Presented by MongoDB We have been building databases as an industry for roughly 60 years. We have been building AI agents, in the form most people mean when they say the word today, for about 18 months.Sit with that ratio for a second, because it explains almost everything about the state of agentic development right now. Six decades versus a year and a half. We are not in the middle of this learning curve. We are standing at the very bottom of it, squinting up.There is no LAMP stack for agents yet. There is no settled, boring, default set of choices that lets a team stop re-litigating architecture and just ship.One of the earliest lessons came from the industry’s brief obsession with token-maxxing. For a stretch in early 2026, token consumption became a vanity metric. The backlash was f [...]
DeepSeek’s announcement over the weekend that it has made its 75% price cut permanent on its flagship V4 Pro model is a disruptive assault on the capital-heavy business models of Silicon Valley’s [...]
For the last 24 months, one narrative justified every over-provisioned data center and bloated IT budget: the GPU scramble. Silicon was the new oil, and H100s traded like contraband. Reserve capacity [...]
AI agents forget. Every time a coding assistant loses track of a debugging thread, or a data analysis agent re-ingests the same context it already processed, the team pays in latency, token costs, and [...]
Nvidia researchers have introduced a new technique that dramatically reduces how much memory large language models need to track conversation history — by as much as 20x — without modifying the mo [...]
A few hours ago, Chinese delivery app company Meituan officially unveiled LongCat-2.0 on GitHub, Hugging Face, and its native platform, unmasking the model as the computational engine behind "Owl [...]
When an enterprise LLM retrieves a product name, technical specification, or standard contract clause, it's using expensive GPU computation designed for complex reasoning — just to access stati [...]
As agentic AI workflows multiply the cost and latency of long reasoning chains, a team from the University of Maryland, Lawrence Livermore National Labs, Columbia University and TogetherAI has found a [...]
A VB Pulse survey this June found that 57% of enterprises had traced a confidently wrong agent answer back to missing or inconsistent context — the latest sign of how central context has become to w [...]