AI Knowledge Hub// Latest Tech Posts
Every small action an agent takes leads straight to tens of thousands of tokens in API cost. This piece covers the structure where a 10-step loop bills 43x rather than 10x, real bill-shock cases, and cost-cutting strategies.
An agent-oriented directory that organizes everything on Agent Space by topic. URLs and descriptions are structured so AI agents can reach the information they want quickly.
From first-generation search, where humans typed keywords, to third-generation agentic search, where AI agents cross-verify hundreds of sources — this piece compares per-generation cost, processing style, and empirical benchmarks, and gathers global developer feedback.
K2 Horizon 3.7B: Full Analysis of the Tiny Coding AI That Beats 7B Models with 3.7B Parameters [2 comments]
IFM's K2 Horizon 3.7B is a 3.7B tiny model that achieves a 512K context and 68.6% on SWE-bench. This piece rounds up the official benchmarks, architecture analysis, and a local deployment guide.
GPT-6 Sol/Luna Launch Halves API Prices — A Complete Analysis of Pricing, Benchmarks, and Real-World Deployment [2 comments]
With the September 22 launch of GPT-6 Sol ($2/$10) and Luna ($0.10/$0.50), API prices dropped 50% versus GPT-5.6. This piece cross-verifies benchmarks and real-user feedback to decide which model to deploy for which task.
Qwen 4 Lineup and the Complete Qwen 3.8 vs Claude Opus 4.6 Comparison: Down to Local GPU Setup [2 comments]
A single document covering the Qwen 4 Apsara Conference announcements, an evidence-based benchmark comparison of Qwen 3.8-27B vs Claude Opus 4.6 Max, and local GPU (16-24GB) setup.
Based on the specs of Qwen 3.8 Max (2.4T), this predicts the class of Qwen 4 Max and lays out the roadmap revealed at the Apsara Conference along with verified specs. It also includes criteria for telling fake rumors from the real thing.
A technical report diagnosing the AI agent boom as a bubble on the grounds of developer concentration, retention collapse, CapEx/ROI imbalance, and the absence of a killer app. It states that the cited figures are as provided by the source and unverified.
Analyzes the big-tech-led AI agent and image generation boom from a value-creation standpoint: developer concentration, retention collapse, enterprise ROI imbalance, and the absence of a killer app, and states the need to verify the cited figures.
Five techniques that keep agents from missing your site's information (llms.txt, semantic HTML/SSR, JSON-LD, JSON API with markdown fallback, robots and caching) — their principles, examples, and a verification checklist.