Today we’re launching text-based intelligence classifiers, a powerful new way to train NewsBlur to show you exactly what you want to read. You’ve always been able to train NewsBlur’s intelligence using story titles, authors, tags, and publishers. Now you can train on any phrase that appears in the full text of a story. This feature is available exclusively to NewsBlur Premium Archive subscribers.

Text-based classifiers work just like the intelligence training you’re already familiar with. Find a phrase you care about, mark it as something you like or dislike, and NewsBlur will automatically highlight or hide future stories containing that phrase. Stories with phrases you like are marked with a green focus indicator, while stories with phrases you dislike are hidden unless you choose to view them.

How to use text-based classifiers

Reading a story and spot a phrase you want to see more of? Simply select the text with your mouse or trackpad, then click the “Train” button that appears.

This opens the intelligence trainer where you can mark the selected text as something you like (thumbs up) or dislike (thumbs down). The text classifier appears at the top of the trainer dialog, ready for you to train.

Once you’ve trained a text phrase, NewsBlur will automatically scan the full text of every story from that feed. Stories containing your phrase will be highlighted with a green focus indicator in your story list, making them easy to spot. You can also see the phrase highlighted throughout the story content itself.

Real-world examples

Text-based classifiers shine when you subscribe to broad-interest feeds but only care about specific topics. Here are some examples:

• Subscribe to a food blog that covers everything, but only want to read about vegan recipes? Train on “vegan” and similar terms.
• Reading a tech blog that writes about many frameworks, but you only want stories about your favorite language? Train on that language name.
• Following a news site with mixed content, but only interested in stories about a specific region or topic? Train on location names or topic keywords.

Since text classifiers work on the full article text and not just titles, they catch stories that might not mention your interest in the headline but discuss it in depth within the article.

Green always wins

Just like with other intelligence classifiers, green (focus) always wins. If a story matches both a phrase you like and a phrase you dislike, NewsBlur will mark it as focus and show it in your unread count. This ensures you never miss a story about something you care about, even if it also contains topics you’re less interested in.

You can view your focus stories by choosing between Unread and Focus at the bottom of the feed list. Set it to Focus to show only green stories and see everything NewsBlur knows you want to read.

Why Premium Archive only?

Text-based classifiers require scanning the full article content of every story, not just the RSS feed excerpt. The Premium Archive subscription ensures every story is fetched, archived, and available for full-text search and classification. This means your text classifiers work on every story from every feed you subscribe to, with no gaps in coverage.

The Premium Archive subscription also includes unlimited story archiving, the ability to mark any story as unread forever, full-text search across your entire archive, and the discover stories feature for finding related content across all your feeds.

Available now on the web

Text-based classifiers are available now to all Premium Archive subscribers on the web. Simply highlight any phrase in a story, click the “Train” button, and start training. iOS and Android support is coming soon.

If you’re not yet a Premium Archive subscriber and want to unlock text-based intelligence training along with unlimited archiving and advanced search, you can upgrade directly on the web.

As always, we’d love to hear your feedback on the NewsBlur forum. For every person who shares their thoughts, there are a dozen others thinking the same thing, so your input helps shape where NewsBlur goes next.

💬 Join the discussion on kottke.org →

Think about what you did this morning. You woke up, brushed your teeth, made coffee, maybe scrolled your phone, maybe drove the same route to work that you do every day. How many of those things did you actually think about? According to new research, probably none. Scientists say nearly nine out of every ten daily actions happen on autopilot, with our brains running the show long before conscious thought gets involved.

The study, published in Psychology & Health, tracked 105 people for a week. Participants were pinged six times a day and had to report what they were doing, along with how deliberate or automatic it felt.

Across more than 3,700 reports, researchers found that 88 percent of behaviors were carried out automatically, while about two-thirds were triggered by habit rather than decision-making.

Lead researcher Amanda Rebar, an associate professor at the University of South Carolina, explained that this automation shows up in two ways.

“Habitual instigation occurs when environmental cues automatically trigger the decision to do something, like reaching for your phone when you hear a notification. Habitual execution happens when you perform an action smoothly without thinking about the mechanics, such as brushing your teeth or driving a familiar route,” she said in a statement.

Most people like to imagine themselves as rational actors, carefully weighing each choice they make. In practice, the study shows, life is closer to a string of well-worn loops. And those loops don’t vary much. Age, gender, and relationship status had no real effect on how habitual someone’s behavior looked.

One exception was exercise. People were more likely to start workouts based on cues, which could mean a reminder on their phone or a regular time of day, but still had to engage consciously once they got moving. Running, lifting, or cycling doesn’t complete itself, even if the decision to start feels automatic.

Habits, it turns out, often line up with what people want. Almost half of all reported behaviors were both intentional and automatic, while only a small fraction clashed with someone’s goals. That makes habits a surprisingly strong ally for anyone hoping to change.

Benjamin Gardner, a psychology professor at the University of Surrey and co-author of the study, said strategies for habit formation are more effective than willpower alone.

“For people who want to break their bad habits, simply telling them to ‘try harder’ isn’t enough,” he said. Building cues for healthier choices—or dismantling the ones tied to unhelpful patterns—might be the clearest path to change.

Most of what you do today will unfold without much thought. The trick, researchers suggest, is shaping those automatic moments to get you one step closer in the direction you actually want to go.

The post You’re Running on Autopilot Way More Often Than You Think appeared first on VICE.

ChatGPT Is Blowing Up Marriages as Spouses Use AI to Attack Their Partners

The tension in the vehicle is palpable. The marriage has been on the rocks for months, and the wife in the passenger seat, who recently requested an official separation, has been asking her spouse not to fight with her in front of their kids. But as the family speeds down the roadway, the spouse in the driver’s seat pulls out a smartphone and starts quizzing ChatGPT’s Voice Mode about their relationship problems, feeding the chatbot leading prompts that result in the AI browbeating her wife in front of their preschool-aged children.

Tags: ai, generative-ai, chatgpt, llms, ai-ethics, ai-personality

I've noticed something interesting over the past few weeks: I've started using the term "agent" in conversations where I don't feel the need to then define it, roll my eyes or wrap it in scare quotes.

This is a big piece of personal character development for me!

Moving forward, when I talk about agents I'm going to use this:

An LLM agent runs tools in a loop to achieve a goal.

I've been very hesitant to use the term "agent" for meaningful communication over the last couple of years. It felt to me like the ultimate in buzzword bingo - everyone was talking about agents, but if you quizzed them everyone seemed to hold a different mental model of what they actually were.

I even started collecting definitions in my agent-definitions tag, including crowdsourcing 211 definitions on Twitter and attempting to summarize and group them with Gemini (I got 13 groups).

Jargon terms are only useful if you can be confident that the people you are talking to share the same definition! If they don't then communication becomes less effective - you can waste time passionately discussing entirely different concepts.

It turns out this is not a new problem. In 1994's Intelligent Agents: Theory and Practice Michael Wooldridge wrote:

Carl Hewitt recently remarked that the question what is an agent? is embarrassing for the agent-based computing community in just the same way that the question what is intelligence? is embarrassing for the mainstream AI community. The problem is that although the term is widely used, by many people working in closely related areas, it defies attempts to produce a single universally accepted definition.

So long as agents lack a commonly shared definition, using the term reduces rather than increases the clarity of a conversation.

In the AI engineering space I think we may finally have settled on a widely enough accepted definition that we can now have productive conversations about them.

Tools in a loop to achieve a goal

An LLM agent runs tools in a loop to achieve a goal. Let's break that down.

The "tools in a loop" definition has been popular for a while - Anthropic in particular have settled on that one. This is the pattern baked into many LLM APIs as tools or function calls - the LLM is given the ability to request actions to be executed by its harness, and the outcome of those tools is fed back into the model so it can continue to reason through and solve the given problem.

"To achieve a goal" reflects that these are not infinite loops - there is a stopping condition.

I debated whether to specify "... a goal set by a user". I decided that's not a necessary part of this definition: we already have sub-agent patterns where another LLM sets the goal (see Claude Code and Claude Research).

There remains an almost unlimited set of alternative definitions: if you talk to people outside of the technical field of building with LLMs you're still likely to encounter travel agent analogies or employee replacements or excitable use of the word "autonomous". In those contexts it's important to clarify the definition they are using in order to have a productive conversation.

But from now on, if a technical implementer tells me they are building an "agent" I'm going to assume they mean they are wiring up tools to an LLM in order to achieve goals using those tools in a bounded loop.

Some people might insist that agents have a memory. The "tools in a loop" model has a fundamental form of memory baked in: those tool calls are constructed as part of a conversation with the model, and the previous steps in that conversation provide short-term memory that's essential for achieving the current specified goal.

If you want long-term memory the most promising way to implement it is with an extra set of tools!

Agents as human replacements is my least favorite definition

If you talk to non-technical business folk you may encounter a depressingly common alternative definition: agents as replacements for human staff. This often takes the form of "customer support agents", but you'll also see cases where people assume that there should be marketing agents, sales agents, accounting agents and more.

If someone surveys Fortune 500s about their "agent strategy" there's a good chance that's what is being implied. Good luck getting a clear, distinct answer from them to the question "what is an agent?" though!

This category of agent remains science fiction. If your agent strategy is to replace your human staff with some fuzzily defined AI system (most likely a system prompt and a collection of tools under the hood) you're going to end up sorely disappointed.

That's because there's one key feature that remains unique to human staff: accountability. A human can take responsibility for its action and learn from its mistakes. Putting an AI agent on a performance improvement plan makes no sense at all!

Amusingly enough, humans also have agency. They can form their own goals and intentions and act autonomously to achieve them - while taking accountability for those decisions. Despite the name, AI agents can do nothing of the sort.

This legendary 1979 IBM training slide says everything we need to know:

OpenAI need to get their story straight

The single biggest source of agent definition confusion I'm aware of is OpenAI themselves.

OpenAI CEO Sam Altman is fond of calling agents "AI systems that can do work for you independently".

Back in July OpenAI launched a product feature called "ChatGPT agent" which is actually a browser automation system - toggle that option on in ChatGPT and it can launch a real web browser and use it to interact with web pages directly.

And in March OpenAI launched an Agents SDK with libraries in Python (openai-agents) and JavaScript (@openai/agents). This one is a much closer fit to the "tools in a loop" idea.

It may be too late for OpenAI to unify their definitions at this point. I'm going to ignore their various other definitions and stick with tools in a loop!

Tags: ai, generative-ai, llms, ai-agents, agent-definitions

Beyond Vibe Coding

I'll be honest: I don't feel great about that post. I made an example of those two books to push my own agenda of encouraging "vibe coding" to avoid semantic diffusion but it felt (and feels) a bit mean.

... but maybe it had an effect? I recently spotted that Addy Osmani's book "Vibe Coding: The Future of Programming" has a new title, it's now called "Beyond Vibe Coding: From Coder to AI-Era Developer".

This title is so much better. Setting aside my earlier opinions, this positioning as a book to help people go beyond vibe coding and use LLMs as part of a professional engineering practice is a really great hook!

From Addy's new description of the book:

Vibe coding was never meant to describe all AI-assisted coding. It's a specific approach where you don't read the AI's code before running it. There's much more to consider beyond the prototype for production systems. [...]

AI-assisted engineering is a more structured approach that combines the creativity of vibe coding with the rigor of traditional engineering practices. It involves specs, rigor and emphasizes collaboration between human developers and AI tools, ensuring that the final product is not only functional but also maintainable and secure.

Amazon lists it as releasing on September 23rd. I'm looking forward to it.

Tags: books, oreilly, ai, generative-ai, llms, ai-assisted-programming, addy-osmani, vibe-coding