Ideas, guides, and
production lessons.
What we learn building AI agents, RAG systems, and Claude integrations for real clients.
The RLHF Paradox: Helpful Chatbots Can't Simulate Us
A 208,000-participant study reveals a fundamental trade-off: RLHF training systematically destroys a model's ability to mimic human behavior, and the gap widens with every generation.
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The Citation Crisis: When AI Gets It Right But Points Wrong
A new benchmark reveals that even top AI models frequently support correct answers with fabricated citations—a flaw called 'attribution hallucination' that undermines trust in regulated industries.
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AI Agents Can Now Write Browser Exploits—And That Changes Everything
New benchmark reveals Claude Mythos and GPT-5.5 can autonomously develop real browser exploits, raising urgent questions about AI safety and the future of cybersecurity.
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The End of Scaling Lies: Ernie 5.1 Cuts 94% of Pre-Training Costs Without Sacrificing Performance
Baidu's Once-For-All training method achieves frontier-level performance using only 6% of the compute cost, proving that massive spending isn't the only path to top-tier AI.
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Why Bigger Language Models Actually Work: The Geometry Behind Scaling Laws
MIT researchers trace scaling laws to superposition—a geometric property where LLMs pack more concepts into limited dimensions than theoretically possible.
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Your Job Isn't Vanishing – It's Expanding: Why AI Agents Make Engineers More Essential
A new study argues AI agents don't replace software engineers but expand the discipline into strategy, governance, and societal fit. The real risk is clinging to code.
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