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Roelof Botha joins SpaceX’s board of directors
The former Sequoia Capital leader is filling an "existing vacancy" on SpaceX's board, days after the company went public in the largest IPO ever.
Solar in California surpassed natural gas in the first five months of 2026
Loreline – Tools for writing interactive fiction
After unveiling ridiculously expensive AR glasses, Snap’s stock takes a dive
Snap's long-awaited smart glasses debut hasn't exactly done wonders for the company's stock.
Apple Intelligence may become mandatory in iOS and macOS 27
Now You Don't: When Espionage Meets Magic
FortiBleed – 75k Fortinet firewalls have admin passwords cracked
How Madrid built its metro cheaply
Made a free macOS menu bar app that fixes typing in the wrong keyboard layout
Agentic coding and persistent returns to expertise
I scored 200 blockchain NPM packages for deprecation and hijack risk
Windows 11 25H2 – I'm so happy to not be using this
The hacker sent by Anthropic to calm the government's nerves about AI safety
Readable: https://www.wsj.com/tech/ai/anthropic-mythos-safety-nicholas...
42/60 Days System Design Questions
Your AI agent remembered the user's name. Then it forgot what it was doing. Here's the setup: User asks the agent: book the cheapest flight to NYC, search hotels under $150/night, then compare total trip cost. By step 3, the agent calls the LLM with 8,000 tokens of raw conversation history — and still answers as if it's turn 1. You need a memory architecture before this ships. Which one do you pick? A) In-context window only — full conversation stays in the system prompt. Simple. Breaks at ~15 turns or 8K tokens, whichever comes first. B) Vector memory store — embed past turns, retrieve the top-k by semantic similarity at query time. Works great until "NYC flight" pulls a memory about a past NYC trip instead of the current task. C) Episodic memory with summarization — compress old turns into structured event summaries, inject the relevant ones per request. More complex to build. Much harder to confuse. D) Redis session state — structured key-value store, explicit agent reads/writes. Deterministic. Requires the agent to know what to store and when. One of these collapses past 15 turns. One retrieves the wrong context at exactly the wrong moment. One is the right answer for task-oriented agents. Pick A, B, C, or D — and tell me where you've hit this in production. Full breakdown in the comments.
UK's Social Media Ban: The Monumental Pretext for Total Digital Surveillance
Ask HN: How do you separate intentional test boilerplate from real duplication?
I am maintaining an open-source project (deterministic open source duplicate-code detector) and a user asked for a feature which I don’t have a clear answer on how to implement. This seems a very hard problem to solve: -Tests repeat the same scenario. For a structural detector, this flags as repetition (duplication). However, tests are not something people want to delete from the codebases. -The repetitions from tests (on purpose) end up looking like undesired code duplication and the tools cann