tokens
tokens on Beyond Market Intelligence: a running collection of 11 stories we have gathered and hand-picked because they are worth your time. Every post here touches on tokens 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 tokens, 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.
Shopify Introduces Gisting: Compressing LLM System Prompts into Learned Tokens
Shopify engineers have introduced Gisting, a significant advancement in Large Language Model (LLM) efficiency. This innovative technique compresses lengthy system prompts into a smaller set of learned "gist" tokens, demonstrably improving throughput and reducing inference costs. Gisting represents a practical step toward scaling AI-powered experiences. For those seeking a broader understanding of AI visibility challenges, explore our related article, "The AI visibility gap: Why great brands disappear from AI answers," presented by Contentful. Discover how Shopify is shaping the future of data management.
Prompt caching: this is what most builders ignore #AI #promptcaching #Claude #APIbuilders #tokens
Most AI builders overlook a critical optimization: prompt caching. This simple technique dramatically reduces API token usage and costs, especially with models like Claude. Ignoring it means needlessly spending resources on repetitive prompts. Prompt caching stores previous prompt-response pairs, serving cached results when the same prompt is encountered again. As discussed in "When to Use Claude Code and When to Use Codex," understanding these nuances is vital for efficient AI development. Explore this often-missed strategy to maximize your AI’s performance and minimize expenses.
Article: Post-Quantum Cryptography in Spring Boot: Four Patterns You Can Ship This Sprint
The shift to post-quantum cryptography (PQC) is no longer a distant concern—it’s a present imperative. Pankaj Sharma’s latest article, "Post-Quantum Cryptography in Spring Boot: Four Patterns You Can Ship This Sprint," outlines actionable strategies for integrating PQC into your Spring Boot applications. Explore patterns for securing service payloads, database fields, long-term document signing, and service tokens, acknowledging the growing threat of Harvest Now, Decrypt Later attacks. For broader context on building robust systems, see our article, "Mastering the AI Project Cycle: From Concept to Production."
![Bart- A vintage llm [R]](https://preview.redd.it/27z2aamswclh1.png?width=640&crop=smart&auto=webp&s=ba36a31376435bcec7f675b732595ad9dd2641a7)
Bart- A vintage llm [R]
Unbounded Labs proudly introduces Bart, a 2.82B parameter LLM meticulously trained from scratch on a unique corpus of 20.1B tokens of English text predating 1931. After three months and a modest $800 investment, we’ve achieved a significant milestone: the best-performing vintage base model at its scale on Vintage CORE. Our research, detailed in a comprehensive article, explores the potential for LLMs to replicate historical scientific reasoning—a crucial step toward understanding AI originality. Explore Bart and our methodology at the links provided.
Implementing Watermarking for Language Models [P]
Recently, curiosity surrounding Anthropic's plans to watermark language model responses led to an exploration of subtle statistical patterns – not visible messages – embedded during token selection. I’ve implemented a simplified, educational version of this technique, inspired by SynthID-Text, to better understand the concept. While not a direct reproduction, the core idea remains. Explore the implementation and its potential implications on GitHub: [https://github.com/Saad1926Q/llm-watermark](https://github.com/Saad1926Q/llm-watermark). For a deeper dive into related challenges in AI research, see our discussion on AAA
GLM-5.3 hits the API at $1.4/$4.4 per million tokens
Z.ai has made GLM-5.3, its new open-source language model boasting advanced coding and agent capabilities, accessible via API. Developers can now integrate this frontier model into their applications at a competitive rate of $1.40 per million input tokens and $4.40 per million output tokens—unchanged from its predecessor, GLM-5.2. Independent benchmarks place GLM-5.3 among the world’s top open-weight models, demonstrating strong performance at a notably lower cost than premium alternatives. For teams exploring coding and agent workloads, GLM-5.3 represents a compelling, accessible option.
Presentation: Producing the World's Cheapest Tokens: A How-to Guide
Meryem Arik’s presentation, "Producing the World's Cheapest Tokens: A How-to Guide," offers actionable strategies for dramatically reducing costs in LLM inference. Designed for software architects and engineering leaders, Arik explores critical trade-offs across hardware, runtimes, and decoding techniques to achieve order-of-magnitude savings in high-volume, non-real-time workloads. Discover how smart queue reordering and other innovations can transform your data management approach. For further exploration of AI governance, see our recent article, "IBM and Red Hat Expand Lightwell."
OpenAI launches GPT-5.6-Cyber with reduced refusals, 95% completion on advanced cybersecurity tasks
OpenAI has launched GPT-5.6-Cyber, a specialized model engineered to excel at advanced cybersecurity tasks like vulnerability research and exploit development for authorized defenders. Fine-tuned from GPT-5.6 Sol, this model demonstrates a remarkable 95% completion rate on complex cybersecurity benchmarks—a significant leap from its predecessor. Crucially, GPT-5.6-Cyber reduces refusals on higher-risk requests, enabling deeper exploration within secure environments. Access is granted through the new Daybreak Red tier, emphasizing trusted defenders with validated security programs. As AI-led attacks multiply, OpenAI launches a new cyber model to help.
Gemini 3.6 Flash Is Here: The Efficiency Release
While the industry awaited Gemini 3.5 Pro, Google quietly released Gemini 3.6 Flash on July 21, 2026—an efficiency-focused update to its speed tier. This release prioritizes streamlined performance, achieving comparable thinking capabilities to 3.5 Flash while reducing token usage, tool calls, and overall processing demands. It’s a practical step forward, demonstrating a commitment to optimized AI workflows. Explore the implications of this shift, and how it impacts agentic AI strategies—as discussed in our article, "Agentic AI vs AI Automation."
![Follow up: GPT-2's vocabulary as a hyperbolic tree — 32,070 tokens in a Poincaré ball you can fly through [P]](https://preview.redd.it/o6l1c96lo6eh1.png?width=640&crop=smart&auto=webp&s=92447206205a44b4f473a41cc8557c245d73a7d0)
Follow up: GPT-2's vocabulary as a hyperbolic tree — 32,070 tokens in a Poincaré ball you can fly through [P]
Explore the fascinating architecture of GPT-2's vocabulary with a unique visualization: a hyperbolic tree containing 32,070 tokens rendered within a Poincaré ball. This interactive experience, running directly on your phone, allows you to navigate the relationships between tokens through intuitive drag, pinch, and tap interactions. The structure reveals a natural "forest" of interconnected elements, best represented in hyperbolic space—a design that elegantly accommodates the vocabulary's complex similarity structure. Discover more on this topic with our article, "Kimi: Threat or menace?".
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."