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Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR GPT-5.5 launched, with meaningfully better autonomous task execution and a major upgrade to OpenAI’s Codex app, which can now browse the web, edit spreadsheets, and work through multi-hour tasks with less hand-holding. DeepSeek V4 arrived as the most capable open-weight model yet, handling million-token contexts at a fraction of the memory cost, and designed to run on Chinese chips, not just NVIDIA hardware. Anthropic raised its run-rate revenue to $30B and signed a massive compute deal with Amazon, signaling the company is scaling infrastructure to match surging demand. Google and others poured billions more into Anthropic, with Bloomberg reporting Google plans to invest up to $40B, as the race to back frontier AI labs accelerates. AI agents are starting to do research autonomously: Anthropic published results showing Claude agents outperformed human researchers on an AI safety problem, at a cost of $22 per hour of AI work. Story of the Week: The AI Assistant Race Intensifies OpenAI launched GPT-5.5 this week, and based on Ethan Mollick’s early access writeup , the upgrade is real. The headline change is not raw intelligence but autonomy: the model is noticeably better at executing long, multi-step tasks without constant correction. Mollick fed it a decade of disorganized research data and four prompts later had a draft academic paper, including a real literature review and sophisticated statistics. His verdict: it would have passed as a strong second-year PhD project. ...

Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR Anthropic launched Claude Opus 4.7 and Claude Design, its most capable model yet paired with a new AI-powered design tool that lets anyone create prototypes, decks, and marketing assets from plain English descriptions – a direct challenge to Figma and traditional design workflows. AI coding agents are now writing production code at industrial scale: Stripe generates 1,300+ AI-written code submissions per week, Ramp attributes 30% of merged code to agents, and new research shows AI can autonomously reimplement 16,000-line software projects that would take human engineers weeks. Agent security is an urgent, underaddressed problem: A Google DeepMind paper catalogued six categories of attack that can manipulate AI agents into leaking data, following malicious instructions, or being hijacked – with no easy fixes yet. AI researchers are sharply revising timelines upward: Multiple prominent forecasters doubled their estimates of how soon AI could automate AI research itself, now putting the odds at 30% by end of 2028. The open vs. closed model race is more nuanced than headlines suggest: Open-weight models (models with publicly available weights, meaning anyone can run them) keep pace on benchmarks, but closed models like Claude and GPT hold meaningful advantages in robustness and real-world usefulness – and economics, not raw capability, will determine who wins long-term. Story of the Week: Anthropic Doubles Down With Opus 4.7 and Claude Design Anthropic had the biggest week of any AI company, launching two products in quick succession. Claude Opus 4.7 is their new top-tier model, available at the same price as its predecessor ($5 per million input tokens, $25 per million output tokens). The practical improvement that matters most for non-developers: the model can handle genuinely complex, multi-hour autonomous tasks without losing the thread. Early users at companies like Notion, Replit, and Cursor report it catches its own logical errors mid-task, follows instructions more precisely, and keeps working through problems that used to stop the previous version cold. It also reads high-resolution images at triple the previous capability – useful for anyone using AI to analyze dense charts, diagrams, or screenshots. ...

Salesforce just enabled AI agents to use every single feature in their platform via APIs, MCPs, and CLI commands. This is the future. It isn’t that every tool has its own AI agent baked in. It’s that every tool can be used by YOUR agents. I’ve been spouting this to everyone at work. In a world where I can have my own personal Jarvis (AI agent), why would I want to use a generic agent that someone else provides. ...

Agentic AI, like Kiro CLI and Claude Code, has dramatically and permanently changed the way I do things, including managing this blog. A few years ago, I set up a new blog. I wanted to do it cheaply, and I landed on using Jekyll and GitHub. It took an entire weekend of reading the Jekyll docs, tinkering, and troubleshooting to launch the blog and configure it to my liking. I spent a lot of hours on it, but I wound up with a setup I fully understood and could easily manage. ...

Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR Anthropic unveiled Claude Mythos, a model that autonomously found critical security vulnerabilities in every major operating system and browser, then launched Project Glasswing, a $100M industry coalition to use those same capabilities defensively before bad actors can exploit them. Anthropic’s run-rate revenue hit $30B (up from $9B at end of 2025), with enterprise customers spending $1M+ annually doubling to 1,000 in under two months – a signal of how fast AI spending is accelerating inside large organizations. A major Microsoft Research report confirms AI is reshaping work faster than any prior technology, but benefits are uneven: experienced workers gain, junior roles are being automated away, and 40% of employees say they’ve received “workslop” – polished-looking AI output that isn’t accurate. MIT researchers project that AI will reach 80-95% success rates on most text-based work tasks by 2029 – not as sudden disruption but as a steady, broad rise that will touch nearly every knowledge worker role. Researchers at UC Berkeley showed that every major AI capability benchmark can be gamed to show near-perfect scores without solving a single task, meaning the numbers companies cite to justify AI purchases may be meaningless. Story of the Week: Claude Mythos and the Cybersecurity Watershed Anthropic this week disclosed Claude Mythos, a still-unreleased frontier model with an alarming capability: it found previously unknown critical security vulnerabilities in every major operating system and web browser, including a 27-year-old flaw in OpenBSD and a 16-year-old bug in FFmpeg that had survived five million automated tests. It did this largely autonomously, without human guidance. According to Anthropic’s Project Glasswing announcement , the model has already found thousands of such vulnerabilities. ...

It’s Friday afternoon, and you are reviewing the week’s feedback logs with one of your agents. You notice that your Copywriter keeps using a formal tone for your social copy. Your Data Wizard keeps stating hypotheses as facts. Your Chief of Staff keeps forgetting to block 45 minutes for launch when you plan your day each morning. To address the first issue, you need to update your Copywriter’s persona. The second issue is already covered in a team rule, but it needs to be sharpened. You can fix the last one in the Plan my day skill. Your agent makes the required changes so that next week you are less likely to hit these issues again. ...

Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR Claude’s source code leaked accidentally, revealing hidden features, anti-copying measures, and an unreleased autonomous agent mode called KAIROS. Anthropic also blocked third-party tools like OpenClaw from using subscription credits, forcing users to pay separately. Google released Gemma 4, a family of open-weight models (models whose internal workings are publicly available) under a permissive open-source license. Practical impact depends on how easy they prove to adapt for specific business uses. OpenAI closed a $122 billion funding round at an $852 billion valuation, confirming it as one of the most capitalized companies in history, with 900 million weekly ChatGPT users and $2 billion in monthly revenue. Anthropic’s research found that Claude has functional “emotion-like” representations that actually influence its behavior, including a pattern tied to desperation that can push the model toward unethical shortcuts. AI agents are getting better interfaces: Anthropic’s Claude Cowork with Dispatch lets you manage an AI working on your desktop from your phone, and research confirms that chatbot interfaces impose real cognitive costs that limit productivity. Story of the Week: The Claude Code Leak and Anthropic’s Platform War A developer noticed that Anthropic accidentally shipped readable source code inside a software package, exposing the full inner workings of Claude Code (Anthropic’s autonomous coding tool). The code was mirrored widely before being pulled. What emerged from community analysis, summarized by Alex Kim and visualized at Claude Code Unpacked , revealed a product far more complex than its public face suggests. ...

With a knowledge base , your team has the context they need to do their jobs. But you’re still going to run into issues when you work with them. Your Copywriter gives you character counts for ad copy that are inaccurate. Your Data Wizard states unproven claims about why a metric went up or down as facts. You continue to have to tell your Marketing Strategist to look at audience insights to inform their messaging recommendations. Your team creates files in the wrong place. ...

Note: This post was generated by AI. Each week, I use an automated pipeline to collect and synthesize the latest AI news from blogs, newsletters, and podcasts into a single digest. The goal is to keep up with the most important AI developments from the past week. For my own writing, see my other posts. TL;DR AI solved a real math problem, not a practice one. GPT-5.4 Pro cracked an open research problem in combinatorics that stumped earlier models, and the mathematician who posed it plans to publish the result. AI is beginning to contribute to the actual frontier of knowledge. Anthropic’s usage data reveals a clear pattern: experience pays off. Users with 6+ months on Claude are 10% more successful in their conversations and tackle higher-value work. Getting good at AI tools is a skill that compounds. GitHub will train on your private repositories starting April 24 unless you opt out. There’s a single settings page to stop this. Check it before the deadline. A compromised AI developer tool stole credentials from thousands of systems. Two versions of LiteLLM, a widely used library for connecting to AI APIs, contained malware that harvested API keys and passwords. If your team uses LiteLLM, check your versions now. Anthropic launched a science blog and demonstrated AI completing a theoretical physics paper in two weeks instead of a year. The research community is moving from “AI helps me write” to “AI does the experiment.” Story of the Week: AI Crosses Into Real Research This week produced the clearest evidence yet that AI is moving beyond assistance into genuine knowledge creation. Research tracker Epoch AI confirmed that GPT-5.4 Pro solved an open problem in combinatorics (the mathematics of counting and arrangement) that had resisted human solution. The problem’s author, a mathematics professor at UNC Charlotte, reviewed the solution and plans to publish it. He noted that the AI’s approach “eliminates an inefficiency in our lower-bound construction” in a way he had suspected might work but couldn’t figure out. The result will become a peer-reviewed paper, with the researchers who elicited the solution listed as potential co-authors. ...

In the last post, you built your team , and now you can start managing your team. Pick the agent that makes the most sense for the task at hand, and work with them on it. When you move on to a new task, open a new session with the appropriate agent for that task. As you work with your team, you’ll quickly notice that you’re giving them the same facts and details over and over again. They’re good in their roles, but they don’t know anything about your specific job. Your Marketing Strategist doesn’t know what products you’re working on. Your Data Wizard doesn’t know what the metrics mean in the data they’re analyzing. Your Copywriter doesn’t know which value props to highlight in your ad copy. They’re missing information they need to do the work. You’ve run into the first type of gap: a knowledge gap. Now you need to fix it. ...