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Posts tagged with "LLMs"

Post-Chat UI

Fascinating analysis by Allen Pike on how, beyond traditional chatbot interactions, the technology behind LLMs can be used in other types of user interfaces and interactions:

While chat is powerful, for most products chatting with the underlying LLM should be more of a debug interface – a fallback mode – and not the primary UX.

So, how is AI making our software more useful, if not via chat? Let’s do a tour.

There are plenty of useful, practical examples in the story showing how natural language understanding and processing can be embedded in different features of modern apps. My favorite example is search, as Pike writes:

Another UI convention being reinvented is the search field.

It used to be that finding your flight details in your email required typing something exact, like “air canada confirmation”, and hoping that’s actually the phrasing in the email you’re thinking of.

Now, you should be able to type “what are the flight details for the offsite?” and find what you want.

Having used Shortwave and its AI-powered search for the past few months, I couldn’t agree more. The moment you get used to searching without exact queries or specific operators, there’s no going back.

Experience this once, and products with an old-school text-match search field feel broken. You should be able to just find “tax receipts from registered charities” in your email app, “the file where the login UI is defined” in your IDE, and “my upcoming vacations” in your calendar.

Interestingly, Pike mentions Command-K bars as another interface pattern that can benefit from LLM-infused interactions. I knew that sounded familiar – I covered the topic in mid-November 2022, and I still think it’s a shame that Apple hasn’t natively implemented these anywhere in their apps, especially now that commands can be fuzzier (just consider what Raycast is doing). Funnily enough, that post was published just two weeks before the public debut of ChatGPT on November 30, 2022. That feels like forever ago now.

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Sycophancy in GPT-4o

OpenAI found itself in the middle of another controversy earlier this week, only this time it wasn’t about publishers or regulation, but about its core product – ChatGPT. Specifically, after rolling out an update to the default 4o model with improved personality, users started noticing that ChatGPT was adopting highly sycophantic behavior: it weirdly agreed with users on all kinds of prompts, even about topics that would typically warrant some justified pushback from a digital assistant. (Simon Willison and Ethan Mollick have a good roundup of the examples as well as the change in the system prompt that may have caused this.) OpenAI had to roll back the update and explain what happened on the company’s blog:

We have rolled back last week’s GPT‑4o update in ChatGPT so people are now using an earlier version with more balanced behavior. The update we removed was overly flattering or agreeable—often described as sycophantic.

We are actively testing new fixes to address the issue. We’re revising how we collect and incorporate feedback to heavily weight long-term user satisfaction and we’re introducing more personalization features, giving users greater control over how ChatGPT behaves.

And:

We also believe users should have more control over how ChatGPT behaves and, to the extent that it is safe and feasible, make adjustments if they don’t agree with the default behavior.

Today, users can give the model specific instructions to shape its behavior with features like custom instructions. We’re also building new, easier ways for users to do this. For example, users will be able to give real-time feedback to directly influence their interactions and choose from multiple default personalities.

“Easier ways” for users to adjust ChatGPT’s behavior sound to me like a user-friendly toggle or slider to adjust ChatGPT’s personality (Grok has something similar, albeit unhinged), which I think would be a reasonable addition to the product. I’ve long argued that Siri should come with an adjustable personality similar to CARROT Weather, which lets you tweak whether you want the app to be “evil” or “professional” with a slider. I increasingly feel like that sort of option would make a lot of sense for modern LLMs, too.

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What Siri Isn’t: Perplexity’s Voice Assistant and the Potential of LLMs Integrated with iOS

Perplexity's voice assistant for iOS.

Perplexity’s voice assistant for iOS.

You’ve probably heard that Perplexity – a company whose web scraping tactics I generally despise, and the only AI bot we still block at MacStories – has rolled out an iOS version of their voice assistant that integrates with several native features of the operating system. Here’s their promo video in case you missed it:

This is a very clever idea: while other major LLMs’ voice modes are limited to having a conversation with the chatbot (with the kind of quality and conversation flow that, frankly, annihilates Siri), Perplexity put a different spin on it: they used native Apple APIs and frameworks to make conversations more actionable (some may even say “agentic”) and integrated with the Apple apps you use every day. I’ve seen a lot of people calling Perplexity’s voice assistant “what Siri should be” or arguing that Apple should consider Perplexity as an acquisition target because of this, and I thought I’d share some additional comments and notes after having played with their voice mode for a while.

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