All posts tagged: LLM

Monitoring LLM behavior: Drift, retries, and refusal patterns

Monitoring LLM behavior: Drift, retries, and refusal patterns

Published by skeptic

The stochastic challenge Traditional software is predictable: Input A plus function B always equals output C. This determinism allows engineers to develop robust tests. On the other hand, generative AI is stochastic and unpredictable. The exact same prompt often yields different results on Monday versus Tuesday, breaking the traditional unit testing that engineers know and love. To ship enterprise-ready AI, engineers cannot rely on mere “vibe checks” that pass today but fail when customers use the product. Product builders need to adopt a new infrastructure layer: The AI Evaluation Stack. This framework is informed by my extensive experience shipping AI products for Fortune 500 enterprise customers in high-stakes industries, where “hallucination” is not funny — it’s a huge compliance risk. Defining the AI evaluation paradigm Traditional software tests are binary assertions (pass/fail). While some AI evals use binary asserts, many evaluate on a gradient. An eval is not a single script; it is a structured pipeline of assertions — ranging from strict code syntax to nuanced semantic checks — that verify the AI system’s intended …

Why We Should Be Reading Paul Churchland Right Now: Neurophilosophy and AI

Why We Should Be Reading Paul Churchland Right Now: Neurophilosophy and AI

Published by Jenni Sidey

The more I get into philosophical and philosophy-adjacent discussions of current-generation “artificial intelligence” (large language models and the like), the more dismayed I am not to see any discussion of the large body of relevant work by Paul Churchland. (Full disclosure: he was my dissertation chair.) Paul is not ordinarily thought of as a philosopher of AI, but rather as a philosopher of mind and of neuroscience. However, for reasons I hope to make clear in this post, Paul was one of the first philosophers to engage in detail with the predecessors of the technology behind systems like ChatGPT, and he provides quite extensive conceptual resources for beginning to address many of the ontological and epistemological questions about this type of AI. Paul Churchland is a naturalistic philosopher who has written widely on philosophy of science, philosophy of mind, epistemology, and philosophy of language. For most of his career, the primary naturalistic lens through which he has pursued a variety of philosophical concerns is that of the neurosciences, and from roughly 1986 on, that primarily …

Anthropic releases Claude Opus 4.7, narrowly retaking lead for most powerful generally available LLM

Anthropic releases Claude Opus 4.7, narrowly retaking lead for most powerful generally available LLM

Published by skeptic

Anthropic is publicly releasing its most powerful large language model yet, Claude Opus 4.7, today — as it continues to keep an even more powerful successor, Mythos, restricted to a small number of external enterprise partners for cybersecurity testing and patching vulnerabilities in the software said enterprises use (which Mythos exposed rapidly). The big headlines are that Opus 4.7 exceeds its most direct rivals — OpenAI’s GPT-5.4, released in early March 2026, scarcely more than a month ago; and Google’s latest flagship model Gemini 3.1 Pro from February — on key benchmarks including agentic coding, scaled tool-use, agentic computer use, and financial analysis. But also, it’s notable how tight the race is getting: on directly comparable benchmarks, Opus 4.7 only leads GPT-5.4 by 7-4. Annotated Claude Opus 4.7 benchmark chart. Credit: Anthropic edited by VentureBeat using Google Gemini 3.1 Pro Image It currently leads the market on the GDPVal-AA knowledge work evaluation with an Elo score of 1753, surpassing both GPT-5.4 (1674) and Gemini 3.1 Pro (1314). GPDVal-AA knowledge work benchmark comparison chart of Opus …

I tried an abliterated local LLM and it feels nothing like the others

I tried an abliterated local LLM and it feels nothing like the others

Published by skeptic

Local LLMs are great for privacy; they run locally and require no subscription. That is, until you realize they suffer the same problems their cloud counterparts do: extremely restricted safety guardrails. Safety measures online are one thing, but if you’re running your AI model locally, bypassing them should be easy, right? Turns out, it actually is. If you’ve ever gone down the local LLM rabbit hole, you must have come across abliterated models. Local LLMs have already made cloud versions obsolete for certain tasks, and once you run them without restrictions, you’ll never look at regular models the same way. What abliterated LLMs actually are Stripped-down models with their guardrails removed Yadullah Abidi / MakeUseOf AI models go through a process called RLHF or reinforced learning from human feedback before launch. This process trains the model to refuse requests it deems harmful or sensitive. The model learns a specific direction in its activation space that teaches it what requests to refuse, hence implementing safety guardrails that prevent it from abuse when implemented in online services. …

TurboQuant Algorithm Lowers LLM Costs Without Accuracy Loss

TurboQuant Algorithm Lowers LLM Costs Without Accuracy Loss

Published by skeptic

Google’s TurboQuant is making waves in the AI hardware sector by addressing long-standing challenges in memory usage and processing efficiency. Developed with components like the Quantized Johnson-Lindenstrauss Algorithm, TurboQuant achieves up to sixfold reductions in memory requirements while preserving model accuracy. This compression algorithm also accelerates processing speeds by as much as eight times, allowing faster and more cost-effective deployment of large language models (LLMs). As Wes Roth explains, these advancements are reshaping how enterprises approach AI infrastructure, with significant implications for both operational efficiency and the broader hardware market. Explore how TurboQuant’s capabilities translate into practical benefits, from reducing inference costs by 50% to optimizing GPU utilization for existing hardware. Gain insight into its potential to extend context windows and support larger models, opening doors for more sophisticated AI applications. Additionally, understand the ripple effects on the memory chip market, where declining demand for high-capacity components signals a shift in industry dynamics. This overview provides a clear breakdown of TurboQuant’s impact on AI accessibility, cost structures and future adoption trends. Key Innovations Behind TurboQuant …

AI joins the 8-hour work day as GLM ships 5.1 open source LLM, beating Opus 4.6 and GPT 5.4 on SWE-Bench Pro

AI joins the 8-hour work day as GLM ships 5.1 open source LLM, beating Opus 4.6 and GPT 5.4 on SWE-Bench Pro

Published by skeptic

Is China picking back up the open source AI baton? Z.ai, also known as Zhupai AI, a Chinese AI startup best known for its powerful, open source GLM family of models, has unveiled GLM-5.1 today under a permissive MIT License, allowing for enterprises to download, customize and use it for commercial purposes. They can do so on Hugging Face. This follows its release of GLM-5 Turbo, a faster version, under only proprietary license last month. The new GLM-5.1 is designed to work autonomously for up to eight hours on a single task, marking a definitive shift from vibe coding to agentic engineering. The release represents a pivotal moment in the evolution of artificial intelligence. While competitors have focused on increasing reasoning tokens for better logic, Z.ai is optimizing for productive horizons. GLM-5.1 is a 754-billion parameter Mixture-of-Experts model engineered to maintain goal alignment over extended execution traces that span thousands of tool calls. “agents could do about 20 steps by the end of last year,” wrote z.ai leader Lou on X. “glm-5.1 can do 1,700 …

Karpathy shares ‘LLM Knowledge Base’ architecture that bypasses RAG with an evolving markdown library maintained by AI

Karpathy shares ‘LLM Knowledge Base’ architecture that bypasses RAG with an evolving markdown library maintained by AI

Published by skeptic

AI vibe coders have yet another reason to thank Andrej Karpathy, the coiner of the term. The former Director of AI at Tesla and co-founder of OpenAI, now running his own independent AI project, recently posted on X describing a “LLM Knowledge Bases” approach he’s using to manage various topics of research interest. By building a persistent, LLM-maintained record of his projects, Karpathy is solving the core frustration of “stateless” AI development: the dreaded context-limit reset. As anyone who has vibe coded can attest, hitting a usage limit or ending a session often feels like a lobotomy for your project. You’re forced to spend valuable tokens (and time) reconstructing context for the AI, hoping it “remembers” the architectural nuances you just established. Karpathy proposes something simpler and more loosely, messily elegant than the typical enterprise solution of a vector database and RAG pipeline. Instead, he outlines a system where the LLM itself acts as a full-time “research librarian”—actively compiling, linting, and interlinking Markdown (.md) files, the most LLM-friendly and compact data format. By diverting a …

Xiaomi stuns with new MiMo-V2-Pro LLM nearing GPT-5.2, Opus 4.6 performance at a fraction of the cost

Xiaomi stuns with new MiMo-V2-Pro LLM nearing GPT-5.2, Opus 4.6 performance at a fraction of the cost

Published by skeptic

Chinese electronics and car manufacturer Xiaomi surprised the global AI community today with the release of MiMo-V2-Pro, a new 1-trillion parameter foundation model with benchmarks approaching those of U.S. AI giants OpenAI and Anthropic, but at around a seventh or sixth the cost when accessed over proprietary API — and importantly, sending less than 256,000 tokens-worth of information back and forth. Led by Fuli Luo, a veteran of the disruptive DeepSeek R1 project, the release represents what Luo characterizes as a “quiet ambush” on the global frontier. Furthermore, Luo stated in an X post that the company does plan to open source a model variant from this latest release, ” when the models are stable enough to deserve it.” By focusing on the “action space” of intelligence—moving from code generation to the autonomous operation of digital “claws”—Xiaomi is attempting to leapfrog the conversational paradigm entirely. Prior to this foray into frontier AI, Beijing-based Xiaomi established itself as a titan of “The Internet of Things” and consumer hardware. Globally recognized as the world’s third-largest smartphone manufacturer, …

I gave my local LLM access to my files and it replaced three apps I was paying for

I gave my local LLM access to my files and it replaced three apps I was paying for

Published by skeptic

If you’ve got tons of files that you constantly need to search through, you’re likely paying for software that’s reading and summarizing them under the hood. But considering local LLMs can turn any file into a mind map, what if you give yours access to your files? That’s exactly what I did, and to my surprise, the results turned out great. No cloud, no API keys, nothing leaving your machine, and before you know, it might just replace apps you would otherwise be paying for. How local AI indexing actually works Letting your local LLM access your files isn’t as daunting as it sounds Giving your local LLM access to your files might sound intimidating, but it’s actually simpler than you think. I used an approach called RAG or Retrieval-Augmented Generation. Instead of dumping an entire document into the models’ context window, which is slow, expensive in tokens, and hits limits quickly, RAG can break your files into smaller chunks and convert them into vector embeddings that are stored in a local database. When you …

Nvidia says it can shrink LLM memory 20x without changing model weights

Nvidia says it can shrink LLM memory 20x without changing model weights

Published by skeptic

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 model itself. The method, called KV Cache Transform Coding (KVTC), applies ideas from media compression formats like JPEG to shrink the key-value cache behind multi-turn AI systems, lowering GPU memory demands and speeding up time-to-first-token by up to 8x. For enterprise AI applications that rely on agents and long contexts, this translates to reduced GPU memory costs, better prompt reuse, and up to an 8x reduction in latency by avoiding the need to recompute dropped KV cache values. Serving large language models at scale requires managing a massive amount of data, especially for multi-turn conversations and long coding sessions. Every time a user adds to a prompt, the system relies on stored memory to avoid recomputing the entire conversation history from scratch. However, this memory footprint grows rapidly, creating a severe bottleneck for latency and infrastructure costs. Why KV cache becomes a bottleneck at …