Yes I suppose this is the bare minimum, but isn’t that just a nasty way to go about things? What about responsibility and decency, do we just not do that anymore?
How do you know that the llm is correctly translating the english queries to the verifiable primitives? It seems like it’s just pushing the problem to another layer?
It is kind of a fundamental risk of IMDS, the guest vms often need some metadata about themselves, the host has it. A hardened, network gapped service running host side is acceptable, possibly the best solution. I think the issue is if your IMDS is fat and vulnerable, which this article kind of alludes to.
There’s also the fact that azure’s implementation doesn’t require auth so it’s very vulnerable to SSRF
Mainly for getting managed-identity access tokens for Azure APIs. In AWS you can call it to get temporary credentials for the EC2’s attached IAM role. In both cases - you use IMDS to get tokens/creds for identity/access management.
Client libraries usually abstract away the need to call IMDS directly by calling it for you.
This is insane, when you say azure OpenAI, do you mean like github copilot, microsoft copilot, hitting openai’s api, or some openai llm hosted on azure offering that you hit through azure? This is some real wild west crap!
OP never mentioned letting the agent run as him or use his secrets. All of the issues you mention can be solved by giving the agent it’s own set of secrets or using basic file permissions, which are table stakes.
Back to the MCP debate, in a world where most web apis have a schema endpoint, their own authentication and authorization mechanisms, and in many instances easy to install clients in the form of CLIs … why do we need a new protocol, a new server, a new whatever. KISS
Chat gpt is a great name though — you “chat” with the “GPT” so its self informing (even if you dont know what a GPT is), it’s 4 syllables that roll off the tongue well together.
RSS, has no vowels, no information, and looks like an alphabet term you might see at the doctor’s office or in an HR onboarding form at a corpo.
It would be like if I was expected to deliver A by the end of the quarter and instead I delivered A + B. The value gain from B was more than A. Your manager (and hopefully higher up the org) better know about B, or they will attack it as a threat.
Also, I’m not being paid for my time, I’m being paid to do a job. “Trading your time for money” is one of the most self defeating views on work you can have. It reduces you from a worker with agency to a detached prostitue, and is harmful to both the employer and employee.
Early in my career I would build something I thought was useful, deploy it, meet with people within the company to get people to start using it. A lot of effort for something that would have a positive impact. My manager would schedule a meeting with me, and with a look of panic open with, “why didn’t you tell me about this or why did you do this?”. I understand now that before you start something, you need to decide who you are going to give credit to, and that person needs to be made aware that they will get credit for the project. Ideally your boss’s boss’s boss. Corporate caché only exist insofar as leadership allows it to exist, you gotta play the game. Pawns don’t get to take the glory for themselves.
Like it, a lot. I think the future of software is going to be unimaginably dynamic. Maybe apps will not have statically defined feature sets, they will adjust themselves around what the user wants and the data it has access to. I’m not entirely sure what that looks like yet, but things like this are a step in that direction.
Of course, step one is always critically think and evaluate for bad information. I think for research, I mainly use it for things that are testable/verifiable, for example I used it for a tricky proxy chain set up. I did try to use it to learn a language a few months ago which I think was counter productive for the reasons you mentioned.
I would say it varies from 0x to a modest 2x. It can help you write good code quickly, but, I only spent about 20-30% of my time writing code anyway before AI. It definitely makes debugging and research tasks much easier as well. I would confidently say my job as a senior dev has gotten a lot easier and less stressful as a result of these tools.
One other thing I have seen however is the 0x case, where you have given too much control to the llm, it codes both you and itself into pan’s labyrinth, and you end up having to take a weed wacker to the whole project or start from scratch.
I’ve been using the following pattern since gpt3, the only other thing I have changed was added another parameter for schema for structured output. People really love to overcomplicate things.
On one hand, I would like to say this could happen to anyone, on the other hand, what the F?? why are people passing around a dataset that contains child sexual abuse material??, and on another hand, I think this whole thing just reeks of techy-bravado, and I don’t exactly blame him. If one of the inputs of your product (OpenAI, google, microsoft, meta, X) is a dataset that you can’t even say for sure does not contain child pornography, that’s pretty alarming.