In 2023, the science fiction literary magazine Clarkesworld
stopped accepting new submissions because so many were generated by artificial intelligence. Near as the editors could tell, many submitters pasted the magazine’s detailed story guidelines into an AI and sent in the results. And they weren’t alone. Other fiction magazines have also reported a high number of AI-generated submissions.
This is only one example of a ubiquitous trend. A legacy system relied on the difficulty of writing and cognition to limit volume. Generative AI overwhelms the system because the humans on the receiving end can’t keep up. — Read More
Recent Updates Page 15
Reinforcement World Model Learning for LLM-based Agents
Large language models (LLMs) have achieved strong performance in language-centric tasks. However, in agentic settings, LLMs often struggle to anticipate action consequences and adapt to environment dynamics, highlighting the need for world-modeling capabilities in LLM-based agents. We propose Reinforcement World Model Learning (RWML), a self-supervised method that learns action-conditioned world models for LLM-based agents on textual states using sim-to-real gap rewards. Our method aligns simulated next states produced by the model with realized next states observed from the environment, encouraging consistency between internal world simulations and actual environment dynamics in a pre-trained embedding space. Unlike next-state token prediction, which prioritizes token-level fidelity (i.e., reproducing exact wording) over semantic equivalence and can lead to model collapse, our method provides a more robust training signal and is empirically less susceptible to reward hacking than LLM-as-a-judge. We evaluate our method on ALFWorld and Bench and observe significant gains over the base model, despite being entirely self-supervised. When combined with task-success rewards, our method outperforms direct task-success reward RL by 6.9 and 5.7 points on ALFWorld and Bench respectively, while matching the performance of expert-data training. — Read More
Python or C++ for AI? Here’s the Honest Answer After Years of Using Both
Forget the hype. This is what really happens when you build AI systems in Python and C++, and why the “one language” debate misses the point.
On the surface, it sounds like a simple “this or that” question. But if you’ve actually built stuff — broken stuff, fixed stuff at midnight, and argued with teammates over which language is better — you know it’s not that simple. The short version? You probably need both. The long version? Well, that’s what this post is about. — Read More
Authentication Downgrade Attacks: Deep Dive into MFA Bypass
Phishing-resistant multi-factor authentication (MFA), particularly FIDO2/WebAuthn, has become the industry standard for protecting high-value credentials. Technologies such as YubiKeys and Windows Hello for Business rely on strong cryptographic binding to specific domains, neutralizing traditional credential harvesting and AitM (Adversary-in-the-Middle) attacks.
However, the effectiveness of these controls depends heavily on implementation and configuration. Research conducted by Carlos Gomez at IOActive has identified a critical attack vector that bypasses these protections not by breaking the cryptography, but by manipulating the authentication flow itself. This research introduces two key contributions: first, the weaponization of Cloudflare Workers as a serverless transparent proxy platform that operates on trusted Content Delivery Network (CDN) infrastructure with zero forensic footprint; second, an Authentication Downgrade Attack technique that forces victims to fall back to phishable authentication methods (such as push notifications or OTPs) even when FIDO2 hardware keys are registered. — Read More
My AI Adoption Journey
Mitchell Hashimoto, a HashiCorp co-founder, shares his approach to AI adoption.
My experience adopting any meaningful tool is that I’ve necessarily gone through three phases: (1) a period of inefficiency (2) a period of adequacy, then finally (3) a period of workflow and life-altering discovery.
In most cases, I have to force myself through phase 1 and 2 because I usually have a workflow I’m already happy and comfortable with. Adopting a tool feels like work, and I do not want to put in the effort, but I usually do in an effort to be a well-rounded person of my craft.
This is my journey of how I found value in AI tooling and what I’m trying next with it. In an ocean of overly dramatic, hyped takes, I hope this represents a more nuanced, measured approach to my views on AI and how they’ve changed over time. — Read More
Ships Passing in the Night (OpenAI’s GPT-5.3/Anthropic’s Opus 4.6)
OpenAI just introduced a new model that unlocks even more of what Codex can do: GPT‑5.3-Codex, the most capable agentic coding model to date. The model advances both the frontier coding performance of GPT‑5.2-Codex and the reasoning and professional knowledge capabilities of GPT‑5.2, together in one model, which is also 25% faster. This enables it to take on long-running tasks that involve research, tool use, and complex execution. Much like a colleague, you can steer and interact with GPT‑5.3-Codex while it’s working, without losing context.
Meanwhile, Anthropic counter with the new Claude Opus 4.6 improves on its predecessor’s coding skills. It plans more carefully, sustains agentic tasks for longer, can operate more reliably in larger codebases, and has better code review and debugging skills to catch its own mistakes. And, in a first for our Opus-class models, Opus 4.6 features a 1M token context window in beta.
… Both companies are advancing beyond simple code completion. We’re now talking about AI agents that can tackle complex, multi-step projects with a new level of independence. They are evolving from assistants into collaborators and, in some cases, independent workers. — Read More
Project Genie | Experimenting with infinite interactive worlds
Kimi K2.5
… Artificial Analysis calls Kimi the new leading open weights model, ‘now closer than ever to the frontier’ behind only OpenAI, Anthropic and Google.
Kimi K2.5 gets to top some benchmarks: HLE-Full with tools (50%), BrowseComp with Agent Swarp (78%), OCRBench (92%), OmiDocBench 1.5 (89%), MathVista (90%) and InfoVQA (93%). It is not too far behind on AIME 2025 (96% vs. 100%), SWE-Bench (77% vs. 81%) and GPQA-Diamond (88% vs. 92%).
[B]enchmarks are highly useful, but easy to overinterpret.
Inference is cheap, and speed is similar to Gemini 3 Pro, modestly faster than Opus. — Read More
Enterprises Don’t Have an AI Problem. They Have an Architecture Problem
Over the last year, I keep hearing the same statements in meetings, reviews, and architecture forums:
“We’re doing AI.” “We have a chatbot now.” “We’ve deployed an agent.”
When I look a little closer, what most organizations really have is not enterprise AI. They have a tool.
Usually it is a chatbot, or a search assistant, or a workflow automation, or a RAG system. All of these are useful. I have built many of them myself. But none of these, by themselves, represent enterprise AI architecture.
AI is not a feature. AI is not a product.
AI is a new enterprise capability layer. And in large organizations, capability layers must be architected. — Read More
Before ChatGPT, this simple machine changed everything
Today’s neural networks feel almost magical.
They write, see, reason, and talk to us like nothing before.
But all of this traces back to one extremely simple machine.
When this machine appeared in the late 1950s, it quietly changed how people thought about intelligence. — Read More
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