Browse Comments — LLM coded
Close reading of the corpus at each pipeline stage: raw → clean → relevant → coded.
5
comments matched
· page 1 of 1
This points to a deeper shift than cost curves. What’s breaking isn’t just the price of inference, it’s the assumption that intelligence must live inside monoliths. When scaling hits diminishing returns, architecture becomes the lever. Ensembles move the bottleneck from raw compute to orchestration, signal interpretation, and aggregation logic. That’s a fundamentally different design philosophy, and it aligns much more closely with how real-world intelligence actually works. If this holds, the next frontier isn’t bigger models competing with OpenAI or DeepSeek AI on brute force. It’s smaller, purpose-built systems coordinated intelligently, closer to perception, context, and decision-making. The age of scaling was inevitable. An age of architecture was always next.
Mike Pappas How do you know that they "want individual AI-powered solutions to specific tasks"? Seems like a reach (unless people have told you that directly in an interview). I feel like it may be more accurate to say: 1. They want AI to cost less (so they aren't worried about driving their bill through the roof -> see Jackson Oaks) 2. Give them faster responses (so they don't sit there staring at it wondering what to do in the meantime) 3. While still accurately and actually solving their problem (e.g. doing the task) There's a reason I use Opus for almost everything: I trust the response will actually be good and be a solution to my problem: good email, working software, etc. Personally I don't care in any way shape or form about "individual AI-powered solutions to specific tasks" I just want my "task" (problem) done (solved) for cheap. xD
Hey man! We gotta talk. I have built a Distributed AI Infrastructure platform, and one of my platform’s core abilities is the ability to analyze media formats for enterprises to learn from with cross domain learning capabilities. We also do this, while ensuring our clients keep their data sovereign. I have an open-source version of my Distributed AI Infrastructure platform on GitHub (doesn’t come with the above capability, that is the full version - which is already built), but it comes with highly useful capabilities that you and your dev team can easily play around with and integrate into your platform for efficiency gains, intelligence expansion, and cost mitigation on compute/energy within your organization. Excited to talk about AI and orchestration as we are mutually aligned in that category!
The #1 most used model on OpenRouter right now costs $0.07 per million tokens (Hy3 preview). The #2 costs $5.00 (Claude Opus 4.7). Wild gap. check out for more upto date information
This is where AI gets interesting to me — not as one tool trying to do everything, but as coordinated systems built around specific work. In operations, that same thinking applies. Safety, quality, inventory, uptime, delivery, SOPs, and KPIs should not live in separate silos. The real value is when the system connects the signals and helps people act faster. That is the future of practical execution. Thanks for sharing.