Forem: Daisy Auma

How I Built Customer-Specific Documentation in Mintlify

Daisy Auma — Tue, 03 Feb 2026 07:25:01 +0000

A practical guide to serving different audiences without maintaining multiple doc sites

Here's a challenge I didn't expect when I started documenting an AI platform: different customers need different documentation.

Some customers want security details, compliance information, and deployment guides. Others want quick starts and code examples. Some customers use specific features that others never touch.

The obvious solution? Multiple documentation sites. One for each customer type, one for each use case.

The obvious solution is also a maintenance nightmare.

Here's how I actually solved it using Mintlify's group-based access control, serving different audiences from a single doc site without losing my mind.

The Problem: One Product, Many Audiences

When you're documenting a platform with diverse customers, you quickly realize that "one size fits all" documentation doesn't work.

The challenges I faced:

Different companies needed different content based on their use cases
Different use cases required different getting-started paths
Some features were only relevant to specific customer segments
Maintaining multiple doc sites would mean duplicating shared content across all of them

I needed a way to personalize the documentation experience without creating separate silos.

The Primary Solution: Group-Based Access Control

For truly customer-specific content that should only be visible to certain users, Mintlify supports group-based access control. This is the approach I went with.

How it works:

Enable authentication (OAuth or JWT)
Your auth system returns user groups
Pages can be restricted to specific groups

Example: Restricting a page to specific users

---
title: "Advanced Configuration Guide"
groups: ["pro-users"]
---

# Advanced Configuration Guide

This guide covers features available to pro users...

Users not in the "pro-users" group won't see this page. They'll get a 404 if they try to access it directly.

Example: User info returned by your auth system

{
  "groups": ["pro-users", "beta-testers"],
  "expiresAt": 1735689600
}

This user can access pages tagged with either "pro-users" or "beta-testers" groups.

When to use group-based access:

Paid features that free users shouldn't see
Beta documentation for early adopters
Partner-specific integrations
Customer-specific content based on their plan or use case

This approach lets me maintain one documentation site while serving completely different content to different customer segments.

Tabs for Different Audiences

The simplest way to serve multiple audiences on the same page is tabs.

Mintlify's tab component lets you present alternative content side-by-side, letting readers choose what's relevant to them.

Example: Different code languages

<Tabs>
  <Tab title="Python">
    ```

python
    import requests

    response = requests.get("https://api.example.com/data")
    print(response.json())


    ```
  </Tab>

  <Tab title="JavaScript">
    ```

javascript
    fetch("https://api.example.com/data")
      .then(response => response.json())
      .then(data => console.log(data));


    ```
  </Tab>

  <Tab title="cURL">
    ```

bash
    curl https://api.example.com/data


    ```
  </Tab>
</Tabs>

When to use tabs:

Content that's parallel (same topic, different approaches)
Users typically only need one option
All options should be equally visible/accessible

When NOT to use tabs:

Content that most users need to read entirely
Options that build on each other
More than 4-5 alternatives (gets unwieldy)

Conditional Navigation Paths

Not all customers need to see all sections of your documentation.

Mintlify lets you organize navigation in docs.json to create logical groupings. Combined with authentication and groups, you can show different navigation to different users.

Example: Organized navigation

{
  "navigation": {
    "groups": [
      {
        "group": "Getting Started",
        "public": true,
        "pages": [
          "quickstart",
          "installation",
          "first-api-call"
        ]
      },
      {
        "group": "Core Features",
        "pages": [
          "feature-overview",
          "basic-usage",
          "best-practices"
        ]
      },
      {
        "group": "Advanced",
        "pages": [
          "custom-integrations",
          "api-reference",
          "troubleshooting"
        ]
      }
    ]
  }
}

The key insight: You don't always need to hide content. Sometimes, clear organization is enough.

Users will naturally gravitate to the sections relevant to them. The content is available to everyone, but the navigation makes it easy to find what's relevant.

Public vs. Protected Content

Sometimes you want most of your docs behind authentication, but certain pages public.

Mintlify makes this straightforward with the public property.

Make a single page public:

---
title: "API Overview"
public: true
---

Make an entire section public:

{
  "navigation": {
    "groups": [
      {
        "group": "Public Docs",
        "public": true,
        "pages": [
          "overview",
          "quickstart",
          "pricing"
        ]
      },
      {
        "group": "Protected Docs",
        "pages": [
          "api-reference",
          "advanced-guides",
          "internal-tools"
        ]
      }
    ]
  }
}

My approach:

Public: Overview, quickstart, basic concepts (helps with SEO and sales)
Protected: Detailed API reference, advanced guides, customer-specific content

This gives potential customers enough to evaluate the product while keeping detailed documentation for actual users.

How It All Fits Together

Here's how I think about the hierarchy:

┌─────────────────────────────────────────────┐
│              All Documentation               │
├─────────────────────────────────────────────┤
│  Public Pages                                │
│  (Anyone can access)                         │
│  - Overview, quickstart, basic concepts      │
├─────────────────────────────────────────────┤
│  Authenticated Pages                         │
│  (Logged-in users)                           │
│  - Full API reference, guides, tutorials     │
├─────────────────────────────────────────────┤
│  Group-Restricted Pages                      │
│  (Specific user groups)                      │
│  - Pro users: advanced features              │
│  - Beta: upcoming features                   │
│  - Partners: integration guides              │
└─────────────────────────────────────────────┘

Within each level, I use tabs and clear navigation to help users find what's relevant without hiding content unnecessarily.

Lessons Learned

1. Start with organization, not restriction

Before adding authentication or group controls, ask: "Can I solve this with better organization?"

Clear sections and good navigation often eliminate the need for access control. Users self-select into the content they need.

2. Tabs are your friend

Any time you're tempted to create parallel pages (Python vs. JavaScript, beginner vs. advanced), consider tabs first. They keep related content together and reduce maintenance burden.

3. Don't over-segment

It's tempting to create hyper-specific documentation for every customer segment. Resist this urge.

Every segment you create is content you have to maintain. Start broad and only segment when you have clear evidence that users need it.

4. Test with real users

The best way to know if your documentation structure works? Watch someone use it.

Do customers find the content they need? Do new users complete the quickstart? Where do people get stuck?

User testing reveals gaps that internal review never will.

The Result

With this approach, I maintain a single Mintlify documentation site that serves:

New users exploring the product (public quickstart)
Active customers building integrations (authenticated API reference)
Specific customer segments with unique requirements (group-restricted docs)
Beta testers trying new features (group-restricted beta docs)

No duplicate content. No multiple sites to maintain. One source of truth with flexible presentation.

Resources

How do you handle customer-specific documentation? I'd love to hear what approaches have worked for you.

The Lonely Side of Being a Solo Technical Writer

Daisy Auma — Mon, 02 Feb 2026 08:27:17 +0000

What it's really like to be the only one who does what you do

I have a great team. Engineers review my documentation for accuracy. Product managers answer my endless questions. My work gets feedback before it ships.

And yet, being a solo technical writer is still lonely.

Not lonely in the "no one talks to me" sense. Lonely in the "no one else thinks about what I think about" sense.

Here's what that actually looks like.

No One to Discuss the Craft With

Last week, I spent an hour deciding whether a piece of content should be a tutorial or a conceptual guide.

Tutorials are step-by-step. Conceptual guides explain the "why." The distinction matters for how users navigate documentation. It affects the structure, the tone, the level of detail.

I made the decision. It was the right call. But I made it alone.

Engineers review my docs for technical accuracy. They catch errors, clarify edge cases, and make sure the code examples actually work. That's invaluable.

But they're not thinking about information architecture. They're not debating whether this content belongs in the quickstart or the API reference. They're not wondering if users will find this page through search or navigation.

That's my job. And I'm the only one doing it.

Wins That Don't Translate

A few weeks ago, I restructured our documentation sidebar. It took hours — reorganizing pages, fixing broken links, testing navigation paths, making sure users could find what they needed in fewer clicks.

When I finished, I was genuinely proud. The documentation was meaningfully better.

I mentioned it in our team Slack. People responded positively — thumbs up, a few nice comments. But I could tell it wasn't a priority for anyone else. And why would it be? They had features to ship, bugs to fix, customers to support.

The documentation sidebar wasn't keeping anyone else up at night. Just me.

But it means celebrating alone. The wins that matter most to me are often invisible to everyone else.

Carrying the Vision Solo

Here's something I didn't expect about this role: I'm the only one who sees the full picture.

Engineers know their features deeply. Product managers know the roadmap. Support knows the common questions.

But I'm the only one who sees how all of it connects in the documentation. I'm the only one thinking about the user's journey from first visit to successful implementation. I'm the only one noticing that we have three pages explaining the same concept slightly differently.

That's powerful. It's also heavy.

When documentation has gaps, I feel responsible. When users get confused, I wonder what I missed. There's no one to share that weight with, no peer to say "I noticed that too" or "here's how I'd approach it."

Different Mental Models

I think in user journeys. Engineers think in features.

When a new feature ships, engineers are excited about what it does. I'm thinking about how users will discover it, understand it, and actually use it.

"What does this feature do?" is an engineering question.
"What problem does this solve for users, and how do we explain it so they succeed?" is a documentation question.

Both are valid. But they're different languages. And sometimes it feels like I'm the only one speaking mine.

The Flip Side: Freedom

I don't want this to sound like complaining. Because honestly? There's a flip side.

Being the solo technical writer means I own the documentation voice. I set the standards. When I decide we're going to write in a certain style, that's the style. No committee. No endless debates. No death by consensus.

I can experiment. I can try new approaches. If something doesn't work, I can change it without convincing five people first.

The autonomy is real. And it's valuable.

But autonomy and loneliness are two sides of the same coin. You can't have one without accepting some of the other.

Finding Your People Outside Work

The thing that's helped most? Finding community outside my company.

The Write the Docs Slack has been invaluable. Thousands of technical writers sharing challenges, asking questions, and celebrating wins that actually make sense to each other.

"I finally got the API reference structure right" gets real excitement there. People understand.

Twitter and LinkedIn have helped too. Following other technical writers, reading about their processes, realizing that my challenges aren't unique — that's been grounding.

You don't have to work with other technical writers to have a community of them.

What I've Learned

A few things I'd tell someone starting as a solo technical writer:

Build relationships with engineers who explain things well. Not all engineers communicate the same way. Find the ones who naturally translate complexity into clarity. They'll make your job easier and your documentation better.

Create your own feedback loops. If no one's reviewing your work for documentation quality, find ways to get that feedback. Watch new users navigate your docs. Ask support what questions keep coming up. The feedback exists — you just have to seek it out.

Celebrate your own wins. No one else will fully understand why that sidebar restructure mattered. Celebrate it anyway. Keep a list of improvements you've made. Look at it when you need a reminder that your work matters.

Find your community. Write the Docs, DevRel communities, Twitter, LinkedIn — the people who understand your work exist. They're just not in your office.

Accept the trade-off. Autonomy comes with loneliness. Freedom comes with responsibility. You can't have the good parts without accepting the harder parts too.

It's Worth It

Being a solo technical writer is lonely sometimes. That's the truth.

But it's also deeply rewarding. You own something meaningful. You see the direct impact of your work. You build something that helps real people accomplish real things.

The loneliness isn't a bug. It's just part of the deal.

And once you accept that, you can build the support systems you need — inside and outside your company — to thrive anyway.

Are you a solo technical writer? What's been your experience? I'd love to hear what's worked for you.

Writing API Documentation That Developers Actually Read

Daisy Auma — Tue, 20 Jan 2026 10:40:47 +0000

Lessons from documenting AI APIs as a solo technical writer

Here's a truth that took me too long to learn: Most API documentation is written for the writer, not the reader.

We document endpoints in alphabetical order because it's easy to organize. We list every parameter because we want to be thorough. We write comprehensive reference docs and wonder why developers still ask basic questions in support channels.

As the only technical writer at an AI startup, I've had to rethink how I approach API documentation. Here's what actually works.

Start With Use Cases, Not Endpoints

The biggest mistake I made early on was organizing documentation by endpoint.

POST /users/createGET /users/listPUT /users/update

Alphabetical. Comprehensive. Completely useless for someone trying to figure out how to get started.

What developers actually want to know:

How do I analyze a video file?
How do I monitor sensor data in real-time?
How do I get my first successful API call working?

Now I start every API section with a use case. Not "here's what this endpoint does" but "here's what you're trying to accomplish, and here's how."

The endpoint reference still exists. But it's not the entry point. The use case is.

Stripe's API documentation is the gold standard here, they lead with use cases like "Accept a payment" before diving into endpoint references.

The Quickstart Is Everything

If your quickstart doesn't work, nothing else matters.

I learned this the hard way when we had a hackathon approaching and our quickstart guide wasn't returning results. The API reference was complete. The conceptual docs were polished. But developers couldn't get past step one.

A good quickstart should:

Work in under 5 minutes
Require minimal setup
Show a real result (not just a 200 OK)
Be testable by someone who knows nothing about your product

I now test every quickstart on a fresh environment before publishing. If I can't get it working in 5 minutes with just the docs, it's not ready.

Twilio's quickstarts are excellent examples — they get you to a working "Hello World" in minutes.

Code Examples Must Actually Run

This sounds obvious. It's not.

I've lost count of how many times I've found code examples that:

Use deprecated endpoints
Have typos in parameter names
Work in the old API version but not the current one
Are missing required authentication headers

My process now:

Write the code example
Actually run it
Copy the exact code that worked into the docs
Add a test to catch when it breaks

That last part is crucial. APIs change. If you don't have automated checks, your examples will rot.

One specific lesson: JSON formatting matters more than you think. I once spent hours debugging why an API call failed, only to discover that multi-line JSON with whitespace caused errors, but compact single-line JSON worked fine. That's now documented with a warning.

Error Documentation Is Not Optional

Most API docs show you the happy path. Request goes in, response comes out, everyone's happy.

Real developers spend most of their time in the unhappy path.

For every endpoint, I now document:

What errors can occur
What each error code means
What likely caused it
How to fix it

Example:

400 - Missing Required Field
{
  "code": "missing_field",
  "message": "Missing field: user_id",
  "suggestion": "Ensure your request body includes the required user_id field"
}

This takes more time upfront but dramatically reduces support tickets. When developers can self-serve their way out of errors, everyone wins.

Stripe's error handling documentation is worth studying, they document every error code with causes and solutions.

Structure Matters More Than You Think

A few formatting lessons I've learned:

Use tabs for multi-language examples. If you support Python, JavaScript, and cURL, don't make developers scroll past two languages they don't care about.

Lead with the most common case. If 90% of users need the simple version, show that first. Put the advanced options in an expandable section.

Be consistent. If you show request first, then response in one section, do it that way everywhere. Developers build mental models of your docs. Don't break them.

Make copy-paste easy. If developers need to copy an API key, a URL, or a code snippet, make sure it copies cleanly without extra whitespace or formatting artifacts.

If you're using a docs-as-code approach, tools like Mintlify, ReadMe, or Redocly have built-in components for tabs, code groups, and interactive examples.

Test Your Own Documentation

The best feedback on my docs comes from watching someone use them.

When we onboarded new team members, I asked them to mark every point where they got confused. Every time they had to ask a question, that was a documentation failure.

Questions I now ask myself:

Can someone with no context get from zero to working API call using just these docs?
When they hit an error, can they figure out what went wrong?
Are they copying code examples directly, or having to modify them?

If the answer to any of these is "no," the docs aren't done.

The Write the Docs community has been invaluable for getting feedback and learning from other technical writers facing similar challenges.

The 3 AM Test

Here's my final quality check:

Imagine a developer at 3 AM, deadline looming, trying to integrate your API. They're tired. They're frustrated. They don't want to read your carefully crafted conceptual overview.

Can they find what they need in 30 seconds? Can they copy a working example? Can they troubleshoot errors without filing a support ticket?

If yes, your docs are ready. If no, keep iterating.

What I'm Still Learning

API documentation is never done. I'm still figuring out:

How to document AI outputs that aren't deterministic ("results may vary" is true but not helpful)
How to balance comprehensive reference docs with practical guides
How to keep docs updated when the API changes weekly

But the fundamentals stay the same: start with use cases, make the quickstart bulletproof, test everything, and always write for the developer at 3 AM.

Resources That Have Helped Me

Google's API Design Guide — Principles for designing (and documenting) APIs
Docs for Developers — The book that shaped how I think about developer docs
I'd Rather Be Writing — Tom Johnson's blog on API documentation
Write the Docs Slack — Community of technical writers

What's the best API documentation you've encountered? What made it work?

How I Turned Slack Messages Into Documentation

Daisy Auma — Tue, 13 Jan 2026 06:40:25 +0000

The unsexy process behind gathering information as a solo technical writer

At startups, knowledge doesn't live in neatly organized wikis or comprehensive handoff documents. It lives in Slack threads, Notion pages with outdated information, and most critically — in engineers' heads.

As the only technical writer at an AI startup, my job isn't just writing documentation. It's excavating knowledge before it disappears.

Here's the actual process I use to turn scattered internal conversations into documentation that developers can actually use.

The Reality of Information Gathering

When I joined my company, I inherited documentation that was outdated and AI-generated. The product had evolved significantly, but the docs hadn't kept pace. The knowledge existed — it was just trapped in places I couldn't access.

I quickly learned that the best explanations weren't in formal documents. They were in Slack messages where an engineer answered a customer question at 11 PM. In casual conversations during standups. In the comments of pull requests.

My job became part technical writer, part archaeologist.

Step 1: Build Relationships Before You Need Them

This is the step most technical writers skip, and it's the most important one.

Engineers are busy. They're shipping features, fixing bugs, and attending meetings. Documentation is rarely their priority. If you only show up when you need something, you'll always be at the bottom of their to-do list.

What I do instead:

I show up in engineering channels even when I don't need anything. I react to their wins. I ask genuine questions about what they're building. When I see an interesting technical discussion, I engage with curiosity, not just for documentation purposes.

When I finally do need their help documenting a feature, I'm not a stranger asking for a favor. I'm a colleague they've interacted with.

This sounds soft, but it's the difference between getting a response in two hours versus two weeks.

Step 2: Ask Specific Questions, Not Open-Ended Ones

Early on, I made the mistake of asking engineers: "Can you explain how this feature works?"

The responses were either overwhelming (a 30-minute monologue covering edge cases I didn't need) or unhelpfully brief ("It does what the PR says").

What actually works:

Instead of "How does this work?", I ask:

"What happens when a user sends an invalid API key?"
"What's the most common mistake customers make with this endpoint?"
"If this fails, what error message do they see?"

Specific questions get specific answers. Specific answers become useful documentation.

The best question I've learned to ask: "What do you wish customers understood before they start using this?" Engineers love answering this one because it lets them vent about the support tickets they're tired of seeing.

Step 3: Capture Conversations in Real-Time

The gold is in the Slack threads. Here's my actual process:

When I see an engineer explain something well in Slack:

I screenshot or copy the message immediately
I DM them: "That explanation was really clear. Mind if I turn this into docs?"
Most say yes. Many are flattered.

When a customer asks a question in a support channel:

I note the question (this tells me what's unclear in current docs)
I watch how the engineer responds
I add this to my documentation backlog

When I'm in meetings:

I keep a running note of anything that makes me think "wait, is that documented?" It usually isn't.

The key is capturing knowledge when it's fresh. Engineers explain things clearly in the moment, but ask them to re-explain two weeks later and the clarity is gone.

Step 4: Make Reviews Stupidly Easy

Getting engineers to review documentation is like pulling teeth — unless you make it effortless.

What doesn't work:

"Hey, can you review the docs I wrote?"

This gets ignored because it feels like an undefined time commitment.

What works:

"Hey, can you check if the code example in section 3 actually runs? Should take 5 minutes."

I break reviews into tiny, specific asks:

"Is this parameter description accurate?"
"Did I get the error codes right?"
"Any gotchas I should add?"

I also learned to set deadlines with consequences:

"I'm publishing this Thursday at 2 PM. If I don't hear back, I'm assuming it's accurate."

This sounds aggressive, but it works. Engineers don't want wrong documentation published under their name. They'll make time to respond when there's a deadline.

Step 5: Build a System, Not Just Documents

Individual documents are nice. A system is sustainable.

My simple system:

I keep a "docs debt" list — a running note of everything I've noticed needs documentation but haven't had time to write. Every Friday, I spend two hours chipping away at it.

I also maintain templates for common documentation types:

API endpoint documentation
Tutorial structure
Troubleshooting guides
Release notes

Templates mean I spend my mental energy on content, not structure. They also create consistency across documents.

The Slack channel hack:

I created a #docs-updates channel where I post whenever I publish new documentation. Two benefits:

Engineers see their work being documented (makes them more likely to help in the future)
People can flag issues immediately ("Actually, we changed that endpoint last week")

What I've Learned

After months of doing this, a few things have become clear:

Documentation is 20% writing, 80% gathering. The actual writing is the easy part. Getting accurate information from busy people is the real skill.

Timing matters more than asking nicely. Catch engineers right after they ship a feature — they're still excited and remember the details. Wait two weeks and you'll get "I don't remember, check the PR."

Perfect is the enemy of published. I used to wait until documentation was comprehensive before publishing. Now I ship "good enough" and iterate. Something helpful today beats something perfect next month.

Your documentation is only as good as your relationships. I know which engineers explain things clearly, which ones need more specific questions, and which ones prefer async communication. This knowledge is as valuable as any writing skill.

The Unsexy Truth

There's no magic tool that extracts knowledge from your organization. Mintlify makes the publishing beautiful. GitHub makes version control seamless. But the actual information gathering?

That's Slack messages at 9 PM. That's coffee chats that turn into impromptu explanations. That's asking "dumb questions" in engineering channels and not caring if you look uninformed.

It's not glamorous. But it's how documentation actually gets made at startups.

What's your biggest challenge in gathering information for documentation? I'd love to hear what works (and what doesn't) for others.

# Why I Chose Mintlify (And What I Wish I Knew Earlier)

Daisy Auma — Mon, 05 Jan 2026 20:03:20 +0000

An honest review from a solo technical writer at an AI startup

Let me be upfront: I didn't choose Mintlify. When I joined my current company as the first and only technical writer, the platform had already been selected. The documentation needed a complete overhaul, and Mintlify was what I had to work with.

At first, I wasn't sure what to expect. I'd heard of Mintlify but never used it. Now, after months of building documentation from scratch on this platform, I have opinions, and honestly? I'd choose it myself if I had to start over.

Here's my honest take on Mintlify what works, what's tricky, and why it might (or might not) be right for you.

How Mintlify Compares to Alternatives

Even though I didn't make the initial choice, I've since evaluated whether it was the right one. Here's how Mintlify stacks up against what else is out there:

GitBook is the go-to for many teams. It's polished, collaborative, and has a gentle learning curve. But for a developer-focused product with heavy API documentation, I found it limiting. The customization options felt restrictive, and the pricing scales quickly as your team grows.

ReadMe is fantastic for API documentation specifically. The interactive API explorer is genuinely impressive. But if you need more than API docs; tutorials, conceptual guides, getting started content, it starts to feel like you're fighting the platform.

Docusaurus gives you complete control. It's open-source, React-based, and incredibly flexible. The trade-off? You're essentially maintaining a website. For a solo technical writer at a startup, that overhead wasn't something I could justify.

Mintlify sits in an interesting middle ground: docs-as-code workflow, beautiful defaults, and enough customization to not feel boxed in.

What I Love About Mintlify

The Docs-as-Code Workflow Changed Everything

This was the game-changer for me. My documentation lives in a GitHub repository, written in MDX (Markdown with components). Every change goes through a pull request. Engineers can review docs the same way they review code.

Why this matters:

Version control: I can see exactly what changed, when, and why
Collaboration: Engineers can suggest edits directly in the files they know
Deployment: Push to main, docs update automatically
Backup: My documentation is just files in a repo, not locked in someone's database

Coming from tools where documentation lived in a web interface, this felt like a revelation. No more copy-pasting between platforms. No more wondering if I saved my changes. Just Git.

The Defaults Are Actually Beautiful

I've used documentation tools where "out of the box" meant spending a week on styling before you could show anyone. Mintlify's defaults look professional from day one.

The typography is clean. The navigation makes sense. Dark mode works properly. Code blocks have syntax highlighting. These sound like basics, but you'd be surprised how many platforms get them wrong.

For a startup where I needed to ship documentation fast, not spending time on design decisions was a gift.

MDX Components Save Hours

Mintlify comes with built-in components that would take forever to build yourself:

Tabs for showing multiple code examples (Python, JavaScript, cURL)
Callouts for notes, warnings, and tips
Cards for navigation and feature highlights
Steps for sequential instructions
Code groups for switching between related examples
API playground that lets developers test endpoints directly in the docs

These aren't just nice-to-have. They're the difference between documentation that people skim and documentation that people actually use.

The API Reference Generation

If you're documenting APIs, Mintlify can generate reference documentation from your OpenAPI spec. You maintain your spec file, and the docs stay in sync.

Is it perfect? No. I still end up writing custom content around the generated reference. But having the baseline automatically generated with request/response examples, parameter descriptions, and authentication details saves significant time.

What I Wish I Knew Earlier

The Learning Curve Is Real

Mintlify markets itself as simple, and it is once you understand the patterns. But there's a learning curve that caught me off guard.

The docs.json configuration file controls your entire site structure. Navigation, styling, integrations, redirects — it's all in one file. When things break, you're often debugging JSON structure.

My first few weeks involved a lot of trial and error. "Why isn't this page showing up in the sidebar?" Usually: I forgot to add it to the navigation config. "Why is this redirect not working?" Usually: path formatting issues.

If you're not comfortable with JSON configuration and Git workflows, budget time for learning.

Image Handling Has Quirks

This one bit me multiple times. Images in Mintlify need to be in specific locations, and the path syntax depends on where your MDX file lives.

I've debugged countless "image not displaying" issues. The culprit is usually:

Wrong path (relative vs. absolute)
Case sensitivity in filenames
Forgetting to commit the image file to Git

My advice: establish a clear image organization system from day one and stick to it religiously.

Some Features Need Workarounds

Not everything works exactly as documented. I've had to get creative with:

Dark/light mode images: Needed custom CSS classes to swap images based on theme
Nested navigation: The sidebar structure has limits that required restructuring my content
Custom components: Possible, but requires understanding the underlying system

None of these are dealbreakers, but they're the kind of friction that adds up when you're trying to move fast.

Enterprise Features Cost Extra

If you need authentication, personalization, or analytics beyond the basics, you're looking at higher-tier plans. I implemented customer-specific documentation using Mintlify's authentication features, it works well, but it wasn't part of the base package.

For a startup, be realistic about what features you need now vs. later. The free and lower tiers are generous for getting started, but factor in growth.

Who Should (and Shouldn't) Use Mintlify

Mintlify is great if you:

Want docs-as-code without building your own system
Have developer-focused documentation with API content
Value beautiful defaults over unlimited customization
Are comfortable with Git and Markdown
Need to ship documentation quickly at a startup

Consider alternatives if you:

Need heavy collaboration features for non-technical writers
Want a completely custom design (Docusaurus gives more control)
Primarily need internal documentation (Notion or Confluence might be simpler)
Have a large team that needs granular permissions

The Bottom Line

I didn't choose Mintlify but after months of daily use, I'm glad someone else did. If I were starting fresh at a new company and had to pick a documentation platform myself, Mintlify would be at the top of my list.

The docs-as-code workflow fits how I think about documentation. The defaults are beautiful. The components save time. The platform gets out of my way and lets me focus on writing.

Is it perfect? No. The learning curve is steeper than advertised. Some features need workarounds. Enterprise features add up.

But for a solo technical writer at a startup building developer documentation? It's been the right tool for the job.

What documentation platform do you use? I'm curious what's worked (and what hasn't) for others. Drop a comment — I read every one.

The Python Web Stack You've Never Heard Of: Building Apps Without Frontend Code

Daisy Auma — Mon, 05 Jan 2026 20:02:43 +0000

I recently interviewed with Anvil, and despite not getting the role, I found what they do to be really cool. As a Python developer, I’ve hit the same wall countless times: I can wrangle data, build ML models, automate workflows, but the moment someone says “put a web UI on that,” I’m stuck learning React, wrestling with JavaScript tooling, and coordinating between frontend and backend.

Anvil takes a contrarian approach: what if you could write your entire web application including the client-side code in Python? No JavaScript required. But here’s the technical puzzle: browsers only understand JavaScript. So how does this actually work?

Architecture Overview

Anvil’s stack consists of four main layers that work together to enable full-stack Python development:

Client Layer: Python code that runs in the browser, compiled to JavaScript via Skulpt
Server Layer: Standard Python with full ecosystem access, running on Anvil’s servers
Communication: RPC over WebSockets connecting client and server seamlessly
Database: Built-in PostgreSQL (Data Tables) or connect to external databases

There’s also an optional fifth component called the Uplink, which lets you connect local Python scripts or Jupyter notebooks to your web app (more on this later).

The key innovation here is a unified language across the entire stack. No context switching between Python and JavaScript, no REST API boilerplate, no separate frontend and backend codebases.

The Technical Magic: Client-Side Python

Here’s the fundamental problem: web browsers only execute JavaScript. For decades, the solution has been clear: learn JavaScript frameworks, coordinate between frontend and backend teams, manage two entirely different codebases.

Anvil’s solution relies on Skulpt, a JavaScript implementation of Python that runs in the browser. When you write Python code for the client-side, it gets compiled to JavaScript at runtime. This means your Python code is actually executing in the browser, handling events, manipulating the DOM, and updating the UI.

Here’s what this looks like in practice:

# This Python code runs IN THE BROWSER
from anvil import *

class MyForm(MyFormTemplate):
  def button_click(self, **event_args):
    # Client-side event handling in Python
    alert("Hello from the browser!")

    # Call server seamlessly (looks like a regular function call)
    result = anvil.server.call('process_data', self.text_box.text)
    self.label.text = result

This is genuinely running as Python in your browser. The button_click method handles a UI event, displays an alert, calls a server function, and updates a label, all without writing a single line of JavaScript.

Benefits:

Familiar Python syntax and patterns with no mental context switching
Seamless client-server communication where RPC calls look like regular Python functions
Unified codebase with one language and one set of conventions
Rapid development without frontend/backend coordination overhead

The crucial insight is understanding where code actually runs. UI event handlers execute in the browser (Python via Skulpt), but heavy data processing, machine learning, and pandas operations run on the server in native Python. Database queries happen server-side. The architecture naturally guides you toward the right separation of concerns.

Real Code Example

Let’s build a mini data dashboard that demonstrates how everything fits together:

# === SERVER MODULE (server.py) ===
import pandas as pd
import plotly.express as px
import anvil.server

@anvil.server.callable
def analyze_sales(uploaded_file):
  # Full Python ecosystem available here
  df = pd.read_csv(uploaded_file)
  df = df.groupby('category')['revenue'].sum().reset_index()

  fig = px.bar(df, x='category', y='revenue', 
               title='Sales by Category')
  return fig

@anvil.server.callable  
def save_to_database(data):
  # Built-in database access
  app_tables.sales.add_row(
    category=data['category'],
    amount=data['amount'],
    date=datetime.now()
  )
  return "Saved successfully"


# === CLIENT MODULE (Form1.py) ===
from anvil import *
import anvil.server

class Form1(Form1Template):
  def upload_change(self, file, **event_args):
    # This runs in the browser
    self.progress.visible = True

    # Call server function - seamless RPC
    chart = anvil.server.call('analyze_sales', file)
    self.plot.figure = chart

    self.progress.visible = False

  def save_button_click(self, **event_args):
    data = {
      'category': self.dropdown.selected_value,
      'amount': self.text_box.text
    }
    result = anvil.server.call('save_to_database', data)
    alert(result)

What’s happening here:

File upload is handled client-side (Python in browser via Skulpt)
Heavy processing with pandas happens server-side with full Python capabilities
RPC calls look like regular Python function calls (no HTTP requests to construct)
No JSON serialization code (Python objects are serialized automatically)
Security is built-in: server functions decorated with @anvil.server.callable form your API boundary

Notice there’s no REST API definition, no endpoint configuration, no fetch calls with headers and error handling. The anvil.server.call() function handles all of that transparently.

Integration & Ecosystem

What Works Where

Server-side, you have access to the full Python ecosystem. Install any pip package: pandas, scikit-learn, requests, TensorFlow. This is native Python execution.

Client-side is more limited. Plotly works for visualizations, and you have access to a subset of the standard library, but compute-heavy libraries like NumPy won’t run in the browser. This limitation actually makes sense architecturally, heavy computation belongs on the server anyway.

JavaScript libraries can be wrapped and called from Python using Anvil’s JavaScript integration features. You’re not completely cut off from the JavaScript ecosystem when you need it.

Anvil includes built-in integrations for common services: Stripe for payments, Google and Microsoft for authentication and APIs, and email sending. These work out of the box with minimal configuration.

The Uplink: The Secret Weapon

The Uplink is perhaps Anvil’s most unique feature. It lets you connect existing Python code running anywhere; your laptop, a Jupyter notebook, a GPU server to your web application.

Plain textANTLR4BashCC#CSSCoffeeScriptCMakeDartDjangoDockerEJSErlangGitGoGraphQLGroovyHTMLJavaJavaScriptJSONJSXKotlinLaTeXLessLuaMakefileMarkdownMATLABMarkupObjective-CPerlPHPPowerShell.propertiesProtocol BuffersPythonRRubySass (Sass)Sass (Scss)SchemeSQLShellSwiftSVGTSXTypeScriptWebAssemblyYAMLXML# Your local Jupyter notebook or scriptimport anvil.serveranvil.server.connect("YOUR_UPLINK_KEY")@anvil.server.callabledef run_ml_model(data): # Use your local GPU, existing code, trained models return model.predict(data)

Your web app calls this like any server function, but it executes on your machine. This is brilliant for connecting data science workflows to web interfaces without rewriting everything. Your Jupyter notebook becomes a backend service.

When to Use This

Choose Anvil when:

Your team is Python-heavy but frontend-light
Building internal tools, dashboards, or admin panels
Rapidly prototyping data applications
Connecting Jupyter notebooks or data science code to web UIs
You value development speed over pixel-perfect custom UI
Creating CRUD applications where Python’s full ecosystem matters more than complex client-side interactions

The Bottom Line

Anvil makes a specific technical bet: for certain types of applications, the productivity gains from using a unified language outweigh the performance overhead and ecosystem constraints of client-side Python.

This isn’t about replacing React or Vue. It’s about offering an alternative path for Python developers who view web UIs as a necessary interface layer rather than the core of what they’re building. When your value is in data processing, business logic, or machine learning models, Anvil lets you wrap those capabilities in a web interface without learning an entirely new stack.

The platform is part of a broader trend of Python expanding beyond its traditional backend role.

Is this the future of web development? Probably not. But for specific use cases (particularly in data science, internal tooling, and rapid prototyping) it’s a genuinely productive alternative that more Python developers should know about.

This article may not be perfect and I would appreciate any feedback!

Buy me a coffee: https://buymeacoffee.com/daisyauma

The Python Web Stack You've Never Heard Of: Building Apps Without Frontend Code

Daisy Auma — Thu, 09 Oct 2025 16:56:34 +0000

Architecture Overview

Anvil’s stack consists of four main layers that work together to enable full-stack Python development:

There’s also an optional fifth component called the Uplink, which lets you connect local Python scripts or Jupyter notebooks to your web app (more on this later).

The key innovation here is a unified language across the entire stack. No context switching between Python and JavaScript, no REST API boilerplate, no separate frontend and backend codebases.

The Technical Magic: Client-Side Python

Here’s what this looks like in practice:

# This Python code runs IN THE BROWSER
from anvil import *

class MyForm(MyFormTemplate):
  def button_click(self, **event_args):
    # Client-side event handling in Python
    alert("Hello from the browser!")

    # Call server seamlessly (looks like a regular function call)
    result = anvil.server.call('process_data', self.text_box.text)
    self.label.text = result

This is genuinely running as Python in your browser. The button\_click method handles a UI event, displays an alert, calls a server function, and updates a label, all without writing a single line of JavaScript.

Benefits:

Familiar Python syntax and patterns with no mental context switching
Seamless client-server communication where RPC calls look like regular Python functions
Unified codebase with one language and one set of conventions
Rapid development without frontend/backend coordination overhead

Real Code Example

Let’s build a mini data dashboard that demonstrates how everything fits together:

# === SERVER MODULE (server.py) ===
import pandas as pd
import plotly.express as px
import anvil.server

@anvil.server.callable
def analyze_sales(uploaded_file):
  # Full Python ecosystem available here
  df = pd.read_csv(uploaded_file)
  df = df.groupby('category')['revenue'].sum().reset_index()

  fig = px.bar(df, x='category', y='revenue', 
               title='Sales by Category')
  return fig

@anvil.server.callable  
def save_to_database(data):
  # Built-in database access
  app_tables.sales.add_row(
    category=data['category'],
    amount=data['amount'],
    date=datetime.now()
  )
  return "Saved successfully"


# === CLIENT MODULE (Form1.py) ===
from anvil import *
import anvil.server

class Form1(Form1Template):
  def upload_change(self, file, **event_args):
    # This runs in the browser
    self.progress.visible = True

    # Call server function - seamless RPC
    chart = anvil.server.call('analyze_sales', file)
    self.plot.figure = chart

    self.progress.visible = False

  def save_button_click(self, **event_args):
    data = {
      'category': self.dropdown.selected_value,
      'amount': self.text_box.text
    }
    result = anvil.server.call('save_to_database', data)
    alert(result)

What’s happening here:

File upload is handled client-side (Python in browser via Skulpt)
Heavy processing with pandas happens server-side with full Python capabilities
RPC calls look like regular Python function calls (no HTTP requests to construct)
No JSON serialization code (Python objects are serialized automatically)
Security is built-in: server functions decorated with @anvil.server.callable form your API boundary

Notice there’s no REST API definition, no endpoint configuration, no fetch calls with headers and error handling. The anvil.server.call() function handles all of that transparently.

Integration & Ecosystem

What Works Where

Server-side, you have access to the full Python ecosystem. Install any pip package: pandas, scikit-learn, requests, TensorFlow. This is native Python execution.

JavaScript libraries can be wrapped and called from Python using Anvil’s JavaScript integration features. You’re not completely cut off from the JavaScript ecosystem when you need it.

The Uplink: The Secret Weapon

The Uplink is perhaps Anvil’s most unique feature. It lets you connect existing Python code running anywhere; your laptop, a Jupyter notebook, a GPU server to your web application.

# Your local Jupyter notebook or script
import anvil.server
anvil.server.connect("YOUR_UPLINK_KEY")

@anvil.server.callable
def run_ml_model(data):
  # Use your local GPU, existing code, trained models
  return model.predict(data)

When to Use This

Choose Anvil when:

Your team is Python-heavy but frontend-light
Building internal tools, dashboards, or admin panels
Rapidly prototyping data applications
Connecting Jupyter notebooks or data science code to web UIs
You value development speed over pixel-perfect custom UI
Creating CRUD applications where Python’s full ecosystem matters more than complex client-side interactions

The Bottom Line

The platform is part of a broader trend of Python expanding beyond its traditional backend role.

This article may not be perfect and I would appreciate any feedback!

Buy me a coffee: https://buymeacoffee.com/daisyauma

# Build an Interactive Sales Chart with Plotly

Daisy Auma — Thu, 25 Sep 2025 02:07:11 +0000

Static sales charts in Excel or spreadsheets do not let you explore your data dynamically. Want to zoom in on a specific month? Compare different products? You need to create new charts manually. In this tutorial, you will build an interactive sales chart in Python using Plotly that lets users hover for details, zoom into timeframes, and filter by product.

Prerequisites

Before we begin, make sure you have:

Python 3.7 or higher installed on your system. You will need two libraries: Plotly for creating interactive visualizations and Pandas for handling data. Install them using pip:

pip install plotly pandas

You can run this code in any Python environment: VS Code, Jupyter Notebook, PyCharm, or even the command line. The interactive charts will open in your default browser.

This tutorial assumes you are comfortable with basic Python syntax and have used Pandas DataFrames before. You should know how to load data and access columns. You do not need any prior experience with Plotly. We will be using Plotly Express, the high-level interface that makes creating charts simple and intuitive.

The Data

We will work with a year's worth of monthly sales data for three products: Laptops, Tablets, and Smartphones. The dataset contains revenue figures (in dollars) for each product across all 12 months of 2024.

I have hosted the data on GitHub (https://raw.githubusercontent.com/Daisy-Faith-Auma/plotly-sales-tutorial/refs/heads/main/sales_data.csv) for easy access. Let us load it:

import pandas as pd
import plotly.express as px

# Load the sales data
df = pd.read_csv('https://raw.githubusercontent.com/Daisy-Faith-Auma/plotly-sales-tutorial/refs/heads/main/sales_data.csv')

# Convert Month column to datetime for proper time-series plotting
df['Month'] = pd.to_datetime(df['Month'])

# Take a look at the data
print(df.head())

You should see a DataFrame with three columns: Month, Product, and Revenue. The data shows interesting patterns: Laptops have steady growth, Tablets show some seasonal variation, and Smartphones spike during the holiday season.

Step 1: Basic Line Chart

Let us create our first chart. Plotly Express makes this remarkably simple:

# Create a basic line chart
fig = px.line(df, x='Month', y='Revenue', title='Monthly Sales Revenue')
fig.show()

When you run this, a browser window opens showing your visualization. Try hovering over the line. You will see data points appear. You can also zoom by clicking and dragging, and pan by holding shift while dragging.

But there is a problem: this chart combines all three products into a single line, which is not useful for comparison. We are summing up all the revenue without distinguishing between Laptops, Tablets, and Smartphones.

Notice what Plotly provides: automatic axis labels, a title, grid lines, and basic interactivity.

Step 2: Add Hover Interactivity

Now let us separate our products and make the hover information more useful. We will tell Plotly to create a different line for each product:

# Create separate lines for each product
fig = px.line(df, 
              x='Month', 
              y='Revenue', 
              color='Product',
              title='Monthly Sales Revenue by Product')

fig.show()

Now you have three distinct lines, each in a different color. The legend appears automatically, and you can click on product names to show or hide specific lines.

Let us improve the hover tooltips to show properly formatted currency:

# Customize hover information
fig = px.line(df, 
              x='Month', 
              y='Revenue', 
              color='Product',
              title='Monthly Sales Revenue by Product',
              labels={'Revenue': 'Revenue ($)', 'Month': 'Month'})

# Format the hover template
fig.update_traces(hovertemplate='<b>%{fullData.name}</b><br>' +
                                'Month: %{x|%B %Y}<br>' +
                                'Revenue: $%{y:,.0f}<br>' +
                                '<extra></extra>')

fig.show()

Now when you hover, you see formatted information: the product name in bold, the full month name with year, and revenue formatted as currency with comma separators. The <extra></extra> removes the redundant trace name that appears by default.

Try hovering over different points. The formatting makes the data much more readable and professional.

Step 3: Multiple Products with Legend

Our chart already shows multiple products, but let us polish it further. We will improve the layout and make the legend more functional:

# Enhanced multi-product chart
fig = px.line(df, 
              x='Month', 
              y='Revenue', 
              color='Product',
              title='2024 Sales Performance Dashboard',
              labels={'Revenue': 'Revenue ($)', 'Month': 'Month'})

# Customize hover information
fig.update_traces(hovertemplate='<b>%{fullData.name}</b><br>' +
                                'Month: %{x|%B %Y}<br>' +
                                'Revenue: $%{y:,.0f}<br>' +
                                '<extra></extra>')

# Improve layout
fig.update_layout(
    hovermode='x unified',  # Show all products when hovering over a month
    legend=dict(
        title='Product Line',
        orientation='h',
        yanchor='bottom',
        y=1.02,
        xanchor='right',
        x=1
    )
)

fig.show()

Two key improvements here:

First, hovermode='x unified' shows data for all products simultaneously when you hover over a specific month. This makes comparisons easier. You can instantly see which product performed best in any given month.

Second, we have repositioned the legend horizontally above the chart. This saves vertical space and keeps all product information visible while you explore the data. Try clicking on legend items. You can show or hide products to focus on specific comparisons.

Step 4: Dropdown Product Selector

For the final enhancement, let's add a dropdown menu that lets users filter by product. This is particularly useful when presenting to stakeholders who want to focus on specific product lines:

# Create the base figure
fig = px.line(df, 
              x='Month', 
              y='Revenue', 
              color='Product',
              title='2024 Sales Performance Dashboard',
              labels={'Revenue': 'Revenue ($)', 'Month': 'Month'})

# Customize hover
fig.update_traces(hovertemplate='<b>%{fullData.name}</b><br>' +
                                'Month: %{x|%B %Y}<br>' +
                                'Revenue: $%{y:,.0f}<br>' +
                                '<extra></extra>')

# Add dropdown menu
fig.update_layout(
    updatemenus=[
        dict(
            buttons=list([
                dict(label="All Products",
                     method="update",
                     args=[{"visible": [True, True, True]}]),
                dict(label="Laptop",
                     method="update",
                     args=[{"visible": [True, False, False]}]),
                dict(label="Tablet",
                     method="update",
                     args=[{"visible": [False, True, False]}]),
                dict(label="Smartphone",
                     method="update",
                     args=[{"visible": [False, False, True]}])
            ]),
            direction="down",
            showactive=True,
            x=0.5,
            xanchor='center',
            y=1.15
        )
    ]
)

fig.show()

The dropdown menu appears at the top of your chart. Users can select "All Products" to see everything, or choose individual products for focused analysis. This transforms your chart into an interactive dashboard that serves multiple purposes.

Wrap-up

You have now built an interactive sales dashboard with hover tooltips showing formatted data, synchronized hovering across products, and a dropdown filter. These features make data exploration intuitive and engaging.

You can save your chart as a standalone HTML file with fig.write_html('sales_dashboard.html') for easy sharing. The HTML file can be opened in any browser without requiring Python or any additional software.

Try experimenting with your own sales data by replacing the CSV URL, or explore other chart types like bar charts and scatter plots using the same Plotly Express patterns you have learned here.

SearchGPT: OpenAI’s Latest Tool Set to Transform the Search Engine Landscape

Daisy Auma — Sun, 28 Jul 2024 23:58:58 +0000

SearchGPT is the latest AI search tool that has been announced by OpenAI. SearchGPT runs on GPT-4 AI models and is expected to be launched as a prototype for a given amount of time before its best features are incorporated into ChatGPT. Here are some of the key features to expect from SearchGPT.

SearchGPT will have real-time access to the internet, which will ensure that users have access to the most current information available. This puts the search engine above other AI models that do not have the feature.
SearchGPT is set to have a conversational interface that will allow users to interact with the tool more naturally and intuitively. Users will be able to ask follow-up questions and have context built up in the conversation. The search engine is expected to summarize and organize information for users.
The results provided by SearchGPT will have in-line attributions and links that will guide you to information sources, while allowing users to check out these links for more information. To improve this experience, OpenAI is collaborating with various publishers to enable them to manage how they appear in SearchGPT results.

What impact is SearchGPT likely to have on the search market?

It will be interesting to see how the current players react to OpenAI’s newest creation. While Google dominates the search market, SearchGPT is bound to make waves and attract some traffic when it is eventually rolled out. Similar products such as Perplexity are also likely to come up with a response to this competitive search engine.

Different Approach

SearchGPT is seeking to gain an edge by partnering with publishers such as News Corp and The Atlantic and actively involving them in the layout of search results. This is particularly significant as a direct competitor, Perplexity, is currently facing legal proceedings brought forth by publishers. SearchGPT’s collaboration with publishers therefore makes for an interesting game-changer.

How to access SearchGPT.

While it will be initially rolled out as a prototype, SearchGPT will only be made available to thousands of people for its trial period. There is currently no information on how long this test period is expected to last. To be able to sign up for the trial, you need to have a ChatGPT account. You can join the waitlist from here.

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Learning to Learn: A Tech Girl’s Guide to Staying Ahead

Daisy Auma — Sun, 26 May 2024 16:37:16 +0000

Let’s be honest, the tech world is a treadmill on fast-forward. By the time you master one programming language like Python, there’s a hot new framework like Django out there threatening to make it seem outdated. Cloud platforms shift from on-premise servers to behemoths like AWS, methodologies evolve from waterfall to agile, and the pressure to stay relevant feels constant. It’s enough to make any ambitious twenty-something like me want to throw in the towel.

But here’s the secret weapon I’ve discovered: it’s not about desperately cramming every new tech trend into your brain. It’s about mastering the art of learning to learn.

Yes, you read that right. In this ever-changing landscape, the most valuable skill you can develop is the ability to adapt and acquire new information efficiently. Here’s how I’m tackling this challenge:

Focus on Fundamentals: Before diving into the newest thing, solidify your grasp of core concepts. Think of these fundamentals as the building blocks of your tech knowledge. Understanding things like data structures (like arrays and linked lists) and algorithms (like sorting and searching) creates a strong foundation for future learning. You can check sites like Leetcode for the most sought after questions. These concepts are evergreen and apply across different programming languages and technologies. Sites like The Odin Project offer fantastic, interactive introductions to these core concepts, with comprehensive curriculums and hands-on projects that make learning engaging and practical.

Befriend Online Resources: The tech world is a goldmine of free and paid resources. From online courses like Coursera to informative blogs and YouTube channels, there’s something for everyone. Find learning styles that resonate with you — some people learn best by reading in-depth articles, while others prefer video tutorials or attending live webinars. There are fantastic resources for every budget. Sites like Coursera and edX offer affordable courses from top universities, while platforms like YouTube have a wealth of free, high-quality content from tech experts. For instance, Google offers a free course on its own developer platform, Google Developers, covering a wide range of topics from building web applications to machine learning.

Embrace the Power of Communities: Don’t underestimate the power of connecting with other tech enthusiasts. Online forums like Stack Overflow are great places to get help with specific coding problems, while meetups and conferences allow you to network with like-minded individuals. There are even dedicated online communities for women in tech, like Women Who Code, which offer mentorship, workshops, and a supportive network. You can also find tech communities on platforms like Meetup, where you can connect with local developers and attend events focused on specific technologies. Organizations like Women Techmakers and GDG provide resources and programs specifically geared towards women in technology.

Learn by Doing: The best way to solidify your knowledge is to apply it. Don’t be afraid to experiment with personal projects. Maybe you could build a simple website using HTML, CSS, and JavaScript, or develop a mobile app to automate a mundane task. There are also a plethora of open-source initiatives on platforms like GitHub where you can contribute to existing projects and learn from experienced developers. Contributing to open-source not only enhances your skills but also gives you valuable experience to showcase on your resume. You can find open-source projects on GitHub that cater to all skill levels, so don’t be afraid to jump in and start exploring!

Develop a Growth Mindset: Shift your perspective from fearing change to embracing it as an opportunity to grow. Celebrate challenges as learning experiences, and view mistakes as stepping stones to mastery. Instead of getting discouraged when you get stuck on a coding problem, reframe it as a puzzle to be solved. Approach new technologies with curiosity and a willingness to learn. There are countless books and online resources on developing a growth mindset, such as Carol Dweck’s influential book “Mindset: The New Psychology of Success.” You can also find articles and videos on the topic through a quick Google search.

Remember, learning to learn is a continuous journey. There will be days when you feel overwhelmed by the sheer volume of new information, but don’t let that discourage you. Embrace the constant evolution of tech, focus on the joy of discovery, and you’ll find yourself not just keeping up, but thriving in this dynamic field. Here are some additional tips to keep in mind:

Curate Your Learning: Don’t try to learn everything at once. Instead, identify your specific goals and tailor your learning resources accordingly. For example: Are you interested in becoming a web developer? Focus on learning front-end technologies like HTML, CSS, and JavaScript, along with a popular back-end framework like Node.js or Python.

Goodluck!

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Google Announces Major Updates to Gemini AI: Enhancing Capabilities and Expanding Access

Daisy Auma — Thu, 23 May 2024 21:23:19 +0000

In a series of announcements made in May 2024, Google unveiled substantial updates to its Gemini AI models, marking significant advancements in artificial intelligence technology. These updates include the enhancement of the Gemini 1.5 Pro model, the introduction of the Gemini 1.5 Flash model, and new features available through the Gemini API. These developments are designed to make the AI more capable, accessible, and useful across a variety of applications.

Enhancements to Gemini 1.5 Pro

One of the standout updates is the enhancement of the Gemini 1.5 Pro model, which now supports a context window of up to 1 million tokens. This is the longest context window available for any consumer chatbot, allowing the model to process and understand extensive amounts of text. This capability is particularly useful for tasks that involve long documents, detailed email threads, and comprehensive datasets. With this update, users can expect improved performance in generating coherent and contextually accurate responses even when dealing with large volumes of information.

The Gemini 1.5 Pro model has also received updates that improve its data analysis capabilities. Users can now upload various file types, including spreadsheets and documents, directly into the model for analysis. The AI can generate visualisations and charts from the data, making it a powerful tool for businesses and researchers who need to interpret and present complex information quickly and accurately.

Introduction of Gemini 1.5 Flash

In addition to the enhancements to the Gemini 1.5 Pro model, Google introduced the Gemini 1.5 Flash model. This new model is optimized for tasks that require rapid response times, making it ideal for applications where speed is critical. Despite being a smaller model, Gemini 1.5 Flash maintains high performance and accuracy, ensuring that users do not have to compromise on quality for the sake of speed.

New Features in the Gemini API

Developers have much to look forward to with the new features available through the Gemini API. One of the most notable additions is the first-ever 2 million token context window, which allows for even more complex data processing and analysis. This feature is particularly beneficial for applications that require the AI to handle extensive and intricate datasets, providing developers with more flexibility and power in their projects.

The Gemini API also includes new capabilities for native audio understanding, system instructions, and JSON mode. These features enhance the model's ability to interact with and process different types of data, broadening the scope of potential applications. Whether it's interpreting audio files, following detailed system instructions, or handling structured data in JSON format, the updated API provides the tools needed to leverage Gemini AI's full potential.

Expanding Practical Applications

The recent updates to Gemini AI are part of Google's broader strategy to integrate advanced AI functionalities into everyday tools. Users can now upload files via Google Drive or directly from their devices, enabling the AI to perform in-depth analysis and provide insights on dense documents. This feature is particularly useful for professionals who need to analyse reports, research papers, and other extensive documents quickly and efficiently.

Moreover, the Gemini 1.5 Pro model's multimodal capabilities have been enhanced. This means the AI is better equipped to understand and interact with images and audio, making it a versatile tool for a wide range of applications. Whether it's analysing visual data or processing spoken language, the updated model provides a more comprehensive and intuitive user experience.

Availability and Future Prospects

Both the Gemini 1.5 Pro and Gemini 1.5 Flash models are available in preview across more than 200 countries and territories as of May 14, 2024. These models will be generally available in June 2024, allowing a wider audience to benefit from their advanced capabilities. Developers can start using these models by obtaining an API key through the Google AI Studio and exploring the Gemini API Cookbook.

The continuous evolution of Gemini AI reflects Google's commitment to advancing AI technology and making it more accessible. By enhancing the capabilities of its models and expanding their practical applications, Google is paving the way for a future where AI plays a central role in various aspects of daily life and professional work.

For more detailed updates and information, you can visit Google's official blog posts on the recent announcements:

"Community Communication: Slack vs. Discord - Deciding the Ideal Platform"

Daisy Auma — Mon, 08 Apr 2024 19:15:25 +0000

In the ever-evolving landscape of online communities, selecting the right platform is crucial to fostering effective communication and engagement. Two prominent players in this arena, Slack and Discord, offer unique features catering to different needs. To make an informed decision, it’s essential to weigh the pros and cons of each platform based on your community’s goals and characteristics.

Purpose and Audience:
— Slack: Positioned as a professional collaboration tool, Slack is ideal for workplace communication. It exudes a polished and business-oriented environment, making it suitable for teams aiming for a more formal setting.
— Discord: Originally designed for gamers, Discord has transitioned into a versatile platform for communities of all types. Its casual and gamer-friendly vibe appeals to a broader audience beyond traditional workplaces.
Features:
— Slack: Renowned for its extensive integrations with productivity tools, Slack excels in supporting work-related tasks. Its advanced features, including file sharing and project management, make it a powerhouse for professional teams.
— Discord: Offering voice channels, video calls, and screen sharing, Discord shines in real-time communication. It prioritizes community engagement with features like emojis, reactions, and customizable roles, enhancing the overall user experience.
Cost:
— Slack: While a free version is available, many advanced features are reserved for paid plans, with pricing based on the number of users. This makes Slack a potentially more expensive option for larger communities.
— Discord: The majority of Discord’s features are accessible for free. While there is a paid subscription called Nitro with additional perks, it’s not essential for basic community functionality, making Discord a cost-effective choice for many.
User Interface:
— Slack: Boasting a clean and professional interface, Slack follows a more traditional chat and channel structure. Its design caters to those seeking a formal and structured communication environment.
— Discord: Featuring a modern and gamer-friendly interface, Discord emphasizes simplicity and customization. The interface is user-friendly and appeals to a younger and more diverse audience.
Customization:
— Slack: Known for its high level of customization, Slack allows users to tailor channels, notifications, and integrations to suit diverse business needs. This makes it a versatile platform for teams with specific requirements.
— Discord: Offers extensive server customization, enabling the creation of different channels, roles, and permissions. This flexibility caters to a wide range of community types and allows for a tailored user experience.
Moderation and Security:
— Slack: Equipped with robust security features suitable for professional use, Slack provides adequate moderation tools. However, its moderation capabilities may not be as comprehensive as Discord’s, particularly for larger communities.
— Discord: Renowned for its powerful moderation tools, Discord allows for precise control over roles and permissions. While its moderation features are strong, some may argue that it might not be as secure as Slack, especially in a professional context.
Community Size:
— Slack: Better suited for smaller to medium-sized groups due to potential scalability challenges and pricing structures that may become less favourable for larger communities.
— Discord: Built with scalability in mind, Discord excels in hosting larger communities and can comfortably accommodate thousands of members within a single server.

To conclude, the choice between Slack and Discord boils down to the specific needs and characteristics of your community. If you’re seeking a formal, professional environment with a focus on workplace collaboration, Slack may be the preferred option. On the other hand, if your community is more casual, diverse, and large, Discord offers a feature-rich and cost-effective solution for fostering engagement. Carefully evaluating these factors will help you choose the platform that aligns best with your community’s goals and culture.

Tell me which one you prefer! Or better still, do have better options?

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