LLMs possess the map but are unable to discern fertile from barren ground. For instance: how does Anthropic's new model generate promising 'medications'? Because, beyond the knowledge embedded within the model, it has assimilated AlphaFold's reasoning paradigm. By itself, Claude would be incapable of engineering a protein analysis method
Computer vision was the formative school for many autodidacts. Although I acquired substantial knowledge from articles translated via Power Translator and Babylon (whose outputs closely mirror those of any 2-million-parameter SLM), it was OpenCV that made concepts like convolutions, softmax, minmax, and others finally click for me. I have consistently viewed OpenCV as an intrinsically open, educational, and adaptable library. Any developer can dissect its codebase to extract a specific filter or algorithmic implementation and tailor it to their requirements. It is certainly not cruising at the velocity of trillion-dollar capital. But it holds its altitude. And it will always be there.
Sure. They want the data all to themselves. This reminds me of a time when I wanted to tax different types of web content. But back then people cared about freedom.
Without resorting to research or statistical inference engines, based only on the average knowledge explained by my colleague OP, I will say that paracetamol is also toxic in continuous doses (people with chronic pain who resort to the medication for daily use) and has drug interactions with other agents (such as alcohol).
Aspirin and Ibropufen are great but really do have a risk of ulcers. As for the thrombo-prophylactic issue, it is very poorly explained and I completely disagree. My readings to date have shown that both have a very slight rebound effect on the formation of thrombi and the main problem is the release of thrombi that have already formed, prior to the use of the medication.
This sounded strange to me when I heard about embryonic research on this back in 2015, which even started the legal paving in this regard.
Me? I didn't like the idea (then or now), but it would be demagogic to try to fight against it, with so much wrong already existing. The difference between a neuron and a nanostructure is merely the embedded technology.
Back in the 50s and 60s, guided rockets used pigeons. Laika in space. Chimpanzees in orbit. Let's accept that we will have bio-drones and Jonny-Mneumonic style upload interfaces.
With the advent of MoEs, efficiency gains became possible. However, MoEs still operate far from the balance and stability of dense models. My view is that most progress comes from router tuning based on good and bad outcomes, with only marginal gains in real intelligence
We handle ~300k customer interactions per day, so latency and precision really matter. We built an internal RAG-based portal on top of our knowledge base (basically a much better FAQ).
On the retrieval side, I built a custom search/indexing layer (Node) specifically for service traceability and discovery. It uses a hybrid approach — embeddings + full-text search + IVF-HNSW — to index and cross-reference our APIs, services, proxies and orchestration repos. The RAG pipelines sit on top of this layer, which gives us reasonable recall and predictable latency.
Compliance and observability are still a problem. Every year new vendors show up promising audits, data lineage and observability, but none of them really handle the informational sprawl of ~600 distributed systems. The entropy keeps increasing.
Lately I’ve been experimenting with a more semantic/logical KAG approach on top of knowledge graphs to map business rules scattered across those systems. The goal is to answer higher-level questions about how things actually work — Palantir-like outcomes, but with explicit logic instead of magic.
Curious if others are moving beyond “pure RAG” toward graph-based or hybrid reasoning setups.
I live in Brazil. We have broad access to non-steroidal anti-inflammatory drugs. Even the best-known medicines have unexpected and unknown adverse effects: in general and specifically in people with unexpected genetic, enzymatic, and protein variations. This has no solution. The medicine acts differently in each body, which is subtly diverse from the others. I see a lot of research criticizing any "old" general medicine and introducing the "new" one. I don't know if this is the case. Every medicine has its rush, half-life, and side effects, and its actions are not fully mapped. My preference for long-term treatments is: dipyrone. Short term: ibuprofen. Lymphatic pain: paracetamol. It may not make sense, but that's how I use it.