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How I Hacked Claude Code to Do Magic for My Marketing Workflow

29 Jun

Over a year after its February 2025 launch, Claude Code is being used beyond software engineering. Anthropic says more than half of public API tool calls now come from outside the developer world (Source: Measuring AI agent autonomy in practice). I am part of that “other 50%”, using Claude Code for market research, copywriting, document creation, and on brand visual storytelling. And I am loving the results.

Here’s a short FAQ with the why and the how I hacked Claude Code to do magic to my marketing workflow.

First, why Claude Code and not the chat version (Claude.ai)?

Claude.ai is great for focused conversations. Claude Code enables an entirely new workflow because it runs inside a folder on your laptop that can be full of relevant artifacts.

Every brief, deck, template, research document, and half finished draft in that folder is available for Claude Code to search, read, and use as needed. It can work across those artifacts and build something new from them.

Instead of opening a new chat, uploading a handful of files, and restating the business context, I open the workspace and start from where the work already lives.

Second, how to set up Claude Code for a marketer user?

Here’s a README file 😉

Step 1: Build a CLAUDE.md file

Describe who you are, what your company does, how you write, what a good output looks like, and what Claude should never do.

Claude Code loads those project instructions at the beginning of each session. No 15 minute warmup to explain your jobs to be done, your company context, your quality bar, and your writing preferences all over again.

You open the workspace and it is ready to work.

Step 2: Give Claude Code a folder on your laptop instead of a question

In 2023, many of us used AI like a search engine: One question. One answer. Move on.

In 2026, the better workflow is giving an agent a well organized set of source materials and a meaningful task. Strategic narratives. Customer research. EBC decks. Analyst notes. Messaging drafts. Product requirements. Sales data. Existing templates.

Claude Code can search, read, and synthesize the relevant materials as it works through a new project.

That mirrors how marketers actually work.

  • We switch context constantly, from product roadmaps and PRDs to sales forecasts and campaign copy.
  • We synthesize incomplete inputs, deal with messy starting points, and turn them into something useful.

For example, Claude Code can help you:

  1. Build a customer facing, 30,000 foot thematic roadmap from product manager inputs, feature descriptions, and strategy documents.
  2. Turn messaging documents into frameworks that give creative and campaign teams a stronger starting point.
  3. Draft cross functional processes from scattered inputs across Product Marketing, Sales, Product Operations, Sales Operations, Finance, and Sales Enablement.

Step 3: Ask Claude Code to build your marketing artifact for you

Give it a source deck, a template, a spreadsheet, and a clear brief. Then ask it to create a new presentation in the same style.

It can work across the right files, follow the intended structure, match the template, write in your voice, and get you 80% of the way to a finished artifact.

The outcome takes longer than a quick chatbot response. Most of my projects take several minutes. But with the right brief, context, and a few hacks I will share below, you get a real artifact you can open, review, and share with your team after a quick polish.

Three Hacks That Made Claude Code Work for My Marketing Workflow

Hack 1: Turn off the permission prompts – only in the right workspace

Claude Code asks for approval when it is about to take meaningful actions, such as editing files, generating new ones, running commands, or reaching outside the workspace.

That is a good default. But when I am running a contained, end to end workflow in a dedicated project folder, repeated prompts can break the flow.

Fix it with one flag:

claude –permission-mode bypassPermissions

⚠️ Warning: Use bypass permissions only in a dedicated, disposable folder with no sensitive files, no shared drives, no untrusted code, and no untrusted documents. Do not run it from your home folder or an unfamiliar repository.

Hack 2: The CLI looks scary. Yet once you are inside it, it feels like a chat window with access to your files.

The terminal is the first thing that might put marketers off. For those of us who joined marketing from engineering, it may bring the scaries of getting stuck in VIM forever (q!).

But once you’re inside a terminal window, you’re just chatting. You type what you need, it responds, it works on the files in your folder.

If you’ve used Claude in a browser, the experience is almost identical, just without the upload button, because it doesn’t need one. Your files are already there.

Pro tip: you can use the chatbot of your choice, like Claude.ai to help you refine your brief for Claude Code to get better results faster. That’s the only copy-past you’ll need 😉

Hack 3: Experiment often to optimize over time

Anthropic found that experienced Claude Code users approve more actions automatically, while also interrupting more often when they need to redirect the work.

That matches my experience. Give Claude room to work. Watch the output. Step in when the brief needs to change, the quality slips, or the task needs human judgment.

My first artifacts were good. After a few prompt refinements, a stronger CLAUDE.md file, and better source materials, they started to look like something I could plausibly have created myself.

Who’s using Claude Code, ChatGPT Agents, Gemini CLI, or another agentic tool for non-coding work? What’s the most valuable workflow you’ve discovered?

My 2025 AI Predictions: INs & OUTs

28 Jan

Read the original post on Linkedin here

With the latest AI darling, DeepSeek AI, wiping billions off the market value of US tech giants just yesterday, 2025 is already shaping up to be a fascinating year for AI. The rapid evolution of AI, its promises, pitfalls, and shifting priorities, sets the stage for a year full of disruption. Here are my predictions for what’s IN and what’s OUT in AI for 2025:

AI Tech Stack: OUT with Training Obsession, IN with Inference*

The obsession with training massive models is OUT. What’s IN? Ruthlessly efficient inference. In 2025, if you’re not optimizing for inference, you’re already behind. Here’s why.

The cost of achieving OpenAI o1 level intelligence fell 27x in just the last 3 months, as my Google Cloud colleague Antonio Gulli observed – impressive price-performance improvement.

https://www.linkedin.com/feed/update/urn:li:activity:7289697297397944320/

The recent DeepSeek AI breakthrough proves this point perfectly. Their R1 model (trained for just $5.6 million, a fraction OpenAI’s rumored 500 million budget for its o1 model), achieves feature-parity and even outperforms major competitors in key benchmarks:

https://arxiv.org/pdf/2501.12948

We clearly figured out how to make LLM training more effective and cost efficient. Time to reap the benefits and use the models for inference.

*We will still be enhancing LLMs’ capabilities, developing smaller, purpose-built models and re-training them with new data-sets.

AI Architecture: OUT with Cloud-First, IN with Edge-First**

The pioneers in the most AI-advanced industries like Manufacturing have exposed the limitations of cloud-first AI approaches. According to Gartner, 27% of manufacturing enterprises have already deployed edge computing, and 64% plan to have it deployed by the end of 2027. Why the rush to edge-first AI architectures?

In industrial applications, especially those requiring real-time control and automation, latency requirements as low as 1-10 milliseconds demand a fundamental rethinking of distributed AI system design. At these speeds, edge-to-cloud roundtrips are impractical; systems must operate as edge-native, with processing and decision-making happening locally at the edge.

One of Synadia‘s most innovative customers, Intelecy, a No-Code AI platform that helps industrial companies optimize factory and plant processes with real-time machine learning insights, perfectly illustrates this paradigm shift. Their initial cloud-first approach had processing delays of 15-30 minutes. By redesigning their AI architecture for the edge, they achieved less than one-second round-trip latencies. This dramatic improvement enabled real-world applications like automated temperature control in dairy production, where ML models can provide real-time insights for process optimization.

Processing data where it is generated isn’t just more efficient—it’s becoming a competitive necessity for every industry. Gartner predicts that by 2029, 50% of enterprises will use edge computing, up from just 20% in 2024.

**The cloud’s role in AI isn’t disappearing (of course), but the default is shifting rapidly towards edge-first thinking.

AI Impact: OUT with What-If, IN with What-Now***

Focusing on model capabilities is OUT. What’s IN? Solving real business problems. The most compelling AI stories in 2025 won’t mention model architecture. Instead, they’ll focus on measurable business impact.

Intelecy’s Chief Security Officer 🔐 Jonathan Camp explains how AI can help ensure quality in manufacturing: “A dairy can use a machine learning forecast model to set temperature control systems using the real-time predicted state of the cheese production process. The process engineering team can use Intelecy insights to identify trends and then automate temperature adjustments on a vat of yogurt to ensure quality and output are not compromised.”

Source: https://www.intelecy.com/industries/food-and-beverage

The shift is clear: success is no longer measured in model capabilities, but in hard metrics like revenue gained, costs saved, and efficiency improved. The question isn’t “What can AI do?” but “What value did it deliver this quarter?”

***As an innovation-obsessed marketer, I’l never give up on “what-if” dreams but “what-now” is the state of AI in 2025.

The Elephant in the Room: Can gen AI be trusted?

We’ve solved training costs. We’ve started to crack real-time processing. Now, the focus shifts to trust: Can AI deliver consistent, reliable, and verifiable results at scale?

For example, try to ask 3x gen AI bots this prompt 3x, and see for yourself:

Name top 3 ski resorts in Europe by the total length of ski runs that are truly interconnected (no bus transfers)

We’re entering the era of agentic AI where AI-made decisions will be automatically implemented by chains if AI-functions. Are we ready?

What’s on your IN/OUT list for 2025?

#AIin2025 #Data #AI #DataAndAI #Tech2025 #FutureOfAI #Inference #Training

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  • insightfullikesupport4Simone Morellato and 3 others
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