New AI models reviewed and compared, how they work, and what the labs behind them are betting on.
Most read by people in Engineering, Executives and Legal & Compliance.
Klarna's AI assistant did the work of 700 agents, yet the company rebuilt a human support layer in 2025. See what changed, why, and how its hybrid model works.
Claude Haiku 5.5 vs. local models for coding, compared on API token prices, electricity, and the engineering time each takes to run and maintain for a team.
Can AI solve complex math problems? Top models reached gold-medal scores at the 2025 IMO but still miss basic word problems. Here is where AI math works and where it breaks.
AI decision models vs. LLMs compared on how they work, cost, latency, and benchmark results, with guidance on when to use each for routing and classification.
Compare GPT-6 Astra, Claude Opus 5.5, and open-weight models like DeepSeek V4.1 Flash for internal tools, with pricing, benchmarks, and self-hosting costs.
How ChatGPT chooses which sources to cite, from query rewriting and retrieval to ranking by authority and freshness, and why most retrieved pages go uncited.
Ranking #1 on Google doesn't guarantee an AI citation. Learn what AEO is and which tactics work, based on Ahrefs and Princeton data on stats, structure and freshness.
Learn how to structure content so ChatGPT, Perplexity, and Google AI Overviews cite it, using answer-first sections, FAQs, and real citation data.
Track traffic from AI assistants in GA4 and server logs with a custom source-only channel group, crawler log analysis, and IP-based bot verification.
AEO vs SEO for AI search visibility: how each earns results, where they differ, and which matters more now. Includes click data and a checklist.
Compare dense, MoE, reasoning, and state-space AI models for internal tools by cost per task, latency, self-hosting hardware needs, and governance.
Decision models return typed choices and scores; execution models and sandboxed code act on them. Compare latency, cost, and real benchmarks.
Electric bills, water tables, bot traffic, and egress fees: who actually pays for the cloud. A data-backed look at cloud computing's externalized costs.
Copilot+ PCs launched in 2024 with big claims. Two years of weak sales, Recall's security backlash, and enterprise indifference later, here's what actually happened.
Vendor benchmarks for GPT-6.1 Sol and Claude Opus 5.5 barely overlap. Here's what independent evals, aggregators, and drift trackers show.
Jev made fast, cheap decision models mainstream. CLM makes them self-hostable and fine-tunable. A numbers-first comparison of speed, cost, accuracy, and ownership.
RLCD trains models like Jev to output calibrated decisions, not text. Here's how it works, the real speed and cost numbers, and why 'can't hallucinate' isn't 'can't be wrong.'
Claude Opus 5.5 wins on raw coding capability. GPT-6 Sol wins on cost per task. Here's what that means for building internal tools instead of buying SaaS.
Jev-class decision models trade text generation for typed choices, scores, and probabilities, running 40-400x cheaper and up to 200x faster than chat LLMs.
What is a System 1 AI model versus JEV? A grounded look at fast, calibrated classifiers versus reasoning LLMs for GUI and form-filling agents.
Infinite-parameter LLMs turn live data into model weights instead of prompts. Here's how they work, and what they change for data engineering teams.
GPT-6 Astra ties Fable 5.1 on coding benchmarks at a fraction of the cost, but Fable 5.1 wins on cache economics and long runs. Here's how to pick.
GPT-6 Astra costs 2.5x more than its predecessor and still isn't private by default. Here's the real cost and privacy math against local coding models.
Same list price, different bills. Compare GPT-6 Astra and Claude Fable 5.1 on cost-per-task, cache pricing, and benchmarks for internal AI agents.