Cognaptus Insights

Rules of Engagement: Why LLMs Need Logic to Plan When it comes to language generation, large language models (LLMs) like GPT-4o are top of the class. But ask them to reason through a complex plan — such as reorganizing a logistics network or optimizing staff scheduling — and their performance becomes unreliable. That’s the central finding from ACPBench Hard (Kokel et al., 2025), a new benchmark from IBM Research that tests unrestrained reasoning about action, change, and planning. ...

Problem For years, crafting a compelling content persona in influencer marketing has been expensive, time-consuming, and resource-heavy. Building a consistent voice and personality online required cross-functional teams—strategists, writers, designers, and analysts—to maintain authenticity across posts and platforms. This made persona-based content marketing largely inaccessible to smaller brands or solo marketers. Hidden Insight Generative AI doesn’t just speed up content creation—it reshapes the entire cost structure and creative workflow of persona-driven marketing. With the right prompt design and persona template, anyone can now launch a consistent, human-like virtual persona and scale content production at near-zero marginal cost. This shift not only reduces content creation time but also redefines how marketing teams collaborate, ideate, and scale messaging across platforms. ...

📺 Watch this first: Jimmy O. Yang on “Guess How Much” “Because the art is in the savings — you never pay full price.” 💬 “Guess How Much?” — A Philosophy for AI Developers In his stand-up comedy, Jimmy O. Yang jokes about how Asian families brag not about how much they spend, but how little: “Guess how much?” “No — it was $200!” It’s not just a punchline. It’s a philosophy. And for developers building LLM-powered applications for small businesses or individual users, it’s the right mindset. ...

Gemini 2.5 and the Rise of the 2 Million Token Era In March 2025, Google introduced Gemini 2.5 Pro with a 2 million token context window, marking a major milestone in the capabilities of language models. While this remains an experimental and high-cost frontier, it opens the door to new possibilities. To put this in perspective (approximate values, depending on tokenizer): 📖 The entire King James Bible: ~785,000 tokens 🎭 All of Shakespeare’s plays: ~900,000 tokens 📚 A full college textbook: ~500,000–800,000 tokens This means Gemini 2.5 could, in theory, process multiple entire books or large document repositories in one go—though with substantial compute and memory costs that make practical deployment currently limited. ...

Introduction In the recent paper LLM-Gomoku: A Large Language Model-Based System for Strategic Gomoku with Self-Play and Reinforcement Learning, by Hui Wang (Submitted on 27 Mar 2025), the author demonstrates how Large Language Models (LLMs) can learn to play Gomoku through a clever blend of language‐based prompting and reinforcement learning. While at first glance this sounds like yet another AI approach to a classic board game, the innovative aspects of integrating prompts, self‐play, and local move evaluations offer fresh insights into how LLMs might tackle real‐world decision problems—especially where traditional AI often struggles to handle complexities or requires enormous labeled data. ...

Introduction Generative AI continues to impress with its breadth of capabilities—from drafting reports to designing presentations. Yet despite these advances, it is crucial to understand the evolving cost structure, risk exposure, and strategic options businesses face before committing to full-scale AI adoption. This article offers a structured approach for business leaders and AI startups to evaluate where and when generative AI deployment makes sense. We explore cost-performance tradeoffs, forward-looking cost projections, tangible ROI examples, and differentiation strategies in a rapidly changing ecosystem. ...

Introduction In the race to develop increasingly powerful AI agents, it is tempting to believe that size and scale alone will determine success. OpenAI’s GPT, Anthropic’s Claude, and Google’s Gemini are all remarkable examples of cutting-edge large language models (LLMs) capable of handling complex, end-to-end tasks. But behind the marvel lies a critical commercial reality: these models are not free. For enterprise applications, the cost of inference can become a serious bottleneck. As firms aim to deploy AI across workflows, queries, and business logic, every API call adds up. This is where a more deliberate, resourceful approach can offer not just a competitive edge—but a sustainable business model. ...

“Fine-tuning and prompting don’t just teach—sometimes, they mislead. The key is knowing how much to trust new information.” — Cognaptus Insights 🧠 Introduction: When Models Learn Too Eagerly In the world of Large Language Models (LLMs), LoRA fine-tuning and prompt engineering are popular tools to customize model behavior. They are efficient, modular, and increasingly accessible. However, in many practical scenarios—especially outside elite research labs—there remains a challenge: Enterprise-grade LLM deployments and user-facing fine-tuning workflows often lack structured, scalable mechanisms to handle input quality, model confidence, and uncertainty propagation. ...

Introduction: Seeing Opportunity in the Unseen In developing economies across Southeast Asia and Latin America, many businesses—especially SMEs—still rely heavily on paper-based workflows. According to the World Bank, over 65 million SMEs operate across these regions, and in countries like the Philippines, Vietnam, Colombia, and Peru, up to 70% of invoicing remains manual and paper-driven 12. Despite the growth of digital tools, invoice scanning, expense tracking, and compliance reporting remain highly fragmented and inefficient. ...

🏘️ Why Real Estate Brokerage in Southeast Asia Resists Change — And How AI-Powered SaaS Might Finally Break Through For years, founders and VCs have dreamed of creating the “Lianjia of Southeast Asia.” Yet platform after platform has failed to break through the chaotic, relationship-driven, deeply human world of SEA real estate brokerage. Why does this industry remain so stubborn to automation — and could a new generation of AI-powered SaaS finally change that? ...