# Optimization Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Optimization", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

How Token-Hungry is Claude Code? A Comparative Experiment Shows Up to 30x Difference Across Three Frameworks

Claude Code's Token Consumption Exposed: Comparison Experiment Shows Up to 30x Difference Between Frameworks A recent experiment by the Composio team tested the same model (Kimi K3) across three different agent frameworks (Claude Code, Hermes, and Kimi Code) on 28 identical tasks. While task completion rates were similar, token consumption varied dramatically. The median token usage was approximately 61k for Kimi Code, 67k for Hermes, and a staggering 340k for Claude Code – about 6 times more than Kimi Code. For individual tasks, the maximum difference reached 30x. In terms of cost, using Claude Code averaged $2 per task compared to $0.22 for Kimi Code and $0.28 for Hermes (based on Kimi K3 pricing). Speed also differed, with Hermes being the fastest. Analysis suggests Claude Code's high token usage stems from its harness repeatedly feeding extensive context (previous messages, tool calls, command outputs, file contents) back into the model across multiple interaction rounds, significantly inflating input tokens rather than generating longer outputs. This highlights a crucial trend: the agent framework (harness) is becoming as important as the model itself for cost and efficiency. A separate study from Writer showed that simply switching the orchestration layer to their optimized harness reduced average task cost by 41% and latency by 44% across various models without sacrificing quality. The conclusion is clear: for cost-effective AI agents, optimizing the harness may yield greater savings than changing the model. The future of agent competition may hinge not just on capability ("can it do it?") but on efficiency ("who does it for less?").

marsbitYesterday 12:26

How Token-Hungry is Claude Code? A Comparative Experiment Shows Up to 30x Difference Across Three Frameworks

marsbitYesterday 12:26

NVIDIA's 20-Year CUDA Moat Collapsed Over a Weekend, Claude Single-Handedly Got AMD's New GPU Running

In a single weekend, Claude, an AI agent from Anthropic, successfully ported and optimized its cutting-edge model to run on a brand-new AMD MI355X server rack without any manual code intervention. This feat demonstrates a potential breakthrough in overcoming NVIDIA's long-established CUDA software ecosystem dominance, built over two decades. Anthropic's team simply instructed Claude to get the AMD machine running. By Monday, it not only worked but was showing a continuously improving performance curve. The achievement impressed AMD CEO Lisa Su and accelerated a major deployment partnership: Anthropic plans to deploy up to 2GW of AMD Instinct GPUs starting in 2027. The key enabler is AMD's new ROCm.AI platform, a toolbox designed specifically for AI agents like Claude. It provides AI-readable documentation, including chip instruction sets (ISA), and tools like the Hyperloom service that allows agents to autonomously profile performance, identify bottlenecks, test configurations, and generate optimized kernels. In a demo, Hyperloom boosted the output speed of a model by 38%. This represents a fundamental shift. While CUDA's strength lies in its vast, human-expert-driven ecosystem of tools and tacit knowledge, AMD's strategy is to make its hardware and software stack directly accessible and optimizable by AI agents. An agent can parallelize tasks—debugging, profiling, coding—that would take human engineers years to master, compressing the traditional software adaptation timeline from years to tasks. The competition is no longer just about peak hardware specs but also about how well AI can read, utilize, and tune a platform.

marsbit07/28 00:09

NVIDIA's 20-Year CUDA Moat Collapsed Over a Weekend, Claude Single-Handedly Got AMD's New GPU Running

marsbit07/28 00:09

Claude Code Slashes 80% of Prompt Tokens, But Opus 5 Just Adds Them Right Back In

Claude Code, the AI coding assistant from Anthropic, recently announced a massive reduction of over 80% in its system prompt content for models like Opus 5 and Fable 5. The goal was to remove verbose, often conflicting, rules (like strict commenting and documentation requirements) and replace them with a simpler directive: write code that matches the style of the surrounding project. This "pruning" aims to make the model more efficient by reducing internal conflict from overlapping instructions, with no measurable performance drop reported. However, a developer's (@chenchengpro) investigation revealed a twist. While the prompt was drastically cut from 15,225 characters in Opus 4.7 to 4,467 in Opus 4.8, it *increased* by approximately 72% to 7,694 characters in Opus 5. This isn't a contradiction. The "over 80% cut" refers to the overall shift from the old, detailed rulebook-style prompts to a new, streamlined system. The 72% increase for Opus 5 represents new, targeted instructions added to manage the model's enhanced capabilities. Opus 5 is more proactive—it likes to report progress, generate longer outputs, use sub-agents, and expand task scope. The added prompt content (roughly 3,755 characters) primarily provides guidelines for "Delivering work" (controlling task scope, progress reporting) and "Corrections" (limiting excessive self-correction). These new rules are necessary to curb potential over-engineering on simple tasks, ensuring efficiency even as the model becomes more independent. In short, the old, restrictive manual was deleted, but new guidelines were written to harness the model's newfound initiative.

marsbit07/27 11:37

Claude Code Slashes 80% of Prompt Tokens, But Opus 5 Just Adds Them Right Back In

marsbit07/27 11:37

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