My favorite metaphor for brutal cognitive dissonance — the one we will likely experience when the Singularity actually arrives — is “ the knight in the supermarket .” I prefer the word “chevalier,” though, so I’ll be using that going forward. Try to imagine the following scene: a medieval chevalier, somewhere on the land of current Germany, riding his horse, heavily armored, helmet on, big sword. The year is 1450, and our chevalier is just charging in a small battle against some equally armed neighbors. But then something happens. A short circuit in the space-time continuum and our chevalier is fast forwarded to the current times, but in the exact same location. Where, of course, there is a supermarket now. The lights. The shiny shelves with thousands of small, colored objects. The cold near the meat sector. The TVs rotating ads with faces of women talking in a slightly similar language, but saying words he cannot understand. At this moment, it’s safe to say that our chevalier is completely lost. He has no idea how light is made (no “electricity” concept in his mind), no way to know how cold is made inside a building (no “refrigerator”), no way to understand what the tiny packages on the shelves are (“chemistry” is closer to alchemy for him) and no way to understand remote communication (“television” doesn’t simply exist). He can still walk around, but the world will feel almost hostile to him. We’re Not in the Singularity. Not Yet Now let’s get back to the current X timeline, where everybody is screaming that we’re in AGI. In the Singularity. The world will never be the same. This changes everything. But does it, really? Are we experiencing the same cognitive gap as our chevalier in the supermarket? I don’t think so. The world is keep worlding right now, except for a very small percentage of people who are subjecting themselves to some AI-related psychosis. All we’ve done so far is cramming a LOT of compute into tiny digital artifacts that are nothing more than ver

How to Audit Hidden Reminders and Context Usage in Claude Code Logs | Agent Lab Journal Agent Lab Journal Guides Glossary Advanced field guide How to Audit Hidden Reminders and Context Usage in Claude Code Logs Advanced · 45 min read · Local analysis · Updated August 1, 2026 The visible transcript in Claude Code is not necessarily a complete representation of everything recorded around a request. Service messages, internal reminder markers, tool payloads, and usage metadata can exist in session logs without appearing as ordinary chat turns. If you want to know how often ip_reminder occurs—or how input, output, cache creation, and cache read tokens are distributed—you need to inspect the stored records directly and preserve enough structure to avoid misleading totals. In this guide What this audit can establish Concrete investigation case Locate and select one session Preserve an auditable copy Run a quick structural check Build the full local report Interpret reminder and token data Verify the report independently Failure cases and repairs Limitations What this audit can—and cannot—establish This workflow examines one local session stored as JSON Lines (JSONL): a text format in which each line is normally an independent JSON value. It creates a report with: the selected file’s path, size, modification time, and SHA-256 digest; the number of physical lines, parsed records, blank lines, and malformed lines; every record containing the exact, case-sensitive string ip_reminder; the JSON paths at which the marker was found; timestamps and record types when those fields are available; per-record and aggregate input, output, cache creation, and cache read token values; a chronological CSV suitable for a spreadsheet or notebook; a machine-readable JSON report for later comparison. The report shows what is present in the selected file. It does not prove why a reminder was inserted, whether it was transmitted to a model exactly as stored, or how the client’s undocumented inte

A discovery in a solar system 73 light-years from Earth is challenging definitions and “blurring the lines between stars, planets, and moons.”

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

The biggest mistake people make about AI is thinking it’s coming for artists, writers, musicians, or programmers. They’re just first. AI is coming for almost every profession that depends more on a brain than a body. Accountants. Lawyers. Teachers. Consultants. Analysts. Customer service. Marketing. Management. Software engineering. Research. Finance. Medicine. Eventually almost every job where the primary product is human thought. Manual labor only looks safe because robotics hasn’t caught up yet. AI doesn’t have to replace an entire profession to destroy it. It only has to let one person do the work of ten. Companies don’t need AI to be perfect. They need it to be cheaper than you. Once that happens, replacing people stops being a technological question and becomes an accounting decision. For most workers, there is no safe career waiting on the other side. People tell themselves we’ll adapt like we always have. We won’t. The Industrial Revolution replaced muscle while making human intelligence more valuable. AI replaces the intelligence behind the work itself. Every previous technological revolution created new industries that still needed millions of people. AI is being built for the opposite purpose: producing more with fewer humans. The next comforting myth is that people will simply buy human-made products instead. No, they won’t. There will always be a luxury market for handmade art, music, books, furniture, and clothing. There are still people who buy mechanical watches and vinyl records. That’s a niche—not an economy. Most people buy whatever is cheaper, faster, easier, and good enough. Businesses care even less. They exist to reduce costs, increase output, and beat competitors. Sentiment doesn’t survive quarterly earnings. There is no hidden human economy large enough to rescue everyone AI makes unnecessary. The consequences don’t stop with unemployment. Workers are also consumers and taxpayers. If hundreds of millions of people lose well-paid jobs, they s

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry. ...

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Marketing teams sit on more data than ever, yet many still spend a large part of the week just assembling reports. By the time the numbers are clean and explained, the window to act has already narrowed. A more practical use of AI in this space focuses on detection and explanation rather than another dashboard. The system watches for unusual movements, surfaces the likely drivers, and presents them in plain language. Analysts spend less time pulling the same weekly views and more time deciding what to do next. The useful part is speed. When something shifts in performance, the team hears about it earlier instead of discovering it during a scheduled review. Of course this only works if the underlying data is reliable, otherwise the explanations become noise. Is anyone here already using AI this way for marketing performance, or are most teams still in the experimental stage? submitted by /u/Cloudy_Day912 [link] [留言]

Hey guys, What is currently the best tool for autocomplete in vscode given the recent changes to GitHub copilot free tier usage limits? Thanks in advance! submitted by /u/TalkHot2112 [link] [留言]