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Today, we will talk about these stories:
- U.S. lost 42 aircraft, including fighter jets, MQ-9 Reaper drones in Iran war
- Google wants AI inside research labs
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U.S. lost 42 aircraft, including fighter jets, MQ-9 Reaper drones in Iran war
The Operation The U.S. launched Operation Epic Fury on February 28, targeting Iran. Joint American-Israeli strikes against Tehran started the war.
Total Losses The U.S. lost or damaged at least 42 aircraft, including fighter jets and drones, according to a Congressional Research Service (CRS) report.
Aircraft Breakdown Losses include: 4 F-15E Strike Eagle fighter jets, 1 F-35A Lightning II, 1 A-10 Thunderbolt II ground-attack aircraft, 7 KC-135 Stratotanker refuelling aircraft, 1 E-3 Sentry AWACS, 2 MC-130J Commando II special ops aircraft, 1 HH-60W Jolly Green II helicopter, 24 MQ-9 Reaper drones, and 1 MQ-4C Triton drone.
Notable Incidents Several aircraft were lost or damaged during a search-and-rescue mission after an F-15E was shot down over Iran in April. An A-10 and two MC-130Js were intentionally destroyed on the ground in Iran during that operation. A KC-135 refueling tanker crashed in friendly airspace over Iraq, killing all six crew members aboard.
Cost The Pentagon’s cost estimate for military operations in Iran has risen to $29 billion, largely due to refined estimates on repair and replacement costs for equipment.
Caveats The figures may still be revised due to classification restrictions, ongoing combat operations, and attribution challenges. Each MQ-9 Reaper costs around $30 million and can carry up to 16 Hellfire missiles.
Google wants AI inside research labs
Google wants Gemini to sit closer to the bench, the code editor, and the paper pile.
The company introduced Gemini for Science, a group of experimental tools for hypothesis generation, computational discovery, and literature review. It is also launching Science Skills in Google Antigravity, with access to more than 30 life science databases and tools.
The useful part is the workflow focus. Google is not just pitching a smarter chatbot here, it is trying to take over the slow middle steps of research, from reading papers to testing code variations.
That makes the product more credible. A scientist staring at a table of citations at 11 p.m. does not need magic, just fewer manual steps and better checks.
The harder question is trust. Google says claims will have clickable citations, and that more than 100 institutions are helping test the systems.
That sounds serious, but scientific judgment is still the scarce part. What happens when the fastest tool in the room also shapes which questions get asked?
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