DEV Community: Muhammad Adil

Self-Hosting Services at Home with Docker

Muhammad Adil — Mon, 07 Sep 2026 01:51:35 +0000

This guide explains how to turn a home machine into a lightweight server for self-hosting applications using Docker and Docker Compose. Whether you're a developer testing cloud-native apps or a user seeking control over your data and infrastructure, self-hosting offers flexibility. Here’s how to get started with a practical, single-machine setup.

Why Self-Host?

The appeal of self-hosting isn’t just about reducing costs or dependence on third-party providers; it’s about control. You gain more control over where your data is stored, how services are configured, and how long you retain them. For developers, it’s a playground for testing cloud-native applications while reducing dependence on a particular provider. For privacy-conscious users, self-hosting can reduce reliance on third-party providers for services like file storage, media streaming, and personal applications.

For larger deployments, hardware, storage, and reliable power become significant considerations. However, modern hardware and container tools make it possible to run many useful self-hosted services on a mid-range PC, mini PC, or Raspberry Pi, depending on the workload. Containerization and modern deployment tools have made self-hosting more accessible, allowing surprisingly capable private setups on a single machine. Residential internet connections may also introduce challenges like NAT traversal, dynamic IP addresses, or ISP restrictions, which can affect remote access to services.

Essential and Optional Tools

Docker: Packages applications and their userspace dependencies into containers that provide process and resource isolation while sharing the host operating system’s kernel.
Docker Compose: Simplifies orchestration for multi-container applications on a single server, making it a practical choice for small home setups.
Traefik: A reverse proxy that can route traffic to the appropriate service, while Portainer lets you manage containers through a web interface.
Kubernetes (K8s): Orchestrates containerized workloads and automates deployment, scheduling, service discovery, and scaling. While it’s often unnecessary for a single-machine home server, it can run on one machine for learning purposes or small-scale experimentation. A single-node cluster is useful for learning but has no node-level redundancy.
Rancher: A Kubernetes management platform designed for managing clusters, primarily useful for advanced homelab setups.

Together, these tools can form a capable self-hosted environment, although a basic setup only requires Docker and Docker Compose. Here, ‘home server’ refers to a computer or other hardware you control that runs self-hosted services. Docker isolates services (like a Next.js site or a self-hosted Git server) in containers, while Docker Compose simplifies orchestration for small setups. Traefik ensures traffic reaches the right service, and Portainer lets you monitor everything with a web interface.

Getting Started: A Step-by-Step Primer

The first step is installing Docker on your machine. The official Docker install guide walks you through it for Linux, macOS, or Windows. Once Docker is running, you can pull pre-built images for services like Nextcloud (file storage) or Jellyfin (media server). Prefer official or well-maintained images, pin versions or digests when reproducibility matters, and keep images updated.

For orchestration, Docker Compose is usually the simpler choice for a small home setup. It’s explicitly designed for deploying applications on a single server. If you want to experiment with Kubernetes or manage a multi-node homelab, you can add it later. Minikube lets you run a local Kubernetes cluster for learning and experimentation before moving to a larger setup. If you prefer a graphical interface, Portainer can provide a web UI for managing supported Docker environments.

Install Docker and verify it’s running (docker --version).
Pull an image and follow that application's documentation for its required volumes, environment variables, database, and networking configuration.
If you need multiple services behind one domain, consider a reverse proxy such as Traefik or Caddy.
Use Portainer to monitor and manage containers.

Pro tip: Start small. Deploy one service at a time, like a self-hosted wiki (Wiki.js) or a lightweight monitoring tool (Uptime Kuma), to test stability before adding more. Monitor resource usage (CPU, RAM, disk) to avoid overloading your machine.

Security Before Exposure

Before exposing services to the internet, take these precautions: avoid exposing unnecessary ports, enforce HTTPS for web services, keep the OS and containers updated, use strong authentication and 2FA where available, and back up important data to a separate physical device or remote location. Test your backups regularly. Do not expose Docker or Kubernetes administration interfaces directly to the public internet. Consider using VPNs or private networking for services that don’t need public access. Remember that containers do not provide a security boundary that eliminates all host risks.

What You Can Actually Run at Home

File storage and synchronization (Nextcloud: a self-hosted alternative to Dropbox or Google Drive; Syncthing: for continuous file synchronization between devices).
Media servers (Jellyfin, Plex): Stream your own media from a self-hosted server, including locally over your home network.
Collaboration tools (Matrix): Can work well for self-hosted messaging, while Jitsi can provide self-hosted video conferencing but requires substantially more resources and network capacity.
Developer infrastructure: GitLab can provide self-managed source control and CI/CD, although it is considerably heavier than lightweight Git hosting options. GitLab Runners execute CI/CD jobs. For security and performance, GitLab recommends running self-managed runners on infrastructure separate from the GitLab instance.
Monitoring: Uptime Kuma can monitor the availability of your services.
Home automation: Home Assistant controls smart devices with open-source software, offering a user-friendly interface and extensive community support.
Password management: Bitwarden can be self-hosted, but deployment and configuration requirements depend on the deployment method.

The Trade-offs

Self-hosting isn’t always simpler or cheaper. You’ll spend time maintaining the setup, troubleshooting issues, and ensuring backups. If your internet connection goes down, remote access and internet-dependent services may become unavailable, although many local services can continue working on your home network. For those running services for family or small teams, the trade-offs may be worthwhile when control, privacy, or local availability are important.

Cost-wise, a home setup can be cheaper for some workloads, especially if you already own suitable hardware, but electricity, storage, backups, hardware replacement, and maintenance costs should also be considered. For lightweight services, a Raspberry Pi or used mini PC can be a practical starting point, but storage reliability, RAM, ARM compatibility, and workload requirements should be considered before choosing one.

Where to Learn More

The Docker and Kubernetes communities are full of resources. For hands-on guides, check out Docker’s official tutorials and Kubernetes’ step-by-step docs. For community-maintained recommendations, the Awesome Selfhosted repository provides a directory of self-hosted software.

If you’re new to containers, start with Docker Compose, as it’s simpler than Kubernetes and great for small setups. Tools like Home Assistant also provide detailed getting-started documentation for self-hosting.

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GPT-6 Astra: Launch & Real Capabilities

Muhammad Adil — Fri, 04 Sep 2026 06:33:57 +0000

OpenAI officially introduced GPT-6 Astra on September 3, 2026, marking a significant milestone in AI development. While speculation once surrounded its existence, Astra is now a reality, designed to push boundaries in reasoning, coding, and professional tasks. Here’s what we know about its capabilities, technical specs, and real-world impact.

What GPT-6 Astra Actually Is

GPT-6 Astra is OpenAI’s most capable model to date, built to excel in complex reasoning, software engineering, cybersecurity, and professional work. Unlike previous iterations, Astra emphasizes not only capability but also speed, computer use, judgment, and alignment. OpenAI positions it as the most intelligent and aligned model yet, with improvements in speed and accuracy across several evaluated tasks.

Key Technical Specifications

OpenAI reports the following specifications for GPT-6 Astra: a 1,050,000-token context window, a maximum output of 128,000 tokens. The model also supports varying levels of reasoning effort: low, medium, high, xhigh, and max. Standard pricing is $10 per 1M input tokens and $50 per 1M output tokens, with higher rates applying to requests exceeding 272K input tokens.

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Atlas: World Labs' new omni world model for spatial intelligence

Muhammad Adil — Wed, 02 Sep 2026 12:15:17 +0000

World Labs introduced Atlas on September 1, 2026. Atlas operates on text, images, video, camera poses, and 3D depth maps, grounding all inputs in a shared spatial context to reconstruct, generate, and simulate spatial environments.

What Atlas actually does

Atlas is a multimodal autoregressive diffusion transformer that combines multimodal inputs into a shared spatial context. It can reconstruct real-world scenes from as few as two or three input images, while also supporting one to dozens of input images. The model supports pixel-perfect camera control, producing images and videos with precise camera control. It can generate videos of up to one minute at 1440p resolution and create 360° panoramas from text or image prompts.

Atlas also models space and time, supporting Real-to-Sim workflows for robotics by reconstructing environments and generating RGB/depth observations from simulated robot viewpoints. It can aid manipulation simulation by helping create varied virtual environments for robotics training and testing, including variations in objects, positions, robot motion, lighting, and backgrounds. Atlas can also produce explicit 3D outputs such as point clouds and 3D Gaussian splats.

Why spatial intelligence matters

For robots to operate effectively in dynamic, unstructured environments, they need more than obstacle detection. They need to understand space in a way that supports simulation, training, and evaluation. Atlas provides a foundation for these capabilities by enabling realistic reconstructions and simulations of physical spaces.

How it compares to existing systems

SLAM systems primarily estimate a robot’s pose while constructing a geometric map. Atlas, in contrast, is designed as a general multimodal world model that can reconstruct, generate, and simulate spatial environments. Atlas is not a direct replacement for SLAM; it targets broader generation, reconstruction and simulation capabilities.

Broad scene coverage: Atlas is designed to operate across varied environments and scene types.
Camera-controlled generation: Produces images and videos with precise camera control, including videos up to one minute at 1440p.
Spatial reconstruction: Can reconstruct real-world scenes from one to dozens of input images, typically producing faithful reconstructions with two or three images.
Space-time simulation: Models space and time from video, supporting Real-to-Sim workflows for robotics.
Text-to-image and image-to-image generation: It can generate images and 360° panoramas from text prompts.

Limitations and open questions

Atlas is currently entering early access with select partners. Sparse-view reconstruction can imagine unseen regions, meaning the generated result isn’t necessarily an exact reconstruction of reality. More input images provide more context and reduce the amount the model has to infer. The benchmark results are reported by World Labs; for camera-controlled generation, third-party human raters were used to judge which model better followed the intended camera path.

What comes next

Potential future directions could include more efficient inference, tighter integration with reinforcement learning, or multi-agent spatial coordination, although these are not announced Atlas features. As world models like Atlas mature, we may see robots that don’t just navigate spaces but use simulated environments to support planning and training. That could redefine how machines interact with the physical world, moving beyond static maps to dynamic, generative understanding.

Source: World Labs, Atlas: A World Model for Spatial Intelligence, September 1, 2026.

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OpenShot 4.0 Adds Recording, Color Tools and AI Masking

Muhammad Adil — Tue, 01 Sep 2026 07:31:35 +0000

OpenShot 4.0, released August 30, 2026, delivers a major refresh to the free, open-source video editor. Created by Jonathan Thomas, the project has modernized its UI, added new creative tools, improved performance, and expanded recording and color grading capabilities without a subscription or watermark. The feature details below are based primarily on OpenShot's official 4.0 release announcement and documentation.

What's new in OpenShot 4.0

OpenShot 4.0 introduces screen, webcam, microphone, and system-audio recording directly in the editor. The update adds color wheels and curves with .cube LUT support, video scopes for analyzing exposure and color, and a local AI-powered Object Mask tool for isolating and tracking subjects. Animated audio visualizations turn sound into motion graphics on the timeline. OpenShot has expanded its Qt 6 support, including PySide6 compatibility, as part of a broader modernization of the UI, build system, and libraries. OpenShot also supports hardware-accelerated decoding and encoding, although its documentation describes GPU acceleration as experimental. Export options have also been refreshed with presets for platforms including TikTok, Instagram, Facebook, Snapchat, and LinkedIn.

Key features in the update

Screen, webcam, microphone, and system-audio capture built directly into the editor.
Color wheels and curves with .cube LUT support for color grading workflows.
Video scopes for analyzing exposure and color, including Luma Waveform, Histogram, RGB Parade, and Vectorscope.
A local AI-powered Object Mask tool for isolating and tracking subjects to apply effects or compositing.
Animated audio visualizations that turn sound into motion graphics directly on the timeline.
Expanded Qt 6 support, including PySide6 compatibility, for smoother file handling.

OpenShot's editing workflow

OpenShot uses a traditional timeline-based editing workflow and includes animation and keyframe tools. It aims to be the practical middle ground: free, cross-platform, and packed with features that matter for everyday creators.

OpenShot uses FFmpeg as part of its media pipeline, giving it broad support for common video and audio formats. The project is split across three main repositories: the Python-based openshot-qt application, the C++ libopenshot video library, and the C++ libopenshot-audio library. This architecture supports OpenShot's cross-platform builds for Windows, macOS, and Linux.

Who should try OpenShot 4.0

Casual creators who need quick cuts, titles, and simple effects without a steep learning curve.
Budget-conscious teams looking for a free alternative to subscription-based editors.
Educators or students who want a tool they can study, modify, and redistribute under its open-source licensing terms.

Performance and testing

In OpenShot's performance testing, version 4.0 was 61.8% faster for the Blur benchmark, 12.8% faster for Sharpen, 3.4% faster for Timeline operations, and 5.1% faster for Timeline with transforms compared to version 3.5.1. These are project benchmarks, so actual performance will vary by hardware, media, and workload.

The open-source advantage

Because OpenShot is open-source, users can inspect the source code, submit fixes, and modify or contribute to the project.

Sources

OpenShot, “OpenShot 4.0: Record, Edit, and Color Like Never Before”, official release announcement and feature information.

OpenShot 4.0 brings recording, color grading, local AI masking, animated graphics, and timeline improvements into a single free, open-source editor. Download OpenShot from the official website.

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How AI Chips Actually Work Under the Hood

Muhammad Adil — Mon, 24 Aug 2026 10:18:41 +0000

AI models don't run on magic. They run on silicon, and the way that silicon is designed determines how fast, how efficient, and how expensive your training and inference jobs will be. If you're building or deploying AI systems, you need to understand the trade-offs between different chip architectures. This isn't just academic, it directly impacts your cloud bills, your latency, and what kinds of models you can even run.

Why General-Purpose CPUs Fall Short

Traditional CPUs are built for versatility. They handle everything from web browsing to database queries, which means they're optimized for single-threaded performance and low-latency task switching. But AI workloads, especially deep learning, have very different needs. They involve massive parallelism, thousands of identical operations happening at once. CPUs simply don't have enough cores or the right memory hierarchy to keep up.

For example, training a large language model might require multiplying matrices with billions of elements. A CPU would process these sequentially or with limited parallelism, while an AI-specific chip can handle thousands of multiplications in a single clock cycle. The difference isn't marginal, it's orders of magnitude.

GPUs: The First Wave of AI Acceleration

Graphics Processing Units were originally designed for rendering 3D graphics, but their architecture turned out to be a near-perfect fit for AI. GPUs excel at parallel computation because they're built to process thousands of pixels or vertices simultaneously. This same capability translates directly to matrix operations, which form the backbone of deep learning.

Massive parallelism: Modern GPUs have thousands of smaller cores optimized for concurrent operations.
High memory bandwidth: AI workloads are memory-bound, and GPUs provide the throughput needed to feed data to all those cores.
Flexibility: GPUs can handle a wide range of AI tasks, from training to inference, and are programmable via frameworks like CUDA.

The downside is power consumption. GPUs are power-hungry, which makes them expensive to run at scale. They're also not always the most efficient choice for inference, where latency and cost per query matter more than raw throughput.

TPUs: Google's Custom AI Silicon

Google's Tensor Processing Units were designed from the ground up for AI workloads. Unlike GPUs, which are general-purpose accelerators, TPUs are highly specialized for matrix operations. This specialization comes with trade-offs, but it also delivers significant performance and efficiency gains for the right workloads.

Systolic arrays: TPUs use a grid of processing elements that pass data directly to each other, reducing memory access and improving efficiency.
Quantization: TPUs are optimized for lower-precision arithmetic, which speeds up computation and reduces power consumption without sacrificing much accuracy.
Tight integration: TPUs are designed to work seamlessly with Google's AI frameworks, like TensorFlow, which can simplify deployment.

TPUs aren't a silver bullet. They're less flexible than GPUs and are primarily available in Google Cloud, which can lock you into a specific ecosystem. They also struggle with certain types of models, like those with irregular memory access patterns.

Other Players: NPUs, FPGAs, and More

GPUs and TPUs dominate the AI hardware landscape, but they're not the only options. Neural Processing Units are specialized chips designed for edge devices, where power efficiency is critical. Companies like Apple and Qualcomm have integrated NPUs into their mobile and laptop chips to enable on-device AI features without draining the battery.

Field-Programmable Gate Arrays offer another path. FPGAs are reconfigurable chips that can be customized for specific workloads. They're not as fast as GPUs or TPUs for general AI tasks, but they shine in scenarios where low latency and power efficiency are paramount, like real-time video processing or financial modeling.

Memory and Bandwidth: The Hidden Bottleneck

No matter how fast your chip is, it's useless if it can't get data fast enough. AI workloads are memory-bound, meaning the speed of your memory and the bandwidth between memory and compute units often determine performance. This is why high-bandwidth memory technologies, like HBM, are a critical part of modern AI chips.

Memory hierarchy also plays a role. GPUs and TPUs use a combination of on-chip memory, high-bandwidth memory, and traditional DRAM to balance speed and capacity. The challenge is keeping the compute units fed with data without wasting cycles waiting for memory access.

Choosing the Right Chip for the Job

There's no one-size-fits-all answer. The best chip for your AI workload depends on your specific requirements. If you're training large models in the cloud, GPUs or TPUs are likely your best bet. For edge devices, NPUs or FPGAs might be the way to go. And if you're deploying models in production, you'll need to consider factors like latency, throughput, and cost per query.

Training: GPUs or TPUs, depending on your framework and cloud provider.
Inference in the cloud: GPUs for flexibility, TPUs for cost efficiency if your model is supported.
Edge devices: NPUs or FPGAs for power efficiency and low latency.

Understanding these trade-offs isn't just about picking the fastest chip. It's about making informed decisions that balance performance, cost, and practicality. The right choice can save you time, money, and headaches down the line.

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Why Claude Loses Users to Cheaper AI Tools

Muhammad Adil — Mon, 24 Aug 2026 06:01:05 +0000

Claude 3.5 Sonnet is fast, accurate, and handles complex tasks better than most models out there. Yet, when you check usage stats or talk to teams actually deploying AI, you’ll notice something odd. The cheaper options, often half the price or less, are winning. This isn’t about quality. It’s about economics, workflows, and a few key gaps Anthropic hasn’t closed yet.

The price gap is too wide for most

At $3 per million input tokens, Claude 3.5 Sonnet isn’t just expensive, it’s a premium product in a market where most users don’t need premium. Startups, indie developers, and even mid-sized companies run the numbers. For the same budget, they can get 2-3x the volume on GPT-4o Mini or Llama 3.1. That extra capacity means more experiments, more users, and faster iteration. When the output quality is close enough, the math is simple.

Free tiers shape user habits early

Anthropic offers a free tier, but it’s restrictive. Rate limits are tight, and the model selection is limited. Compare that to competitors: OpenAI’s free tier is generous, and Meta’s Llama models are fully open. Developers build their first prototypes on these platforms. By the time they’re ready to scale, switching feels like extra work. The free tier isn’t just a trial, it’s where loyalty forms.

Integration friction adds up

Claude’s API is solid, but it’s not as deeply embedded in the tools developers already use. Want to deploy on Vercel? There’s a template for GPT. Need a quick chat interface? Streamlit has built-in support for OpenAI. Anthropic’s ecosystem is growing, but it’s playing catch-up. Every extra step, manual API calls, custom middleware, or missing SDKs, makes the cheaper, easier option more appealing.

Where Claude actually wins

Tasks requiring deep reasoning or long context windows, like legal document analysis or multi-file code reviews.
Use cases where safety and bias mitigation are non-negotiable, such as healthcare or financial compliance.
Projects where output consistency matters more than cost, like enterprise-grade content generation.

For these scenarios, teams are willing to pay. But most workflows don’t need that level of precision. A startup building a customer support bot doesn’t care if the model nails a 10-page research summary, they just need answers that sound human and don’t hallucinate too often.

The hidden cost of switching

Even when Claude is the better choice, switching costs keep users locked in. Fine-tuning, prompt engineering, and evaluation pipelines are built around a specific model. Migrating to Claude means retraining teams, rewriting prompts, and revalidating outputs. For a small gain in quality, most teams decide it’s not worth the effort. The inertia of existing workflows is stronger than any feature sheet.

What Anthropic could do differently

Introduce a high-volume, low-cost tier for startups and indie developers, even if it means slightly lower margins.
Expand free tier limits to match competitors, focusing on model variety and rate limits that allow real prototyping.
Invest in tighter integrations with popular dev tools, think one-click deployments on Vercel, Replit, or Hugging Face.
Offer migration tools that simplify switching, like prompt conversion guides or automated output validation.

Until then, the cheaper tools will keep winning. Not because they’re better, but because they fit into how people actually work. Claude’s strength is its performance, but performance alone doesn’t build user bases, accessibility does.

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Autolith: A live runtime for coding agents

Muhammad Adil — Sun, 23 Aug 2026 09:13:51 +0000

AI coding assistants are great at writing code snippets, but they fall apart when the environment changes. A variable name shifts, a dependency updates, or the runtime state drifts. Autolith fixes that by giving agents a live connection to the actual execution context. It’s not just autocomplete, it’s a two-way mirror between the agent and your running code.

What Autolith actually does

Most agents work with static files or isolated sandboxes. Autolith attaches to a real process, whether it’s a local script, a server, or a notebook. The agent can read variables, inspect call stacks, and even inject small patches without restarting. Think of it like a debugger that speaks natural language.

Reads live variable values and types, not just file contents
Executes agent-suggested code in the same process context
Watches for runtime errors and surfaces them to the agent immediately
Supports Python, JavaScript, and Go out of the box

Why live runtime matters

Static analysis only gets you so far. An agent might write perfect code for an API that’s down, or a function that expects a string but gets a buffer. With Autolith, the agent sees the same errors you do. It can catch type mismatches, missing environment variables, or even race conditions that only appear at runtime.

This also speeds up iteration. Instead of guessing what’s wrong, the agent can test its own fixes. If it suggests a new query, it can run it right away and see the result. No more back-and-forth with “try this” and “it didn’t work.”

How it fits into your workflow

Autolith isn’t a replacement for your editor or IDE. It’s a bridge between the agent and the runtime. You can use it with any agent that supports the Autolith protocol, which includes most open-source frameworks. Just attach it to your process, and the agent gets a real-time view of what’s happening.

Start your app or script as usual
Attach Autolith with a single command or API call
Let the agent observe and interact with the live state
Detach when done, your process keeps running

Debugging with an agent that sees the truth

The biggest win is debugging. An agent with live runtime access can trace a bug from the error message back to the root cause. It can check variable states at each step, run assertions, and even suggest fixes that account for the actual data flowing through the system. No more “works on my machine” guesswork.

With Autolith, the agent isn’t just writing code, it’s part of the runtime. That’s the difference between a tool and a teammate.

Where it falls short

Autolith isn’t magic. It can’t fix fundamental design flaws or rewrite entire systems. It also adds overhead, attaching to a process means the agent can see everything, which might be a security concern in some environments. And like any tool, it’s only as good as the agent using it. A weak agent will still write weak code, just with more confidence.

Getting started

If you’re using Python, the setup is simple. Install the package, start your script with the Autolith flag, and point your agent to the runtime endpoint. JavaScript and Go require a small wrapper, but the pattern is the same. The protocol is open, so you can build your own integrations if needed.

Autolith won’t replace your brain, but it will save you hours of debugging. For anyone working with AI agents, it’s the closest thing to having a second pair of eyes on the runtime itself.

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How a student caught an AI hacking attack in Texas

Muhammad Adil — Sun, 23 Aug 2026 08:33:50 +0000

Last spring, a computer science student at the University of Texas noticed something odd in the network logs. A script was probing campus servers, but it wasn’t using the usual brute-force methods. It was adapting, learning from failed attempts, tweaking its approach in real time. That’s when he realized: this wasn’t just another script kiddie. Someone had built an AI to do the hacking for them.

The first red flag

The student, let’s call him Jake, was running a routine check on the university’s firewall logs. Most attacks followed predictable patterns: repeated login attempts, common password lists, or known exploit signatures. This one was different. The requests were spaced irregularly, and the payloads changed slightly after each failure. It looked like the attacker was testing different combinations, but with a level of variation that didn’t match human behavior.

Jake dug deeper. He set up a honeypot, a fake server designed to attract attackers, and mirrored the traffic. Within hours, the script had shifted its focus to the honeypot, probing for vulnerabilities with increasing precision. That’s when he knew: this was an AI-driven attack. The script wasn’t just following a pre-written list. It was analyzing responses and refining its strategy on the fly.

How the AI worked

The attacker had built a lightweight reinforcement learning model. It started with a basic set of credentials and exploit attempts, then adjusted based on server responses. If a login failed, it would try a slightly different username or password next time. If a payload triggered a 403 error, it would modify the request headers. Over time, it became more effective, not because the attacker was smarter, but because the AI was learning from its mistakes.

The model used a small neural network, trained on public exploit databases and leaked credential lists.
It ran on a cloud VM, likely rented with stolen credit cards, to avoid IP-based blocking.
The script included rate-limiting to avoid triggering automated defenses, making it harder to detect.
It targeted low-priority systems first, like student portals, to avoid immediate attention.

Stopping the attack

Jake didn’t panic. He isolated the honeypot, ensuring the AI couldn’t pivot to real systems, then contacted the university’s IT security team. Together, they set up a tarpit, a server that deliberately slows down responses, to waste the AI’s time. The longer the AI spent waiting for replies, the less effective it became. Meanwhile, they traced the originating IP to a cloud provider in Eastern Europe.

The cloud provider shut down the VM within hours, but not before Jake’s team captured the AI’s model. Analyzing it revealed a sloppy but functional design: the attacker had cobbled together open-source tools and a basic reinforcement learning framework. It wasn’t sophisticated, but it was effective enough to fly under the radar, until Jake noticed the patterns.

What developers can learn

This incident wasn’t an isolated case. AI-driven attacks are becoming more common, and they’re harder to detect than traditional hacking attempts. The good news is that the same principles that caught this attack can help defend against others.

Monitor for irregular patterns, not just known signatures. AI attacks won’t follow the usual scripts.
Use honeypots to lure and study attackers without risking real systems.
Rate-limiting and tarpits can disrupt AI-driven attacks by forcing them to waste time.
Collaborate with cloud providers to shut down malicious VMs quickly.
Assume attackers are using AI. Build defenses that adapt, not just block.

The bigger picture

This wasn’t a nation-state attack or a high-profile breach. It was a low-budget experiment by someone testing how far they could push AI-driven hacking. The fact that a student caught it before it caused damage is a win, but it’s also a warning. As AI tools become more accessible, so do AI-powered attacks. Defenders need to stay ahead, not just by patching vulnerabilities, but by understanding how attackers are evolving.

Jake’s story isn’t just about one attack. It’s about the future of cybersecurity. The next time you check your logs, ask yourself: is this a human, or is this an AI learning how to break in?

credit: @Leo_Marchandon
Thomson Reuters

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Cut Rust LSP RAM Use Without Losing Core Features

Muhammad Adil — Sun, 23 Aug 2026 05:27:33 +0000

Rust-analyzer is the most widely recommended and used language server for Rust. It works well, but on large codebases it can consume significant memory. That memory load slows down your editor, forces swapping, and eats into the RAM available for your actual build. There’s a leaner alternative that gives you core features for a fraction of the cost.

Why the default LSP eats RAM

Rust-analyzer builds a full semantic model of your project. It indexes every crate, resolves every symbol, and keeps the entire syntax tree in memory. That’s great for refactoring and deep static analysis, but most edits don’t need all of that. A quick syntax check or a single completion only requires a small slice of the data. The rest sits idle, consuming RAM.

Enter rust-glancer

Rust-glancer is not a drop-in replacement for rust-analyzer; it is an alternative LSP with lower memory usage but does not support all rust-analyzer features. It uses the same protocol and exposes many of the same editor features. Under the hood, it skips the heavy indexing and only loads what the current request needs. The result is memory use that scales with the size of the visible file, not the entire project.

Real-world memory use

On large Rust workspaces, rust-analyzer can consume several gigabytes of memory. Rust-glancer, running the same editor session, uses significantly less. The trade-off is a slight delay when you first open a file, because it has to parse on demand. Subsequent edits are fast, since the data is cached for the duration of the session.

What you still get

Code completion for local and imported symbols
Inline error squiggles and quick fixes
Go-to-definition and find references
Basic refactoring like rename symbol
Hover documentation and type hints

What you lose is deep project-wide analysis. If you rely on features like call hierarchy or precise unused-code detection, rust-glancer won’t replace rust-analyzer. For most day-to-day coding, though, the difference is invisible.

How to switch

Installation involves downloading, moving the binary, and updating editor settings. Rust-glancer is distributed as a pre-built binary.

Download the latest release from the GitHub repo repourl
Move the binary to /usr/local/bin or your preferred location
Update your editor’s LSP settings to use rust-glancer
Restart your editor and verify the new server is active

When to stick with rust-analyzer

If you work on a small project or have RAM to spare, the default server is still the better choice. It’s more mature, has better error recovery, and supports the full range of IDE features. Rust-glancer is for situations where memory is the bottleneck, like large monorepos or machines with limited resources.

The choice isn’t permanent. Switching is straightforward but may require minor setup adjustments. Try rust-glancer for a week; if it doesn’t slow you down, keep it. If you miss a feature, revert to rust-analyzer. Either way, you’ll have a leaner Rust setup.

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How to Rename Columns in Polars

Muhammad Adil — Sat, 22 Aug 2026 16:19:14 +0000

Renaming columns is one of the first things you do after loading messy data. Polars keeps it simple, and nothing mutates in place: every rename returns a new frame. Here is each clean way to do it.

Rename one column or several at once

The main tool is df.rename, which takes a dict that maps old names to new ones. One pair or many, it works the same.

df = df.rename({"old": "new"})
df = df.rename({"a": "alpha", "b": "beta"})
# only the columns you name change; the rest stay as they are

Replace column names with a dict

If you already have a mapping dict, hand it straight to rename. This is the answer to replacing column names from a dictionary.

mapping = {"col1": "id", "col2": "date", "col3": "amount"}
df = df.rename(mapping)

Rename every column with a function

To transform all the names at once, build the dict from df.columns with a comprehension. This covers lowercasing, trimming, and swapping spaces for underscores.

# lowercase every column name:
df = df.rename({c: c.lower() for c in df.columns})
# strip spaces and use snake_case:
df = df.rename({c: c.strip().lower().replace(" ", "_") for c in df.columns})

Rename inline with alias

When you are already selecting or transforming, rename in the same step with .alias instead of a separate rename call. This is the idiomatic Polars style inside select and with_columns.

df.select(pl.col("old").alias("new"))
df.with_columns((pl.col("price") * 1.2).alias("price_with_tax"))

Everything here works the same on a LazyFrame. Renaming only relabels, so it is cheap: use it freely early in a lazy pipeline before you collect. For the full toolkit, see the Python Polars cheat sheet. Next up: creating DataFrames and filtering rows.

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View and Inspect Polars DataFrames

Muhammad Adil — Sat, 22 Aug 2026 16:18:49 +0000

Polars truncates output by default so a print does not flood your terminal. That is great until you actually want to see everything. Here is how to inspect a frame, print all rows, add row numbers, and pull data out.

Print all rows and columns

Raise the display limits with pl.Config. Pass -1 to show every row. Wrap it in a context manager if you only want it for one print.

pl.Config.set_tbl_rows(-1) # show all rows
pl.Config.set_tbl_cols(-1) # show all columns
print(df)
# temporary, just for this block:
with pl.Config(tbl_rows=-1):
print(df)

Add row numbers

with_row_index adds a 0-based index column, which is the Polars way to get line numbers for a DataFrame. (In older versions this was with_row_count.)

df = df.with_row_index("row")
# start at 1:
df = df.with_row_index("row", offset=1)

Convert a column to a list

Pull a column out as a plain Python list with to_list. This answers using to_list() in Polars.

values = df["a"].to_list()
# or explicitly:
values = df.get_column("a").to_list()

Quick looks

A few one-liners for a fast read on any frame.

df.head(5)
df.tail(5)
df.sample(5)
df.describe()
df.schema # column names and types
df.shape # (rows, columns)

Stack frames: hstack and vstack

hstack adds columns side by side, vstack stacks rows. For many frames at once, pl.concat is cleaner. This is what hstack does in Polars.

df.hstack([other]) # add columns (same number of rows)
df.vstack(other) # add rows (same columns)
pl.concat([df1, df2]) # stack many frames by rows
pl.concat([df1, df2], how="horizontal") # side by side

To build the frames you are inspecting, see creating DataFrames; to narrow them down, see filtering rows. Everything else is in the Python Polars cheat sheet.

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Create Polars DataFrames: Dict, NumPy, Pandas, pyreadstat

Muhammad Adil — Sat, 22 Aug 2026 16:18:19 +0000

Data comes from everywhere: a quick dict, NumPy arrays, an existing Pandas frame, or a stats file. Polars converts from all of them directly. Here is every path into a Polars DataFrame.

From a Python dict

The fastest way to hand-build a frame. Keys become column names, lists become columns.

import polars as pl
df = pl.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
# or from a list of row dicts:
df = pl.from_dicts([{"a": 1, "b": "x"}, {"a": 2, "b": "y"}])

From NumPy arrays

Pass a 2D array with a schema for the column names, or build columns from separate 1D arrays with a dict.

import numpy as np
arr = np.array([[1, 2], [3, 4], [5, 6]])
df = pl.from_numpy(arr, schema=["a", "b"])
# from separate 1D arrays:
df = pl.DataFrame({"a": np.arange(3), "b": np.random.rand(3)})

From a Pandas DataFrame

One call, and it keeps your column names and types. Use this to move a hot path from Pandas to Polars.

df = pl.from_pandas(pandas_df)
# go back when a library needs Pandas:
pandas_df = df.to_pandas()

From SPSS or SAS files with pyreadstat

Polars does not read .sav or .sas7bdat directly. Read them with pyreadstat into a Pandas frame first, then convert. This is how you turn a pyreadstat DataFrame into a Polars DataFrame.

import pyreadstat
pdf, meta = pyreadstat.read_sav("survey.sav") # or read_sas7bdat(...)
df = pl.from_pandas(pdf)

Grab a single value

To pull one scalar out of a frame, use .item. This answers getting a DataFrame item in Polars.

value = df.select("a").item(0, 0)
# or the single value of a 1x1 result:
total = df.select(pl.col("a").sum()).item()

From here you will want to filter rows and read and write files. The full reference lives in the Python Polars cheat sheet.

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