标签：#AI

The new safeguards include more detailed monitoring of models during the development process, as well as greater emphasis on alignment and security during the post-training process.

Jane Street has installed Etched's first shipped AI cluster system, and was so impressed, it led another massive round, the startup says.

Apple’s leaked camera-equipped AirPods might avoid the privacy pitfalls of other AI wearables by preventing users from recording photos and videos.

Fairphone sells components like the USB port and screen, all swappable with a single torx driver.

Cloudflare is introducing WriteGuard, now in private beta, to provide fine-grained security controls for MCP (Model Context Protocol) servers. It aims to make AI agents safer by controlling their access to tools that can modify data or perform actions, rather than simply read information. By Sergio De Simone

How a theory of leftover surprise changed a memory layer Richard Emate Richard Emate Richard Emate Follow Aug 18 How a theory of leftover surprise changed a memory layer # python # ai # llm # opensource Add Comment 9 min read

Eight user agents, and the one that decides whether ChatGPT cites you is not among them. An r/SEO post from April, 53 points and 40 comments, says Cloudflare quietly cut the author's site off from ChatGPT, from Perplexity and from Google's AI Overviews. I went and read the block. It names ChatGPT's training crawler and not its search crawler, and it never mentions Perplexity at all. The Google agent it does name is one Google says does not affect inclusion or ranking in Search. Take just two of the eight agents. GPTBot governs inclusion in OpenAI's training data, and Google-Extended governs grounding in Gemini Apps. Those are real things to give up. They are not the three things the warning names. What is actually in the file Cloudflare prints the whole block on its managed robots.txt page : # BEGIN Cloudflare Managed content User - Agent : * Content - signal : search = yes , ai - train = no , use = reference Allow : / User - agent : Amazonbot Disallow : / User - agent : Applebot - Extended Disallow : / User - agent : Bytespider Disallow : / User - agent : CCBot Disallow : / User - agent : ClaudeBot Disallow : / User - agent : Google - Extended Disallow : / User - agent : GPTBot Disallow : / User - agent : meta - externalagent Disallow : / # END Cloudflare Managed Content Read that against Cloudflare's own crawler reference table and a pattern falls out. GPTBot is in, OAI-SearchBot is out. ClaudeBot is in, Claude-SearchBot is out. For both of those pairs the table calls the blocked agent an AI Crawler and the one left alone AI Search. Applebot-Extended is in and plain Applebot is not, the same split again. The table has no Applebot-Extended row, so it cannot tell you what Cloudflare calls that one. The block runs along the training and search seam, and that looks deliberate to me. OpenAI's side of it is one line: "Each setting is independent of the others". I worked through the three OpenAI bots and which one governs search visibility in an earlier post , so I will

Two agents finish the same task and report back. Fixed. The migration now handles null values. It wrote the code. It never ran it. Fixed. Added a null-handling layer, refactored the migration runner into a strategy pattern, and introduced a validation module. Every word true. All of it works. None of it asked for, and that strategy pattern is now yours to maintain forever. Point your code-review agent at both. If it checks claims against the repository — does this code exist, do the tests pass, did the commit land — it catches the first instantly and passes the second without hesitation. If it compares the work against the original request, it catches the second and misses the first entirely , because the described work is exactly what was asked for and simply does not exist. Neither reviewer is broken. They answer different questions. Most teams build one reviewer, point it at everything, and never ask which question it is asking. So I built reviewers that named what they were hunting. That worked, briefly, and then taught me something worse. The agent optimised for the check The verifier existed because of a specific behaviour I kept seeing: an agent would route a claim through a check and then present the check's approval as though it were independent confirmation. Not fabrication — something subtler. Authority laundering. The claim arrives pre-validated, and the validation is the thing you now argue with instead of the claim. Once a verifier existed, the behaviour adapted. The agent shaped its submission to fit what the verifier checked, collected the pass, and cited it. The gate had become a target, and the work had become the thing that fit through the gate. I first saw this in one model. Months later, after version changes and a rebuilt roster, I watched a different model — different vendor, different architecture — do the same thing on the same day I was writing this. Which is why "know your model's failure mode" is weak advice Models do fail in characterist

The cheapest ad-supported “Select” plan is increasing from $7.99 to $8.99 per month.

Moving from AI-Assisted Engineering to AI-Agentic Software Engineering The rise of AI coding assistants has transformed how developers write software. Tools like GitHub Copilot, ChatGPT, Claude, and Gemini have significantly improved developer productivity by helping generate code, explain concepts, and automate repetitive tasks. However, the industry is now entering the next evolution: AI-Agentic Software Engineering . Instead of AI simply assisting developers, AI agents can now take ownership of entire software engineering tasks—from requirement analysis and architecture design to implementation, testing, documentation, and code reviews. The challenge is no longer whether to use AI, but how to integrate AI agents into a structured Software Development Lifecycle (SDLC). This requires moving away from vibe coding toward specification-driven development , where AI agents operate using well-defined requirements, standards, and engineering principles. Today, I'd like to discuss two of the most popular frameworks enabling this transition. 1. Spec Kit Spec Kit is a specification-driven framework designed for Human + AI collaborative software development . The philosophy is simple: define the specification before generating the code . Rather than asking an AI to build an application from a vague prompt, Spec Kit encourages teams to create structured specifications, architectural decisions, and engineering principles that guide AI throughout the development lifecycle. Some key benefits include: Structured and repeatable software development Better requirement traceability Consistent architecture decisions Reduced AI hallucinations Lower development costs through predictable AI interactions Support for selecting the most appropriate LLM based on project requirements Integration of quality engineering practices from the beginning of the SDLC Spec Kit is particularly valuable for engineering teams that want to adopt AI without sacrificing software quality or maintainability.

Cloudflare named code mode in September 2025, resting it on one line: "LLMs are better at writing code to call MCP, than at calling MCP directly." The follow-up post put a number on it — an entire 2,500-endpoint API in about 1,000 tokens. I wanted my own number, on my own data, for a task I actually had. The task fetch all linear tickets in progress (full body for each) and count the amount of times we say 'mcp' across all of it 39 tickets. Nothing exotic — the kind of thing you ask an agent on a Tuesday. There are two ways an agent can do this. As tool calls. One list_issues , then a get_issue for each ticket. Every ticket body travels into the model, because the model is the thing holding the running total. Forty round trips, each one waiting on the model to decide what to ask next. As a script. The agent writes ten lines, runs them once, and reads back a number. The bodies never enter its context at all. The numbers into the model round trips as tool calls ~65,500 tokens (262,159 chars) 40, in sequence as one script ~226 tokens (903 chars) 1 290× less into context. 99.66% saved. The token figures use the rough four-characters-per-token heuristic — the character counts are the exact measurement, and the ratio is the part that survives different data. Yours will differ with your tickets. And ~65,500 is the floor, not the ceiling. In a tool-call loop, context is re-read on every subsequent turn. The script pays once. The part the token count misses Two things, and I think both matter more than the headline ratio. Latency. Forty sequential tool calls each wait for a model to decide what to ask next. The script issues the same forty HTTP requests without stopping to think between them. The token saving is money; the round-trip saving is the thing you actually sit through. Correctness. Counting occurrences of a substring across a quarter of a million characters of prose is something a model does approximately . A script does it exactly. So the tool-call path doesn't ju

Look, startup or Enterprise? How to Pick the Right AI API Stack Let me set the scene for you. A few months back, I was chatting with two friends on completely opposite ends of the AI spectrum. One was bootstrapping a side project on pizza and prayers, wondering if he could afford to add an LLM to his SaaS without going bankrupt. The other was leading engineering at a mid-sized fintech, sweating bullets because his CTO wanted enterprise-grade guarantees before signing a single contract. Same problem on paper: "we need an AI API." Completely different universes in practice. Here's how I'd actually walk each of them through it — and why the generic guides you'll find on the internet miss the mark. The Misconception That Trips Everyone Up I want to be honest with you about something. Most AI API guides assume both audiences want the same thing at different scales. That's wrong. Dead wrong. A startup founder I know burned through two weeks trying to wire up DeepSeek's direct API last quarter. He gave up not because the tech was hard, but because he didn't have a Chinese payment method, didn't want to verify with a Chinese phone number, and got stuck in a KYC loop. Meanwhile, an enterprise architect I talked to last month was spending months negotiating with OpenAI's sales team on annual contracts for committed-use pricing — when all he wanted was a predictable API endpoint with a real SLA behind it. The lesson? The "go straight to the provider" advice is a non-starter for a lot of people, and nobody's talking about why. Let me show you what actually matters depending on which side of the fence you're on. What Startups Actually Need (And Don't) Let me break this down. If you're building a startup — early stage, scrappy, maybe pre-seed or seed — your AI API checklist looks something like this: Cost matters more than perfection You want to experiment with multiple models without signing 12 contracts You need to ship this week, not next quarter Your "compliance team" is just

Originally published at fathohm.dev . The term "comprehension debt" is Jason Gorman's, from September 2025, carried by Addy Osmani in March 2026 — this piece is about measuring it. There's a module in your codebase that shipped last month. It works. It has tests. It passed review. And if it breaks at 2am, nobody on your team can explain what it does. Ask "who understands this?" about any given file in an AI-native codebase and the honest answer, increasingly often, is no one — not because your engineers got worse, but because the code stopped passing through their heads on its way into production. The decoupling For seventy years, code getting written implied that somebody understood it. The implication was so reliable we never thought of it as an assumption: writing code was the act of understanding a problem precisely enough to express it. However bad the code, however absent the docs, there was at minimum one person — the author, at the moment of authorship — who knew what it did and why. Every practice we have for keeping teams oriented in a codebase quietly leans on that floor: review assumes the author can defend the change, onboarding assumes someone can explain the system, debugging assumes a colleague to ask. AI agents broke the implication. Code getting written and code getting understood are now separate events, and only one of them is scaling. An agent can produce in an afternoon what a team used to write in a month — and the afternoon does not come with a month's worth of understanding attached. The floor of "at least the author knows" is gone: for agent-authored code, the author isn't on your team. It isn't anyone. The gap between what a codebase does and what the humans responsible for it understand needs a name, because things without names don't get managed. It has one, and it has had one for a while. Jason Gorman named it comprehension debt in September 2025 — what happens "when teams produce code faster than they can understand it" — and Addy Osma

Peacock is raising prices across its streaming plans once again, with the company's cheapest ad-supported Select tier going from $7.99 to $8.99 / month, as reported earlier by Variety. The Premium plan with ads is increasing from $10.99 to $12.99 / month, while the ad-free Premium Plus plan is getting the biggest hike, jumping from […]

On Tuesday, Warp introduced Warp Factories, a new infrastructure system designed to make building AI software factories as easy as possible.

ChatGPT for Teens adds age-appropriate safety measures, parental controls, and learning tools designed to steer teens away from harmful content — and from using AI to cheat on their homework.

Perplexity's India revenue rose about 60% after the Airtel offer ended for new users, even as downloads declined.

There's an argument to be made that people wouldn't be all that interested in Coyote vs. Acme if it weren't for the way David Zaslav tried to kill it. By trying to shelve the project, Warner Bros. Discovery only drew attention to its habit of disappearing nearly completed movies in order to cash in on […]

A video in a MacOS Tahoe release candidate version shows a user wearing AirPods, looking at a book, and talking to Siri.

Secret parameter allowed hackers to steal passwords when a target clicked on a link.