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ChatGPT agent: bridging research and action

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Re: ChatGPT agent: bridging research and action

#191

Post by stpedgwdgfhgdd »

samtp wrote: post #82
twalkz wrote: post #30

The "spreadsheet" example video is kind of funny: guy talks about how it normally takes him 4 to 8 hours to put together complicated, data-heavy reports. Now he fires off an agent request, goes to walk his dog, and comes back to a downloadable spreadsheet of dense data, which he pulls up and says "I think it got 98% of the information correct... I just needed to copy / paste a few things. If it can do 90 - 95% of the…

This is the exact same issue that I've had trying to use LLMs for anything that needs to be precise such as multi-step data pipelines. The code it produces will look correct and produce a result that seems correct. But when you do quality checks on the end data, you'll notice that things are not adding up. So then you have to dig into all this overly verbose code to identify the 3-4 subtle flaws with how it transform…

In my experience using small steps and a lot of automated tests work very well with CC. Don’t go for these huge prompts that have a complete feature in it.

Remember the title “attention is all you need”? Well you need to pay a lot of attention to CC during these small steps and have a solid mental model of what it is building.

Re: ChatGPT agent: bridging research and action

#192

Post by simonw »

bigyabai wrote: post #3

I do not know what an agent is and at this point I am too afraid to ask.

That's because there are dozens of slightly (or significantly) different definitions floating around and everyone who uses the term likes to pretend that their definition is the only one out there and should be obvious to everyone else.

I collect agent definitions. I think the two most important at the moment are Anthropic's and OpenAI's.

The Anthropic one boils down to this: "Agents are models using tools in a loop". It's a good technical definition which makes sense to software developers. https://simonwillison.net/2025/May/22/tools-in-a-loop/

The OpenAI one is a lot more vague: "AI agents are AI systems that can do work for you independently. You give them a task and they go off and do it." https://simonwillison.net/2025/Jan/23/introducing-operator/

I've collected a bunch more here: https://simonwillison.net/tags/agent-definitions/ but I think the above two are the most widely used, at least in the LLM space right now.

Re: ChatGPT agent: bridging research and action

#193

Post by seydor »

bryanhogan wrote: post #26

One the one hand this is super cool and maybe very beneficial, something I definitely want to try out. On the other, LLMs always make mistakes, and when it's this deeply integrated into other system I wonder how severe these mistakes will be, since they are bound to happen.

also LLMs mistakes tend to pile up , multiplying like probabilities. I wonder how scrabled a computer will be after some hours of use

Re: ChatGPT agent: bridging research and action

#194

Post by Karrot_Kream »

ameliaquining wrote: post #157
danny_codes wrote: post #95

Earlier quoted context omitted.

It’s past the hype curve and into the trough of disillusionment. Over the next 5,10,15 years (who can say?) the tech will mature out of the trough into general adoption. GenAI is the exciting new tech currently riding the initial hype spike. This will die down into the trough of disillusionment as well, probably sometime next year. Like self-driving, people will continue to innovate in the space and the tech will be…

The Gartner hype cycle assumes a single fundamental technical breakthrough, and describes the process of the market figuring out what it is and isn't good for. This isn't straightforwardly applicable to LLMs because the question of what they're good for is a moving target; the foundation models are actually getting more capable every few months, which wasn't true of cryptocurrency or self-driving cars. At least some…

> If scaling doesn't stall out soon, then I honestly have no idea what to expect the visibility curve to look like. Is there any historical precedent for a technology's scope of potential applications expanding this much this fast?

Lots of pre-internet technologies went through this curve. PCs during the clock speed race, aircraft before that during the aeronautics surge of the 50s, cars when Detroit was in its heydays. In fact, cloud computing was enabled by the breakthroughs in PCs which allowed commodity computing to be architected in a way to compete with mainframes and servers of the era. Even the original industrial revolution was actually a 200-year ish period where mechanization became better and better understood.

Personally I've always been a bit confused about the Gartner Hype Cycle and its usage by pundits in online comments. As you say it applies to point changes in technology but many technological revolutions have created academic, social, and economic conditions that lead to a flywheel of innovation up until some point on an envisioned sigmoid curve where the innovation flattens out. I've never understood how the hype cycle fits into that and why it's invoked so much in online discussions. I wonder if folks who have business school exposure can answer this question better.

Re: ChatGPT agent: bridging research and action

#195

Post by gorbypark »

apwell23 wrote: post #62
pyman wrote: post #39

It's great to see at least one company creating real AI agents. The last six months have been agonising, reading article after article about people and companies claiming they've built and deployed AI agents, when in reality, they were just using OpenAI's API with a cron job or an event-driven system to orchestrate their GenAI scripts.

> It's great to see at least one company creating real AI agents. I am already doing the type of examples in that post with claude code. claude code is not just for code. this week i've been doing market research in real estate with claude code.

I opened up the app bundle of CC on macOS and CC is incredibly simple at its core! There’s about 14 tools (read, write, grep, bash, etc). The power is in the combination of the model, the tools and the system prompt/tool description prompts. It’s kind of mind blowing how well my cobbled together home brew version actually works. It doesn’t have the fancy CLI GUI but it is more or less performant as CC when running it through the Sonnet API.

Works less well on other models. I think Anthropic really nailed the combination of tool calling and general coding ability (or other abilities in your case). I’ve been adding some extra tools to my version for specific use cases and it’s pretty shocking how well it performs!

Re: ChatGPT agent: bridging research and action

#196

Post by ivape »

Aurornis wrote: post #127
twalkz wrote: post #30

The "spreadsheet" example video is kind of funny: guy talks about how it normally takes him 4 to 8 hours to put together complicated, data-heavy reports. Now he fires off an agent request, goes to walk his dog, and comes back to a downloadable spreadsheet of dense data, which he pulls up and says "I think it got 98% of the information correct... I just needed to copy / paste a few things. If it can do 90 - 95% of the…

> how it normally takes him 4 to 8 hours to put together complicated, data-heavy reports. Now he fires off an agent request, goes to walk his dog, and comes back to a downloadable spreadsheet of dense data, which he pulls up and says "I think it got 98% of the information correct... This is where the AI hype bites people. A great use of AI in this situation would be to automate the collection and checking of data. Se…

”The people who YOLO it with prompting cycles until the code passes tests and then submit a PR are causing problems almost as fast as they’re developing new features in non-trivial codebases.”

This might as well be the new definition of “script kiddie”, and it’s the kids that are literally going to be the ones birthed into this lifestyle. The “craft” of programming may not be carried by these coming generations and possibly will need to be rediscovered at some point in the future. The Lost Art of Programming is a book that’s going to need to be written soon.

Re: ChatGPT agent: bridging research and action

#197

Post by simantel »

ricardobayes wrote: post #76
twalkz wrote: post #30

The "spreadsheet" example video is kind of funny: guy talks about how it normally takes him 4 to 8 hours to put together complicated, data-heavy reports. Now he fires off an agent request, goes to walk his dog, and comes back to a downloadable spreadsheet of dense data, which he pulls up and says "I think it got 98% of the information correct... I just needed to copy / paste a few things. If it can do 90 - 95% of the…

Of course, Pareto principle is at work here. In an adjacent field, self-driving, they are working on the last "20%" for almost a decade now. It feels kind of odd that almost no one is talking about self-driving now, compared to how hot of a topic it used to be, with a lot of deep, moral, almost philosophical discussions.

> It feels kind of odd that almost no one is talking about self-driving now, compared to how hot of a topic it used to be

Probably because it's just here now? More people take Waymo than Lyft each day in SF.

Re: ChatGPT agent: bridging research and action

#198

Post by seydor »

testfrequency wrote: post #7
jboggan wrote: post #5

[flagged]

Hardly. Is Apple a doomed company because they are chronically late to ~everything bleeding edge?

Apple products are leading edge. Imagine if they waited until Samsung makes the perfect phone , then copy it.

We re talking about european tech businesses being left behind, locked in a basement.

Re: ChatGPT agent: bridging research and action

#199

Post by [deleted] »

ishita159 wrote: post #114
dingnuts wrote: post #106

Earlier quoted context omitted.

The critics of the current AI buzz certainly have been drawing comparisons to self driving cars as LLMs inch along with their logarithmic curve of improvement that's been clear since the GPT-2 days. Whenever someone tells me how these models are going to make white collar professions obsolete in five years, I remind them that the people making these predictions 1) said we'd have self driving cars "in a few years" bac…

I think people don't realize how much models have to extrapolate still, which causes hallucinations. We are still not great at giving all the context in our brain to LLMs. There's still a lot of tooling to be built before it can start completely replacing anyone.

[deleted]

Re: ChatGPT agent: bridging research and action

#200

Post by [deleted] »

twalkz wrote: post #30

The "spreadsheet" example video is kind of funny: guy talks about how it normally takes him 4 to 8 hours to put together complicated, data-heavy reports. Now he fires off an agent request, goes to walk his dog, and comes back to a downloadable spreadsheet of dense data, which he pulls up and says "I think it got 98% of the information correct... I just needed to copy / paste a few things. If it can do 90 - 95% of the…

[deleted]
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