Put all the timestamps you want in the post mortem about what you observed, but you haven't addressed the root cause.

The "this doesn't make sense" part of the story likely has a real explanation that nobody wants to reveal yet.

Technically all of my replies are from an LLM, too; they all went through transformer-backed STT.

Absolutely agree the deterministic performance-oriented mindset is still essential for large workloads. Are you expecting that this supplements a traditional vector/semantic store or that it superceeds it?

My focus has absolutely been on relatively small corpii, and which is supported by forcing a subset of data to be included by design. There are intentionally no conventions for things like "we talked about how AI is transforming computing at 1AM" and instead it attempts to focus on "user believes AI is transforming computing", so hopefully there's less of the context poisoning that happens with current memory.

Haven't deployed WVF at any scale yet; just a casual experiment among many others.

The fundamental breakthrough with LLMs is that they handle semantic mapping for you and can (albeit non-deterministically) interpret the meaning and relationships between concepts with a pretty high degree of accuracy, in context.

It just makes me wonder if you could dramatically simplify the schema and data modeling by incorporating more of these learnings.

I have a simple experiment along these lines that’s especially relevant given the advent of one-million-token context windows, although I don’t consider it a scientifically backed or production-ready concept, just an exploration: https://github.com/tcdent/wvf

Everyone is looking for a simple solution, but simple solutions don't take into account human social dynamics.

Chopping the fenders on a Porsche 911 to install a widebody kit does not have the same weight as rolling the seams on an Jeep Cherokee.

On a nonzero number of occasions I have priced the cost of running an inference server with a model that is actually usable and the annual cost is astronomical.

Comments URL: https://news.ycombinator.com/item?id=47596290

Points: 4

# Comments: 0

"Yes" Proceeds to talk about AI.

1. Up until about six months ago, modifying prompts by hand and incorporating terminology with very specific intent and observing edge cases and essentially directing the LLM in a direction to the intended outcome was somewhat meticulous and also somewhat tricky. This is what the industry was commonly referring to as prompt engineering.

2. With the current state of SOTA models like Opus 4.6, the agent that is developing my applications alongside of me often has a more intelligent and/or generalized view of the system that we're creating.

We've reached a point in the industry where smaller models can accomplish tasks that were reserved for only the largest models. And now that we use the most intelligent models to create those systems, the feedback loop which was patterned by DSPy has essentially become adopted as part of my development workflow.

I can write an agent and a prompt as a first pass using an agentic coder, and then based on the observation of the performance of the agent by my agentic coder, continue to iterate on my prompts until I arrive at satisfactory results. This is further supported by all of the documentation, specifications, data structures, and other I/O aspects of the application that the agent integrates in which the coding agent can take into account when constructing and evaluating agentic systems.

So DSPy was certainly onto something but the level of abstraction, at least in my personal use case has, moved up a layer instead of being integrated into the actual system.

With that said, people are trying to extend VRAM into system RAM or even NVMe storage, but as soon as you hit the PCI bus with the high bandwidth layers like KV cache, you eliminate a lot of the performance benefit that you get from having fast memory near the GPU die.

Did you think the hesitancy of westerners engaging and relying on Chinese labs was due to vibes? There are fundamental cultural differences at play, wether we are comfortable admitting that or not.