LeetCode MEDIUM 833 Find And Replace in String Summary

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Quick facts

Problem gist

The input is a base string s and three parallel arrays:

  • indices[i] says where a possible replacement starts.
  • sources[i] says what text must already appear there.
  • targets[i] says what text to write if the source matches.

Every replacement is checked against the original string, not against a partially edited string. That “simultaneous” detail is the core of the problem. If s = "abcd", indices = [0, 2], sources = ["a", "cd"], and targets = ["eee", "ffff"], both checks happen on "abcd", so the answer is "eeebffff".

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Scientific American 202606 The New Moon Race Summary

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Artemis II was designed as a test flight, but Nadia Drake frames it as something larger: the moment human spaceflight returned to the moon after more than half a century. The mission did not land. Instead, commander Reid Wiseman, pilot Victor Glover, mission specialist Christina Koch and Canadian mission specialist Jeremy Hansen rode NASA’s Orion spacecraft on a looping 10-day path around the moon, proving out the systems that are supposed to support later lunar landings and, eventually, a sustained human presence near the lunar south pole.

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The Economist 20260509 The rent is too damn AI Summary

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This summary covers The Economist’s May 9th, 2026 Finance & economics article on artificial intelligence and China’s office-property market, published under the headline The rent is too damn AI and listed in the contents as AI rescues Chinese landlords.

The article uses a neat irony to explain a real-estate rebound. A court in Hangzhou, one of China’s leading AI hubs, ruled that firms cannot simply dismiss workers and replace them with artificial intelligence. That is good news for employees. It is also good news for commercial landlords, because office towers do not earn rent from software agents. They need people in seats, and China’s newest source of people in seats is the AI boom.

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The Pragmatic Engineer 20260527 Building OpenCode with Dax Raad Summary

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The Pragmatic Engineer surfaced this May 27, 2026 podcast episode, and the original episode page is Building OpenCode with Dax Raad.

Open source becomes the product wedge

The episode is interesting because OpenCode is not framed as another AI coding tool trying to win by having slightly better prompts or a slicker interface. Dax Raad’s account is more strategic: OpenCode saw that no one had clearly claimed the open-source AI coding harness category, then moved hard into that position. In a market crowded with proprietary agents, wrappers, IDEs, and model-specific workflows, that positioning gave developers a simple reason to care.

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2026-05-27 Social General Briefing Summary

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Several killed as train and school bus collide in Belgium, local media report (r/news)

US strikes were ‘gross violation’ of ceasefire, Iran says (r/news)

Ireland to ban goods from Israeli settlements in West Bank by July (r/worldnews)

What is virtually inevitable at this point, yet most people don’t see it coming? (r/AskReddit)

TIL that some archeologists have theorised that the Trojan Horse story is a metaphor for a massive earthquake which destroyed Troy’s walls and let the Greeks in. (r/todayilearned)

2026-05-27 Social Tech Briefing Summary

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My manager is now super supportive after I quit. (Blind)

SWE Salaries are about to collapse (Blind)

Erin Brockovich launches map of over 4,200 data centres in the US, appeals for local communities to report environmental impact and other costs (r/technology)

US Law Enforcement Warns of ‘Anti-Tech Extremism’ as AI Hatred Grows (r/technology)

Uber burned through its entire 2026 AI budget in four months. Now its COO is questioning whether it’s worth it (r/technology)

Anthropic 20260525 How We Contain Claude Across Products Summary

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What happened

Anthropic’s official engineering blog published How we contain Claude across products, a May 25, 2026 post about the containment architectures behind claude.ai, Claude Code, and Claude Cowork.

The core argument is that agent safety is becoming a blast-radius engineering problem. As agents get more capable, the value of giving them real access rises, but so does the damage they could do if they misbehave, follow malicious instructions, or are steered by hostile content. Anthropic frames risk as two separate quantities: how likely a failure is, and how much harm a failure can cause. Better models, classifiers, prompts, and training can reduce the first quantity, but the second has to be capped by deterministic boundaries such as sandboxes, virtual machines, filesystem controls, and network egress policy.

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Scientific American 202606 What's a Quantum Computer Good For, Anyway? Summary

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The hard question after the race to build one

This article starts from a useful shift in perspective. The field has spent decades asking whether quantum computers can be built at all. Now that companies and laboratories can assemble machines with hundreds or thousands of qubits, the sharper question is what those machines will actually be good for.

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The Economist 20260509 A MAD Problem Summary

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This summary covers The Economist’s May 9th, 2026 China article listed in the contents as US-China AI negotiations and published under the headline A MAD problem.

The article argues that artificial intelligence is becoming a strategic problem of the sort usually associated with nuclear weapons: both America and China fear the danger, but neither wants to slow down if the other might race ahead. That tension is what makes AI diplomacy urgent and difficult. The technology promises economic power, military leverage and scientific advantage. It also raises the prospect of cyberattacks, biological misuse and systems that become hard for humans to control.

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The Pragmatic Engineer 20260526 State of the software engineering job market in 2026 Summary

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The Pragmatic Engineer surfaced this May 26, 2026 deepdive through its latest archive and feed, and the original piece is State of the software engineering job market in 2026.

The useful thing about this report is that it refuses the two simplest stories about engineering hiring. The market is not back to the easy-growth world of 2021, but it is also not frozen. Hiring is recovering, unevenly, and the shape of that recovery says a lot about what companies now value: proven engineering ability, AI-adjacent skills, industry context, and proximity to the places where companies are still willing to concentrate teams.

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