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Elon Musk denies a report about SpaceX’s AI phone prototype

Elon Musk says a report about a SpaceX AI phone prototype is "utterly false." The report, published on Wednesday by The Wall Street Journal, says SpaceX showed off a "handset-like prototype" to some investors before launching its record-breaking initial public offering in June. The device was "slimmer than an iPhone," and they were told it […]

2026-07-02 原文 →
AI 资讯

Stratagems #4: P Walked Into an AI Monitoring POC. P Didn't Run a Single Test.

Exhaust the enemy's strength without fighting. Weaken the strong by nurturing the soft. — The 36 Stratagems, " Wait at Leisure While the Enemy Labors " P flipped the business card over and wrote one letter on the back: P . Then P walked into the conference room. P didn't do opening lines. P doesn't have a name — not yet, not in this series anyway. But if you've read the earlier stories, you'd recognize the signature. The first story — P's own article got flagged as "low quality" by the company's AI moderation system. P dug into the internal API, pulled 347 flagged records — effective accuracy came out to 38%. More false positives than correct identifications. The second story — an AI payment gateway processing $2.8 billion. The CTO backed it with formal verification, claimed it was "mathematically bulletproof." P spent eight months quietly building an adversarial testing pipeline, and proved the gateway would approve illegal transactions. P won both times. P left zero fingerprints both times. After those two jobs, P stopped working for other people. This time, P got brought in as an independent evaluator. Two Companies, One Customer, Zero Questions The customer was a mid-sized industrial IoT firm called FirmCore . Their production-line gear had been running for almost a decade. The monitoring system was going down once a month, and management had finally had enough. They decided to bring in an AI monitoring platform. A good call — right up until they decided to run two vendors through POC at the same time and pick a winner. "We want to see who can actually cover our failure modes," the VP said in the meeting. "We've also brought in an independent evaluator." P was that evaluator. The two AI monitoring companies were MonitorAI and SentryWave . MonitorAI's pre-sales team went first, slides blazing with "99.3% fault coverage, validated across 3 manufacturing customers." SentryWave followed right behind: "99.7% coverage, 7-day deployment" — bigger numbers, bolder font.

2026-07-01 原文 →
AI 资讯

What Feature Makes You Leave a Resume Builder Website?

I'm curious... What's the one feature that instantly makes you stop using a resume builder? For me, it was simple: You spend time creating your resume, everything looks great, and then the site asks you to pay just to download it. That experience inspired me to build Resumship, a resume builder where downloading your resume is completely free. Now I'm thinking about the next features to add, and I'd love to hear from the community. If you were building the ideal resume builder, what features would you include? AI-powered resume suggestions? Better ATS optimization? More templates? Portfolio integration? Cover letter generation? Something completely different? If you have a minute, I'd also love for you to try Resumship and share your honest feedback. 🌐 https://resumship.com Your feedback will directly influence what gets built next. Every suggestion, bug report, or feature request helps make the platform better for everyone. Looking forward to hearing your ideas! 🚀

2026-07-01 原文 →
AI 资讯

Nobody wants to review the robot's 600-line pull request

An agent opened a pull request on our service last week. Six hundred lines. It rewrote how we handle webhook retries and deduplication, an area that is fiddly and easy to get subtly wrong. The diff was clean. The tests were green. The commit messages were better than mine usually are. And I felt the specific dread that I think a lot of engineers are starting to feel in 2026. I was the reviewer. I had not written any of this. I had no idea why it was shaped the way it was. To review it properly, the way I would want my own code reviewed, I was looking at the better part of an hour of carefully reconstructing intent from the code itself. I did not have that hour. So I did what almost everyone does in that situation, which is skim it, decide it looked reasonable, and approve. That moment is the actual problem with AI-written code, and it is not the one people argue about. The bottleneck moved, and most teams have not adjusted The tired debate is whether agents write good code. In 2026 that argument is mostly over. They do. They plan, they read the codebase, they run the tests, they back out of dead ends, they open pull requests that clear most review bars. If you are still litigating whether the code is any good, you have not used a current agent in a while. But here is what follows from that, and it is the part teams have not absorbed: if writing the code is no longer the slow step, then reviewing it is. And review does not scale the way generation does. An agent can produce five well-tested pull requests before lunch. Your senior engineers cannot deeply review five pull requests before lunch, not on top of their own work. The volume went up and the review capacity did not, and something has to give. What gives is the depth of review. It degrades, quietly, into a skim. People approve fluent diffs they have not truly read, because reading them properly costs more time than anyone has. The green check still appears. It just means less than it used to. That is a governan

2026-07-01 原文 →