LLM
LLM on Beyond Market Intelligence: a running collection of 17 stories we have gathered and hand-picked because they are worth your time. Every post here touches on llm in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around llm, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Writer's AI harness cuts token spend nearly 40% — without sacrificing accuracy
Enterprise AI faces a growing ROI challenge: while powerful foundation models excel in experimentation, production costs can quickly become unsustainable. New research from Writer demonstrates a solution accessible to engineering teams, revealing dramatic reductions—up to 41%—in task costs by optimizing the AI harness, the orchestration layer surrounding these models. This approach, which cuts token spend by nearly 40% without sacrificing accuracy, highlights the critical need to shift focus from simply increasing model size to refining system design.
Loop Engineering with Adaptive Parsing in Action: Parsing Flat Tables with Azure and Figures with a Vision LLM
Loop Engineering presents a progressive approach to enterprise document intelligence, demonstrating Adaptive Parsing in action. This initial installment, "Parsing Flat Tables with Azure and Figures with a Vision LLM," explores utilizing Large Language Models (LLMs) as a critical last line of defense. We detail two complete escalations: extracting data from flat tables via Azure and interpreting figures through a vision model. For those seeking to optimize agent performance, consider "How to Run Claude Code Agents for 24+ Hours" for deeper insights into long-running coding agents.
How to Run Claude Code Agents for 24+ Hours
Unlock sustained coding productivity with Claude Code Agents running continuously – even for 24+ hours. This guide explores how to leverage these powerful AI assistants to streamline your engineering workflows and tackle complex projects with unprecedented efficiency. Discover practical techniques for maintaining and optimizing long-running agents, transforming your coding process. For a foundational understanding of setup and configuration, see "A Beginner’s Guide to Setting Up Claude Code for High Performance Agentic Programming" and elevate your agentic programming skills.
A Beginner’s Guide to Setting Up Claude Code for High Performance Agentic Programming
Unlock the full potential of Claude Code for agentic programming with this practical guide. We detail the essential configuration—permissions, hooks, and command habits—that distinguish a functional installation from a robust, production-ready setup designed for sustained agentic workflows. This isn’t theory; it’s a step-by-step walkthrough to optimize performance. For those seeking broader context on the evolving AI landscape, consider our recent discussion, "Am I focusing on the wrong skills as a CS student in the AI era?", to ensure you're building a future-focused skillset.
Did blatant AI Slop just win a 25K USD Deepmind / Kaggle Grand Prize? [D]
A recent DeepMind/Kaggle competition, "Measuring Progress Toward AGI," has sparked considerable debate following the announcement of its results. The 25,000 USD grand prize was awarded to a submission critiqued as presenting “nonsensical number generation” and questionable methodology. The work, intended to assess LLM reasoning through viewpoint comparison, appears to have been overlooked for critical review. Explore a deeper investigation of this outcome, detailing the methodology and data—a journey that may challenge conventional understanding.
Looking for JEPA devil advocates [R]
The emergence of JEPA-like world models presents a compelling, future-focused direction for robot learning, as highlighted by recent research. While Yann LeCun’s vision is undeniably ambitious, a critical evaluation is warranted. We're seeking perspectives that challenge the current trajectory – "devil's advocates" who can identify potential downsides compared to alternative world model approaches. Are there overlooked limitations or vulnerabilities within JEPA’s framework? Explore this discussion, and consider “Are Current AI Memory Architectures Optimizing for the Wrong Abstraction?” for a deeper dive into related challenges.
Tried testing qwen 35b moe model on s26 ultra , without compromising on precision [R] ,[D]
Early testing reveals promising results for running a private Qwen 35B MoE LLM on an S26 Ultra, demonstrating a potential for approximately 90 tokens/second input processing and 8 tokens/second output generation after optimization. This achievement, realized through self-directed AI/ML exploration and leveraging available compute resources, highlights the accessibility of advanced model deployment. The author, without disclosing implementation details, is actively seeking collaborators to further test and refine this mobile runtime.
ExTernD: Expanded-Rank Ternary Decomposition Ternary LLM PTQ with Accuracy Approaching Any Quantization Level [P]
ExTernD introduces a novel approach to Post-Training Quantization (PTQ) for Large Language Models, resolving a critical limitation of traditional ternary quantization. Unlike fixed-size methods that plateau in accuracy, ExTernD decomposes matrices into ternary components alongside a scalable diagonal scaling matrix. This innovative architecture allows for arbitrarily fine-grained accuracy control with a minimal increase in VRAM—often comparable to existing quantization techniques. Explore the full details of this transformative method in the arXiv paper: [https://arxiv.org/pdf/2607.13511](https://arxiv.org/pdf/2607.13511).
Context Engineering Isn’t Enough — A Loop Engineering Experiment With No LLM Inside the Loop
The conversation around loop engineering often centers on Large Language Models (LLMs), but can the architecture itself drive improved performance? This article presents a novel experiment, rigorously testing a deterministic, zero-dependency Python benchmark to isolate failures—without an LLM. Results across 300 random seeds demonstrate that goal-directed controllers consistently outperform linear pipelines in completing independent branches. Explore the architecture, benchmark, and debugging process, revealing that failure isolation is, in fact, a measurable property of control flow.
The agent evaluation gap: Enterprise AI organizations have a reality-alignment problem, not a coverage problem — and most are shipping to production anyway
Enterprise AI organizations face a critical reality-alignment problem: an “evaluation gap” where increasing agent autonomy outpaces trust in the evaluations meant to govern it. A recent VentureBeat Pulse Research survey of 157 enterprises reveals that half have already deployed an agent that passed internal evaluations but subsequently failed a customer. Only 5% fully trust automated evaluation, citing a key weakness – evaluations often don't reflect real-world outcomes. Despite this, two-thirds are moving toward fully automated deployments, highlighting a pressing need for more reliable assurance.
Context Engineering for RAG Question Parsing: From a Raw Question to Typed Fields That Steer Retrieval and Generation
Unlock the full potential of Retrieval-Augmented Generation (RAG) with Context Engineering for Question Parsing. This approach transforms raw, unstructured questions into precisely typed fields, directly steering both retrieval and generation processes. Published in Enterprise Document Intelligence [Vol.1 #6quater], this post details a critical technique for maximizing AI agent effectiveness. Addressing the "AI context gap," as explored in our related article, "The AI context gap: Enterprise AI organizations have a trust problem…", this method ensures your AI agents operate with clarity and precision.
Most RAG Hallucinations Are Retrieval Failures: How the Retrieval Brick Decides What the Model Can Invent
RAG (Retrieval-Augmented Generation) hallucinations aren't primarily model flaws; they're overwhelmingly retrieval failures. Enterprise Document Intelligence, Vol.1 #7quinquies, reveals that the retrieval component—the “brick” selecting context—is often the root cause. Simply put, garbage retrieval leads to garbage output. Addressing retrieval shortcomings is the most impactful step toward mitigating hallucinations, as it limits the model’s opportunity to invent information. As Vint Cerf explores with his work on identifying AI agents, ensuring reliable data sources is paramount.
AWS Ships Claude Apps Gateway as Self-Hosted Control Plane for Claude Code and Claude Desktop
AWS now offers the Claude Apps Gateway, a self-hosted control plane streamlining access to Claude Code and Claude Desktop. This innovative solution centralizes crucial functions—identity, policy, telemetry, routing, and spend management—within a single, stateless container. Inference requests are efficiently directed to either Amazon Bedrock or the Claude Platform on AWS. This marks a significant step toward greater control and flexibility for developers. For a deeper understanding of Claude’s underlying architecture, explore our breakdown of the Claude Fable 5 system prompt.
Inside the Claude Fable 5 System Prompt: A Full Breakdown
Delve into the inner workings of Claude Fable 5 with a comprehensive breakdown of its 3,826-line system prompt, now accessible via a public GitHub archive. This detailed rulebook governs Claude’s behavior within the Claude app, outlining critical parameters for safety, tone, and restraint. Examining this prompt reveals a key insight: advanced AI is fundamentally an engineered system, far more defined by carefully crafted instructions than inherent sentience.
How Much Does It Actually Cost to Run a Local LLM? (Euros per Million Tokens, Measured)
Running Large Language Models (LLMs) locally presents a compelling alternative to cloud-based solutions, but what's the real cost? We measured the actual GPU electricity consumption for eight different local LLMs on a single RTX 3090, revealing surprising results – the most efficient wasn't necessarily the smallest or largest. Discover how costs vary per million tokens, and gain practical insights into optimizing your local LLM deployment. For a deeper dive into the computational challenges of generative AI, explore "A Gentle Introduction to Autoencoders & Latent Space."
What is Meta Prompting and How does it work?
Prompt quality directly impacts large language model (LLM) output. While clear instructions yield focused results, achieving consistency across teams—especially for repetitive tasks—can be challenging. Meta-prompting addresses this by leveraging the LLM itself to design reusable prompts, templates, checklists, or even entire workflows. Essentially, the model crafts the instructions *before* you use them, ensuring standardized and predictable outcomes. For deeper exploration of related AI architecture complexities, see our article, "Article: Comprehension at AI Speed: Building a Context Store for Evolutionary Architecture."
12 Ways to Reduce LLM Latency and Inference Costs in Production
Scaling large language models (LLMs) effectively moves beyond simply adding more GPUs. It demands a rigorous focus on optimizing request efficiency. This article details 12 proven strategies to reduce LLM latency and inference costs in production environments. Ranked by impact, these methods address wasted work within each request—from caching and quantization to optimized prompting and batching. Discover practical techniques to empower your LLM deployments and maximize performance.