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Most Dockerfiles work. That's the problem — "it builds and runs" hides a lot of quiet costs in security, speed, and size that don't announce themselves until an audit, an incident, or a cloud bill does it for them. Here are seven mistakes I see constantly, and what to do instead. 1. Running as root By default, the process in your container runs as root — and if someone breaks out, they're root on a surface they shouldn't be. Add a non-root user and switch to it: RUN useradd --system --uid 10001 appuser USER appuser Cheap, and it closes off a whole category of "well, at least it wasn't root" incidents. 2. FROM some-image:latest latest is not a version — it's "whatever was newest when this happened to build." Two builds a week apart can produce different images with no diff to explain it, and a surprise base upgrade is a fun way to spend a Friday. Pin a specific tag, ideally by digest: FROM node:20.11.1-slim 3. Baking secrets into layers COPY .env . or ARG API_KEY followed by using it — and now the secret lives in an image layer forever , recoverable by anyone who pulls the image, even if a later layer deletes the file. Layers are immutable and additive; you can't delete your way out of a leak. Use build secrets ( --mount=type=secret ) or inject at runtime, never at build. 4. No .dockerignore Without one, COPY . . sweeps your .git directory, local env files, node_modules , and test data into the build context — bloating the image and, worse, potentially baking credentials and history into a layer. A five-line .dockerignore is one of the highest-leverage files in the repo. 5. Layer order that destroys your cache COPY . . RUN npm ci # ← reinstalls on EVERY code change Docker invalidates every layer after the first change. Copy the lockfile and install dependencies before copying the rest of your source, so a one-line code change doesn't trigger a full reinstall. This is a build-speed bug hiding as a style choice. 6. Leaving package manager cruft in the image RUN apt-get

I wanted to understand how malloc actually works under the hood. Most explanations I found online described what an allocator does, but completely skipped over the "why" behind its design decisions. Rather than stopping at theory, I decided to build a cross-platform allocator in C that implements malloc , calloc , realloc , and free from scratch. This article documents the design of that allocator, the architectural tradeoffs I faced, and the core concepts I had to learn along the way. Table Of Contents Design Decisions Architecture Modern Allocator Strategies Core Concepts What's Next Project Overview A custom memory allocator does not create physical memory. Instead, it requests pages of raw virtual memory from the operating system and manages how that memory is partitioned, reused, resized, and released by the application. The goal of this educational project is to implement C's core memory management API using a modular, cross-platform architecture inspired by design principles found in modern allocators, rather than relying on legacy, single-platform tricks. Design Decisions #1: Why I am Skipping sbrk While many classic tutorials use sbrk for educational implementations, I deliberately chose a 100% mmap -based approach for two major reasons: sbrk is a fragile global bottleneck. It works by moving a single pointer (the program break) up and down. This means the allocator assumes it owns a contiguous line of memory. If a third-party library or another thread in the program secretly calls sbrk behind the scenes, the allocator's memory layout can break instantly. mmap , by contrast, provides isolated, independent chunks of memory. Cross-Platform Symmetry. Windows has absolutely no equivalent to sbrk , but it has a direct equivalent to mmap : VirtualAlloc . If we used sbrk , our architectural abstraction ( os_alloc ) would become awkward because Linux would deal with a moving pointer while Windows dealt with independent pages. Using mmap keeps the abstraction perfec

There are a lot of fast-growing AI startups, but some are growing even faster, they say.

The robotaxi stopped, called 911, and waited for the San Mateo Police to show up.

What if the AI you chat with every day is quietly running something that looks a lot like a train of thought, and we just never had the right tool to see it? On 7th July, 2026, Anthropic published a research paper that honestly feels a little spooky. The team behind the Transformer Circuits Thread released a long, detailed study called Verbalizable Representations Form a Global Workspace in Language Models . The title is dense, but the idea inside is wild. They found a small, privileged region inside Claude and similar models. A region that behaves a lot like what cognitive scientists call the global workspace , the part of the brain associated with conscious access. The part that lets you say, I am thinking about a banana right now . In this post, I want to walk you through what they found, in plain English, with no math fear and no jargon walls. We will cover what the workspace is, how they found it, what they can do with it, and why it matters for anyone building or using AI. Grab a coffee. This one is worth your time. First, a Quick Brain Detour Before we get to the model, we need a tiny bit of background from neuroscience. For decades, scientists have noticed that the brain seems to operate on two tracks. Most of what your brain does, like parsing the sounds coming into your ears or keeping you balanced, happens automatically and quietly. You cannot really talk about it . It just runs in the background. But a smaller slice of brain activity is different. It is reportable . You can put it into words. You can hold a concept in mind, dismiss it, chain it to another concept, and use it for reasoning. Cognitive scientists call this access consciousness . One popular theory, called the Global Workspace Theory , says this happens because the brain has a shared hub. Specialized processors do their own thing in parallel. But every now and then, a representation gets posted to this central workspace, and once it is there, lots of other brain systems can read it, reason w

Hello fellow React developers! In this article we will be breaking down what React component instance is and scenarios where React component instance is at play. What is a React Component ? Before we can understand and really appreciate what a React component instance is, we first need to understand what a component itself is. Basically, components are the fundamental building blocks of any React application. They are independent, reusable pieces of code that allow you to split your application into distinct, manageable bits of logic and UI. From our knowledge of JavaScript, you can think of components in a way as what a function is. Just as we create and use functions to avoid repeating code and separate logic, components are used to divide our application into reusable visual chunks. However, they work in isolation and return HTML (via JSX) to describe what appears on the UI. Let take a look at a simple Greetings component used in a demo; Instead of writing the HTML layout for a greeting over and over again, we define it once as a component and reuse it multiple times in our application by passing different props (arguments). React Component Instances: What are they ? Now that we understand what a React component is, let's move on to React component instances. In programming, an instance is a concrete object created from a specific template (such as a JavaScript class or a Constructor function). In React, a component instance is the actual implementation of a component in a React application. It is a long-lived object that holds contextual information about a particular component. Every time a component is rendered in our application, React creates a new instance of that component. To help you visualize this, let’s take a look at a simple Counter component; // A Counter Component import React , { useState } from ' react ' ; export default function Counter () { const [ count , setCount ] = useState ( 0 ); return < button onClick = {() => setCount ( count + 1 )} > C

Most agent demos are one-shot loops. You open a terminal, give the model a task, watch it call tools, and then the process dies. That is fine for coding sessions. It is a weak shape for an assistant that is meant to live in your actual workflow. Talon is built around the other shape: a persistent agent process with frontends, memory, tools, background jobs, and swappable model backends. What it runs on Talon can expose the same agent core through: Telegram Discord Microsoft Teams terminal chat a desktop/mobile companion bridge That means the agent is not tied to one UI. The chat app is just a mouth. The core state, tools, memory, goals, and model backend live behind it. Backends are swappable The same harness can run through: Claude Agent SDK OpenAI Agents Codex Kilo OpenCode Each backend implements the same capability interface, so the rest of the system does not need to care which model runtime is active. It has real operating machinery The important parts are not flashy. They are the things that let an agent keep working after the first message: MCP plugins for tools cron jobs for scheduled actions triggers for condition-based wakeups persistent goals for multi-session work long-term memory heartbeat mode for background progress dream mode for consolidation per-chat model and effort settings This is the difference between "chat with a model" and "run an assistant". Install npm install -g talon-agent talon setup talon start Repo: https://github.com/dylanneve1/talon If this is the kind of agent infrastructure you want more of, a GitHub star helps the project get found.

We've all seen password strength meters on sign-up forms. Most of them rely on simplistic, static rules: "Must contain at least 8 characters, one number, and one special character." But from a mathematical standpoint, these rules are a poor proxy for actual password security. A password like Tr0ub4dor&3 conforms to these rules but is far easier to compromise than a randomly generated four-word passphrase like correct-horse-battery-staple . To truly measure password security, we have to look at information theory and compute its Shannon Entropy . Here is how password entropy works, the math behind it, and how you can calculate it directly in the browser with 100% client-side privacy. What is Password Entropy? In cryptography, entropy is a measure of the unpredictability or randomness of a password. It is expressed in bits . An entropy of $N$ bits means there are $2^N$ possible combinations that an attacker would have to guess in a worst-case brute-force search. < 28 bits: Very weak (easily guessed in milliseconds). 28 to 35 bits: Weak (cracked in minutes or hours). 36 to 59 bits: Reasonable protection (days to months). 60 to 127 bits: Very strong (takes years to decades to crack). 128+ bits: Extremely secure (mathematically unfeasible to crack). The Mathematical Formula To calculate the entropy ($E$) of a password, we use the following equation: $$E = L \times \log_2(R)$$ Where: $L$ is the length of the password (number of characters). $R$ is the size of the pool of unique characters from which the password is drawn. $\log_2(R)$ is the binary logarithm of the pool size, representing the amount of information carried by each character. Determining Pool Size ($R$) To find $R$, we analyze which character sets are present in the password string: Lowercase letters ( a-z ): 26 characters Uppercase letters ( A-Z ): 26 characters Numbers ( 0-9 ): 10 characters Common special characters/punctuation: 33 characters (e.g., !@#$%^&*()-_=+[]{}|;:',.<>/? etc.) If a password uses ch

Every junior developer or solo software engineer falls into the exact same engineering trap: They conflate writing code with building a business. They spend their initial excitement phase setting up intricate user database authentication schemas, writing custom cron jobs for automated subscription reminders, or building heavy background pipelines just to resize a user’s uploaded logo image. By the time their local environment is "infrastructure perfect," weeks have passed. The momentum is gone, burnout sets in, and the repository is abandoned before ever tasting real production traffic. In 2026, computing power has completely shifted to specialized edge layers. Infrastructure has become commoditized. If you are wasting creative bandwidth trying to compete on backend pipelines instead of focusing entirely on your unique value proposition, you are systematically killing your startup. Here is the architectural matrix to decouple your operational infrastructure and shift to a lean, hyper-scalable API-first codebase. Part 1: The Production Infrastructure Decoupling Layer The golden rule of modern systems design is clear: Your application should only maintain two core pillars internally—your proprietary business logic and your core user state database. Everything else—from security to user tracking—is a solved problem that should be offloaded to third-party micro-services. Let’s look at the financial and time trade-offs of building versus outsourcing across critical technical vectors: Microservice Vector The Native Way (High Friction) The 2026 API Standard Launch Velocity Impact Merchant of Record Raw Stripe API + Custom Tax Calculators Lemon Squeezy / Paddle Saves 5 days of legal & accounting setup Feature Rollouts Custom Postgres feature-flag logic loops GrowthBook / LaunchDarkly Zero deployment overhead for major pivots Customer Feedback Manual tables + Admin CRUD boards Featurebase API Instant roadmaps directly inside frontend Media Compression AWS S3 triggers + Edge

The crust expands at mid-ocean rifts. But how?

Political operatives in Trumpworld hope that US Senate candidate Graham Platner stays in the race as long as possible.

What if you had a drone that wasn't a buzzy, annoying fly people wanted to swat - but rather a cute dog that runs and jumps? What if it could do tricks on command, and film your tricks as well? What if it could get right back up after a nasty-looking crash, dozens of times […]

Vermilion Cliffs Ventures announced Wednesday the close of a $25 million Fund II.

We’re excited to announce our first ever Summer Bug Smash! Every app has its share of rockstar bugs....

Hi guys, I've been using Claude Code for almost everything lately. Have given one an email account so it can research business leads, draft emails, fact-check them and clear them with me before sending (works really well by the way). I also tend to have a few Claude Code agents running at any one time for coding. I used to create a split terminal to manage them from there, but found working in the terminal all day pretty depressing and, more importantly, found it hard to follow Claude Code's pro

It’s not a great time to shop for a gaming laptop. The unprecedented rise in cost for RAM (and storage to a slightly lesser extent) has made laptops with low-to-midrange specs cost much more than they should. Recent deals on gaming laptops worth your attention have been uninspiring, but one recently caught my eye: MSI’s […]