Adaptive thinking: when Claude ignores your memory
I ask Opus 4.7 to solve an obvious question. It fails. The memory instructions that used to work no longer guarantee anything.
Posts about llm
Claude Code's Silent Downgrade
AMD analyzed 6,852 Claude Code sessions and the data is damning. Leaked source code confirms silent Opus-to-Sonnet fallback.
Why AI Agents Fail in Production (And Nobody Tells You)
AI agents work in demos. They break in production. Six failure modes nobody explains — and what actually helps.
Claude Sonnet 4.6: Flagship Performance at Mid-Tier Pricing
Sonnet 4.6 matches or beats Opus 4.6 on several benchmarks at 40% less cost. Frontier intelligence is no longer expensive. What it means for your AI budget.
Claude Opus 4.6: the model that tanked the market
Claude Opus 4.6 brings 1M context and agent teams. Cowork plugins wiped $285B off software stocks. What changed and why it matters.
LLM Sycophancy: AIs That Only Tell You What You Want
Stanford identifies artificial flattery as one of AI's major problems in 2026. If you've used ChatGPT lately, you know exactly what they're talking about.
ChatGPT drinks from Elon Musk's fountain
OpenAI integrates X/Twitter data into ChatGPT. What it means when your chatbot drinks from Musk's fountain.
How AI thinks: System 1 and System 2 in LLMs
Do language models reason or just improvise? The answer through Kahneman's framework and what it means for how we use them.
AI Trends in 2026: What Actually Matters
The 7 AI trends defining 2026: autonomous agents, small models, MCP, ROI focus and more. A guide for data professionals.
ChatGPT vs Gemini vs Claude: 2026 Comparison
Practical guide to choosing between the big three LLMs. Pricing, capabilities, use cases, and which one fits your situation.
DeepSeek: what it is and how to use it for free
Complete DeepSeek guide for 2026. Free ChatGPT alternative: web, mobile app, and local installation with Ollama.
Prompt Engineering Guide: How to Talk to LLMs
Everything you need to know to write effective prompts. From beginner to advanced, with practical examples and the limits nobody tells you about.
World Models: AI that predicts the physical world
What World Models are, how V-JEPA works, and why Yann LeCun is betting $3.5 billion on them.
LeCun leaves Meta: LLMs won't reach human-level AI
The godfather of AI bets $3.5 billion on a different architecture: World Models.
Taxonomy of LLM failures
Language models fail in four distinct ways. Each requires a different fix: prompt tuning, RAG, fine-tuning, or guardrails. A practical taxonomy.
The model knows how to reason. It just won't commit
17 prompt iterations revealed that the model finds the correct answer but self-censors for not being standard
More tokens doesn't mean better results
How an exhaustive meta-prompt caused context overflow and reached the same error on a random walk problem
3 Coins, Tails Always Even: Is It 0 or 1/13?
P(heads)=1/3, number of tails is always even. Is P(all heads) 0 or 1/13? Both are valid — full math, why AI always picks wrong, and how to fix it.
Why LLMs Reject Their Own Correct Answers
Two-Box separates contexts so the LLM reviews without bias. Problem: counterintuitive answers get discarded.