Turing Post

Turing Post recommends: Agent memory gives AI systems continuity, context, and learning across sessions – not just one-off responses. Prompt-based apps can look impressive in a single interaction and still fall apart over time. In a new MongoDB resource article, "Bringing Attention To Memory In AI Agents and Agentic Systems," we explore why without real memory, even strong models stay stateless. The result is they answer what's in front of them, then start from zero the next time.

TL;DR: AI is already helping improve the systems it runs on, and OpenAI is changing its programming tools to give models more control. Cheaper implementation also makes previously impractical experiments worth trying. Whether this becomes recursive self-improvement depends on what happens next: do those gains make the next round of research more effective? Choosing worthwhile problems and reliably checking the results remain difficult parts of the process.

Today’s editorial: we discuss what models have learned that we don’t yet understand, and why OpenAI’s chief scientist thinks that should affect how quickly we develop them. 79% of enterprises are building AI agents. Only 11% have reached production. The bottleneck isn’t the model – it’s memory and retrieval.

TL;DR: Current AI agents can iterate and improve results, but rarely rethink the strategy behind that improvement. New research explores how to fix this limitation: Metaⁿ recursively adds layers that improve the agent’s strategy, while Recuris evolves how agents store, retrieve, verify, and apply experience through memory. Housekeeping, folks: AI 101 is becoming ∇ Guide and moving to Friday.

Frontier AI models can now chain vulnerabilities and move through infrastructure on their own, no human required. The target, almost always, is identity: credentials, machine access, and the keys that both agents and attackers need. On September 15, Rubrik is hosting a free virtual AI + Identity Resilience Summit to unpack what that means for security teams.

Today’s editorial:Why we believe world models are becoming a distinct field of AI research, and why Turing Post is making them our new editorial backbone. From our partners: SciSpace Agent: Hand Off the Whole Research Task, Not One Step Research still runs on the same slow loop: search, skim, discard, read, extract, synthesize, cite. Most of that isn't thinking. It's finding papers and keeping track of them. SciSpace Agent takes the whole task instead of one step of it.

A coding agent is rarely told exactly where a change belongs. You give it the task, and it has to find the relevant code itself. Today, that usually means searching for a name, opening several files, working out how they connect, then searching again. A surprising amount of the agent’s time and token budget can be wasted before it writes a single line.

Today’s editorial: The superintelligence we are racing toward – and how to record our path to it. We recommend: 🛠 Move from AI Investment to Outcomes Building AI applications at scale requires solving fluid challenges across models, agents, and infrastructure. AWS offers capabilities at every layer of the stack with a partner ecosystem enabling ISVs to build and operate AI more efficiently at scale. Permanent Dawn I want to stop.

Turing Post recommends: Memory, state, security, evals, and the data layer determine what actually works. Agent prototypes are easy. Surviving security review, cost ceilings, and production traffic is the hard part. In our recent blog post, "Designing an Agentic Platform: The Infrastructure That Makes Agents Work," we make the case that the issue usually isn't a weak model; it's thin infrastructure around the model.

Today’s editorial: We discuss why DeepSeek Harness is even bigger than it seems We recommend: 👨‍🔧 MongoDB Agent Skills: Now Live Official MongoDB Agent Skills are live! Bring MongoDB best practices into Claude Code, Cursor, Gemini CLI, and VS Code. Friday / New: I’m pondering an idea for a new type of media that I’d like to share with you. Sunday / Library: Chinese LLMs in 2026: DeepSeek, Qwen3, Kimi K2 and More When DeepSeek released DeepSeek-R1 in January 2025, it blew everyone’s mind.