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TECH & AI
Join the Gemma 4 Challenge: $3,000 prize pool for TEN winners!
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Local AI is having a moment, and we want you to be part of it!

Running through May 24, the Gemma 4 Challenge invites you to explore open models. With the release of Gemma 4, Google’s most capable open model family yet, we now have access to native multimodal capabilities, advanced reasoning, a 128K context window, and models that range from running on a Raspberry Pi, to phones, to powering large-scale deployments.

Whether you love to build or love to write, there’s a prompt for you and a $3,000 prize pool up for grabs!

Read on to learn more.

Our Prompts

Build With Gemma 4

Your mandate is to build something useful or creative with any Gemma 4 model. The scope is wide open — you can build anything from an IoT integration to a multimodal tool to a long-context reasoning app. What matters is that Gemma 4 is doing real work at the heart of your project. 

Build With Gemma 4 Submission Template

 

The most compelling submissions will make a clear case for why you chose the model you did and what that model unlocked.

Write About Gemma 4

Your mandate is to publish a post about Gemma 4 that educates, inspires, or sparks curiosity. There’s no single right format — what matters is that your post offers something genuine and useful to the community.

Not sure what to write about? Here are some ideas:

How-to guide: Walk through setting up and running a Gemma 4 model locally, fine-tuning it for a specific task, or integrating it into a real project

Comparison piece: Break down the three Gemma 4 model variants and help readers decide which one is right for their use case

Personal essay or opinion piece: Share your experience building with Gemma 4, or make a case for something — what does a model this capable running locally mean for the future of AI?

Deep technical breakdown: Explore a specific capability like multimodal input, the 128K context window, or reasoning mode

 

Write About Gemma 4 Submission Template

 

Need inspiration? Check out how Google’s own team fine-tuned Gemma 4 with Cloud Run Jobs. That’s the kind of hands-on, shareable knowledge is exactly what we’re looking for.

Note: If you are primarily showing off a project, please submit to the Build with Gemma 4 prompt instead! Each submission is only allowed to be eligible for one of the two categories.

Which Model Is Right for You? 🤔

Gemma 4 model family spans three distinct architectures tailored for specific hardware requirements:

Small Sizes: 2B and 4B effective parameter models built for ultra-mobile, edge, and browser deployment (e.g., Pixel).

Dense: A powerful 31B parameter dense model that bridges the gap between server-grade performance and local execution.

Mixture-of-Experts: A highly efficient 26B MoE model designed for high-throughput, advanced reasoning.

No matter which you choose, judges will be looking for intentional model selection — show us why your model was the right tool for the job.

Prizes 🏆

Five winners from the “Build with Gemma 4” prompt will receive:

$500 USD cash prize

DEV++ Membership
Exclusive DEV Badge

Five winners from the “Write About Gemma 4” prompt will receive:

$100 USD cash prize

DEV++ Membership
Exclusive DEV Badge

All participants with a valid submission will receive a completion badge on their DEV profile.

Judging Criteria

Build With Gemma 4 submissions will be evaluated for:

Intentional and effective use of the chosen Gemma 4 model
Technical implementation and code quality
Creativity and originality
Usability and user experience

Write About Gemma 4 submissions will be evaluated for:

Clarity and depth of explanation
Originality of perspective or insight
Practical value to the community
Quality of writing

Getting Started With Gemma 4 🚀

Gemma 4 is open — you have several ways to get started, including completely free options:

Gemini API via Google AI Studio: Access Gemma 4 through the Gemini API.

Run locally (free, no credit card required): Download any Gemma 4 model directly from Hugging Face or Kaggle. The E2B model runs on high-end phones — or even a Raspberry Pi 5.

OpenRouter (free tier available): Access Gemma 4 31B via OpenRouter’s free tier — no credit card required.

Important Dates

May 6: Gemma 4 Challenge begins!

May 24: Submissions due at 11:59 PM PDT

June 4: Winners Announced

We can’t wait to see what you build and write! Questions about the challenge? Drop them in the comments below.

Good luck and happy coding!



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TECH & AI
Stop Letting AI Write Your Database Migrations
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The era of “just ask the LLM” has made us remarkably productive, but it has also made us dangerously comfortable. We are currently witnessing a shift where developers are offloading critical infrastructure decisions to generative models. While having an AI suggest a React component or a regex pattern is relatively low-stakes, letting it dictate your database schema transitions is playing with fire.

The problem isn’t that AI is “bad” at SQL; it’s that AI lacks context. It doesn’t know your traffic patterns, it doesn’t understand your locking mechanisms, and it certainly doesn’t care if your production environment goes dark at 3:00 AM because of a table lock that lasted ten minutes too long.

The Illusion of “It Works”

When you ask an AI to generate a migration — say, adding a non-nullable column with a default value to a table with five million rows — the code it gives you will likely be syntactically perfect. You run it in your local environment with fifty rows of seed data, and it finishes in milliseconds.

The issue arises when that same script hits a production environment.

The Before (AI-Generated Standard):

–Generated by AI: Simple, clean, and potentially catastrophic
ALTER TABLE orders ADD COLUMN status_code VARCHAR(255) NOT NULL DEFAULT ‘pending’;

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On a massive table, this operation can trigger a full table rewrite. In PostgreSQL, for instance, versions prior to 11 would lock the entire table while writing that default value to every single row. If your application is high-traffic, your API starts throwing 504 Gateway Timeouts because every connection is waiting for that lock to release.

The After (Human-Engineered Safe Migration):

— Step 1: Add the column as nullable first (instant operation)
ALTER TABLE orders ADD COLUMN status_code VARCHAR(255);

— Step 2: Set the default for future rows
ALTER TABLE orders ALTER COLUMN status_code SET DEFAULT ‘pending’;

— Step 3: Update existing rows in small batches to avoid long-held locks
— (This would typically be handled via a background job or scripted loop)

— Step 4: Add the NOT NULL constraint after data is populated
ALTER TABLE orders ALTER COLUMN status_code SET NOT NULL;

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When “Convenience” Costs Millions

We don’t have to look far to see where automated or poorly planned migrations caused genuine wreckage. One of the most famous examples of migration-related downtime was the 2017 GitLab outage. While that was a human error during a manual intervention, it highlights the fragility of database state.

More recently, several tech startups have reported “silent” data corruption when AI-generated migrations suggested changing column types (like INT to BIGINT) without account for how the underlying ORM would handle the transition during a rolling deployment. If your AI-written migration drops a column before the new version of your application code is fully deployed across all nodes, your “After” state is a series of 500 errors.

The Context Gap

AI models operate on patterns, not performance profiles. They don’t know:

The Lock Hierarchy: Will this ALTER TABLE block SELECT queries?
Replication Lag: Will this massive update stall your read replicas?
Deployment Strategy: Is this a blue-green deployment or a rolling restart?

A migration is not just a script; it is a bridge between two states of a living system.

Moving Forward: Use AI as a Drafter, Not an Architect

I am not suggesting we go back to the Stone Age. AI is a phenomenal tool for boilerplate. If you need to scaffold a complex set of join tables, let the AI write the initial DDL.

But the moment that code touches a migration file, the “AI” portion of the task ends. You must take over as the engineer. You need to verify the locks, check the execution plan, and most importantly, simulate the migration against a production-sized data set.

If you’re interested in seeing how I’ve handled high-performance, SEO-optimized database architectures without relying on “magic” scripts, you can check out my project documentation on my GitHub or follow my updates on LinkedIn.

The database is the heart of your application. Don’t let a probabilistic model perform open-heart surgery on it.

You can find me across the web here:



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TECH & AI
Announcing LightningChart JS 8.3: sunburst charts, multithreading, new dashboards
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What is LightningChart JS?

LightningChart JS is a data visualization library for building high-end JavaScript charting applications in complex scenarios that demand high-performance data processing.

Introducing the Sunburst Chart

LightningChart JS introduces the sunburst chart, used for displaying hierarchical data as a series of aligned rings. LightningChart JS now makes it easier to explore part-to-whole relationships simultaneously across multiple levels.

Open this example online

Multithreading Performance Optimizations

Just when you thought that LightningChart JS could not get any faster, version 8.3 introduces Multi-threading Performance Optimizations as a built-in functionality to make your charts even snappier.

By using multi-threading, we were able to load an absolutely massive data visualization with 40 channels and 10 million data points each in just above 1 second. And you can zoom in/out/reload data without any lag.

JS Drill-Down Nord Pool Map Dashboard

Introducing the JavaScript Drill-Down Nord Pool Map Dashboard, which visualizes day-ahead market data from the Nord Pool energy exchange. It provides a geographic overview of the next day’s planned power generation across the Nordic-Baltic region.

Open this example online

Sunburst Chart Dashboard

Introducing the Sunburst chart dashboard, which combines a regional map with embedded sparkline-style charts, a sortable summary table with status indicators, a dual-axis chart pairing stacked columns with an overlaid line series on a shared time axis, and a row of toggle chips for filtering.

Open this example online

Other Improvements

LightningChart JS 8.3 comes with several more improvements, including:

Built-in axis zebra stripes
Out-of-the-box text color contrast improvements
Built-in chart opacity filter
Rectangle series histogram usability improvements

To see the full list of changes and improvements, visit the changelog, or read the official release note.

Get started with LightningChart JS 8.3 today

Start your 30-day free trial



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