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⚡ Today's Summary

Today’s flow

Anthropic’s new models are not only strong at generating regular text and supporting development—they also show impressive capability in finding and exploiting vulnerabilities, shifting perspectives on safety in a big way [1].
Major companies are moving AI from something you merely try out to something that is used as a tool to actually move work forward. DeepSeek’s new model, Google’s app-building assistance, and Microsoft’s exploration of automated tasks all point to this direction [3][6][7].
At the same time, the longer people use AI, the more complaints appear—such as user quotas dropping faster than expected and extra hassle increasing. As AI becomes more useful, it also demands more thoughtful approaches to governance [4][12].
A practical takeaway right now is that methods like letting AI use your own API directly and developing by having humans review and choose what the AI produces are spreading. This expands the scope of what small teams can build [8][9].

📰 What Happened

Notable changes

Anthropic released its new model, Claude Mythos Preview, and reports say it’s not only strong on general performance but especially capable at identifying software weaknesses and exploiting them. In tests using known Firefox weaknesses, it succeeded 181 times out of 250 attempts, for a success rate of 72.4% [1].
DeepSeek is reportedly rolling out the V4 model through phased web testing. The setup suggests three different modes—one optimized for speed, one for deeper reasoning, and one for situations where it needs to interpret images. Attention is also on its ability to handle long text, work with images and video, and its optimization for cheaper compute infrastructure [3].
At a Google developer event, the audience saw a workflow demonstrated end-to-end: from an idea to a publicly available app in two hours. It used Google AI Studio, Firebase Studio, and Google Antigravity in sequence, minimizing manual creation so the AI handled much of the work [6].
Microsoft is considering adding bot-like functionality that can run automatically in 365 Copilot. In Notepad, Copilot’s prominent display was removed and moved to a less noticeable place. The aim is to avoid pushing AI too hard and instead present it in a way that users are less likely to find intrusive [7][14].
With Anthropic’s Claude Code, dissatisfaction continues: as usage stretches on, the number of remaining uses drops sooner than expected. There are claims that the way caching is handled and how actual usage is counted may not be aligned [4].
For enterprise use cases, there’s increasing emphasis on deciding what you’re going to build correctly first, before handing anything off to AI. By locking down requirements up front, it’s possible to reduce mismatches compared with fixing what AI produces later [9][10].

🔮 What's Next

What could spread next

Going forward, AI may expand beyond returning text and become a presence that actually carries out work. App creation, delegating internal tasks, and automating customer support could all become more commonplace [6][7][13].
At the same time, as models get smarter, security concerns will likely grow as well. If ordinary AI can even assist with abuse, companies will need to think not only about what systems can do, but also about what they should not be allowed to do [1][12].
In situations where people use AI for longer periods, how clearly pricing and usage counts are displayed will matter even more. The more AI is embedded in daily operations, the more important it becomes to have safeguards against overuse and unnecessary re-runs—otherwise the convenience may turn into a burden [4][12].
In development environments, as AI-generated output increases, a workflow where humans set the path first may become more widespread. Companies that make objectives and rules clear up front are likely to be better at leveraging AI effectively than those that build on a whim [9][10].
In retail, phone support, construction, and robotics, AI is likely to accelerate from “seeing and thinking” to making on-the-spot decisions and acting. This could change not only staffing constraints, but also the way work gets done itself [2][5][11][13].

🤝 How to Adapt

How to approach it going forward

First and foremost, it’s important to view AI not as a replacement for magic, but as a powerful tool. While it can do a lot, it can also make mistakes—so it’s reassuring to assume humans make the final call [1][10].
Next, don’t stop at using AI solely to “move faster.” It’s less likely to fail if you also decide what you want to reduce. For example, your usage will differ depending on whether your goal is to reduce effort, reduce missed items, or increase sales [9][11].
The more frequently people use AI day to day, the more they should cultivate habits for checking usage counts, costs, and outcomes. If you get carried away by convenience, you may lose visibility into how many times you used it or how much you spent—and later end up surprised [4][12].
If you’re using AI for work, it’s wiser to add more checkpoints rather than dumping tasks entirely on AI. Especially for work that’s critical or involves money, it’s important not to pass AI outputs straight through as-is, but to shape them into something humans can review and feel confident about [9][10].
In everyday life, a good rule of thumb is test AI in small ways and expand only if it’s good. Getting comfortable with short tasks first is safer and makes it easier to rely on AI long-term without anxiety [6][14].

💡 Today's AI Technique

Let AI use your own API and automate work

If you expose your API in a form that AI can call—rather than simply showing it as-is—AI can invoke it on its own and proceed with tasks. For example, you can execute fixed workflows like calculating charges or checking inventory directly within the flow of a conversation [8].

Steps

Choose one small automated task
Pick something with a clear answer, such as “calculate AI usage fees,” “confirm the number of orders,” or “check inventory.”
Prepare the endpoint that handles that task
Only accept the information needed—e.g., the “model name,” the number of input characters, and the number of output characters.
Register it in a way that AI can call
Make sure the endpoint clearly communicates what you must provide and what it will return, so AI can use it without guessing.
Teach AI how to use it
For instance, if you ask AI to “calculate the fees,” configure it so it knows to use that endpoint.
Start by testing with just one task
Don’t delegate everything at once—verify that a single task works correctly before expanding.

Where to use it

When you perform the same calculations every time
When you want to retrieve fixed internal information
When you want AI to handle not only text, but also some real work, even if only lightly

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