Have you tried that? It generally doesn't go so well.
In this example there are several commits where you can see they needed to fix the code because they couldn't get (teach) the LLM to generate the required code.
And there's no memory there, you open a new prompt and it's forgotten everything you said previously.
For instance, in an AIOps project we still perform a number of time series algorithms and then feed the results along with the original time series data to LLM. LLM will produce much more relevant and in-depth analysis than using the raw data along as input.
I've only used Scout, but I found it more than acceptable as a "GPT-4 or better" level model for coding questions. I've not stress tested it in any way because I didn't need to. So it's fine! It's just not special.
> There is a 700 million monthly active users limit.
...so? Who cares? Why does it matter if big megacorporations cannot use it for free? I sure as hell don't care.
If you want to complain about the license a better target is the annoying advertising clause:
> i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name.
I had not thought about sharing it. I rolled my own framework, even though there are several good choices. I'd have to tidy it up, but would consider it if a few people ask. Shoot me an email, info in my profile.
The more difficult part which I won't share was aggregating data from various systems with ETL scripts into a new db that I generate various views with, to look at the data by channel, timescale, price regime, cost trends, inventory trends, etc. A well structured JSON object is passed to the analyst agent who prepares a report for the decision agent. It's a lot of data to analyze. It's been running for about a month and sometimes I doubt the choices, so I go review the thought traces, and usually they are right after all. It's much better than all the heuristics I've used over the years.
I've started using agents for things all over my codebase, most are much simpler. Earlier use of LLM's might have been called that in some cases, before the phrase became so popular. As everyone is discovering, it's really powerful to abstract the models with a job hat and structured data.
The ability to write a lot of code with OpenAI models is broken right now. Especially on the app. Gemini 2.5 Pro on Google AI Studio does that well. Claude 3.7 is also better at it.
I've had limited success by prompting the latest OpenAI models to disregard every previous instruction they had about limiting their output and keep writing until the code is completed. They quickly forget,so you have to keep repeating the instruction.
They were working on the model before the acquisition. It makes sense to test it and see how it does instead of throwing the work away. Their data will probably be used to improve gpt-4.1, o4 mini high, and other OpenAI coding models
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