You have probably been there. It is 11:30 PM on a Tuesday. You are staring at a legacy codebase that seems to have been written by someone who viewed documentation as a sign of weakness. You have a bug that only appears in production, a stack trace that looks like a cryptographic puzzle, and a deadline that is rapidly approaching. You open a chat window, paste a snippet, and hope for a miracle.
The miracle sometimes happens. But more often than not, the AI suggests a fix that ignores your project structure, halluncinates a library that does not exist, or simply tells you to check your logs. This is the "AI productivity wall." It is the moment where the novelty of a chat interface meets the complexity of real world software engineering.
We have moved past the era of simple autocomplete. We are now in the age of agentic workflows. Anthropic has led this charge with two distinct but complementary tools: the Claude web interface and the newer, terminal based Claude Code. Understanding how to bridge the gap between these two worlds is the difference between a developer who uses AI and an engineer who masters it.
The Two Faces of Claude: Web vs. Code
To use Claude effectively, you have to understand that you are not just talking to a single model. You are interacting with a suite of interfaces designed for different cognitive loads.
The web interface at Claude.ai is your "Architectural Studio." It is designed for high level reasoning, visual prototyping, and long form content generation. Features like Artifacts allow you to render React components, Mermaid diagrams, or HTML pages in real time1. This is where you go when you need to brainstorm a new feature or refactor a complex logic flow that requires a lot of "thinking" time.
Claude Code, on the other hand, is your "Pair Programmer in the Terminal." Released as a research preview, Claude Code is a command line interface (CLI) tool that can actually see your file system, run your tests, and execute git commands2. It is not just predicting the next token; it is acting on your behalf. If the web UI is about "telling," Claude Code is about "doing."
The mistake most teams make is trying to use one for everything. They try to paste 50 files into the web UI, hitting context limits and losing file structure. Or they try to use the CLI to brainstorm high level marketing strategies, which is a bit like using a jackhammer to hang a picture frame. The secret is knowing when to switch tools.
Mastering the Web Interface: Projects and Artifacts
The web interface has matured significantly with the introduction of "Projects." This feature allows you to ground Claude in a specific set of documents, code snippets, and style guides.
Leveraging Project Knowledge
When you start a new project in the web UI, do not just upload your code. Upload your "Product Requirements Documents" (PRDs), your internal coding standards, and perhaps even a few examples of "perfect" pull requests from your team. By setting these as the knowledge base, you reduce the "hallucination surface area."
One overlooked strategy is the use of the "Project Instructions." This is a persistent system prompt that applies to every chat within that project. If your team uses a specific variant of Tailwind CSS or a custom internal library for data fetching, define it here. It saves you from repeating "don't use Axios, use our FetchWrapper" in every single prompt.
The Power of Artifacts
Artifacts changed the game for frontend developers. When Claude generates a UI component, it does not just show you the code; it renders it in a side window. This creates a tight feedback loop. You can say "make that button more rounded" or "show me how this looks on mobile," and see the result instantly.
However, a common pitfall is treating Artifacts as the final source of truth. They are prototypes. The real value lies in using them to iterate on design and then moving the finalized logic into your repository. If you find yourself spending three hours tweaking an Artifact, it is time to move that code into your local environment and let Claude Code take over the implementation details.
Claude Code: The Agentic Leap
Claude Code is fundamentally different because it operates within your local development environment. It uses a tool use loop: it proposes a command (like grep or npm test), waits for your permission, and then analyzes the output to decide the next step2.
Why Claude Code is Necessary
Traditional AI coding assistants are often "read only." They suggest code, but you have to copy, paste, and run it. This creates a "context switch tax." Every time you move from your editor to the chat and back, you lose a bit of focus.
Claude Code eliminates this. You can give it a command like: "Find all the places where we use the old Auth provider and migrate them to the new Clerk implementation. Then run the tests to make sure I didn't break the login flow."
The agent will then:
- Search your codebase for references.
- Analyze the differences between the old and new providers.
- Rewrite the files.
- Run your test suite.
- Fix any errors that arise during the tests.
This is not just productivity; it is a force multiplier. It allows you to stay at the level of intent while the AI handles the mechanical execution.
Best Practices for the CLI Agent
To get the most out of Claude Code, you must treat it like a junior engineer with infinite energy but limited "big picture" awareness.
- Be Specific with Scope: Instead of saying "fix the bugs," say "investigate the 500 error in the
/api/userendpoint and check the database connection logic." - Use the
mcpIntegration: Claude Code can connect to the Model Context Protocol (MCP), which allows it to pull in data from external tools3. This is crucial for debugging. If your logs are in a separate system, an MCP server can provide those logs directly to the agent. - Review Before Committing: Claude Code can stage git commits for you. Never use the
git commit -mshortcut without looking at the diff. The agent might solve the problem in a way that works but introduces technical debt.
The Model Context Protocol (MCP): The Missing Link
One of the most significant developments in the Anthropic ecosystem is the Model Context Protocol. It is an open standard that allows AI models to safely and easily access data from any source - whether it is a local database, a SaaS tool, or a cloud service3.
Before MCP, if you wanted Claude to know about your Jira tickets or your Slack discussions, you had to manually copy and paste that data. With MCP, you can point Claude to a "Server" that provides this data in a structured format.
This is why Claude is becoming "aware." It is no longer a brain in a vat; it has eyes and ears in your tech stack. For example, if you are using the Claude desktop app, you can use MCP servers to let Claude read your Google Calendar or search your GitHub repositories directly. This contextual awareness is what separates a generic AI from a specialized engineering assistant.
We have seen teams use MCP to build "Onboarding Bots" that can answer questions about the codebase by searching both the documentation and the recent commit history. This reduces the burden on senior engineers to answer repetitive questions and helps new hires get up to speed in days rather than weeks.
Things People Often Overlook
Even experienced users often miss some of the more nuanced features of the Claude ecosystem.
1. Vision Capabilities in the Web UI
Claude 3.5 and 3.7 models have incredible vision capabilities. You can upload a screenshot of a bug or a handwritten wireframe, and Claude can often generate the corresponding CSS or identify the UI misalignment. This is incredibly useful for "visual debugging" where describing the problem in text would be tedious.
2. The "System Prompt" Psychology
Claude is highly sensitive to the "persona" it is given. If you tell Claude it is a "skeptical senior security engineer," it will find more potential vulnerabilities in your code than if you just ask it to "review this." Tailoring the persona to the task at hand is a high leverage move that many ignore.
3. XML Tags for Structure
Anthropic models are trained to be particularly responsive to XML tags. If you are providing a lot of data, wrap it in tags like <code_snippet> or <requirements>. This helps the model distinguish between your instructions and the data it needs to process. It significantly improves the reliability of complex prompts.
4. Context Window Management
While Claude has a massive context window, it is not infinite. More importantly, the "middle" of a very long prompt can sometimes be less prioritized than the beginning and the end. If you are working on a massive task, it is often better to break it into smaller "Checkpoints." Use the web UI for the high level breakdown and Claude Code for the individual file executions.
Common Pitfalls and How to Avoid Them
The biggest danger in the current AI era is not that the AI is "wrong," but that it is "convincingly wrong."
The "Vibe Debugging" Trap
It is easy to get into a loop where you just keep saying "it's still not working" and the AI keeps suggesting different random fixes. This is "vibe debugging." It feels like you are making progress, but you are actually just spinning your wheels.
The Fix: Force the AI to explain its reasoning before it gives you the code. Use a prompt like: "Analyze why the previous solution failed. State your hypothesis for the root cause, and then suggest a new approach based on that hypothesis."
The Documentation Lag
AI models are trained on snapshots of the internet. This means they are often behind on the very latest library updates. If you are using a framework that just released a major version last week, Claude might suggest deprecated syntax.
The Fix: Always provide the latest documentation as a reference in your Claude Project or via an MCP server. Do not assume the model knows the 2026 version of your favorite library.
Security and PII
Claude Code can see your local files. While Anthropic has built in safety measures, you should still be cautious about what is in your environment variables. Ensure that your .env files are in your .gitignore so the agent does not accidentally read them and include them in a prompt or a commit message.
Best Practices for Teams
If you are a lead engineer or a CTO, how do you roll this out to a whole team without creating a mess of AI generated spaghetti code?
- Establish a "Review First" Culture: No AI generated code should ever be merged without a human review. The reviewer should check not just for functionality, but for "intent." Does this code follow our architectural patterns, or did the AI just take the path of least resistance?
- Shared Project Knowledge: Create a "Core" project in the Claude web UI for your company. Include your design tokens, your API patterns, and your error handling strategies. Share this project with every developer. This ensures that everyone's AI assistant is "singing from the same songbook."
- Measure Outcome, Not Output: Do not track how many lines of code your developers are generating. Track how many "tickets" are being closed and the "stability" of the production environment. AI makes it easy to write more code, but the goal is to solve more problems.
- Encourage Small Commits: Agentic tools like Claude Code work best when the tasks are atomic. Encourage your team to use the agent for small, well defined changes rather than "refactor the entire billing system."
Building a Unified Engineering Context
The real challenge today is not the intelligence of the model; it is the fragmentation of our tools. We have our tasks in Linear or Jira. We have our conversations in Slack. We have our code in GitHub. We have our meetings in Google Calendar.
Claude is an incredible engine, but an engine needs fuel. That fuel is context. When you use Claude Code or the web UI, you are often manually feeding it that context. You are the "glue" between the AI and your business tools.
You spend your morning summarizing a Slack thread so Claude can understand a bug. You spend your afternoon copying Jira requirements into a Project so Claude can write a test plan. You are essentially acting as a high paid data entry clerk for your AI.
This is where the next leap in productivity happens. The "Future of Work" isn't about having a better chatbot; it is about having a system that automatically connects these dots.
Imagine if your AI assistant already knew what was discussed in your 9:00 AM standup. Imagine if it could see the specific GitHub issue you were assigned and automatically pull in the relevant documentation from your internal Wiki. Imagine if it could cross reference a performance dip in your logs with a recent PR and alert you in Slack with a suggested fix before you even knew there was a problem.
This level of integration is what transforms AI from a "tool you use" into a "platform you work within."
Closing the Visibility Gap with One Horizon
While Claude is an amazing partner for the "writing" and "doing" parts of engineering, the "understanding" and "coordinating" parts are still remarkably manual. This is the gap that One Horizon was built to close.
One Horizon acts as the central nervous system for your engineering team. It doesn't replace Claude; it makes Claude—and your entire team—significantly more effective by providing a unified layer of visibility.
By integrating with the tools you already use—like Slack, Google Calendar, Linear, Jira, and GitHub—One Horizon creates a continuous stream of context. You no longer have to wonder why a certain decision was made or what the status of a complex migration is.
For those looking to dive deeper into how this works with Anthropic's ecosystem, our documentation on Claude and Claude Code provides a blueprint for connecting your workspace to the power of agentic AI.
The goal is to stop spending time "managing" your AI and start "collaborating" with it. When your tools talk to each other, you can finally stop playing the middleman and get back to what you actually love: building great software.
Start collaborating with One Horizon
Footnotes
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Anthropic (2024). "Introducing Claude 3.5 Sonnet." https://www.anthropic.com/news/claude-3-5-sonnet ↩
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Anthropic (2025). "Claude Code: A new way to code with Claude in your terminal." https://www.anthropic.com/news/claude-code ↩ ↩2
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Anthropic (2024). "Model Context Protocol." https://www.anthropic.com/news/model-context-protocol ↩ ↩2
