <title>Can AI predict the safest ocean route? Safety as the optimization objective</title>
    <link>https://maritime-gpt.com/insights/ai-safest-ocean-route/</link>
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    <description>The safest route isn't the fastest or cheapest — it's the one that minimizes the casualty probability. How AI frames safety as an explicit objective and what 'safest' even means.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Collision avoidance under uncertainty: deciding with incomplete knowledge</title>
    <link>https://maritime-gpt.com/insights/ai-collision-uncertainty/</link>
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    <description>Real collision avoidance happens under uncertainty — noisy sensors, unknown intentions, imperfect tracks. The AI that must decide without complete information is the honest version of the problem.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI buoy detection: the small object that matters where the water narrows</title>
    <link>https://maritime-gpt.com/insights/ai-buoy-detection/</link>
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    <description>Buoys are small, hard to see, and safety-critical in channels — the detection problem that's easy in the open sea and hard where it matters. The CV case for the navigation aid.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI night-time maritime vision: the half of the voyage the light doesn't cover</title>
    <link>https://maritime-gpt.com/insights/ai-night-maritime-vision/</link>
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    <description>Half the voyage is dark — and the camera's daytime performance doesn't transfer. Low-light and thermal imaging, the AI that enhances them, and why night is the test the perception stack has to pass.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Radar + camera fusion: the sensor pair that covers each other's blindness</title>
    <link>https://maritime-gpt.com/insights/ai-radar-camera-fusion/</link>
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    <description>Radar finds the contact at range; the camera identifies what it is. Fusing them gives the perception stack range, detail, and the robustness that no single sensor has.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI nautical-chart interpretation: reading the densest document on the ship</title>
    <link>https://maritime-gpt.com/insights/ai-nautical-chart-interpretation/</link>
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    <description>The nautical chart is a century-deep language of symbols, soundings, and warnings — can AI read it well enough to assist passage planning or answer the navigator's question?</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI engine-degradation prediction: the trajectory, not the threshold</title>
    <link>https://maritime-gpt.com/insights/ai-engine-degradation-prediction/</link>
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    <description>Degradation is a trajectory, not a state — the engine that's healthy today is on a curve that ends at failure. AI predicts the curve, not just the point, turning maintenance from reactive to scheduled.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI generator-failure prediction: the machine whose failure blacks out the ship</title>
    <link>https://maritime-gpt.com/insights/ai-generator-failure-prediction/</link>
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    <description>The ship's generators carry every electrical load from propulsion motors to the coffee maker — predicting their failure before the blackout is the machinery AI's highest-stakes problem.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI turbocharger monitoring: the signature of the 30,000 RPM rotor</title>
    <link>https://maritime-gpt.com/insights/ai-turbocharger-monitoring/</link>
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    <description>The turbocharger is the engine's highest-stress rotating component — a rotor at tens of thousands of RPM where a bearing failure is a cascade. AI reads the vibration and temperature signature before the rotor lets go.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI bearing-failure prediction: the one failure mode that's everywhere</title>
    <link>https://maritime-gpt.com/insights/ai-bearing-failure-prediction/</link>
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    <description>Bearings are the universal failure point — in engines, generators, pumps, and compressors alike. The vibration signature is the common language; the AI that reads it is the cross-machinery predictor.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI for pumps and compressors: watching the machines nobody watches</title>
    <link>https://maritime-gpt.com/insights/ai-pumps-compressors-monitoring/</link>
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    <description>Pumps and compressors are the ship's unglamorous workhorses — dozens of them, running constantly, and each a single point of failure for a system the ship needs. AI watches the fleet of auxiliaries no human can babysit.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI fuel-injection monitoring: the per-cylinder read the aggregate gauge misses</title>
    <link>https://maritime-gpt.com/insights/ai-fuel-injection-monitoring/</link>
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    <description>The fuel injector is where fuel economy, emissions, and engine health meet — a fouled injector is wasted fuel, higher emissions, and a cylinder running hot. AI reads the per-cylinder signature the aggregate gauge misses.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Ship-to-twin synchronization: the engineering that keeps the virtual vessel true</title>
    <link>https://maritime-gpt.com/insights/ship-twin-synchronization/</link>
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    <description>A digital twin is only as good as its sync — the moment the model drifts from the physical ship's actual condition, the predictions become fiction. The AI synchronization problem is the twin's hardest engineering challenge.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Can AI read a radar screen? The multimodal test that separates understanding from captioning</title>
    <link>https://maritime-gpt.com/insights/ai-radar-screen-interpretation/</link>
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    <description>Show a vision-language model a radar PPI display and ask it to explain the contacts — the test that reveals whether multimodal AI actually understands maritime displays or just describes pixels.</description>
    <pubDate>Mon, 12 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Can LLMs actually command an autonomous ship? The honest verdict</title>
    <link>https://maritime-gpt.com/insights/can-llms-command-autonomous-ships/</link>
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    <description>The research frontier is LLMs reasoning about navigation — but 'reasoning about navigation' and 'commanding a ship' are different things. What an LLM can and cannot do at the helm.</description>
    <pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate>
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  <item>
    <title>Can AI make collision-avoidance decisions safely? Both sides of the evidence</title>
    <link>https://maritime-gpt.com/insights/ai-collision-decision-safety/</link>
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    <description>The core question of autonomous navigation — can an AI be trusted with the collision-avoidance decision? The honest answer weighs the demonstrated competence against the failure modes that remain.</description>
    <pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI cybersecurity for autonomous ships: when the ship itself is the attack surface</title>
    <link>https://maritime-gpt.com/insights/autonomous-ship-cybersecurity/</link>
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    <description>An autonomous ship is a cyber-physical system where the cybersecurity IS the safety case — every sensor, model, and link the ship trusts is a way in for an attacker.</description>
    <pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI intrusion detection for ships: the network's watchkeeper</title>
    <link>https://maritime-gpt.com/insights/ai-ship-intrusion-detection/</link>
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    <description>A ship's OT network carries the systems that sail her. AI-driven intrusion detection watches the traffic for the attack signatures conventional firewalls miss — the vessel's internal alarm.</description>
    <pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI detection of abnormal vessel behaviour: the pattern that shouldn't be there</title>
    <link>https://maritime-gpt.com/insights/ai-abnormal-vessel-behavior/</link>
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    <description>A vessel that stops in a traffic lane, circles offshore, or deviates from its route pattern is a signal — of smuggling, distress, or a casualty. AI learns the normal so it can flag the abnormal.</description>
    <pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Cloud vs edge AI for vessels: where the model should live</title>
    <link>https://maritime-gpt.com/insights/cloud-vs-edge-ai-vessels/</link>
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    <description>Where should the AI run — on the ship or in the cloud? The answer is neither simple nor either/or: a layered architecture that puts the reflexes aboard and the reasoning ashore.</description>
    <pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI decision support vs automation: the line between advise and do</title>
    <link>https://maritime-gpt.com/insights/ai-decision-support-vs-automation/</link>
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    <description>The maritime AI stack splits into two kinds of system — the one that advises a human and the one that acts. The line between them is the safety case's most important boundary.</description>
    <pubDate>Sun, 11 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Cybersecurity requirements for AI ships: autonomy's attack surface</title>
    <link>https://maritime-gpt.com/insights/mass-cybersecurity-ai-ships/</link>
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    <description>An autonomous ship is a cyber-physical system — its sensors, links, and decision software are all attack surface. What the MASS Code and class rules require, and what the threat model actually looks like.</description>
    <pubDate>Sat, 10 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>The future role of the shipmaster: authority without a bridge</title>
    <link>https://maritime-gpt.com/insights/shipmaster-future-role/</link>
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    <description>Autonomy doesn't eliminate the master — it relocates them. From the bridge to the remote ops centre, from direct control to supervisory authority — what the role becomes.</description>
    <pubDate>Sat, 10 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>MASS vs conventional automation: the difference is who decides</title>
    <link>https://maritime-gpt.com/insights/mass-vs-conventional-automation/</link>
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    <description>Ships have been automated for decades — autopilots, UMS engine rooms, integrated bridges. So why does MASS need its own code? The difference is decision authority, not automation degree.</description>
    <pubDate>Sat, 10 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Human responsibility aboard AI ships: who answers when the software errs?</title>
    <link>https://maritime-gpt.com/insights/mass-human-responsibility/</link>
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    <description>When an autonomous ship errs, someone must still answer — the Code's hardest question is where human responsibility lives when the decision-maker is software.</description>
    <pubDate>Sat, 10 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>LLMs in maritime operations: the 2026 state of play</title>
    <link>https://maritime-gpt.com/insights/maritime-llm-operations-overview/</link>
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    <description>From chartering desks to bridge wings, LLMs have moved from novelty to operational layer in shipping — the honest map of where they're deployed, where they're useful, and where they're still theatre.</description>
    <pubDate>Sat, 10 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Making an LLM safe for the bridge: the guardrails, not the model</title>
    <link>https://maritime-gpt.com/insights/safe-llm-bridge-operations/</link>
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    <description>An LLM on the bridge is a fluency risk in a safety-critical environment. The architecture that makes it safe isn't the model — it's the guardrails around what it can say, do, and decide.</description>
    <pubDate>Sat, 10 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Digital twin vs AI model: the difference that matters</title>
    <link>https://maritime-gpt.com/insights/digital-twin-vs-ai-model/</link>
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    <description>A digital twin is a model of a specific asset that stays synchronized with it; an AI model is a learned function. They're confused constantly — and the confusion matters for what each can actually do.</description>
    <pubDate>Sat, 10 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>ML vs traditional weather routing: the physics model vs the learned one</title>
    <link>https://maritime-gpt.com/insights/ml-vs-traditional-weather-routing/</link>
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    <description>Traditional routing optimizes against the forecast with ship-response curves; ML routing learns from sailed voyages. The honest comparison of what each approach changes and where each wins.</description>
    <pubDate>Sat, 10 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>How the MASS Code regulates AI decision-making</title>
    <link>https://maritime-gpt.com/insights/mass-code-ai-decision-making/</link>
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    <description>The MASS Code has to regulate a decision-maker that isn't human — how do you certify an algorithm's judgement, bound its authority, and keep a person responsible?</description>
    <pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>MASS Code 2026 → mandatory 2032: the roadmap and its risks</title>
    <link>https://maritime-gpt.com/insights/mass-code-2030-roadmap/</link>
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    <description>The IMO roadmap points to a non-mandatory MASS Code in 2026 and a mandatory one by 2030-2032 after an experience-building phase. What the timeline means, what's still open, and where it could slip.</description>
    <pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>What the MASS Code means for seafarers: the occupation being rewritten</title>
    <link>https://maritime-gpt.com/insights/mass-code-seafarers/</link>
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    <description>The MASS Code doesn't just regulate ships — it redefines the seafarer's role, the master's authority, and the human presence aboard. What autonomy actually does to the people who sail.</description>
    <pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI literacy for maritime officers: the competency that isn't taught yet</title>
    <link>https://maritime-gpt.com/insights/maritime-ai-literacy/</link>
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    <description>An officer supervising AI needs to know what it can and can't do — its failure modes, its confidence, its blind spots. AI literacy is becoming a core maritime competency, and the syllabus doesn't exist yet.</description>
    <pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI collision avoidance with real ship dynamics: the gap between the model and the sea</title>
    <link>https://maritime-gpt.com/insights/collision-real-ship-dynamics/</link>
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    <description>Most collision-avoidance research runs on kinematic point models — but a real ship has inertia, sway, yaw lag and slow response. The dynamics gap is where simulation-trained policies meet their limits.</description>
    <pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI inference on shipboard hardware: compute that survives the sea</title>
    <link>https://maritime-gpt.com/insights/shipboard-inference-hardware/</link>
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    <description>A shipboard AI has to run on hardware that survives vibration, salt, heat, and a ship's power budget — and produces answers without a cloud. The marine edge is a different kind of compute problem.</description>
    <pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>TinyML for marine sensors: intelligence at the instrument itself</title>
    <link>https://maritime-gpt.com/insights/tinyml-marine-sensors/</link>
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    <description>TinyML puts the model inside the sensor itself — a microcontroller that detects the anomaly at the source, on milliwatts, without a server. The bottom layer of the ship's intelligence stack.</description>
    <pubDate>Fri, 09 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI speed optimization: the fuel knob nobody uses fully</title>
    <link>https://maritime-gpt.com/insights/ai-speed-optimization/</link>
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    <description>Speed is the most powerful fuel variable — consumption scales roughly with the cube. AI's job is finding the speed profile that meets the ETA while spending the least bunker.</description>
    <pubDate>Thu, 08 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AIS-trained route prediction: the ocean's memory of where ships go</title>
    <link>https://maritime-gpt.com/insights/ais-route-prediction/</link>
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    <description>Billions of AIS positions form the largest dataset of actual ship behaviour ever recorded. Models trained on it learn the routes ships take — and flag the ones that don't belong.</description>
    <pubDate>Thu, 08 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Routing under forecast uncertainty: optimize against the ensemble, not the point</title>
    <link>https://maritime-gpt.com/insights/uncertain-weather-forecast-routing/</link>
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    <description>A route optimized against a single forecast is optimal for a weather that may not happen. Ensemble routing optimizes against many plausible futures — the honest answer to forecast uncertainty.</description>
    <pubDate>Thu, 08 Oct 2026 12:00:00 GMT</pubDate>
  </item>
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    <title>AI for auxiliary machinery: monitoring the equipment that actually fails</title>
    <link>https://maritime-gpt.com/insights/ai-auxiliary-machinery/</link>
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    <description>Generators, pumps, compressors, purifiers — auxiliary machinery fails more often than the main engine and gets less attention. AI monitoring extends to the plant that actually causes the blackout.</description>
    <pubDate>Thu, 08 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>LLM vs RL for ship navigation: the reasoner and the reflex</title>
    <link>https://maritime-gpt.com/insights/llm-vs-reinforcement-learning-navigation/</link>
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    <description>LLMs reason about COLREGS in language; RL policies learn collision avoidance by trial. The honest comparison: what each actually computes, where each fails, and why the answer is probably both.</description>
    <pubDate>Thu, 08 Oct 2026 12:00:00 GMT</pubDate>
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    <title>AI visual COLREGS interpretation: reading the rules from the pixels</title>
    <link>https://maritime-gpt.com/insights/ai-visual-colregs-interpretation/</link>
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    <description>COLREGS is ultimately a visual problem — the lookout sees the lights, the aspect, the manoeuvre. Can computer vision interpret an encounter well enough to reason about the rules?</description>
    <pubDate>Thu, 08 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Digital twins for autonomous navigation: the sea you can afford to fail in</title>
    <link>https://maritime-gpt.com/insights/digital-twin-autonomous-navigation/</link>
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    <description>An autonomous ship can't learn at sea — the digital twin is where it practices. How twins become the training ground, test harness, and shadow for navigation autonomy.</description>
    <pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate>
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    <title>Real-time ship digital twins: the synchronization problem</title>
    <link>https://maritime-gpt.com/insights/real-time-ship-digital-twin/</link>
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    <description>A digital twin that lags the ship by hours is a history model, not a twin. Real-time synchronization — the data pipeline, latency, and drift correction — is the engineering that makes a twin earn its name.</description>
    <pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Digital twins for voyage optimization: rehearsing tomorrow's passage</title>
    <link>https://maritime-gpt.com/insights/digital-twin-voyage-optimization/</link>
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    <description>A voyage twin that mirrors the ship's current state lets you test tomorrow's route against tomorrow's weather before you sail it — the optimization runs on the copy so the ship doesn't pay for the experiment.</description>
    <pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Port digital twins: simulating the terminal before it happens</title>
    <link>https://maritime-gpt.com/insights/port-digital-twin/</link>
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    <description>Ports were early digital-twin adopters — a terminal is a bounded, instrumentable system where simulation pays. How port twins went from visualization to operational planning and where AI fits.</description>
    <pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Digital twins + predictive maintenance: the engine's live baseline</title>
    <link>https://maritime-gpt.com/insights/digital-twin-predictive-maintenance/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/digital-twin-predictive-maintenance/</guid>
    <description>Predictive maintenance needs to know what 'healthy' looks like for this engine right now — and a calibrated twin does. The combination turns anomaly detection into actual prognosis.</description>
    <pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Multi-agent AI for fleet management: a fleet of agents for a fleet of ships</title>
    <link>https://maritime-gpt.com/insights/multi-agent-fleet-management/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/multi-agent-fleet-management/</guid>
    <description>A fleet is a distributed system of autonomous actors sharing infrastructure and weather. Multi-agent AI assigns each vessel an agent — and the fleet problem becomes coordination, not command.</description>
    <pubDate>Wed, 07 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI copilots for chartering: the assistant in the inbox</title>
    <link>https://maritime-gpt.com/insights/ai-copilot-chartering/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-copilot-chartering/</guid>
    <description>Chartering runs on email, recaps, and judgement under time pressure. An LLM copilot can read the market correspondence — but where does drafting end and deal-making begin?</description>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI copilots for port operations: coordinating the choreography</title>
    <link>https://maritime-gpt.com/insights/ai-copilot-port-operations/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-copilot-port-operations/</guid>
    <description>A port is a real-time optimization problem wrapped in communication — berth plans, ETAs, tug bookings, stowage sequences. An AI copilot's job is the coordination layer, not the quay crane.</description>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI copilots for maritime compliance: the research assistant that must cite its sources</title>
    <link>https://maritime-gpt.com/insights/ai-copilot-compliance/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-copilot-compliance/</guid>
    <description>Maritime compliance is a mountain of overlapping rules — IMO, flag, class, port state, regional. An LLM copilot can retrieve and cross-reference it, but in compliance a confident wrong answer is worse than none.</description>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Maritime LLM vs general-purpose LLM: the build-or-retrieve question</title>
    <link>https://maritime-gpt.com/insights/maritime-llm-vs-general/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/maritime-llm-vs-general/</guid>
    <description>Does maritime AI need a domain-trained model, or is GPT-4 with good retrieval enough? The honest answer splits by task — and matters for where the money goes.</description>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Maritime AI with tool calling: from chatbot to operator</title>
    <link>https://maritime-gpt.com/insights/maritime-ai-tool-calling/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/maritime-ai-tool-calling/</guid>
    <description>An LLM that can only talk is a chatbot. An LLM that can query AIS, pull weather, check the CP and run the calculator is an operator. Tool calling is the bridge — and its failure modes are maritime-relevant.</description>
    <pubDate>Tue, 06 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Computer vision for autonomous ships: the lookout problem</title>
    <link>https://maritime-gpt.com/insights/computer-vision-autonomous-ships/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/computer-vision-autonomous-ships/</guid>
    <description>COLREGS Rule 5 demands a proper lookout 'by sight and hearing'. A survey of maritime computer vision — datasets, failure modes, and why cameras remain an assistant sensor, not the lookout.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>YOLO for maritime object detection: benchmark vs bridge</title>
    <link>https://maritime-gpt.com/insights/yolo-maritime-object-detection/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/yolo-maritime-object-detection/</guid>
    <description>YOLO variants dominate maritime detection research, but the published benchmarks tell a narrower story — strong results on shore cameras, thin evidence on real bridge footage.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI detection in fog and darkness: the vision stack's hardest test</title>
    <link>https://maritime-gpt.com/insights/ai-detection-fog-visibility/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-detection-fog-visibility/</guid>
    <description>COLREGS Rule 5 doesn't pause for weather. A review of the actual evidence on camera and infrared detection in restricted visibility — and why fusion is the only honest answer.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI navigation-light recognition: the night-watch COLREGS problem</title>
    <link>https://maritime-gpt.com/insights/ai-navigation-lights-recognition/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-navigation-lights-recognition/</guid>
    <description>Navigation lights encode a vessel's type, size, status and heading. Reading them correctly is a COLREGS classification task — and one of the least-solved problems in maritime vision.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI detection of small vessels: the rare-class problem</title>
    <link>https://maritime-gpt.com/insights/ai-small-vessel-detection/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-small-vessel-detection/</guid>
    <description>The targets that kill in COLREGS encounters are the ones datasets have least of — small craft, buoys, and debris. What the research shows about small-object detection at sea.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Vision Transformers for ship detection: global context, scarce data</title>
    <link>https://maritime-gpt.com/insights/vision-transformers-ship-detection/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/vision-transformers-ship-detection/</guid>
    <description>ViTs dominate land-based vision benchmarks and bring global context to the image. Do they survive maritime data scarcity and shipboard compute budgets?</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI maritime cyberattack detection: the regulated frontier</title>
    <link>https://maritime-gpt.com/insights/maritime-cybersecurity-ai-detection/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/maritime-cybersecurity-ai-detection/</guid>
    <description>Ships are now regulated cyber targets — IACS UR E26/E27 is mandatory on newbuilds since July 2024. What AI intrusion detection can actually do aboard, and where the evidence is thin.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Adversarial attacks on maritime AI: the sensor stack's weakest layer</title>
    <link>https://maritime-gpt.com/insights/adversarial-attacks-maritime-ai/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/adversarial-attacks-maritime-ai/</guid>
    <description>Adversarial perturbations can fool vision and ML classifiers on land. Can the same trick fool a ship's camera, AIS anomaly detector, or collision-avoidance model — and what does the research show?</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI detection of GNSS jamming: the denial problem</title>
    <link>https://maritime-gpt.com/insights/gnss-jamming-detection-ai/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/gnss-jamming-detection-ai/</guid>
    <description>GNSS jamming is the crude sibling of spoofing — deny the signal rather than deceive it. What AI detection of jamming, interference and meaconing actually shows.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>LLM cybersecurity assistants aboard: the SOC-in-a-box question</title>
    <link>https://maritime-gpt.com/insights/llm-cybersecurity-assistants-vessels/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/llm-cybersecurity-assistants-vessels/</guid>
    <description>Ships have no SOC and crews with no security training. Could an LLM triage alerts, explain anomalies, and guide response aboard? What the evidence supports — and the risks it doesn't.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Remote master vs. autonomous master: the command question</title>
    <link>https://maritime-gpt.com/insights/remote-master-vs-autonomous-master/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/remote-master-vs-autonomous-master/</guid>
    <description>The MASS Code splits command between onboard crew, a Remote Operations Centre and the autonomy itself. Where legal authority actually sits — and the gap nobody has filled.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI navigation in congested ports: the density wall</title>
    <link>https://maritime-gpt.com/insights/ai-navigation-congested-ports/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-navigation-congested-ports/</guid>
    <description>Open-sea collision avoidance is hard; a congested approach channel is a different regime — crossing traffic, small craft, tugs, pilots, and inches of margin. What AI actually does there.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI navigation through narrow channels: the physics the rules don't cover</title>
    <link>https://maritime-gpt.com/insights/ai-navigation-narrow-channels/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-navigation-narrow-channels/</guid>
    <description>Canals, narrows and dredged channels are where hydrodynamics, not just rules, decide safety — bank suction, squat, interaction. What AI can and cannot do in confined water.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI decision-making in restricted visibility: the caution problem</title>
    <link>https://maritime-gpt.com/insights/ai-decision-restricted-visibility/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-decision-restricted-visibility/</guid>
    <description>Rule 19 doesn't just demand a lookout in fog — it changes what a safe manoeuvre is. Can AI encode 'every vessel shall proceed with caution' — and does the research even try?</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>How to build a maritime foundation model</title>
    <link>https://maritime-gpt.com/insights/maritime-foundation-model/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/maritime-foundation-model/</guid>
    <description>OceanGPT, AIS-LLM, TG-GPT — maritime foundation models are emerging but fragmented. What 'foundation model' would actually mean at sea, and where the effort stands.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>RAG vs fine-tuning for maritime AI: the freshness-vs-consistency trade</title>
    <link>https://maritime-gpt.com/insights/rag-vs-fine-tuning-maritime/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/rag-vs-fine-tuning-maritime/</guid>
    <description>Should a maritime LLM learn the domain in its weights or fetch it at query time? The RAG-vs-fine-tuning trade, evaluated for rules that change and answers that must be citable.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Hallucination in maritime AI: fluent, confident, wrong</title>
    <link>https://maritime-gpt.com/insights/maritime-ai-hallucination/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/maritime-ai-hallucination/</guid>
    <description>LLMs fabricate fluently — invented citations, wrong rules, plausible-sounding positions. In a domain where the answer can be checked against a regulation, hallucination is the design constraint.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>The AI copilot for shipmasters: advice, not orders</title>
    <link>https://maritime-gpt.com/insights/ai-copilot-shipmaster/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-copilot-shipmaster/</guid>
    <description>Between the pilot book and the bridge watch, a copilot that reads the situation, surfaces the options and flags the risk. What such a tool can be — and the hard line it must not cross.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>The AI copilot for marine engineers: the memory the watch lost</title>
    <link>https://maritime-gpt.com/insights/ai-copilot-marine-engineer/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-copilot-marine-engineer/</guid>
    <description>The engineer's hardest problem is the unfamiliar alarm on an understaffed watch. A copilot that retrieves the manual, the fault history and the last similar case is the tool the role has always needed.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>When the autonomous ship leaves its envelope: the designed exit</title>
    <link>https://maritime-gpt.com/insights/mass-code-operating-envelope/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/mass-code-operating-envelope/</guid>
    <description>The MASS Code's core mechanism is the operating envelope — the conditions under which autonomy is certified. Exiting it isn't an error; it's the designed transition to a fallback the code requires.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI software assurance: certifying the uncertifiable</title>
    <link>https://maritime-gpt.com/insights/mass-code-software-assurance/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/mass-code-software-assurance/</guid>
    <description>The MASS Code needs to verify AI, but AI doesn't submit to type approval like a diesel does. The assurance problem — verifying software that changes, that's opaque, and that's only as good as its data.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>MASS connectivity and the ROC: the link is the system</title>
    <link>https://maritime-gpt.com/insights/mass-code-connectivity-remote-ops/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/mass-code-connectivity-remote-ops/</guid>
    <description>A remotely operated ship is only as autonomous as its worst satellite link. The MASS Code's connectivity requirements and the ROC's role — bandwidth, latency, redundancy, and the failure case.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI risk assessment under the MASS Code: the HAZID that never ends</title>
    <link>https://maritime-gpt.com/insights/mass-code-risk-assessment/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/mass-code-risk-assessment/</guid>
    <description>The MASS Code requires risk assessment, not equipment lists — which means the whole safety case is a hazard-identification exercise for a system whose failure modes are still being discovered.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>What is a maritime digital twin — and what isn't</title>
    <link>https://maritime-gpt.com/insights/what-is-maritime-digital-twin/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/what-is-maritime-digital-twin/</guid>
    <description>Every vendor sells a 'digital twin'. The actual definition is narrower: a live, synchronised model of a physical asset. The difference between a twin and a dashboard is the sync.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>The propulsion digital twin: where the technology is already proven</title>
    <link>https://maritime-gpt.com/insights/digital-twin-propulsion/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/digital-twin-propulsion/</guid>
    <description>Propulsion is the best-instrumented, most-bounded system aboard — which is why it's where digital twins actually work first. What a propulsion twin demonstrates about the wider twin problem.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>DQN vs PPO for collision avoidance: the false algorithm debate</title>
    <link>https://maritime-gpt.com/insights/dqn-vs-ppo-collision-avoidance/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/dqn-vs-ppo-collision-avoidance/</guid>
    <description>Deep Q-Networks and Proximal Policy Optimisation dominate the maritime RL literature — and the honest finding is that the reward shaping matters more than the algorithm.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Computer vision + AIS for collision avoidance: covering each other's blind spots</title>
    <link>https://maritime-gpt.com/insights/cv-ais-collision-avoidance/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/cv-ais-collision-avoidance/</guid>
    <description>AIS sees identity but not the unregistered skiff; the camera sees the skiff but not its intent. The fusion of the two is the perception architecture the research converges on.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Multi-objective voyage optimization: the trade-space problem</title>
    <link>https://maritime-gpt.com/insights/multi-objective-voyage-optimization/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/multi-objective-voyage-optimization/</guid>
    <description>Real voyage planning trades fuel against ETA against weather margins against crew fatigue. Multi-objective optimization formalises the trade — but the weights are a judgement, not a number.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI routing + ECA avoidance: regulation as an optimization constraint</title>
    <link>https://maritime-gpt.com/insights/ai-eca-avoidance-routing/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-eca-avoidance-routing/</guid>
    <description>Emission Control Areas add a hard regulatory boundary to a problem already multi-objective. What the optimization literature does with the ECA constraint — and what it can't.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Which maritime jobs automate first — and which resist</title>
    <link>https://maritime-gpt.com/insights/maritime-jobs-automation-outlook/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/maritime-jobs-automation-outlook/</guid>
    <description>Not all seafaring tasks automate equally. A function-by-function look at what's actually automatable — watchkeeping, paperwork, diagnostics — and what resists: judgement, improvisation, liability.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
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  <item>
    <title>AI fatigue detection: the camera that watches the watchkeeper</title>
    <link>https://maritime-gpt.com/insights/ai-fatigue-detection/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-fatigue-detection/</guid>
    <description>Fatigue is a documented cause of maritime casualties — and now detectable by the same cameras already on the bridge. What fatigue-monitoring AI actually measures, and the privacy line it must hold.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Training seafarers for AI ships: the missing curriculum</title>
    <link>https://maritime-gpt.com/insights/seafarer-training-ai-ships/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/seafarer-training-ai-ships/</guid>
    <description>STCW certifies the seafarer for the ship that was — not the one with AI aboard. The training gap is the unglamorous bottleneck on the whole autonomy programme.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Quantized LLMs aboard: the compute budget that decides what's possible</title>
    <link>https://maritime-gpt.com/insights/quantized-llm-shipboard/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/quantized-llm-shipboard/</guid>
    <description>A ship's compute budget is watts, not datacentre. Quantization — shrinking weights to 4-bit or less — is the lever that makes an LLM run aboard, and the trade-off is the real constraint.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI at sea with intermittent connectivity: the offline-first constraint</title>
    <link>https://maritime-gpt.com/insights/ai-intermittent-connectivity/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-intermittent-connectivity/</guid>
    <description>A ship's satellite link is weather-dependent, coverage-gapped, and contested. Maritime AI must work when it's down — which changes what the architecture is allowed to be.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI-assisted bridge watchkeeping: the co-lookout</title>
    <link>https://maritime-gpt.com/insights/ai-bridge-watchkeeping/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-bridge-watchkeeping/</guid>
    <description>The bridge watch is the oldest safety ritual at sea — and the one AI can honestly augment without replacing. What an assisted watch actually is, and what it must not become.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AI-assisted engine-room watchkeeping: the monitor that reads drift</title>
    <link>https://maritime-gpt.com/insights/ai-engine-watchkeeping/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ai-engine-watchkeeping/</guid>
    <description>The engine-room watch is a human reading gauges for drift the alarm thresholds miss. AI condition monitoring is the strongest case for assisted watchkeeping — and it's the one closest to working.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Vision-language models for ships: the screen that explains itself</title>
    <link>https://maritime-gpt.com/insights/vision-language-models-ships/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/vision-language-models-ships/</guid>
    <description>VLMs can read images and talk about them — the bridge is a wall of images that need reading. Can a VLM interpret a radar scope or an ECDIS chart for a tired watchkeeper?</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>AIS + SAR + optical: the satellite fusion that watches the sea</title>
    <link>https://maritime-gpt.com/insights/ais-sar-optical-surveillance/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/ais-sar-optical-surveillance/</guid>
    <description>Satellite AIS sees the reported; SAR sees through cloud and dark; optical sees detail. Fused, they form the surveillance stack that watches ships that don't want to be seen.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Human trust in autonomous ships: the calibration problem</title>
    <link>https://maritime-gpt.com/insights/human-trust-autonomous-ships/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/human-trust-autonomous-ships/</guid>
    <description>A ship's AI fails if the human trusts it too much or too little. Trust calibration — matching reliance to reliability — is the human-factors problem at the centre of every autonomy stack.</description>
    <pubDate>Mon, 05 Oct 2026 12:00:00 GMT</pubDate>
  </item>
  <item>
    <title>Agentic AI for autonomous ships</title>
    <link>https://maritime-gpt.com/insights/agentic-ai-autonomous-ships/</link>
    <guid isPermaLink="true">https://maritime-gpt.com/insights/agentic-ai-autonomous-ships/</guid>
    
    
  </item>
  <item>
    <title>Agentic AI for marine engineering diagnostics</title>
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