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Claude Sonnet 4.5: 30-Hour Autonomous Coding

Pranay
Pranay
Issue #21 • Oct 01, 2025 • 8 min read

INFOLIA AI

Issue #21 • October 01, 2025 • 4 min read

Making AI accessible for everyday builders

Claude Sonnet 4.5 autonomous coding capabilities visualization

From 7-hour to 30-hour autonomous sessions: Claude's latest model marks a 4x improvement in sustained focus

👋 Hey there!

Anthropic just launched Claude Sonnet 4.5, and developers are calling it the best coding model they've tested. The standout feature: it can code autonomously for 30 hours straight, building production-ready apps while you sleep. Plus: GitHub shipped Copilot CLI for terminal-native coding, and a METR study revealed AI actually slowed experienced developers down by 19%. Let's break it down.

💡 Claude Sonnet 4.5 Can Code Autonomously for 30 Hours Straight

On September 29, Anthropic launched Claude Sonnet 4.5, which the company claims is the best coding model in the world. The model can work autonomously for up to 30 hours straight while maintaining sustained focus on complex, multistep tasks (TechCrunch, Sept 2025). That's a 4x jump from Claude Opus 4.1, which maxed out at 7 hours back in May.

What makes this compelling for developers: it's not just about completing tasks, it's about producing production-ready code. During early customer trials, engineers watched Claude Sonnet 4.5 autonomously build entire applications, stand up database services, purchase domain names, and run SOC 2 audits (TechCrunch, Sept 2025). Cursor CEO Michael Truell called it state-of-the-art for longer horizon tasks.

The model scores state-of-the-art on SWE-Bench Verified, outperforming both GPT-5 and Gemini 2.5 Pro on software engineering benchmarks (Anthropic, Sept 2025). But here's what matters more: Anthropic claims it's also their most aligned model yet, with lower rates of sycophancy and deception, plus improved resistance to prompt injection attacks.

Pricing stays competitive at $3 per million input tokens and $15 per million output tokens, matching Claude Sonnet 4 (TechCrunch, Sept 2025). Cursor, Windsurf, and Replit are already integrating it. Anthropic also released the Claude Agent SDK alongside this launch, giving developers the same infrastructure that powers Claude Code to build their own agents.

The rapid release cycle tells the story: every six months, Anthropic's new models can handle tasks twice as complex as before. This isn't incremental improvement, it's a pattern pointing toward AI that works more like a colleague than a code suggestion tool.

Bottom line: 30 hours of autonomous work suggests we're approaching the threshold where AI can handle entire feature implementations end-to-end, not just snippets.

🛠️ Tool Updates

Composio.dev - Integration platform connecting AI agents to 250+ apps and services

You can give your AI agent access to Gmail, Slack, GitHub, and 250+ other tools? Here's the thing: Composio handles all the authentication headaches so your agents can actually DO things instead of just suggesting them. Get this—developers are using it to build agents that autonomously handle customer support tickets, update CRMs, and coordinate across multiple SaaS tools without writing custom integrations for each one. One team reported it cut their go-to-market time by six months. The platform supports every major AI framework (OpenAI, Anthropic, LangChain) and just released V3 SDKs with improved performance and developer experience. Free tier includes 100 monthly actions to test your workflows. Worth checking out if you're building agents that need to interact with the real world beyond just generating text.

GitHub Copilot CLI - Terminal-native AI coding agent with full GitHub integration

GitHub launched Copilot CLI in public preview on September 25, bringing the coding agent directly to your command line. The cool part: it's not just autocomplete for terminal commands, it's an agentic system that understands your repos, issues, and PRs through natural language (GitHub Changelog, Sept 2025). Ships with Model Context Protocol support out of the box, so you can extend it with custom MCP servers. Available for Copilot Pro, Pro+, Business, and Enterprise users.

GitHub MCP Registry - Centralized discovery hub for Model Context Protocol servers

Finding MCP servers was a mess until GitHub launched this registry on September 16. Now there's one place to discover, explore, and verify MCP servers instead of hunting through scattered repos (GitHub Changelog, Sept 2025). Makes extending AI agents dramatically easier.

💰 Cost Watch

Claude Sonnet 4.5 maintains competitive pricing: At $3 per million input tokens and $15 per million output tokens, Anthropic kept pricing identical to Claude Sonnet 4 despite significant capability improvements (TechCrunch, Sept 2025). For context, that's roughly 750,000 words of input (more than the entire Lord of the Rings series) for three dollars.

💡 Money-saving insight: If you're running AI coding tools at scale, compare usage patterns between Claude Sonnet 4.5 and GPT-5. Despite similar performance on benchmarks, Claude's longer autonomous sessions could reduce the back-and-forth token costs of iterative debugging, potentially lowering your monthly bill by 20-30% depending on your workflow.

🔧 Quick Wins

🔧 Test Claude Sonnet 4.5's autonomous mode: Identify a feature you've been putting off because it requires touching multiple files across your codebase. Give Claude Sonnet 4.5 a detailed spec and let it run for a few hours. Review the PR it generates. Early adopters report 30-40% less time spent on cross-cutting concerns like updating tests, documentation, and type definitions across the entire codebase.

🎯 Set up GitHub Copilot CLI for terminal workflows: Install Copilot CLI with npm, authenticate with your GitHub account, and try delegating repetitive terminal tasks like "find all TODOs in this repo and create GitHub issues" or "analyze these error logs and suggest fixes." Takes 5 minutes to set up, saves hours on context switching between your terminal and browser. Works best for exploring unfamiliar codebases.

⚡ Audit your AI tool usage patterns: Track which AI coding tasks actually save you time this week versus which ones you end up debugging longer than if you'd written the code yourself. The METR study found experienced developers were 19% slower with AI on their own codebases—not because AI is bad, but because they used it on tasks they already knew how to do quickly. Focus your AI usage on unfamiliar territory.

🌟 What's Trending

A METR study published in July tracked 16 experienced open-source developers working on their own repositories and found they were 19% slower when using AI tools, despite believing they were 20% faster (METR, July 2025). Here's the kicker: developers used frontier tools like Cursor Pro with Claude 3.5/3.7 Sonnet on tasks averaging 2 hours each. The study suggests experienced developers on familiar codebases don't benefit as much from AI because they already know exactly what to do—AI adds overhead through code cleanup and context switching. Where AI shines: unfamiliar codebases, research-heavy tasks, and situations requiring exploration over execution. The lesson for teams: be strategic about when developers use AI, not dogmatic about using it everywhere.
Google's 2025 DORA report reveals 90% of software professionals now use AI, up 14% from last year, with developers spending a median of two hours daily with AI tools (Google Cloud Blog, Aug 2025). But there's a trust paradox: while 80% report productivity gains and 59% see improved code quality, only 24% report high trust in AI outputs, and 46% actively distrust AI accuracy (Stack Overflow Developer Survey, 2025). Experienced developers show the most caution, with the highest distrust rates. This split between usage and trust explains why developers continue using AI despite skepticism—it's useful for exploration and iteration even when outputs need verification. The implication: successful AI adoption requires cultural acceptance that AI is a thinking partner requiring review, not an infallible authority.

💬 Are you using AI differently after learning it might slow you down?

The METR study showed experienced developers were slower with AI on familiar code. Has this changed how you decide when to use AI coding tools versus when to just write it yourself? Hit reply—I read every message and I'm curious about your real-world experience.

— Pranay, INFOLIA AI

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Pranay
Pranay
Infolia.ai

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