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Last night, Trump gave the longest, most incredible State of the Union address in American history — almost two hours, a record, nobody’s ever done anything like it.

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The Problem with “Just Migrate”

Most organizations are sitting on MySQL deployments they can’t easily change — a mix of community editions, managed cloud services, and legacy versions. Teams want RAG pipelines and natural language querying, but adding AI capabilities typically means schema migrations, new vector database infrastructure, dual-write synchronization headaches, and AI logic sprawled across every application layer. The operational cost is real, and the governance risk is worse.
ProxySQL v4.0 takes a different approach: don’t touch your database at all.

The Transparent AI Layer

The core thesis is elegant — put the intelligence at the proxy layer, not in the database or the application. ProxySQL already sits between every client and every MySQL backend, which makes it a natural choke point for centralized governance, auth, auditing, and now AI capabilities. No connection string changes, no schema migrations, no new infrastructure your DBA team has to babysit.
The comparison with app-layer integration is stark. Where app-layer AI means fragmented governance across every service, schema changes, and multiple network hops, the ProxySQL AI layer provides unified query rules, zero schema changes, and a single enforced access path.

What’s Actually in v4.0

The MCP (Model Context Protocol) server is the centerpiece. Running on port 6071 over HTTPS with bearer token auth, it exposes 30+ tools that any MCP-compatible agent — Claude Code, GitHub Copilot, Cursor, Warp — can discover and call via standard JSON-RPC. Tools span schema discovery (list_schemas, list_tables, list_columns), safe read-only execution (run_sql_readonly, explain_sql), and full RAG search capabilities (rag.search_fts, rag.search_vector, rag.search_hybrid).

All MCP requests pass through MCP Query Rules — analogous to ProxySQL’s existing mysql_query_rules — which can allow, block, rewrite, or timeout requests before they ever reach MySQL. This is where you enforce read-only access, prevent data exfiltration, and audit everything agents are doing.

The Autodiscovery system (discovery.run_static) runs a two-phase process: static schema harvesting followed by LLM-enriched metadata including summaries and domain analysis. Everything lands in a local SQLite catalog (mcp_catalog.db) that agents can then search semantically via llm.search.

The NL2SQL workflow builds on this: agents search the catalog for relevant schemas, synthesize or reuse SQL templates, execute safely via run_sql_readonly, and optionally store successful query patterns as templates for future reuse — a continuous learning loop that improves accuracy over time.

What’s Still Coming

The presentation is upfront that this is a prototype showcase. Still on the roadmap: automatic embedding generation (with local or external model options), real-time indexing via MySQL replication/binlog without touching source tables, DISTANCE() SQL semantics for vector search on AI-blind MySQL backends, and additional MCP endpoints for config management, cache inspection, and observability.

The Bottom Line

The proxy layer argument is compelling: it’s operationally mature, protocol-aware, already deployed in front of critical databases, and has a battle-tested policy engine. Adding AI there rather than in application code means one place to enforce rules, one place to audit, and zero changes to the workloads that depend on MySQL stability.

The Code is on the v4.0 branch at github.com/sysown/proxysql, and the Generative AI documentation section at sysown.github.io/proxysql covers the new features.

Download the Bringing GenAI to every MySQL Instance Presentation given by
René Cannaò at preFOSDEM MySQL Belgian Days 2026 in Brussels.

The post Bringing GenAI to Every MySQL Instance: ProxySQL v4.0 appeared first on ProxySQL.

Planet MySQL

Los Angeles County has sued Roblox for "unfair and deceptive business practices," claiming the platform’s moderation and age-verification systems are inadequate. "Roblox portrays its platform as a safe and appropriate place for children to play," the complaint states. "In reality, and as Roblox well knows, the design of its platform makes children easy prey for pedophiles." 

Representatives accused Roblox of failing to implement adequate platform safety features to prevent child endangerment. "Specifically, Roblox has not effectively moderated game content or enforced age-appropriate restrictions and warnings established by the creators, allowing the predatory and inappropriate language and interactions between users to persist," the County stated. It also said the platform failed to disclose any danger to children, including sexual content and the risk of predators. 

Roblox rejected the allegations, saying the platform was built around safety. "We have advanced safeguards that monitor our platform for harmful content and communications, and users cannot send or receive images via chat, avoiding one of the most prevalent opportunities for misuse seen elsewhere online," the company told the AFP. 

The LA county complaint is the latest in a string of lawsuits from US regions including Florida, Texas and Kentucky. The Attorney General in Louisiana also accused the company of having a "lack of safety protocols" that endanger the safety of children in favor of "growth, revenue and profits." That state’s lawsuit cited a specific example of a subject arrested last year that used voice-altering tech to mimic a younger feminine voice to lure and sexually exploit young players.

Roblox has said that it has about 144 million daily active users around the world, with over 40 percent of them under the age of 13. However, it has faced repeated accusations that it doesn’t do enough to protect young players. In 2024, Roblox banned players under 13 from accessing some types of in-game content and restricted their ability to message with other players outside of specific games. Last year, the company also began asking tens of millions of children to verify their age with a selfie. 

This article originally appeared on Engadget at https://www.engadget.com/gaming/la-county-lawsuit-accuses-roblox-of-exposing-children-to-grooming-and-exploitation-124523028.html?src=rssEngadget

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The Patriot Post — Current Articles

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Artificial intelligence (AI) is having some unexpected consequences, and they’re still shaking themselves out as the impact spreads.  Jeff Childers reports:

The Financial Times reported that KPMG— one of the world’s Big Four accounting firms— bullied its own auditor into a 14% fee cut. Their argument was elegant in its simplicity: if your AI is doing the work, your people shouldn’t be billing for it. KPMG’s hapless auditor, Grant Thornton, tried to kick but quickly folded like a WalMart lawn chair, dropping its auditing fee from $416,000 to $357,000.

And now every CFO on Earth is reaching for a calculator.

Here’s the dark comedy. Grant Thornton’s UK audit leader bragged in a December blog post that AI was making their work “faster and smarter.” KPMG took note, and immediately asked why it was still paying the slower-and-dumber price. This is why lawyers tell their clients to stop posting on social media. The marketing department just became the billing department’s worst enemy.

As a lawyer who bills by the hour —and I suspect many of you work in professions that do the same— I can assure everyone that this story sent a terrifying chill racing through the spines of every white-collar professional who’s been out there cheerfully babbling about AI adoption at industry conferences.

The billable hour has survived the fax machine, personal computers, email, electronic filing, spreadsheets, and the entire internet. The billable hour has the survival instincts of a post-apocalyptic cockroach and the institutional momentum of a Senate tradition. But AI might finally be the dinosaur killer, and KPMG just showed everyone exactly how the asteroid hits: your client reads your own press release and demands a discount.

. . .

The billable hour won’t die overnight. But it just got a terminal diagnosis. Every professional services firm that’s spent the last two years bragging about AI efficiency is now staring at the same problem: you can’t brag to your clients you’re faster and also charge them for the same number of hours. As they say at KPMG, it doesn’t add up. Somewhere in a law firm right now, a partner is quietly deleting a LinkedIn post about how AI is “transforming their practice.” Smart move.

There’s more at the link.

It’s not just company-to-company billing, either.  How many professional services do we, as consumers, use, and get charged by the hour?

• Service your car – hourly charge for the mechanic.
• File your taxes – hourly charge by the tax preparer.
• Domestic services such as plumber, electrician, etc. – hourly rate for labor, plus parts, etc.

How many of these services will be affected by AI?  Quite a few, I’m guessing.  A mechanic can use AI to finish his repairs more quickly, as the software guides him through the process on an unfamiliar vehicle.  The tax preparer is almost certainly going to use AI to do his job, so the number of hours they spend on the job should go down – and so should your bill.  Even domestic service calls should be quicker and easier if the technician or professional can look up a reference to what he’s doing, possibly on equipment on which he’s never been trained, and do the job faster and better.

I think AI can be considered the monkey wrench that just got tossed into the professional billing pool.  This should be interesting . . .

Peter

Bayou Renaissance Man

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composer require zappzerapp/laravel-ingest

# Publish config & migrations
php artisan vendor:publish --provider="LaravelIngest\IngestServiceProvider"

# Create tables
php artisan migrate

namespace App\Ingest;

use App\Models\User;
use LaravelIngest\Contracts\IngestDefinition;
use LaravelIngest\IngestConfig;
use LaravelIngest\Enums\SourceType;
use LaravelIngest\Enums\DuplicateStrategy;

class UserImporter implements IngestDefinition
{
    public function getConfig(): IngestConfig
    {
        return IngestConfig::for(User::class)
            ->fromSource(SourceType::UPLOAD)
            ->keyedBy('email') // Identify records by email
            ->onDuplicate(DuplicateStrategy::UPDATE) // Update if exists
            
            // Map CSV columns to DB attributes
            ->map('Full Name', 'name')
            ->map(['E-Mail', 'Email Address'], 'email') // Supports aliases
            
            // Handle Relationships automatically
            ->relate('Role', 'role', Role::class, 'slug', createIfMissing: true)
            
            // Validate rows before processing
            ->validate([
                'email' => 'required|email',
                'Full Name' => 'required|string|min:3'
            ]);
    }
}

use LaravelIngest\IngestServiceProvider;

public function register(): void
{
    $this->app->tag([UserImporter::class], IngestServiceProvider::INGEST_DEFINITION_TAG);
}

php artisan ingest:run user-importer --file=users.csv

curl -X POST \
  -H "Authorization: Bearer <token>" \
  -F "file=@users.csv" \
  https://your-app.com/api/v1/ingest/upload/user-importer

# Clone the demo
git clone https://github.com/zappzerapp/Laravel-Ingest-Demo.git
cd Laravel-Ingest-Demo

# Start and benchmark
docker compose up -d
docker compose exec app php artisan benchmark:ingest

$schedule->command('model:prune', [
    '--model' => [LaravelIngest\Models\IngestRow::class],
])->daily();

IngestConfig::for(Product::class)
    // Sources: UPLOAD, FILESYSTEM, URL, FTP, SFTP
    ->fromSource(SourceType::FTP, ['disk' => 'erp', 'path' => 'daily.csv'])
    
    // Performance
    ->setChunkSize(1000)
    ->atomic() // Wrap chunks in transactions
    
    // Logic
    ->keyedBy('sku')
    ->onDuplicate(DuplicateStrategy::UPDATE_IF_NEWER)
    ->compareTimestamp('last_modified_at', 'updated_at')
    
    // Transformation
    ->mapAndTransform('price_cents', 'price', fn($val) => $val / 100)
    ->resolveModelUsing(fn($row) => $row['type'] === 'digital' ? DigitalProduct::class : Product::class);

# Start Docker
composer docker:up

# Run Tests
composer docker:test

# Check Coverage
composer docker:coverage

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