_pdp_

7 ms·

91.
by _pdp_ 3mo ago
They do use these tools but they are not as efficient as codex multi-file patch which can perform file move, and edit in a single generation.
92.
Exploiting LLM Agent Supply Chains via Payload-Less Skills(arxiv.org)
5 points by _pdp_ 3mo ago|0 comments
93.
It's Hard to Eval Is a Product Smell(hamel.dev)
6 points by _pdp_ 3mo ago|1 comments
94.
Startup Targets Datacenters with 3D-Printed Nuclear Reactor Module(theregister.com)
1 points by _pdp_ 3mo ago|0 comments
95.
by _pdp_ 3mo ago
Yes. I built recently an agent that has very broad set of objectives and nothing in particular. I don't even know what it does most of the time but hopefully it will do something useful eventually. You track its progress here https:&#
96.
by _pdp_ 3mo ago
Saving time does not automatically translate into higher productivity, or even lower costs. That should be obvious? In fact, I would argue the that with AI, companies should expect to spend more on average, without necessarily seeing any me
97.
by _pdp_ 3mo ago
Cool. The main thing I like about MCP is the authentication story, particularly the standardisation around OAuth. Because of that, I think it is often worth wrapping an API in an MCP server. I actually had to do this recently. I have been w
98.
by _pdp_ 3mo ago
This is what we do. The same agent writing the code can also write the docs.
99.
by _pdp_ 3mo ago
I was thinking the same and I changed my mind. Also you don't need to believe me. There is enough evidence in the open source space.
100.
by _pdp_ 3mo ago
I am not surprised it is not open source. These harnesses are hard to build - they are not just wrappers - and often they contain business logic that is not suitable for public distribution for all kinds of reasons.
101.
by _pdp_ 3mo ago
I might be in the minority here, but although x402 sounds useful, it seems to me that adoption will be an uphill struggle, especially for per-request micropayments. The most likely scenario is Stripe, or someone similar, creating an agentic
102.
by _pdp_ 3mo ago
It is just a small agent using a remote harness, so the local dependencies are limited. It is specialised in the sense that the prompt, tools, and skills are all custom and baked into the binary, including the key to the harness API. I don&
103.
by _pdp_ 3mo ago
I wrote a small agent (single go binary) that does all the monitoring and maintenance for me. Possibly overkill but it is amusing to think there is a little ghost in the machine.
104.
by _pdp_ 3mo ago
Too expensive?
105.
by _pdp_ 3mo ago
> OOP to me means only messaging, local retention and protection and hiding of state-process, and extreme late-binding of all things. This sounds a lot like microservice architecture.
106.
by _pdp_ 3mo ago
You can exploit both ways.
107.
by _pdp_ 3mo ago
There are so many proxies like this now but I can tell you from first hand experience this is not going to work. You cannot just route away from a situation at such a high level especially when we are talking about models that are quite dif
108.
by _pdp_ 3mo ago
You think this is not happening with open weight models?
109.
by _pdp_ 3mo ago
Nooice /s
110.
by _pdp_ 3mo ago
That's my point too. If you take GLM and call it ChatGPT or Claude Opus is anyone going to notice? If you are not into agentic AI I would argue that the model type makes zero difference for day to day use because GLM 5.2 is hitting the
111.
by _pdp_ 3mo ago
GLM 5.2 has replaced "normie" agentic workflows previously backed by Sonnet and Opus. So I don't know. From my end it seems to me they are perfectly capable of working agenticly.
112.
by _pdp_ 3mo ago
Frankly it does not matter if there is gap because for most practical use-cases the end user can barely perceive the difference in intelligence. On paper frontier models will be ahead of the curve but I don't think hardly anyone will b
113.
by _pdp_ 3mo ago
Cool.. but I still don't get how this is going to save money. It seems to me that it might actually burn more money just because the whole system now seems to be coming from different LLMs. Also, small LLMs are prone to stop before com
114.
by _pdp_ 3mo ago
I mean have you tried to tokenmax? It is not that hard. Just launch 10 different windows and make sure to loop back in after every turn and you will be burning billions of tokens per month in no time.
115.
by _pdp_ 3mo ago
My thinking is the same. I believe that AI will be the predominant forms of "intelligence" online probably taking as much as 90-99% of all traffic. It is not hard to see where this is going. The price for tokens will become a prox
116.
by _pdp_ 3mo ago
Prices will go down one way or another. That is of course unless the market gets cornered by restricting model use, restricting supply of essential hardware components or raw materials to make this hardware, etc. In terms of running the mod
117.
by _pdp_ 4mo ago
Not Claude but we have AI agents operating like that already. We have 10 different ones actually all deployed in Slack and accessible via DM, or in a shared team chat. The difference between this and our agents is that they are context awar
118.
by _pdp_ 4mo ago
Who is GLM 5.2?
119.
by _pdp_ 4mo ago
Actually the math is worse. Every new line increases dramatically the complexity of the software which requires more cost and most maintenance. If you stop at a single tool this is still manageable. Imagine now that you do the same for 10 o
120.
by _pdp_ 4mo ago
There are downsides depending on how good is your harness. Switching the model is easy enough. Ensuring that the harness continues working the way it did is a completely different thing. This is not just about the prompts but also general b

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