DEV Community: TildAlice

GitHub's Metered Copilot: Predictability Dies

TildAlice — Sat, 20 Jun 2026 21:02:14 +0000

When the Meter Runs, Trust Runs Out

GitHub officially moved Copilot to usage-based billing on June 1, 2026, replacing flat subscriptions with AI Credits consumed per token. The backlash was immediate and brutal: developers reported burning through a month's credits in hours, with one Pro+ user ($39/month) watching 8% of their 7,000-credit quota vanish in two hours of work. Another developer made a single refactoring request and burned $6. The core issue isn't the price table—it's that predictable consumption just died.

The new model works like this: code completions remain unlimited, but chat and agentic features now drain monthly AI Credit allotments based on token usage (input, output, cached). Pro costs $10/month with $15 in credits; Pro+ is $39 with $70; the new Max tier is $100 with $200. Business and Enterprise get pooled credits—1,900 and 3,900 per user respectively, with promotional bumps to 3,000 and 7,000 through September 1. One AI Credit equals one cent.

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Git Stash vs Worktree: 4 Patterns for Context Switching

TildAlice — Sat, 20 Jun 2026 15:05:05 +0000

The Problem: Stash Breaks on Uncommitted Index State

You're mid-refactor, three files staged, when a production bug lands in Slack. git stash sounds perfect — until you discover it didn't save your staged/unstaged distinction, or worse, it mangles conflicts when you pop.

Here's what actually happens when you stash with mixed index state:

# File: api/middleware.py (staged)
class AuthMiddleware:
    def __init__(self, app):
        self.app = app
        self.rate_limiter = None  # Half-finished feature

# File: api/handlers.py (unstaged, working changes)
async def handle_request(req):
    # Debugging print you forgot to remove
    print(f"Request: {req.path}")
    return await process(req)

Run git stash, fix the bug, then git stash pop. Your staged changes are now unstaged. The distinction you carefully maintained — "this is ready, this isn't" — vanished.

I'm not entirely sure why Git's default stash behavior merges staged and unstaged into one blob when git stash --keep-index exists, but in practice, most developers just use git stash and lose that information.

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One Hardware Wallet for 100+ Chains: My OneKey Setup

TildAlice — Sat, 20 Jun 2026 00:02:59 +0000

Credit-card sized, four physical buttons, a small but crisp OLED display.

I've been juggling crypto across a dozen chains for years, and the wallet fragmentation was driving me insane. Then I consolidated everything onto a single OneKey device. Here's what I learned.

Why Multi-Chain Support Actually Matters

If you're only holding Bitcoin, any hardware wallet works fine. But the moment you start exploring DeFi on Arbitrum, bridging to Solana, or picking up tokens on Avalanche, you hit a wall. Most hardware wallets were designed for a simpler era.

Trezor, for instance, supports around 20+ blockchains. That sounds decent until you realize the crypto ecosystem now spans hundreds of networks. Every new L2, every alt-L1 with interesting yields—you're constantly checking compatibility lists.

OneKey took a different approach: support everything. Their devices handle 100+ chains and over 30,000 tokens natively. Bitcoin, Ethereum, Solana, Cosmos chains, Polkadot parachains, the entire EVM universe—all from one seed phrase, one device.

The Hardware Lineup: Which OneKey to Choose

OneKey offers three main hardware options, each targeting different users:

Model	Price	Key Features	Best For

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SpaceX's $75B IPO: The AI Bet Hidden in the Rocket

TildAlice — Fri, 19 Jun 2026 21:02:19 +0000

The Real Story Isn't Starlink

SpaceX closed the largest IPO in history on June 12, raising $75 billion at $135 per share and a $1.77 trillion valuation. The stock jumped 19% on day one. Wall Street celebrated the rocket company going public. But here's what most missed: this isn't a space play anymore — it's an AI infrastructure bet dressed up in rocket fuel.

Look at where the money actually goes. According to the S-1 filing breakdown, xAI — the AI division SpaceX acquired in February 2026 — is bleeding $2.5 billion per quarter on data center buildout. That's $10 billion annually. The entire $75 billion raise? A significant chunk funds AI compute infrastructure and Starlink expansion, not rockets.

Starlink contributes 61% of revenue at $11.4 billion and runs 39% operating margins — it's the only profitable segment. Launch services brought in $3.8 billion. xAI? $3.2 billion in revenue, but operating at a massive loss. The company posted a $4.9 billion net loss in 2025 despite $18.7 billion in revenue, up 33% year-over-year.

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MLflow Quickstart 2026: Track Your First Experiment in 10 Minutes

TildAlice — Fri, 19 Jun 2026 15:05:50 +0000

MLflow Tracking in 90 Seconds

Here's the entire workflow. Install MLflow, log a single training run, and view it in the UI:

# pip install mlflow==2.11.3
import mlflow
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

X, y = load_iris(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

mlflow.set_experiment("iris-quickstart")

with mlflow.start_run():
    n_est = 100
    max_d = 5

    clf = RandomForestClassifier(n_estimators=n_est, max_depth=max_d, random_state=42)
    clf.fit(X_train, y_train)

    preds = clf.predict(X_test)
    acc = accuracy_score(y_test, preds)

    mlflow.log_param("n_estimators", n_est)
    mlflow.log_param("max_depth", max_d)
    mlflow.log_metric("accuracy", acc)
    mlflow.sklearn.log_model(clf, "model")

    print(f"Logged run with accuracy: {acc:.3f}")

Run mlflow ui in your terminal, open http://localhost:5000, and you'll see your experiment with params, metrics, and the saved model artifact.

That's it. The entire MLflow tracking API distills down to three calls: log_param(), log_metric(), log_model(). Everything else is just organization.

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The Versailles MOU: What the US-Iran Deal Actually Achieves

TildAlice — Thu, 18 Jun 2026 21:03:36 +0000

A Two-Page Agreement After Four Months of War

The optics were deliberately chosen: President Trump signing a peace agreement at the Palace of Versailles, echoing Woodrow Wilson's 1919 treaty that ended World War I. But strip away the historical staging, and what we actually have is a 14-point memorandum of understanding that runs less than two pages—compared to the 159-page JCPOA it's meant to replace.

The core question isn't whether this deal is better than Obama's. It's whether it achieves anything Obama's didn't, given that we bombed Iran for nearly four months to get here.

Photo by DMV Photojournalism on Pexels

What the MOU Actually Contains

Let's be precise about what was signed. The memorandum commits Iran to:

Reopen the Strait of Hormuz (toll-free for 60 days only)
Downblend its stockpile of 60% enriched uranium under IAEA supervision
Cease military operations, including in Lebanon
Reaffirm it "shall not procure or develop nuclear weapons"

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Warsh Drops the Dot Plot: What It Actually Signals

TildAlice — Wed, 17 Jun 2026 21:02:48 +0000

The Fed Chair Who Won't Tell You Where Rates Are Going

Kevin Warsh just did something no Federal Reserve chair has done in two decades: he refused to participate in the dot plot, the Fed's quarterly projections of where interest rates will be in the future. At his first FOMC meeting on June 17, 2026, while nine of the 18 voting members signaled rate hikes are coming, Warsh abstained entirely.

This isn't a technicality. It's a statement about how the Fed should operate — and it reveals more about where monetary policy is headed than any dot would.

The conventional wisdom is that the dot plot provides transparency. Markets get a peek at what each Fed official thinks will happen, extrapolate the median, and price accordingly. But Warsh's refusal exposes what practitioners have known for years: the dot plot is fiction dressed up as forecasting. It's a quarterly ritual of guessing games that the Fed itself walks back within weeks.

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PySpark vs Dask vs Polars: 1TB Cloud Cost Breakdown

TildAlice — Wed, 17 Jun 2026 15:04:50 +0000

The $247 Cloud Bill That Made Me Question Everything

I ran the same 1TB data aggregation job on AWS three times — once with PySpark, once with Dask, and once with Polars. The total cloud compute cost across all three runs was $247. The speed difference was 18x. The winner wasn't what I expected.

Most benchmark posts compare these frameworks on synthetic data or convenient datasets that fit in RAM. I wanted to know what happens when you're processing a real 1TB CSV dump of server logs — the kind where you're burning through EC2 credits and questioning your career choices. This isn't about which framework is "better." It's about which one costs less when you're paying by the hour.

Photo by Alican Helik on Pexels

The Dataset and The Problem

I used a 1TB anonymized server access log dataset (think nginx combined logs, but bigger). The task: compute daily active users, aggregate request counts by endpoint, and calculate 95th percentile response times per day. Standard analytics workload.

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UK Social Media Ban: A 70% Failure Rate Awaits

TildAlice — Tue, 16 Jun 2026 21:02:46 +0000

The Policy That Sounds Better Than It Works

The UK just announced the most aggressive social media restriction in Western history. Prime Minister Keir Starmer's government will ban under-16s from using TikTok, Instagram, YouTube, Snapchat, Facebook, and X, with enforcement expected by spring 2027. The stated goal is to "give kids their childhood back."

I think this is fundamentally misguided—not because protecting children online is wrong, but because the mechanism is destined to fail while creating surveillance infrastructure that affects everyone.

Photo by Boris Hamer on Pexels

Australia Already Ran This Experiment

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$111B Paramount-WBD Merger: The Streaming Bundle Returns

TildAlice — Mon, 15 Jun 2026 21:02:16 +0000

The Streaming Wars Just Ended — By Going Backwards

The Justice Department approved Paramount's $111 billion acquisition of Warner Bros. Discovery on June 12, 2026, creating the second-largest media conglomerate in the world. The deal combines Paramount+, HBO Max, CNN, TBS, the Warner Bros. and Paramount film studios, and IP ranging from Game of Thrones to SpongeBob SquarePants. The DOJ said it "saw no evidence the merger would harm consumers." That conclusion is technically correct and strategically blind.

This isn't a merger. It's an admission of defeat — by the entire streaming-first business model that Hollywood spent the last decade chasing. Paramount and WBD are consolidating not because they discovered synergies, but because neither could survive alone. The irony is that the solution — bundling content under fewer, larger platforms — is exactly the cable TV model the industry claimed to be disrupting.

The deal reveals three uncomfortable truths about the streaming era: scale is non-negotiable, content spend is unsustainable, and consumers never actually wanted 17 different apps.

Why This Merger Was Inevitable

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SAM Zero-Shot Medical Segmentation: 14% mIoU vs CNN 68%

TildAlice — Mon, 15 Jun 2026 15:05:11 +0000

SAM's Medical Image Problem

Segment Anything Model (SAM) works beautifully on everyday photos. Point at a cat, it segments the cat. Point at a building, it segments the building. Point at a lung nodule in a CT scan, and it segments... roughly half the nodule plus some random rib fragments.

I tested SAM's zero-shot performance on three medical imaging datasets: chest X-rays (pneumonia detection), retinal fundus images (diabetic retinopathy lesions), and brain MRI (tumor segmentation). Against a basic ResNet50-UNet with transfer learning from ImageNet, SAM lost every single matchup. The average mIoU gap was 54 percentage points.

This surprised me. SAM was trained on 11 million images and 1.1 billion masks. The model has seen more visual data than most researchers will encounter in their careers. Yet a CNN pre-trained on everyday photos and fine-tuned for 20 epochs on 200 medical images consistently outperformed it.

Photo by Furkan İnce on Pexels

Why Foundation Models Struggle on Medical Data

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The Knicks' 53-Year Wait: What Championships Reveal About Patience

TildAlice — Sun, 14 Jun 2026 21:03:03 +0000

Why This Title Matters More Than Basketball

The New York Knicks clinched their first NBA championship since 1973 on Saturday night, defeating the San Antonio Spurs 94-90 in Game 5. Jalen Brunson scored 45 points in the closeout game, earning unanimous Finals MVP honors. What strikes me about this story isn't the basketball—it's what 53 years of failure followed by sudden success tells us about how systems actually work.

Let me be clear about what happened: the Knicks didn't just win a close series. They pulled off the largest comeback in NBA Finals history in Game 4, erasing a 29-point deficit to beat the Spurs 107-106. OG Anunoby's tip-in with 1.2 seconds left sealed it. They then closed out Victor Wembanyama—the most hyped prospect since LeBron James—in five games.

Photo by Shabazz Stuart on Pexels

The Real Lesson: Time-in-System vs. Perpetual Rebuild

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