Ideas, guides, and
production lessons.
What we learn building AI agents, RAG systems, and Claude integrations for real clients.
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.
Read article
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.
Read article
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.
Read article
Small But Mighty: Qwen3.6-27B Shatters the Bigger Is Better Myth
Alibaba's new 27B dense open-source model outperforms its 397B MoE predecessor on coding benchmarks, signaling a shift toward efficiency over brute-force scale.
Read article
Your AI Can't Close a Deal: The Brutal BankerToolBench Results
A new benchmark finds top models like GPT-5.4 and Claude Opus fail to produce client-ready investment banking deliverables, exposing deep flaws in business logic, code generation, and data fabrication.
Read article
The Intelligence Tax: Why Your Agent’s LLM is Your New Economic Ceiling
Anthropic’s Project Deal reveals a chilling reality: stronger AI models systematically exploit weaker ones in negotiations, and the human victims are too satisfied to notice.
Read articleGet the AI Implementation Checklist
10 questions every team should answer before building AI systems. Avoid the most common mistakes we see in production projects.
Check your inbox!
We've sent you the AI Implementation Checklist.
No spam. Unsubscribe anytime.