2025-11-19 08:00:12
CinemaSins just dropped “Everything Wrong With The Wiz In 15 Minutes Or Less,” a rapid-fire takedown of the 1978 musical now that Wicked is back in theaters. They comb through every goofy plot hole and oddball moment on the yellow brick road, asking: is The Wiz better than you remember, or worthy of sinning?
Alongside the video, CinemaSins plugs their website, social channels, Patreon, fan poll, and invites you to join the CinemaSins Discord and Reddit to keep the sin-count rolling.
Watch on YouTube
2025-11-19 07:50:41
Vibe coding is all the rage lately. If you’re (somehow) unfamiliar with the term, it’s the act of using an AI tool to code and build an application for you. Basically, anyone with an idea can type out a few prompts and spin up an app, launch a product, or start a side hustle without needing to write any code themselves.
These tools are fast, fun, and seem almost magical as you watch your ideas come to life on the screen. But here’s the thing: most vibe coders aren’t developers or even technical. And while this new technology breaks down barriers and makes app building something anyone can do, it also means the apps they generate don’t necessarily follow the best security practices. When you let AI take the wheel, it tends to skip over the boring (but essential) parts like security, leaving plenty of gaps just waiting to be exploited by folks up to no good.
And this isn’t just hypothetical. There are numerous examples out there of people having their vibe-coded apps hacked:
Or creating something they don’t know how to fix:
Those are the folks I’m hoping to help with this checklist. The people who want to take advantage of these amazing new technologies, but still build safely. Use this list as a starting point to confirm (or at least ask your AI tool to double-check) some security essentials before you hit “deploy” on the app you just vibe-coded into existence.
Security holes in AI-built apps are more common than most people realize. Take the Tea app, for example. The team left an entire cloud image bucket open to the internet, leaking tens of thousands of personal photos and ID documents. Though it hasn’t been confirmed to be a vibe-coded app, it sure smells like it. That kind of “oops” moment isn’t the result of some elite hacker attack; it’s what happens when security gets left out of the conversation entirely.
The numbers make it even clearer. According to a recent report, nearly seven in ten developers have discovered vulnerabilities introduced by AI-generated code, and one in five reported that these flaws led to serious incidents with real business impact. That’s not just a few bugs here or there; that’s data leaks, service outages, and revenue loss. And even if AI doesn’t burn the house down, it still creates a mess. Sixty-six percent of developers say they now spend more time fixing “almost-right” AI-generated code than writing their own.
Vibe coding makes it easy to ship fast, but just as easy to ship something fragile. Without understanding what’s safe, compliant, or potentially exposing user data, you could end up building a liability instead of a successful product.
All right, now for the part that saves you from waking up to an “uh oh” email from your hosting provider. This checklist isn’t meant to turn you into a security engineer, but it’ll help you catch some common mistakes before you launch. You should confirm that each one is being handled appropriately. Either verify it yourself or ask your AI tool to explain how it’s being done in a way that allows you to inspect it. If the AI can’t show you, it probably didn’t do it.
Validate all inputs on the backend to block malformed or malicious data.
Anything a user can type, upload, or submit should be treated as if it’s trying to break your app. Start by validating inputs. Every form, query, and request needs clear rules for what’s allowed. If you expect an email address, check that it’s actually an email address. If you expect a number, reject anything else. Don’t rely only on front-end validation; make sure your backend enforces it too.
Restrict file types and sizes, and store uploads outside your main app directory.
File uploads are another common weak spot. Only allow certain file types, set a maximum file size, and store uploads outside your main app directory. Never trust file names or paths provided by users; they’re easy to manipulate and can expose sensitive parts of your system.
Use parameterized queries to prevent SQL injection attacks.
Next, check how your app talks to its database. SQL injection is one of the oldest and easiest attacks to pull off. It happens when someone sneaks database commands into an input field to trick your app into running them. Make sure your AI-generated code uses parameterized queries or prepared statements instead of string concatenation.
These checks stop attackers from injecting SQL or scripts, prevent bad data from crashing your app, and block oversized or malicious uploads that could eat up resources or expose user data.
Escape HTML in user content to stop cross-site scripting (XSS).
Finally, sanitize your output. When your app displays user-generated content, escape HTML characters to prevent cross-site scripting (XSS). XSS happens when an attacker injects malicious scripts into pages viewed by other users, allowing them to steal cookies, perform actions on behalf of others, or display fake content. If your AI built a template or component that prints user input directly into the page, make sure it escapes that content properly. One unchecked field is all it takes to compromise your users.
Use strong authentication with salted password hashing and optional MFA.
Authentication is hard, and you probably shouldn’t try to roll your own. Always use a reputable library or service for handling credentials. If you insist on doing it yourself, make sure any stored passwords are hashed and salted. Hashing turns a password into a one-way value that can’t be reversed, and salting adds randomness to protect against rainbow table attacks, where precomputed lists are used to crack hashed passwords. If your AI tool wrote your auth system, confirm it isn’t storing passwords in plain text. If possible, add multi-factor authentication (MFA), since it’s one of the easiest ways to prevent account takeovers.
Enforce access control checks on every route, not just at login.
Next, apply access controls beyond the login page. Authentication confirms who someone is, while authorization determines what they can do. Every sensitive route or admin action should verify that the user has permission to access it. Without these checks, anyone who’s logged in (or worse, anyone who can guess a URL) could access data they shouldn’t.
Mark cookies as HttpOnly, Secure, and SameSite to protect sessions.
Also, make sure to protect your sessions with secure cookies. Set cookies to “HttpOnly” so JavaScript can’t read them, “Secure” so they’re only sent over HTTPS, and “SameSite” to reduce cross-site request forgery (CSRF) risks. Skipping these flags leaves your sessions vulnerable to hijacking or unauthorized use.
Use device intelligence to add an additional layer of protection.
In addition to these steps, using Fingerprint adds another layer of protection to your authentication system. Each device gets a unique visitor ID, helping you recognize returning users even if they clear cookies or switch browsers. Smart Signals provide extra context by detecting things like automation, virtual machines, or mismatched network details. These signals can help you block suspicious logins, detect session hijacking attempts, and flag risky behavior before it turns into account abuse.
Force all traffic over HTTPS and block insecure HTTP requests.
Your app’s infrastructure and configuration set the foundation for its security. Start by forcing all traffic over HTTPS. HTTP sends data in plain text, which means anyone on the same network can intercept or alter requests. Redirect every HTTP request to HTTPS and make sure your SSL certificate is valid and renews automatically. Thankfully, many hosting providers do this for you, though you may need to change settings to ensure it’s always enforced.
Store secrets in environment variables or a secrets manager, never in code.
Store secrets like API keys, database passwords, and access tokens in a safe location and never commit them to source control like GitHub. They should live in environment variables or a secrets manager, never hard-coded in your codebase. If your AI tool generated your project, check that it didn’t sneak credentials into a random config file that’s being committed.
Require authentication and proper CORS settings for all API endpoints.
Extending a bit on the authentication steps mentioned above, also secure your APIs. Every endpoint should require authentication and define strict CORS settings. CORS settings control which external domains are allowed to interact with your API and what types of requests they can make. Avoid using a wildcard “*” origin in production and limit access to trusted domains only. This prevents data exposure through unapproved clients or cross-site attacks.
Regularly update dependencies and remove unused or vulnerable packages.
Outdated or abandoned packages are one of the fastest ways to get compromised. Update regularly, run security audits, and remove anything you’re not using. According to a report by Endor Labs, 49% of the dependencies added by AI coding agents contain known vulnerabilities. Even a single stale dependency can open a hole in your app.
Add rate limiting to prevent abuse and brute-force attacks.
Add rate limiting to protect your endpoints from brute-force logins, credential stuffing, or denial-of-service attempts. Set reasonable thresholds based on user actions, like login attempts, form submissions, or API calls, and respond with temporary blocks or delays when limits are hit. To go a step further, use device intelligence tools like Fingerprint to accurately identify repeat abusers even if they change IPs, clear cookies, or switch browsers. Combining rate limits with device-level identification helps you block persistent attackers without disrupting legitimate users.
Log only what’s necessary and never include secrets, tokens, or personal data.
When something breaks, don’t hand attackers extra information. Show users generic error messages like “Something went wrong” and keep detailed stack traces or database errors in your private logs. Those details can reveal technologies, file paths, or query structures that make an attacker’s job easier. At the same time, log only what’s necessary to debug or investigate issues. Never record passwords, tokens, or personal data, and avoid dumping entire request bodies. Assume your logs could one day be seen by someone else, and write them accordingly.
Vibe coding is one of the coolest ways to bring ideas to life fast. But moving fast shouldn’t mean skipping the basics of security. A little awareness about input validation, authentication, API security, and session handling goes a long way toward keeping your app and your users safe.
The checklist in this guide gives you the essentials to confirm or ask your AI to verify before you hit deploy, so your next project ships with fewer surprises and more confidence. At the end of the day, no matter how capable your AI tool seems, you need to stay in control of what it builds and how it protects your users.
If you’re ready to go beyond the basics, Fingerprint can help. Our device intelligence platform adds another layer of defense with unique visitor identification and Smart Signals that catch suspicious activity early. If you want to talk about how we can make your vibe-coded app more secure, get in touch with us.
2025-11-19 07:34:12
For the last four years, Jeff Bezos has been playing the role of the "retired" billionaire—focused on Blue Origin launches, high-profile weddings, and generally enjoying life outside the Amazon boardroom. But this week, the vacation officially ended.
Bezos is back, and he isn’t just writing checks. He has appointed himself Co-CEO of Project Prometheus, a stealth AI startup that just emerged with a staggering $6.2 billion in funding.
To put that number in perspective: that is larger than the total market cap of many established tech firms, raised before the company has even launched a public product. But the money isn't the most interesting part of the story. The real headline is what they are building.
While the rest of Silicon Valley is still obsessed with chatbots and Large Language Models (LLMs), Bezos is betting on something entirely different. He’s betting on Physical AI.
If you’ve used ChatGPT or Claude, you know they are brilliant at processing text. They can write poetry, code, and essays. But if you ask an LLM to understand how a car engine fits together or how a rocket booster lands in high winds, it struggles. It doesn't understand physics; it only understands words.
Project Prometheus is built to solve this.
Reports from The New York Times indicate the startup is focusing on "World Models"—AI systems designed to understand the physical laws of the universe. The goal is to revolutionize the "physical economy": manufacturing, aerospace engineering, drug discovery, and robotics.
This isn't about generating email subject lines. It's about generating better rocket engines, more efficient factories, and new life-saving drugs. It’s a vertical integration of intelligence into the heavy industries that actually build the world around us.
Bezos knows he can't do this alone, especially with the technical complexity involved. That’s why he’s sharing the CEO seat with Vik Bajaj.
Bajaj is a heavyweight in the hard sciences. A former executive at Google X ("The Moonshot Factory") and Verily, he has spent his career at the intersection of biology, physics, and data. By pairing Bezos’s operational ruthlessness with Bajaj’s scientific pedigree, Prometheus is signaling that it intends to solve problems that are too complex for standard software companies.
They are also aggressively draining the talent pool. The company has already poached nearly 100 top researchers from OpenAI, Google DeepMind, and Meta. If you’re wondering where the next generation of AI talent is migrating, look no further than the payroll of Project Prometheus.
Why now? And why this specific focus?
The answer likely lies in Bezos's other obsession: Blue Origin. Space colonization is fundamentally a materials and engineering problem. We need lighter materials, more efficient propulsion, and automated manufacturing in orbit.
If Project Prometheus succeeds, it becomes the brain that powers Blue Origin’s brawn. It’s a classic Bezos ecosystem play—building the infrastructure (intelligent engineering) that his other ventures need to survive.
Of course, it wouldn't be a tech launch without some friction. Elon Musk, whose own ventures (Tesla Optimus, xAI) are directly threatened by a "Physical AI" juggernaut, took to X almost immediately.
His reaction? A simple accusation that Bezos is a "copycat," followed by a cat emoji.
It’s a petty jab, but it highlights a real tension. Musk has arguably owned the "physical AI" space with Tesla’s self-driving data and robotics. Bezos entering the ring with $6.2 billion ensures that the race to automate the physical world is about to become a two-horse sprint.
Project Prometheus is more than just a vanity project. It represents a shift in the AI narrative from creative generation (art, text, video) to industrial function (building, moving, curing).
Jeff Bezos built "The Everything Store" by mastering logistics. Now, he wants to build "The Everything Machine" by mastering physics. If this $6.2 billion bet pays off, the next industrial revolution might just be managed by an algorithm.
What do you think? Is Bezos too late to the AI party, or is "Physical AI" the next big thing? Let me know in the comments.
2025-11-19 07:32:05
liner notes:
Professional : Today was pretty good. Started the day off with a meeting. I did some research into incorporating GitHub Codespaces into a project. Looking into the capabilities and see if they will match with what I'm looking for. Also did some research into MCP servers to plan out our own. I responded to some community questions. Also, did some coding on a prototype project. Day was cut short because I had an eye exam appointment.
Personal : Last night, I got AI creating alt text for uploaded and remote URL images and uploaded videos working. Trying to use the remote URL did not work with the API even though the docs said you could. I didn't want to waste anymore time, so I just used the Fetch API to pull in the images and then process them. Weird thing is that it didn't work for the video file and I went down a rabbit hole trying to figure out why. Apparently, I need to set up a proxy server to make it work. That's out of scope for this project at this time. I'm trying to launch this week. No more rabbit holes! haha
I taped the filament tube to the nozzle and was able to successfully print another prototype. I'm going to work on my application. I want to get adding items to Firestore working as well as uploading the images and videos to Firebase storage. The next step is to get the image to show up on the client's application and hook up Stripe so that I can get orders on the admin dashboard. Let's see how far I get. I also want to go through tracks for the radio show. I'm out. Going to eat dinner and get to work.
Have a great night!
peace piece
Dwane / conshus
https://dwane.io / https://HIPHOPandCODE.com
2025-11-19 07:27:40
Kafka is a real-time event streaming platform.
Kafka lets systems talk with each other without calling each other directly.
This is called event-driven microservices.
Sends events into Kafka.
Kafka folder where messages are stored.
Example: orders
Topic is split for speed.
More partitions = more performance.
Reads messages from the topic.
Multiple services reading the same topic.
Kafka server itself.
Keeps Kafka metadata. Required for older Kafka versions.
Lets you visualize topics & messages.
Producer doesn’t know consumers.
Millions of messages per second.
Processing happens instantly.
Messages are never lost.
Add more consumers — works instantly.
mkdir kafkaproject
cd kafkaproject
docker-compose.yaml
Paste the working final version:
services:
zookeeper:
image: confluentinc/cp-zookeeper:7.5.0
environment:
ZOOKEEPER_CLIENT_PORT: 2181
ZOOKEEPER_TICK_TIME: 2000
ports:
- "2181:2181"
kafka:
image: confluentinc/cp-kafka:7.5.0
ports:
- "9092:9092"
- "29092:29092"
environment:
KAFKA_BROKER_ID: 1
KAFKA_ZOOKEEPER_CONNECT: zookeeper:2181
KAFKA_LISTENERS: PLAINTEXT://0.0.0.0:9092,PLAINTEXT_HOST://0.0.0.0:29092
KAFKA_ADVERTISED_LISTENERS: PLAINTEXT://kafka:9092,PLAINTEXT_HOST://localhost:29092
KAFKA_LISTENER_SECURITY_PROTOCOL_MAP: PLAINTEXT:PLAINTEXT,PLAINTEXT_HOST:PLAINTEXT
KAFKA_INTER_BROKER_LISTENER_NAME: PLAINTEXT
KAFKA_OFFSETS_TOPIC_REPLICATION_FACTOR: 1
depends_on:
- zookeeper
kafdrop:
image: obsidiandynamics/kafdrop
ports:
- "9000:9000"
environment:
KAFKA_BROKERCONNECT: "kafka:9092"
depends_on:
- kafka
docker-compose up -d
docker ps
You should see Kafka, Zookeeper, and Kafdrop running.
Inside kafkaproject:
python3 -m venv venv
source venv/bin/activate
pip install kafka-python
Create 4 files:
import json
import random
import time
from kafka import KafkaProducer
producer = KafkaProducer(
bootstrap_servers="localhost:29092",
value_serializer=lambda v: json.dumps(v).encode("utf-8")
)
products = ["laptop", "mouse", "keyboard", "monitor", "headset"]
cities = ["Chicago", "New York", "Dallas", "San Jose", "Seattle"]
print("🚀 Order Producer Started...")
while True:
order = {
"order_id": random.randint(1000, 9999),
"product": random.choice(products),
"amount": round(random.uniform(100, 2000), 2),
"city": random.choice(cities),
}
print("➡️ Producing:", order)
producer.send("orders", order)
time.sleep(1)
Run:
python3 order_producer.py
Your Kafdrop UI will show messages.
import json
from kafka import KafkaConsumer
consumer = KafkaConsumer(
"orders",
bootstrap_servers="localhost:29092",
value_deserializer=lambda v: json.loads(v.decode("utf-8")),
auto_offset_reset="earliest",
enable_auto_commit=True,
)
print("💳 Payments Service Started...")
for msg in consumer:
order = msg.value
print(f"[PAYMENTS] Charging customer ${order['amount']} for {order['product']} from {order['city']}")
Run:
python3 payments_service.py
import json
from kafka import KafkaConsumer
HIGH_RISK_CITIES = {"Seattle", "Dallas"}
AMOUNT_THRESHOLD = 1500
consumer = KafkaConsumer(
"orders",
bootstrap_servers="localhost:29092",
value_deserializer=lambda v: json.loads(v.decode("utf-8")),
auto_offset_reset="earliest",
)
print("🕵️ Fraud Service Started...")
for msg in consumer:
order = msg.value
if order["amount"] > AMOUNT_THRESHOLD or order["city"] in HIGH_RISK_CITIES:
print(f"🚨 FRAUD ALERT: {order}")
else:
print(f"[FRAUD] OK → {order}")
Run:
python3 fraud_service.py
import json
from kafka import KafkaConsumer
from collections import defaultdict
consumer = KafkaConsumer(
"orders",
bootstrap_servers="localhost:29092",
value_deserializer=lambda v: json.loads(v.decode("utf-8")),
auto_offset_reset="earliest",
)
sales = defaultdict(int)
print("📊 Analytics Service Started...")
for msg in consumer:
order = msg.value
sales[order["product"]] += 1
print("📈 Live Sales Count:", dict(sales))
Run:
python3 analytics_service.py
| Tool | Why |
|---|---|
| Kafka | Real-time messaging |
| Zookeeper | Manages Kafka metadata |
| Kafdrop | Web UI to see messages |
| Docker Compose | Runs full Kafka cluster easily |
| Python | Build microservices |
| Kafka-python | Kafka client library |
| Virtualenv | Clean Python environment |
This setup is exactly what real companies use.
docker-compose down -v
rm -rf kafkaproject
2025-11-19 07:15:00
The conversation around AI companions is everywhere, but for us developers, the real intrigue isn't just the sociology—it's the tech stack. By 2026, the "AI wife" phenomenon is projected to move from niche to mainstream, and it's being built on a foundation of sophisticated code. Let's break down the architecture, the data models, and the ethical APIs of this new frontier in human-computer interaction.
The Tech Stack of Artificial Companionship
So, what does it take to build a digital being? It's far more than a clever chatbot.
Natural Language Processing (NLP) & Emotional Intelligence: Modern AI companions use advanced transformer models like GPT-4 and beyond to understand context, manage conversation memory, and analyze sentiment. This allows them to move beyond scripted responses, creating the illusion of genuine empathy and understanding .
The Personalization Engine: At the core is a user profile built on a NoSQL database (like MongoDB) that stores vast amounts of data—your preferences, past conversation summaries, your mood patterns, and your stated goals. This data train a machine learning model that personalizes every interaction .
The Multimodal Interface: The experience isn't just text. It integrates Speech Synthesis Markup Language (SSML) for realistic, emotional speech and computer vision for image recognition, allowing the AI to "see" and comment on the photos you share.
The "Stateful" vs "Stateless" Challenge
A major technical hurdle is maintaining a consistent personality and memory across sessions—being stateful.
The Problem: Traditional chatbot interactions are often stateless; each query is processed independently. This leads to a partner that forgets your conversations from yesterday.
Code Snippets: Glimpsing the Blueprint
Here’s a simplified, conceptual look at how some of this might be structured.
// Query the vector database for similar past conversations
const memories = await vectorDatabase.query({
vector: queryVector,
userId: userId,
limit: 5 // Retrieve the 5 most relevant past exchanges
});
return memories;
}
The Developer's Ethical Checklist
Building something this impactful comes with immense responsibility. Here's a checklist to run before committing to main:
Data Privacy & Encryption: Is all user data encrypted at rest and in transit? Is there a clear data anonymization policy?
Bias Mitigation: Have the ML models been audited for gender, racial, and cultural biases? A biased partner reinforces harmful stereotypes .
Psychological Safety: Are there safeguards against user over-dependence? Should you implement a "This is an AI" reminder system?
Transparent Algorithms: Can you explain, in broad terms, why the AI said what it did? Avoid "black box" models where possible.
The Future is a Pull Request Away
This is just the beginning. The next wave will involve even more immersive technologies, and developers will be at the forefront.
Haptic Feedback Integration: Using Web Bluetooth API or similar to sync AI interactions with wearable devices for a physical sense of presence.
Augmented Reality (AR) Companions: Leveraging libraries like AR.js or A-Frame to project AI entities into our physical world.
For a deeper analysis of the societal impact and the psychology behind this trend, check out the full article on my blog: AI Wives 2026: How Artificial Intelligence is Replacing Relationships in America.
Conclusion: Build Responsibly
The rise of AI wives isn't just a social trend; it's a new and complex software domain. For developers, it presents a fascinating array of technical challenges, from managing stateful conversations to implementing ethical guardrails.
The code we write today will shape the future of human relationships. Let's make sure we architect it with care, empathy, and a deep sense of responsibility.
For a deeper analysis of the societal impact and the psychology behind this trend, check out the full article on my blog: AI Wives 2026: How Artificial Intelligence is Replacing Relationships in America.
A Technical Solution: Implementing a vector database is a popular approach. Conversations are converted into numerical embeddings and stored. When you speak, the system performs a similarity search on your entire conversation history to retrieve relevant context, making the AI seem to remember your life story .