How to Continuously Improve Your AI Agent's Performance

How to Continuously Improve Your AI Agent's Performance

On AI

Building trust in a freshly deployed AI agent takes time. You run it against your actual work data, watch it closely for days and weeks, constantly weighing whether it's helping or hurting you. Just when you finally start to relax and enjoy the productivity gains, your AI provider pushes a model update—and suddenly everything changes. The responses shift, your instructions get interpreted differently, and you're back to square one.

Here's the hard truth: improving your AI agent isn't a one-time setup. It's an ongoing process, just like maintaining any other tool you depend on.

Part 1: Setting Up for Success

Add Version Control and Build a Sandbox

Version control sounds boring, but tracking and naming each iteration of your AI agent will save you enormous headaches down the road. Without it, you'll struggle to collaborate with teammates and risk re-introducing bugs you've already fixed.

Some AI agent platforms—like Zapier—come with built-in version control. That's ideal. If yours doesn't, save all configuration details to a single source of truth. Here's what you need to track:

• The AI model you're using
• Any system prompts
• Your connected tools list
• Knowledge base versions (including individual document versions)
• Any other factor that changes the agent's behavior when added, modified, or removed

Define Goals and Build a Scorecard

Like any project, start by identifying your destination. First, decide what you're actually fixing:

• Inaccurate responses? Focus on accuracy.
• Wrong tone? Focus on voice and style.
• Unpredictable tool calls? You'll need to dive into schemas, MCPs, and APIs.

Once you've set your goal, create a scorecard. This lets you rank responses and separate what's useful from what isn't.

Metric	0 Points	1 Point	2 Points
Accuracy & Completeness	Inaccurate or missing information	Partially correct but missing key details	Accurate and complete
Factual Grounding	Speculation or fabrication; ignores provided data	Uses some real data but misinterprets it or fills gaps on its own	Clearly based on information provided
Usefulness & Clarity	Confusing, unclear, hard to follow	Acceptable but might leave users confused, doubtful, or trigger escalation	Practical and easy to act on
Tone, Format & Brand Fit	Off-brand, poor formatting, hard to parse	Tonal mismatches, mixed formatting, creates friction	On-brand, well-structured, engaging

Collect Sample Outputs

Now gather recent responses from your agent. Pull 20 to 50 examples—enough to spot real patterns without drowning in data. The key is making sure this set reflects the full range of questions your users actually ask. Otherwise you'll optimize for a narrow use case and cripple your agent's flexibility.

Score Outputs and Identify Top Issues

Add your evaluation columns to a spreadsheet. Include a pass/fail column and columns for each quality metric. Score each response: pass or fail, then award quality points from 0-2. Keep going until everything is scored.

Looking at your scored list, patterns emerge about where to focus. Early on, you might see high-severity issues everywhere. As you improve, you'll shift toward recurring problems, then business impact.

Build a Test Suite

Now that you have a scored list, save those responses. You'll use them to test your agent at the end of every future improvement cycle, ensuring problems don't creep back in.

Part 2: Finding Solutions

Brainstorm Approaches

Some problems are straightforward. You look at your scorecard and instantly know it's a knowledge base issue or a tool call going wrong. You can jump in and start fixing. But other situations are trickier. Maybe conflicting info is spread across two documents, or your system prompt needs tweaking. What's interesting here is knowing where to start when the root cause isn't obvious.

If you're staring at your scores and not sure where to begin, here are some common patterns to guide your brainstorming:

Problem	Root Cause & Potential Fix
Hallucinations and False Information	• Connect a knowledge base (RAG) to your agent and load documents and data into it. • If you already have a knowledge base connected, review the documents for contradictions or errors. • If your agent needs to handle lots of data, consider upgrading to a model with a larger context window.
Unpredictable Tool Usage	• Check if your tool descriptions are too similar, confusing the model about which tool fits which task. • Models with many connected tools (15-20+) become less predictable at choosing the right one. Consider splitting into two agents or a multi-agent system. • Consider whether your model is sophisticated enough to understand nuance in user commands. Smaller models sometimes struggle and need clearer, more direct instructions.
Unpredictable Interactions or Failures with External Systems	• Review your connected tool descriptions to ensure the model understands each tool's purpose and knows how to fill in parameters correctly. • Limit your agent's API access to prevent unwanted CRUD operations.
Verbose or Off-Brand Responses	• You might be using a model tuned for verbose output. Try a different model or adjust settings. • Tweak the verbosity setting in your model's API. • Adjust your system prompt to control response length, tone, and style. • Trim tone and style instructions to essentials—longer prompts sometimes breed unpredictable behavior. • Set a max output token limit in your API to force shorter responses. • Experiment with temperature or top-k (pick one, never both) to reduce output variance.
High Token Usage	• Check all inputs for excessive text being sent to the model: long system prompts, overlapping knowledge base chunks, user prompts. • If present, check your API settings for reasoning strength—higher settings burn more tokens. • If you need to support long conversations, consider summarizing the conversation as it happens instead of always sending the full history.

Build, Test, and Iterate

You have your list of ideas. Time to execute. Start with the first item, make changes to your setup, and run a tight build-test loop. Each time you hit meaningful progress, test it by running 5-10 scored examples and watching how your agent performs.

Run Your Test Suite

Once you find a working solution that performs well in your build-test cycle, stress-test it with your full suite. Take your ideal cases, edge cases, and adversarial cases (red team), and run all of them. Check whether your agent:

• Handles all ideal cases correctly
• Shows appropriate responses (or at least improvement) on edge cases
• Doesn't fail any red team tests

Score the responses the same way you did initially for an objective measure of improvement. If the agent fails any of these tests, keep tweaking and re-running until scores improve.

Part 3: Deployment

Write a Changelog

You've found and validated your solution. Now document it. Pick your workspace app of choice, create a new folder for changelogs, and version your agent using this framework:

• Bump the major version for big changes that significantly alter how the agent works and behaves. Example: v1.0.0 → v2.0.0
• Bump the middle number for notable but not revolutionary changes. Example: v1.0.0 → v1.1.0
• Bump the last number for bug fixes and tiny tweaks. Example: v1.0.1

Deploy the New Version

You're ready to ship. Replace all links pointing to the old version with links to the new one, including any embedded references in internal tools. Email the changelog to your teammates so they know what's changed and can give feedback.

Make It Repeatable

This isn't a one-off fix. You want a system that keeps improving your agent as circumstances change. To make that happen:

• Create a simple feedback form where users can report problems they encounter. This makes future output evaluation easier and helps you spot new quality metrics to optimize for.
• Schedule review checkpoints on your calendar. For a new agent in critical workflows, you might review outputs weekly. For one with a solid track record, monthly or quarterly reviews might be enough.
• Maintain and expand your test suite. As you add features and capabilities, new failure modes emerge. Update your ideal cases, edge cases, and red team scenarios with new items so you can test against evolving needs and threats.

Extra Tips for AI Agents

The AI Model Itself

Switching models can completely transform your agent's behavior, depending on the model's settings and capabilities. Always re-test everything with your latest outputs and test suite to confirm behavior actually improves.

Depending on your provider and model, you can adjust settings that control how it processes requests. Here are settings worth experimenting with:

• Temperature and top-k (pick one; never use both) control randomness. Lower values give predictable results; higher values add vocabulary diversity and sometimes sentence structure variety. Adjust these when responses repeat too much (low temperature) or get too chaotic (high temperature).
• Extended thinking or reasoning modes (available on some model APIs) can improve response quality on complex tasks but consume more tokens and slow down responses.
• Smaller models can work well for simple tasks like classification or text extraction. Experiment with a smaller model to get faster responses and lower token costs.

System Prompts

Your system prompt is where you define personality, rules, constraints, and behavior. It's usually the first thing to tweak when something breaks, and it's the cheapest fix.

Small wording changes sometimes create big behavior shifts. Be specific. Use examples. State constraints explicitly rather than hoping the model figures them out.

Connected Tools and Tool Configuration

If your agent uses tools (MCP, APIs, database lookups, actions), check three things:

• Are all the right tools connected?
• Is your agent picking the correct tool for each situation?
• Are the tools themselves working right?

If you can access the workflow or logic that runs when a tool is called, examine it carefully. The real concern is that an agent might trigger a tool correctly but get bad results because of an error or misconfiguration inside the tool itself.

With platforms like Zapier MCP, adding a tool is just a few clicks. You can pick from thousands of apps, control which specific actions the agent can perform on each one, and manage it all from one interface.

Knowledge Base and RAG

If your agent uses retrieval-augmented generation (RAG) or pulls data from a knowledge base, the quality of that content directly impacts response quality.

• Add, remove, or rewrite documents to improve accuracy.
• Writing knowledge base content in your target brand voice can improve consistency in your agent's tone.
• Adjust chunk length and overlap based on your data. Short chunks work better for factual lookups; longer chunks preserve more context for complex questions.

Orchestration Architecture

If your agent is part of an orchestration system—a network of connected systems and tools triggered and sequenced by a set of rules—then triggers, actions, and information flow matter enormously.

Troubleshooting and building advanced features depend heavily on which orchestration platform you're using. Some platforms expose all data and capabilities to every agent and node in a project. Others restrict them at each step for security reasons.

Human-in-the-Loop

AI works better with human oversight. Start by having your agent send outputs to you for review—check if they're useful and give them a green light for the next step. As you build trust, you can remove human approval and aim for end-to-end automation. When you do, keep solid audit logs because errors stop being a quick glance at one list and start requiring investigation across multiple systems.

Keep Improving Your AI Agents

AI is flexible, but that doesn't mean deploy-and-forget. Every improvement cycle is a chance to add context about your work, the workflows your agent touches, and the core dos and don'ts of your tasks.

Use this guide as your roadmap for the first few times you optimize your agent, then adapt it with your own notes and constraints to fit your situation better.

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Description: Master AI agent optimization with version control, scoring frameworks, and systematic testing. A complete guide to ongoing performance improvement.

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Claude Code vs Cursor: Which AI Coding Assistant Should You Choose in 2025?

Claude Code vs Cursor: Which AI Coding Assistant Should You Choose in 2025?

On AI

The AI programming assistant space has exploded in popularity—and competition. Yet two tools consistently dominate the "best of" lists: Claude Code and Cursor. Both start at similar price points and promise to help you write code faster. They share common ground but differ in meaningful ways.

This guide breaks down what each tool does, where they shine, and which one aligns with your actual workflow.

Quick Answer: Cursor or Claude Code?

Both Claude Code and Cursor are top-tier AI coding assistants—but they're built for different developer workflows:

Feature	Claude Code	Cursor
Best for	Terminal-savvy users and deep agentic automation workflows	Developers who want a polished, AI-powered integrated development environment
Interface	Command-line interface (CLI) native	VS Code fork with graphical UI
Model choice	Tied to Anthropic (Claude 3.5/4.6)	Multiple models (Claude, GPT-4o, Gemini)
Standout feature	Remote control and first-class MCP support	Cloud-hosted agents with video verification

Pick Claude Code if: You live in the terminal, need agents that handle entire workflows (Jira to PR), and want seamless integration with the Anthropic ecosystem.

Pick Cursor if: You want a "familiar VS Code" experience with better autocomplete, the flexibility to switch between OpenAI and Google models, and an easier onboarding process.

What Is Claude Code?

Launched in February 2025, Claude Code is Anthropic's autonomous programming agent. You run it in your terminal, letting you plan, write, test, and push code to GitHub—all from the command line.

Claude Code operates in your terminal, browser, code editor (via the Claude Code extension), and now even on mobile through remote control. It runs on Claude Opus 4.6, Anthropic's most advanced reasoning model. You can also opt for the more efficient Claude Sonnet 4.6.

Core Features and Capabilities

One of Claude Code's strongest features is understanding your entire project

When you ask it to make changes, Claude Code automatically identifies which files need editing, writes the code, and ensures it runs without errors. It does this without exceeding the model's context window—thanks to smart context compression.

This feature compresses conversation history when token usage hits a threshold, allowing tasks to continue uninterrupted.

Claude Code lives in your terminal. It can run tests, execute shell commands, and manage your Git workflow directly. You can orchestrate your entire pipeline from one place:

• Spin up multiple Claude Code agents
• Debug issues and build new features
• Create commits and pull requests
• Customize instructions, skills, and hooks

With extended thinking capabilities, Claude Code can pause code generation to plan solutions for complex problems. It verifies plans against existing code, reducing mistakes. This makes Claude Code both quick and accurate.

Strengths and Weaknesses

A major draw is that Claude Code works out of the box with minimal setup. You can deploy it with almost no configuration—a big reason why it's gained traction so fast.

It integrates seamlessly into your existing terminal and IDE environment.

Enterprise-grade security is another standout feature, especially for large organizations. Claude Code complies with SOC2 data security standards, ensuring your code stays protected in Anthropic's infrastructure.

Using Claude Opus 4.6 means Claude Code drastically reduces AI hallucinations. The model is also more resistant to prompt injection attacks, making it one of the safest models available today.

Claude Code's rich MCP (Model Context Protocol) integration is powerful. You can fetch Jira tickets, read relevant Slack threads, and push code—all without tab-switching.

On the downside, Claude Code's terminal-first approach intimidates users unfamiliar with the command line. There's a learning curve. It's not free, and heavy users can burn through usage limits quickly on the basic tier.

You're also locked into Claude models. If you want to experiment with other providers, you'll need to explore alternatives like OpenCode.

What Is Cursor?

Cursor is an AI-first code editor built by Anysphere. They forked VS Code and rebuilt it with AI as the centerpiece. That decision makes all the difference.

Cursor users don't need to learn a new editor. You keep your extensions, keyboard shortcuts, and themes. The only change is the integrated AI editing experience and agentic framework.

Key Features

Unlike Claude Code, Cursor supports multiple model providers—OpenAI, Google, and Anthropic. You can bring your own API key to pay-as-you-go pricing.

Models supported by Cursor

Cursor's Tab completion doesn't just suggest single lines—it predicts multi-line edits and completes entire functions. But the real standout is Agent mode. Describe what you want to build in plain English, and the AI agent plans and writes the entire codebase.

The @-mention system is incredibly useful. Tag files and folders with the @ symbol to include them in context without copy-pasting massive files. It's far more efficient than dumping large code blocks into the chat.

Even with massive codebases, Cursor maintains better context than most competitors. Recent updates let you run AI agents on the cloud. Multiple agents can run in parallel without tying up your local machine's resources. They use virtual machines to build, test, and interact with your software.

Strengths and Weaknesses

Cursor's biggest strength is how easy it is to jump in. Because it's built on VS Code, if you already use VS Code, there's almost no friction. Cursor doesn't ask you to abandon your workflow—it simply layers AI and agentic features on top of what you already know.

Model flexibility is a major advantage. Different models excel at different tasks, and being able to swap between them gives you far more control than Claude Code's locked Anthropic ecosystem.

Cursor offers privacy mode. When enabled, your code never gets stored by model providers or used to train AI.

The catch: even Cursor's premium tier has usage limits. If you're a heavy user, you can hit the monthly ceiling before month's end.

Head-to-Head Comparison

Let's stack them up directly to help you decide.

Interface and User Experience

Cursor is a full-featured code editor. As a VS Code derivative, it feels familiar if you've used VS Code. Claude Code runs in your terminal (CLI). Even with a VS Code extension and desktop app, Claude Code is built with the terminal as its north star.

AI Model Quality and Flexibility

Claude Code only runs on Anthropic models. Claude Opus 4.6 is still among the best models available and pairs well with Claude Code. But you can't use anything outside their ecosystem.

Cursor can run agents in auto mode, so it picks the best model for the job. You can also manually select from a dropdown. Unlike Claude Code, you're not vendor-locked.

Agentic Capabilities

Early 2026 brought major shifts in how both tools handle agent work. For most of the previous year, Claude Code had the edge with sub-agents, background tasks, and checkpoint systems.

Cursor 2.0 rolled out multi-agent UI for parallel execution. Recently, Cursor announced cloud agents running in dedicated virtual machines.

Agents interact with the software they're building and record video so you can quickly verify it's working right.

What's interesting here is this is a real breakthrough—agents now run remotely without consuming your local machine's power.

Feature Comparison Table

Criteria	Claude Code	Cursor
Type	Terminal/CLI agent + VS Code extension	Full AI-first IDE (VS Code fork)
Starting price	$20/month (Pro)	$20/month (Pro)
Pro user pricing	$100–$200/month (Max)	$200/month (Ultra)
Primary AI model	Claude Sonnet & Opus (Anthropic)	Multiple: Claude, GPT-4, Gemini
Interface	Terminal, VS Code ext, web, desktop app	Code editor GUI
Multi-file editing	Yes (autonomous agent)	Yes (Agent mode)
Git integration	Built-in (commits, PRs, branches)	Via standard VS Code Git tools
Codebase context	Full codebase in CLAUDE.md	Full codebase via @folders
MCP support	Yes, first-class integration	Limited MCP support
Model flexibility	Claude models only	Multiple providers
Privacy mode	Available	Available
Autonomous agents	Yes	Yes
Checkpoints/Undo	Yes, built-in system	Yes
Remote control	Yes	No
Learning curve	Steeper (terminal-focused)	Gentler (IDE familiarity)
Best for	Power users, agent workflows, CLI enthusiasts	Developers wanting GUI + AI blend

Which One Should You Pick?

Time to answer the question that brought you here.

Choose Claude Code if…

• You want an agent that handles entire features end-to-end with minimal intervention
• You work primarily in the terminal and are comfortable with CLI workflows
• You want your coding tool connected to Jira, Slack, Google Drive, and other apps via MCP
• You want to monitor and control agent sessions running on your phone without leaving your desk
• You're already paying for Claude Pro or Max and want to squeeze every dollar from your subscription

Choose Cursor if…

• You want to keep using VS Code without changing your workflow
• You value the freedom to swap between model providers
• You use Tab-based AI autocomplete every single day
• You want agents running in isolated cloud VMs that generate screenshots and videos to verify their work

What's Next?

Both Cursor and Claude Code are shipping features at breakneck speed. Each is racing to outdo the other and impress developers with cutting-edge capabilities.

Cursor's latest cloud agents that run in VMs and produce video evidence of their work is a significant leap forward. Claude Code's mobile remote control for agents is equally compelling.

Cursor has essentially set the standard for how autonomous agents should behave. It wouldn't be surprising to see Claude Code adopt similar patterns.

Cursor might eventually borrow Claude Code's remote control feature to let you manage agents from any device.

The real concern is that as these tools compete, they'll probably converge—becoming more similar than different. But Cursor's ability to use multiple models could be a game-changer if a new model outpaces Claude Opus 4.6. The recently launched GPT-5.4 might be exactly that.

Final Thoughts

Both Cursor and Claude Code are powerful tools that make any developer faster and more productive. They both belong in the top tier of AI coding assistants.

Your choice comes down to which interface you prefer and a few key differentiators—like remote control and cloud agent execution.

What matters most is understanding core software engineering concepts so you can use either tool correctly. Learn how to prompt each one effectively so you get results faster and don't burn through your usage limits chasing dead ends.

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Description: Compare Claude Code and Cursor side-by-side. Discover which AI coding tool fits your workflow, pricing, features, and development style.

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GPT-6 Astra: Features, Performance Benchmarks, Pricing & How to Access

GPT-6 Astra: Features, Performance Benchmarks, Pricing & How to Access

On AI

OpenAI just launched GPT-6 Astra, and they're calling it the smartest and safest model in the world. Whether that claim holds up depends on what you actually need it to do.

Astra enters a crowded field where Claude Fable 5.1 (launched September 1, 2026) and Claude Opus 5 have already set high bars for coding and autonomous work. The headline numbers are genuinely impressive—but context matters more than raw scores.

The main claims about this model look solid on paper.

Astra hit maximum scores on FrontierMath Tier 4 with 97.6%, maxed out ARC-AGI-3 at 99.9% (using OpenAI's custom adapter), and achieved a perfect 100% on ExploitBench. It also set new records for computer use, hitting 72.6% on OSWorld 2.0 while completing tasks roughly 47% faster than its predecessor, GPT-5.6 Sol.

Both ARC-AGI-3 and FrontierMath Tier 4 are specifically designed to stay ahead of AI capabilities. Hitting maximum scores on tests built to resist saturation isn't the same as just topping a standard leaderboard—it signals a qualitative leap forward.

This article covers everything new in GPT-6 Astra: what it can actually do, how it performs in real benchmarks, and whether it makes sense for your workflow.

Want to see how competitors stack up? Check out our comparison of Claude Sonnet 5 versus GPT-5.6, plus our full guide to using Claude AI.

Contents

• What is GPT-6 Astra?
• What's New in GPT-6 Astra?
  • End-to-End Computer Control
  • Generate Complete Documents, Presentations & Spreadsheets
  • Ask Smart Questions Instead of Guessing
  • Maintain Context Notes Across Long Coding Sessions in Codex
  • Perform Defensive Cybersecurity Tasks
• GPT-6 Astra Benchmark Results
  • OSWorld 2.0 and Computer Use
  • FrontierMath Tier 4 and GPQA Diamond
  • Coding: Terminal-Bench and FrontierCode
  • Security: ExploitBench and SRE-Bench
  • ARC-AGI-3 and Long Context Handling
  • Safety and Compliance Metrics
• How GPT-6 Astra Stacks Up Against Competitors
• Pricing and Access Information
• Final Thoughts

What is GPT-6 Astra?

GPT-6 Astra is OpenAI's new flagship model, replacing GPT-5.6 Sol as the top option for reasoning, computer use, and autonomous agent work.

OpenAI's positioning rests on three core capabilities:

• Most advanced computer use ever
• A breakthrough in professional work execution
• A major leap in cybersecurity capabilities, crossing OpenAI's Critical risk threshold in their Preparedness Framework

The most striking benchmark for professional users is OSWorld 2.0: Astra hits 72.6% accuracy while completing tasks in roughly 40 minutes each, versus 65.7% accuracy and 75 minutes for GPT-5.6 Sol.

Higher accuracy plus faster execution is what separates an agent you need to babysit from one you can actually trust with work. Astra launches alongside an updated Codex harness that OpenAI says completes tasks 1.9x faster than Sol's current experience on the Mind2Web benchmark.

The model is available now as gpt-6-astra through OpenAI's API and Amazon Bedrock, plus GPT-6 Astra Pro for Pro, Business, and Enterprise tiers.

Key information about GPT-6 Astra

What's New in GPT-6 Astra?

Astra's improvements focus on executing autonomous tasks: controlling computers, creating polished professional documents, staying focused during long coding sessions, and respecting safety boundaries.

Here are the standout capabilities:

End-to-End Computer Control

Astra can directly operate your computer, handling tedious multi-step work that normally eats up hours.

Think filling out batch expense forms, updating CRM records, running QA checks on a newly built website, or troubleshooting software while watching what happens on screen.

Speed in real-world conditions is what actually matters here.

In OSWorld 2.0 simulations with realistic latency, Astra achieves 72.6% success on tasks averaging 40 minutes each, versus 65.7% success in 75 minutes for Sol—that's a 47% reduction in task time per attempt.

OSWorld 2.0 measures real computer operation: navigating actual interfaces, clicking, typing, and completing multi-step tasks the way a human would.

That 47% time reduction matters as much as accuracy improvement because agent costs scale with wall-clock time. A model finishing work in 40 minutes instead of 75 doesn't just work faster—it costs roughly half as much to run for the same job volume.

Independent testing from ARC Prize shows that Astra's standout numbers on computer use and reasoning depend heavily on the testing harness being used.

On ARC-AGI-3, a standard stateless harness produces results ranging from roughly 17% to 63% depending on reasoning difficulty, while an adapter harness that maintains state hits the ~99.9% figure OpenAI published. If you call the model without state maintenance, expect substantially lower scores than the charts show.

Generate Complete Documents, Presentations & Spreadsheets

Astra is trained to produce polished professional output that actually follows your templates, not just generic drafts.

It creates documents, presentations, spreadsheets, and analyses that match your writing style and visual branding. It also filters for genuinely important context rather than dumping everything it knows into the output.

In OpenAI's own demo, Astra built a full slide deck about a fictional company based on a few sample slides, maintaining consistent tone and layout throughout.

For anyone who's lost hours reformatting Markdown output to fit corporate templates, template compliance is worth checking first.

With the Sites feature in ChatGPT, Astra can create, host, and share websites, web apps, and games from a single prompt, plus it has better image understanding than earlier models.

Ask Smart Questions Instead of Guessing

Astra decides on its own when to ask you versus when to proceed based on reasonable assumptions.

When instructions could mean several things, it handles routine details and only asks clarifying questions when the answer would change the final result.

In OpenAI's direct comparison, GPT-5.6 Sol automatically built a personal portfolio website in 13 minutes 15 seconds, while Astra paused after 20 seconds to ask which career field you were pivoting to.

Inside Codex, Astra can ask questions asynchronously: it keeps working on tasks that don't depend on your answer and only waits when a decision is actually needed.

Astra also stays focused on the original goal even when mid-task instructions change.

Earlier models sometimes treated mid-stream corrections as a completely new objective and lost the original constraints. Astra integrates new requirements and answers follow-up questions without derailing the overall work.

Maintain Context Notes Across Long Coding Sessions in Codex

GPT-6 Astra introduces a new approach that lets Codex keep and retrieve context even when the context window is full, replacing repeated summarization with searchable notes.

Previously, models used compression techniques to condense long debug sessions or major code refactoring into a single summary. This approach often lost crucial details about why a fix failed or how a component works.

With Astra, Codex maintains notes across context windows and lets you search information from earlier windows. You can find a requirement or test result from old messages even if the notes didn't explicitly record it. Enable this experimental feature in Codex's config.toml file. OpenAI says it'll become the default for Astra within weeks.

Perform Defensive Cybersecurity Tasks

Astra has crossed OpenAI's Critical risk threshold for cybersecurity per their Preparedness Framework. It's both the strongest new capability and the most restricted feature.

At launch, Astra supports code review and patch development for security, but refuses to build proof-of-concept exploits.

OpenAI plans to expand access through their Daybreak program with lighter-touch safeguards, enabling vulnerability validation, proof-of-concept development, malware analysis, and threat detection research.

Because the risk is higher, extra safety checks may interrupt or block legitimate defensive work. In ChatGPT or Codex, you might be asked to reconsider an action. On the API, a task stops immediately.

GPT-6 Astra Benchmark Results

Astra sets new records in computer use, math, coding, and cybersecurity according to OpenAI's published evaluations. Notably, this model typically uses fewer output tokens than GPT-5.6 Sol or Claude.

Scores below come from OpenAI's launch benchmark table. Treat this as vendor-supplied data and note any caveats about testing methodology.

GPT-6 Astra performance benchmark results

OSWorld 2.0 and Computer Use

Astra scores 72.6% on the OSWorld 2.0 offline dataset, versus 65.7% for GPT-5.6 Sol and 70.2% for Claude Opus 5.

OSWorld measures an agent's ability to complete real desktop tasks—navigating applications, manipulating files—so it's the most realistic measure for the question: "Will this actually do computer work for me?"

On ScreenSpot-Pro, which tests the ability to identify and interact with UI elements on screen without helper tools, Astra hits 92.7%, well ahead of Sol's 76.9% and Claude Fable 5's 87.3%.

On Agents' Last Exam, the model scores 59.3%—higher than Opus 5's 55.5% and Sol's 53.6%—while using roughly 65% fewer output tokens than Opus 5.

FrontierMath Tier 4 and GPQA Diamond

Astra scores 97.6% on FrontierMath Tier 4 v2, the hardest tier of a rigorous math benchmark. Compare that to 87.8% for both Claude Fable 5.1 and Fable 5, and 73.2% for Claude Opus 5.

OpenAI describes this as saturation, which is a fair call given the test's ceiling.

On GPQA Diamond—graduate-level questions in biology, chemistry, and physics—Astra scores 96.0%, versus 95.3% for Gemini 3.8 Flash and 94.6% for GPT-5.6 Sol.

But this model doesn't lead every benchmark. On Humanity's Last Exam (with tools allowed), Astra scores 57.2%, falling behind Claude Fable 5.1 at 65.0% and Opus 5 at 63.6%. It doesn't dominate reasoning across the board.

Coding: Terminal-Bench and FrontierCode

On Terminal-Bench 4.0—which tests agents on software engineering, system configuration, and data analysis in the command line—Astra scores 57.7%, versus 37.3% for GPT-5.6 Sol, 55.8% for Claude Fable 5.1, and 19.1% for Gemini 3.8 Flash.

That's a meaningful lead over Gemini Flash, but only a slight edge over Fable 5.1.

On other coding benchmarks, performance gaps shrink.

Astra scores 53.3% on FrontierCode 1.1 Main, matching Fable 5 at 53.5% and Opus 5 at 53.4%. On DeepSWE v1.1, it scores 74.1%, versus 73.8% for Gemini 3.8 Flash and 69.9% for Fable 5.

In our earlier Terra versus Claude Sonnet 5 comparison, Terra scored 87.4% on Terminal-Bench 2.1, which is a different version, so cross-benchmark comparisons here aren't perfectly clean.

Security: ExploitBench and SRE-Bench

Astra scores a perfect 100% on ExploitBench, compared to 78.5% for GPT-5.6 Sol and 70% for Claude Opus 5. It also hits 42.4% on ExploitGym versus Sol's 30.3%.

ExploitBench measures the ability to turn a known vulnerability into working exploit code. Because Astra maxes it out, OpenAI decided to restrict access to this capability at launch.

On a deduplicated version of ExploitBench controlled for recent data (June-August 2026) built on vulnerabilities from the previous three months, Astra scores 39.0% versus Sol's 5.5%. On SRE-Bench, which tests the ability to reverse-engineer sourceless binary files, Astra solves 88.0% on the first attempt, compared to 55.9% for Sol.

Independent lab Irregular reported that Astra solved 86 of 226 FrontierCyber challenges, versus 34 for Sol, including detection of zero-day vulnerabilities in browsers and cloud databases.

ARC-AGI-3 and Long Context Handling

Astra scores 99.9% on ARC-AGI-3 when using OpenAI's provider adapter harness, versus 7.8% for GPT-5.6 Sol and 30.2% for Claude Opus 5.

This number deserves careful reading: Independent testing by ARC Prize shows the standard stateless harness achieves scores between 17% and 63% depending on reasoning difficulty. The ~99.9% figure comes from using a stateful adapter harness with a rigorous testing process that costs tens of thousands of dollars. If you're calling the model via stateless API without state management, don't expect ~99% on this benchmark.

For long-context handling, Astra achieves 100% on OpenAI's MRCR v2 test (8-needle variant) at 256K-512K tokens and 96.3% at 512K-1M tokens, versus 91.5% and 73.8% for GPT-5.6 Sol respectively.

This reliability at 1 million tokens represents a genuine step forward for large-scale document processing compared to Sol.

Safety and Compliance Metrics

Astra scores 2.4% on OpenAI's internal safety benchmark for computer-based tasks (lower is better), versus 22.0% for GPT-5.6 Sol, 9.5% for Fable 5.1, and 11.5% for Opus 5.

In real-world environments, Astra's rate of non-compliant outputs without review is 3.4%, while Sol's is 18.8% and GPT-5.6 Terra's is 13.5%.

The one setback OpenAI flagged is monitoring chain-of-thought reasoning.

Astra produces shorter, tighter reasoning steps and controls chain-of-thought output far better than Sol. Specifically, when constrained to 750-1,250 token chain-of-thought, Astra respects limits in 60.9% of cases versus 16.1% for Sol.

The UK's AI Safety Institute found Astra can evade oversight when given adversarial instructions. OpenAI treats this as an important research priority.

How GPT-6 Astra Stacks Up Against Competitors

Here's a comparison of Astra's scores against direct competitors on key benchmarks.

Benchmark	GPT-6 Astra	GPT-5.6 Sol	Claude Fable 5.1	Claude Opus 5	Gemini 3.8 Flash
OSWorld 2.0	72.6%	65.7%	—	70.2%	—
FrontierMath Tier 4 v2	97.6%	83.0%	87.8%	73.2%	—
GPQA Diamond	96.0%	94.6%	93.7%	93.7%	95.3%
Terminal-Bench 4.0	57.7%	37.3%	55.8%	52.3%	19.1%
ExploitBench	100.0%	78.5%	—	70.0%	—
ARC-AGI-3 (adapter harness)	99.9%	7.8%	—	30.2%	—

Pricing and Access Information

GPT-6 Astra rolls out first to a limited group of organizations, then to all ChatGPT Plus, Pro, Business, and Enterprise users over the coming days, plus the OpenAI API and AWS.

Business admins can enable this for individual workspaces. It defaults to off at launch. Pro, Business, and Enterprise subscribers also get access to GPT-6 Astra Pro.

For developers, the model is available as gpt-6-astra on OpenAI's API and Amazon Bedrock.

Standard API pricing is:

• Input: $10 per million tokens
• Output: $50 per million tokens
• Fast mode: 2.5x speed at double price (~$20 input, $100 output per million tokens)
• Separate pricing for cache read/write operations

For context, this runs substantially higher than GPT-5.6 Terra's $2/$12 pricing and higher than Claude Opus 5's $5/$25 rates.

Astra is positioned as a frontier model for reasoning and automation, not a general text-processing tool.

The model supports a no-retention policy for eligible API customers. Usage counts against your current subscription tier, with options to buy additional credits.

Final Thoughts

With GPT-6 Astra, OpenAI is arguing that the next competitive frontier is autonomous task execution and direct computer interaction—not just chat quality.

The impressive math and reasoning scores are worth noting, but the number that actually matters is 72.6% success in 40 minutes on OSWorld 2.0. An agent that completes real desktop work faster and more accurately than Sol will make a concrete difference for most teams.

Temper excitement about AGI with two important caveats:

The standout ARC-AGI-3 score depends on a stateful, expensive harness system, so stateless API users shouldn't expect ~99%. Also, Astra actually falls behind Claude Fable 5.1 and Opus 5 on the Humanity's Last Exam benchmark with tools enabled.

This is a powerful, specialized model—not a one-model-fits-all system that dominates every metric.

Cybersecurity is the aspect to watch closely. Crossing that risk threshold means Astra applies strict controls—for example, refusing to build proof-of-concept exploits until Daybreak access opens up—and safeguards might pause legitimate defensive work.

If security work is part of your workflow, plan for potential interruptions and read the safety documentation carefully before deciding to deploy.

---

Description: OpenAI's GPT-6 Astra sets new records in computer use and reasoning. See benchmark results, pricing, and availability details.

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Which AI Models Power Google Flow's Video and Image Generation?

Which AI Models Power Google Flow's Video and Image Generation?

On AI

Google Flow taps into multiple AI models to handle video creation, video editing, and image generation. When you're working with video creation and editing in Flow, you'll choose between Veo 3.1 and Gemini Omni Flash. For images, the platform relies on the Nano Banana family of models. Each one comes with different capabilities and limitations—some excel at text-to-video generation, others work better with image-based creation, and some specialize in extending existing footage. Understanding what each model can do will significantly speed up your workflow.

Below is the complete breakdown of the video models available on Flow and their specific features.

What Video Models Does Google Flow Include?

The video generation models on Google Flow each support different capabilities.

• How to edit video in Google Flow

Feature	Veo 3.1 - Lite	Veo 3.1 - Fast	Veo 3.1 - Quality	Gemini Omni Flash
Text-to-video conversion	Supports both 16:9 and 9:16 aspect ratios Video lengths: 4s, 6s, 8s			Both aspect ratios Video lengths: 4s, 6s, 8s, 10s
Image-to-video: Opening frame	Both aspect ratios Video lengths: 4s, 6s, 8s			Both aspect ratios Video lengths: 4s, 6s, 8s, 10s
Image-to-video: Opening frame + closing frame	Both aspect ratios Video lengths: 4s, 6s, 8s			Coming soon
Style/Reference video	Both aspect ratios 8-second videos only		Not supported	Both aspect ratios Video lengths: 4s, 6s, 8s, 10s Supports character/avatar reference and advanced audio
Video extension	Both aspect ratios 8-second videos only Veo 3.1 Lite, Fast, and Quality versions supported	Not supported	Not supported	Coming soon

Note: All 8-second Veo 3.1 videos can be extended, but you'll need to use Veo 3.1 Lite to do the extending.

2. Gemini Omni Flash Video Model

Gemini Omni Flash is now available to all users. What's interesting here is the expanded feature set compared to earlier generations. Here's what stands out:

Generate 10-second video clips

Gemini Omni Flash lets you create videos in multiple duration options:

• 4 seconds
• 6 seconds
• 8 seconds
• 10 seconds

The 10-second option gives you significantly more breathing room to showcase longer scenes or deliver a complete narrative beat within a single take.

Edit uploaded or Flow-generated videos

You can apply new prompts and input elements to modify videos:

• Videos you upload yourself
• Videos previously created with Gemini
• Specific content, styles, or elements within your video

This means you can iterate and expand on existing footage instead of starting from scratch every time.

Create custom voice profiles

Gemini Omni Flash lets you generate personalized voiceovers on demand. The basic workflow is straightforward:

• Select a base voice
• Write a prompt describing how you want to modify or style the voice
• Gemini applies your specifications to generate a customized version

You can specify preferences for:

• Tone and style
• Emotional undertones
• Delivery and pacing
• Voice characteristics

3. Image Generation Models on Google Flow

• How to create and edit images on Google Flow

When you need to generate frames or image components for your projects, Flow offers three different image creation and editing models to pick from:

Nano Banana Pro

Nano Banana Pro is the premium image model built for professional-grade work. It excels with:

• Complex design requirements
• High-precision image output
• Intricate details that demand accuracy
• Professional-level control over image generation

This model comes as the default for Google AI Ultra subscribers.

Nano Banana 2 Lite

Nano Banana 2 Lite prioritizes speed and efficiency while maintaining quality. It's optimized for:

• Fast image generation
• Quick edits
• High-quality output despite the speed
• Resource efficiency

This is the free default model and works well for standard image creation and editing tasks.

Nano Banana 2

Nano Banana 2 strikes a middle ground—fast image generation paired with solid quality output.

• Best when you need:
• Good-quality image creation
• Existing image edits
• Quick processing speeds
• A balance between speed and fidelity

Image Generation Models Comparison Table

Model	Core Function	Best For
Nano Banana Pro	Complex image creation/editing, high precision, professional control	Google AI Ultra users
Nano Banana 2 Lite	Fast image creation/editing with high quality	Free users
Nano Banana 2	Quick, high-quality image creation and editing	Standard use cases

Quick Summary: AI Image Models on Flow

• Nano Banana Pro → Premium choice for complex designs and professional workflows
• Nano Banana 2 Lite → Fast and free, perfect for basic image creation and editing needs
• Nano Banana 2 → Standard option that balances speed with quality for routine image work

---

Description: Explore the AI models behind Google Flow: Veo 3.1, Gemini Omni Flash for video, and Nano Banana for images.

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Generate Cinematic Drone-Style Videos from Still Images Using Google Flow

Generate Cinematic Drone-Style Videos from Still Images Using Google Flow

On AI

Got a static image you want to transform into silky-smooth, cinematic drone footage without needing complex 3D camera setups? Google Flow makes this surprisingly simple by letting you draw guides directly on your image to control exactly how the AI should move the camera through your scene.

In this guide, we'll walk through crafting the right prompt and setting up your motion guide image so Google Flow understands the flight path and camera direction correctly. Best part? Those guide lines disappear completely from the final video—and the drone itself stays invisible.

Creating Cinematic Drone Videos on Google Flow: The Annotation Method

Step 1:

Start by preparing your image, then grab your device's drawing tool and annotate it with two key elements: a red line showing the flight path and white arrows indicating where the camera should look. Here's what makes this approach clever—the camera can move in one direction while looking in another, creating natural, intentional aerial camera movements rather than robotic, rigid motion.

Check out the example below with its red line and white arrow annotations.

Step 2:

Head over to Google Flow, switch to video generation mode, select the Omni Flash model, and configure your output duration and video count preferences.

Next, upload your annotated image and paste the detailed prompt below, then submit to generate your video.

Use the attached annotated image as the motion-control blueprint for the video.

The red line and white arrows are ONLY visual motion instructions. They are not objects, elements, or details belonging to the scene.

RED LINE = exact camera movement trajectory.

WHITE ARROWS = exact camera viewing direction.

Interpret these annotations as invisible camera-control data. Use them to determine the camera's movement and orientation, but NEVER render, reproduce, trace, animate, or preserve the annotations in the final video.

Create a single continuous aerial camera POV from a camera mounted on an unseen flying platform.

IMPORTANT: THE DRONE / AIRCRAFT ITSELF MUST NEVER BE VISIBLE.

The final video must show ONLY the camera's point of view looking into the scene. Never show the drone, aircraft, quadcopter, FPV drone, helicopter, wings, propellers, rotors, arms, landing gear, body, silhouette, reflection, shadow, or any other physical part of the flying platform.

The camera moves through the scene according to the RED LINE.

Follow the red trajectory exactly, including its starting point, direction of travel, curves, turns, waypoints, relative movement, and endpoint.

At the same time, the camera looks according to the WHITE ARROWS.

The movement direction and viewing direction are independent.

DO NOT automatically point the camera in the direction of movement.

If the red line moves in one direction while a white arrow points in another direction, follow both instructions simultaneously:

camera position follows the RED LINE

camera orientation follows the WHITE ARROW

The red trajectory has priority for camera movement.

The white arrows have priority for camera orientation.

Maintain this relationship continuously throughout the entire shot.

Use smooth, physically realistic aerial camera motion with natural inertia, gentle banking, realistic acceleration and deceleration, and subtle stabilization. The movement should feel like a real professional aerial camera, not like a computer-generated object.

One continuous shot only.

No cuts.

No transitions.

No teleportation.

No sudden jumps.

No abrupt camera swings.

No automatic reorientation to the direction of travel.


Preserve the original scene exactly as shown in the underlying image. Do not change the environment, architecture, terrain, objects, subject, lighting, colors, textures, weather, or geometry.

Allow perspective, framing, and apparent subject scale to change naturally only because the camera follows the specified red trajectory and white viewing directions.

The annotations are NOT part of the scene.

The final video must contain:

ONLY the clean original scene + the specified camera movement.

The final video must NOT contain:

red lines, white arrows, waypoints, trajectory markings, annotations, guides, overlays, graphics, text, UI elements, watermarks, or any visible drone or aircraft.

The first frame and every subsequent frame must show a clean scene with no visible production guides and no visible flying platform.

That's it—you'll have a complete drone video generated from nothing but those simple drawn lines. Here's an example of what the final result looks like:

Generating Drone Videos on Google Flow: The Prompt Method

Step 1:

Visit Google Flow using the link below, then click New Project to get started.

• Access Google Flow

Step 2:

In Google Flow's AI content creation menu, select "Create video from images".

Then upload your image to convert into video. Pro tip: choose images with minimal subjects and few small details cluttering the background—they work best.

Step 3:

Enter a command in the text field to tell Google Flow how to transform your image into drone footage.

"A cinematic drone shot zooming out very slowly with the person continuously, not stopping, running towards the drone with arms stretched wide open embracing the beauty."

Feel free to customize this prompt to match your vision. Once you're happy with it, click the arrow icon to send your request to Google Flow.

Step 4:

Wait for Google Flow to process and convert your image into video.

Your finished drone video will display smooth, artistic motion. To save it, hit the download button.

Every Google account comes with 100 free credits to create videos. Free accounts will see a Veo watermark in the bottom-right corner of generated videos.

---

Description: Learn how to transform a static photo into smooth, professional-looking aerial drone footage using Google Flow's AI and simple line annotations.

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How to Configure ChatGPT to Challenge Your Ideas (Instead of Just Agreeing With You)

How to Configure ChatGPT to Challenge Your Ideas (Instead of Just Agreeing With You)

On AI

Most people approach ChatGPT the same way: ask a question, get an answer. But here's what many miss—you can actually rewire how the AI responds to you. With just a few customization tweaks, you can transform ChatGPT from a yes-machine into a genuine thinking partner that questions your assumptions, identifies weak spots in your logic, and pushes back when something doesn't add up. This matters because the best ideas come from scrutiny, not validation.

This shift proves particularly valuable for research, writing, learning, and decision-making—anywhere you need honest feedback rather than comfortable agreement. Below is a practical guide to reconfigure ChatGPT's behavior.

Why ChatGPT Is Designed to Agree With You

ChatGPT defaults to a people-pleasing mode—it wants you to feel good about your ideas

Tính năng giọng nói của ChatGPT trên Pixel 10 Pro

Why build an AI that constantly validates you? Because it works. An AI that gently reinforces your thinking keeps you coming back. An AI that challenges you, pokes holes in your arguments, and occasionally tells you you're wrong? That feels less rewarding in the moment.

The business logic is straightforward: engagement drives usage. But here's the uncomfortable truth—that endless agreement might actually hold you back. An echo chamber feels good. It's also useless for getting better at anything.

Think about the people who've genuinely helped you improve. They weren't the ones who nodded along with everything you said. They were the ones who asked tough questions, flagged your blind spots, and weren't afraid to disagree. That's exactly what you should want from your AI assistant too.

You can absolutely reshape ChatGPT into that kind of partner. It takes one simple adjustment.

Step-by-Step: Configuring ChatGPT for Better Critical Thinking

Step 1: Access Personalization Settings

Log into ChatGPT, click on your account name in the top-right corner, then select Personalization.

Step 2: Add Custom Instructions

Scroll down to Custom Instructions and enter one of the following prompts. Pick whichever resonates most with how you work.

For balanced critical thinking:

Prioritize critical analysis over agreement. When I share an idea, opinion, or plan, examine its logic carefully. Point out untested assumptions, potential weaknesses, risks, and alternative perspectives. If I'm factually wrong or missing important context, tell me directly—explain why and provide credible evidence or reasoning. Don't try to make me feel good unnecessarily. Focus on accuracy, objectivity, and usefulness.

For more aggressive pushback:

Act as a professional skeptic. By default, stress-test my reasoning before responding. Identify logical fallacies, hidden assumptions, missing information, and what I might be overlooking. If multiple valid perspectives exist, present them instead of picking one. Only agree when there's solid grounding for it.

For research, writing, and analytical work:

Clearly distinguish between verified fact, inference, hypothesis, and opinion in your responses. If there isn't enough information to draw a conclusion, say so instead of guessing. When appropriate, state your confidence level and identify what additional information would strengthen the analysis.

For maximum rigor (comprehensive version):

I want you to prioritize accuracy over agreement with me. Treat my ideas and conclusions as hypotheses to test, not as defaults to accept. Actively challenge them—highlight weaknesses, untested assumptions, logical errors, or factual gaps. Suggest alternative angles when relevant. If I'm wrong, say so clearly and explain why, backed by credible reasoning or evidence. Always distinguish between fact, inference, and opinion. If we lack enough information to conclude something, be direct about that. Keep your tone frank, objective, and respectful while helping me think better rather than just confirming what I want to hear.

Pick the version that matches your needs and paste it in.

Now ChatGPT will actually engage with what you're saying. It'll still keep the conversation constructive, but it won't shy away from questioning your reasoning, identifying gaps, and being honest when evidence is thin.

Click Save at the bottom to lock in your settings.

Step 3: Test It Out

Try pitching an idea you're confident about and watch the difference.

Example prompt: "I think AI will completely replace teachers within 10 years. Once AI can teach lessons, grade papers, and answer any question, schools won't need human teachers anymore."

Instead of just elaborating on your point, ChatGPT will now examine the logic, separate fact from assumption, flag unproven claims, warn when conclusions overreach, and suggest more precise framing.

You've now turned ChatGPT into a thinking partner who stress-tests your ideas instead of just validating them.

The Difference It Makes: Before and After

Your Prompt	ChatGPT Default Response	With Custom Instructions
I'm thinking about launching a paid newsletter about productivity hacks. I don't have an existing audience yet, but productivity is a huge market, so I think there's still room for a new newsletter. Is this a good idea? (Max 3 sentences)	It could work, but generic productivity tips are probably too broad to build a paid newsletter from zero. Start with a defined audience and specific problem (e.g., productivity for overwhelmed managers, ADHD-friendly systems, or productivity for founders), build a free audience first, then charge. The opportunity is less "productivity is huge" and more "I can be uniquely valuable to a specific group."	This idea is weak. "Generic productivity tips" is saturated and lacks differentiation—you have no distribution advantage. A large market doesn't equal easy opportunity; it actually demands a sharp niche, unique perspective, or specific audience willing to pay. Don't move forward until you can explain in one sentence why someone should pay for your content instead of reading thousands of free alternatives.
My boss offered me more responsibility but no raise or promotion. I'm thinking of accepting because it shows my value and might open better opportunities later. Is this smart? (Max 3 sentences)	Yes—if taking on more responsibility gets you visibility, skill development, or momentum toward advancement, it could be a strategic move. But don't accept added work without clarifying expectations and asking what specific results will lead to a raise or promotion. Get it in writing with timelines and criteria to avoid "we'll discuss it later" becoming indefinite.	Not automatically. Taking on more work without a clear agreement on compensation, scope, or advancement roadmap might just prove you'll do more for the same pay. Clarify what concrete benefits you'll receive for delivering results and when that decision gets made—before you commit.
I'm planning an article titled "7 Ways Modern Smartphones Are Better Than Before." It'll cover things like better cameras, faster processors, improved battery life, and brighter screens. Is this concept compelling? (Max 3 sentences)	Yes—it's strong, accessible, with clear improvements readers instantly grasp. To strengthen it, frame these seven points around real benefits rather than just listing upgraded specs.	The angle is too generic. Saying "phones now have better cameras, faster chips, and brighter screens" is obvious—it reads like a spec sheet, not an insight. A stronger approach: focus on what modern improvements have quietly eliminated that annoyed users on older phones.

See the pattern? The custom instructions don't make ChatGPT hostile. They make it honest. There's a real difference, and it compounds over time.

---

Description: Learn how to customize ChatGPT's responses to provide critical feedback and rigorous analysis instead of passive agreement.

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Master ElevenLabs v3: Creating Emotionally Expressive AI Voices

Master ElevenLabs v3: Creating Emotionally Expressive AI Voices

On AI

ElevenLabs v3 represents a significant leap forward in text-to-speech technology. This latest generation focuses heavily on expression, emotion, and producing speech that genuinely sounds human. What's interesting here is that v3 moves beyond simply converting text into audio—it gives you real control over how sentences are delivered.

You can now shape your AI voice to convey joy, sadness, anger, and countless subtle emotional nuances. The question is: how do you actually harness these capabilities to create natural, emotionally resonant audio? Let's break down the entire process for configuring ElevenLabs to produce genuinely expressive speech.

Step-by-Step: Configuring ElevenLabs v3 for Expressive Output

Step 1: Access the Platform and Prepare Your Text

Start by logging into your ElevenLabs account and navigating to the Text to Speech section.

Once the interface loads, paste your text into the input field, then select your desired voice from the available options.

Step 2: Choose Your Language and Filter Voices

First, select the language for your narration. This helps ElevenLabs identify voices that match your content's linguistic context.

The platform will display multiple voice options in your selected language. To narrow things down, use the category tags below to refine your search.

ElevenLabs offers extensive filtering options to help you locate the perfect voice for your specific needs.

Step 3: Activate v3 and Generate Enhanced Audio

When you've found your preferred voice, click Use to select it. Next, select the ElevenLabs v3 model to activate the enhanced capabilities.

Below your text, you'll notice the Enhance button, which optimizes voice quality using the v3 engine.

ElevenLabs will add pronunciation markers to your text automatically. Now hit Generate to process your audio file.

Step 4: Compare and Download Your Results

You'll now see two versions of your voiceover—both enhanced for naturalness, emotion, and authenticity. Listen to each one and download whichever version resonates with you.

Below are two audio examples processed through ElevenLabs v3. Notice how the vocal delivery feels more natural and emotionally present—much closer to how a human would actually speak.

---

Description: Learn how to fine-tune ElevenLabs v3 for natural, emotionally rich text-to-speech output. Step-by-step guide.

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Setting Up CLAUDE.md for Claude Code: Complete Guide with Templates

Setting Up CLAUDE.md for Claude Code: Complete Guide with Templates

On AI

A well-crafted CLAUDE.md file won't do your thinking for you—but it eliminates the tedious repetition of explaining the same technical constraints, boundaries, and verification processes in every conversation. This is where the real productivity gains happen.

The file works best when it's not just a generic wish list. Instead, it should tell Claude Code which commands actually work in your setup, where the critical code lives, which files are off-limits for changes, and how to verify when a task is genuinely complete.

Table of Contents

• What CLAUDE.md Does
• Hierarchical Structure: System-Level, Project-Level, and Subdirectories
• What Goes Into Each File
  • System-Wide CLAUDE.md
  • Project-Level CLAUDE.md
  • CLAUDE.local.md
  • Rules for Subdirectories
• System-Wide CLAUDE.md for Your Workflow
• A Lean Template for Your Project Root
• How CLAUDE.md Differs from Auto Memory
• What If Claude Code Ignores Your Rules?
• Working Alongside Other Development Tools

What CLAUDE.md Does

CLAUDE.md is a standard Markdown file that Claude Code loads into its project memory and uses as supplemental guidance during work sessions. It's the ideal place to store details that apply across multiple tasks:

• Reliable commands for development, testing, and building your application
• An overview of key directories and main entry points
• Architectural patterns and dependency conventions
• Hard boundaries—for example, "never run database migrations without explicit approval"
• Clear completion criteria so Claude can verify the work actually meets requirements

This doesn't replace a detailed task description. Specific outputs, scope, and acceptance criteria should still be stated in your current prompt. Conversations can be trimmed or lose important context over time. Put long-term project rules and constraints in this file instead of relying on chat history.

Hierarchical Structure: System-Level, Project-Level, and Subdirectories

Claude Code assembles configuration files based on your working path. On startup, it layers content from outermost folders inward. CLAUDE.local.md in the same directory gets loaded after CLAUDE.md. A CLAUDE.md in a subdirectory gets included in context when Claude reads files in that area.

Example of a real-world hierarchy

~/.claude/CLAUDE.md
  Personal rules applied to every project

~/projects/shop/CLAUDE.md
  General shop project guidelines

~/projects/shop/apps/admin/CLAUDE.md
  Specific rules for the admin frontend

~/projects/shop/apps/admin/src/payments/CLAUDE.md
  Extra safeguards for payment processing

Put shared project information in CLAUDE.md at your project root, or in .claude/CLAUDE.md one level up. Pick a convention and stick with it across the repository. Write rules so they don't conflict with each other—Claude Code merges file contents instead of applying a priority system to resolve contradictions.

Claude Code's official documentation on memory hierarchy

Anthropic covers project-level, user-level, and local memory files in detail in the official Memory documentation. The exact interfaces and terminology may shift, so check back there when making major configuration changes.

What Goes Into Each File

System-Wide CLAUDE.md

Your ~/.claude/CLAUDE.md file holds personal rules that apply across multiple projects. This might include formatting preferences, your preferred languages for code and comments, or your personal authentication workflow. It shouldn't contain assumptions tied to any specific repository.

Project-Level CLAUDE.md

This file lives in your repository and describes the overall project state. Keep it under version control when your team needs to stay aligned on common commands and rules. Only include information that actually reflects your codebase, configuration, or decisions the team has agreed on.

CLAUDE.local.md

Use CLAUDE.local.md for personal additions at the same location—like local test data or your personal workflow shortcuts. Add this to .gitignore. Passwords, API keys, customer data, and other sensitive information have no place in any CLAUDE.md file.

Rules for Subdirectories

Creating files in subdirectories makes sense when a specific area has unique risks or conventions. Payment processing, data migrations, and mobile apps are classic examples. Don't duplicate your entire root file here—just add rules specific to that area.

Pro Tip: An effective rule is one that drives specific decisions. "Write clean code" is vague. "Run existing migration tests before altering database schema" is something you can actually verify.

System-Wide CLAUDE.md for Your Workflow

Your system-level file at ~/.claude/CLAUDE.md should be repository-agnostic. This is where you define how Claude works with you in general. Avoid assumptions about specific frameworks or projects.

Sample content for ~/.claude/CLAUDE.md

# Personal Working Guidelines

## Collaboration
- Explain your plan and verification steps before making large changes.
- Ask clarifying questions if expected outcomes or scope aren't clear.

## Code
- Prefer TypeScript if the project already uses it.
- Use the project's existing formatters and test runners.

## Before You Finish
- List changed files and completed checks.
- Flag any remaining risks or unknowns.

A Lean Template for Your Project Root

Create the file directly in your project root, or let Claude Code draft an initial version with /init. Auto-generated drafts won't capture your project's nuances. Review, remove bad assumptions, and only add rules you actually follow.

CLAUDE.md template for projects

# Project Name

One sentence describing the product and who uses it.

## Key Directories
- src/app/: routes and pages
- src/lib/: business logic and integrations
- tests/: automated test suites

## Running and Verifying
bash
npm run lint
npm run test
npm run build


## Core Rules
- Check for existing components before creating new ones.
- Validate all input at API boundaries.
- Document changes to public interfaces.

## Hard Limits
- Never log sensitive information to files or output.
- Don't run data migrations without a confirmed plan.
- Ask before proceeding if requirements are unclear.

## Task Complete When
- All agreed-upon checks pass.
- Documentation and tests reflect actual behavior.

Use the @imports syntax to reference long, stable documents. A line like @docs/architecture.md will load that file at startup. What's interesting here is that imports only support a limited depth, so avoid importing your entire documentation suite by default.

How CLAUDE.md Differs from Auto Memory

Auto Memory stores reusable insights privately and locally. The system discovers stable project patterns as Claude works and learns from them. It's different from guidelines you've vetted and shared in your repository.

CLAUDE.md remains the authoritative source for standards, security boundaries, commands, and architectural decisions. Think of Auto Memory as a helper tool. The /memory command lets you view and manage stored items.

What If Claude Code Ignores Your Rules?

1. Run pwd to confirm Claude Code launched in the intended project directory.
2. Open /context and check what's loaded in the current session.
3. Use /memory to make sure Auto Memory isn't conflicting with a specific project rule.
4. Run claude --version and claude doctor if setup or configuration looks unusual.
5. Try claude --safe-mode if you're troubleshooting customization errors. This mode skips CLAUDE.md files, skills, plugins, hooks, MCP servers, and Auto Memory. Authentication, model selection, built-in tools, and permissions still work normally. Management policies remain active.

If the file loaded but Claude still misbehaved, narrow down your rules and name specific verification steps. Instead of "be careful with emails," write "display the recipient, subject, and body for confirmation before sending."

Working Alongside Other Development Tools

Some teams also use AGENTS.md files. Avoid duplicating rules across multiple files when you can. Claude Code can include a shared file through an import like @AGENTS.md. Then your CLAUDE.md (kept concise) can explain why that file takes the lead and list Claude-specific additions.

Review your actual commands and most critical rules after each major change. A short, accurate file beats a lengthy collection of outdated guidelines every time.

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Description: Learn how to create effective CLAUDE.md files at system, project, and subdirectory levels to guide Claude Code through your workflows.

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