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When I started working on a WooCommerce slider plugin, I noticed that many existing solutions were packed with features that a lot of websites simply don’t need. More features often mean more CSS, more JavaScript, and a bigger impact on performance. So I asked myself a simple question: What would a WooCommerce slider look like if performance came first? My goals Instead of creating another all-in-one slider plugin, I focused on a few principles: Lightweight codebase Fast loading times Responsive by default Easy integration with Gutenberg Elementor support Shortcode support Clean and maintainable architecture Performance matters Every additional request and every unnecessary asset affects page speed. Some optimizations I implemented include: Assets are loaded only when required. Local libraries instead of unnecessary external requests. Server-side rendering where appropriate. Clean HTML output. Developer experience I also wanted the plugin to be simple for users. Instead of a complicated interface, the goal was: Install Create a slider Insert it into a page Done Lessons learned Building a public WordPress plugin taught me a lot: Documentation is almost as important as the code. User feedback quickly reveals edge cases you never considered. Keeping the codebase simple often leads to better long-term maintainability. What’s next? I’m continuing to improve the plugin by adding new features while keeping performance as the top priority. I’d also love to hear how other developers approach WordPress plugin development and performance optimization. Thanks for reading! ⸻ If you’re interested, you can check out my project here: WordPress.org: https://wordpress.org/plugins/amitry-product-category-slider/ GitHub: https://github.com/amitry-de/amitry-product-category-slider Live Demo: https://slider.amitry.de/

The evals for my agent skills scored 0% for as long as I had records. Not low. Not noisy. Exactly zero, every skill, every run. And I believed it. For months I thought my skills were bad, because the number said so and the number never wavered. Then one night I actually read the harness. It had fallen back to the wrong auth token. Every call it made came back 401, and it quietly graded each one a failure. The skills never got a chance to fail on their own. I was not measuring them at all. I was reading a broken thermometer. Real weakness is jagged Here is what took me too long to see. When a system is genuinely bad, it scores 40% one week and 60% the next. It passes the easy cases and trips over the hard ones. It has good days. Incompetence has texture, because an incompetent system is still in contact with the world, and the world varies. A flat number has no texture. A flat number means the measurement stopped touching the thing being measured somewhere upstream, and what you are reading is the instrument's resting state. Doctors know this. A heart monitor drawing a perfectly straight line does not mean the patient is calm. Only a broken thermometer writes the same number every time. Key insight: A performance number with no variance is a reading of the instrument, not of the thing being measured. The same bug in three industries I run systems in advertising, in healthcare billing, and in agent operations, and the same shape shows up in all of them. In advertising I found a dashboard figure that had been hardcoded for two years. Nobody questioned it, because it looked right, and it looked right because it never moved. In agent operations, an account-rotation bug in one of my pipelines overwrote every real error with the same generic message, "no active accounts," so for a while every distinct failure in that system looked identical. And in the denial-assessment engine I run for a medical-billing operation, an agreement metric came back at 44.7%, alarmingly low, un

I used to deride the idea of an AI assistant from the moment they entered the picture (after seeing all the different *Claw variants). Why would regular people like me need an assistant? The best use case I had heard was: "Oh! It helps us decide whether I or my partner should drive the kids today!" Solving that sounded like a silly problem for an AI assistant to handle. Again, I didn't know any better because I didn't have that problem. I thought I could handle one-off tasks with just an AI subscription. What else was there? I only found the answer once I had a specific use case for it. I attended a business event, talked to a dozen people, and collected several business cards. I wanted to send each person a personalized email thanking them and continuing our conversation. If I were to do this manually, the process would look like this: open the email client, manually type in each email address from the business cards, ensure I typed everything correctly, compose my message, and again, make sure I didn't press "send" prematurely. Just thinking about it felt tedious. That is when the idea of an assistant started making sense. I fire up my coding agent(not a *Claw still), I take a single photo of all the business cards together. I ask the it to extract the names and email addresses. Then, I ask it to loop through the list and ask me what I want to send to each person. It creates drafts(which I still manually review - can't trust them enough), I say send, and then it sends them all automatically. It feels exactly like talking to a real assistant: you tell them what you want done, and it gets done without you having to press buttons or navigate a UI. That is exactly what I did; I simply gave it instructions using my voice. This makes me feel that having an AI assistant is indeed helpful. It might have also been useful to jump on this a little sooner, as I could have bought that Mac Mini at the older, lower price.

Cx Dev Log — 2026-07-18: A Moment of Pause Before the Next Push The Cx codebase has taken a breather. It's been quiet for five days, a rarity in the fast-paced world of solo-developed languages. Main is stable at commit 3430e4e , marked by the last 0.3.1 release tag from July 9. Submain's clocked at 3b7b7f8 with the freshly finalized gene/phen design as of July 14. Even the automated matrix stands firm: 321 tests passing, zero failing. But quiet isn't inactivity—it's anticipation. Where the Project Sits The significant chunk of work that wrapped on July 14 was hefty: the gene/phen design's v1.1 spec. It doesn't just wrap dispatch strategies; we're talking about cleaner Ord mappings, robust Self resolutions, and handling phen lookups with cross-module coherence. Those are down in a 13-commit series on submain, the product of a thorough hammering out of all six outstanding design questions. But it wasn't just theoretical. These commits include necessary parser and semantic adjustments, say, coming out of our recent audit. Scoping issues are in check, width-range enforcement leveled up, and we've put any unnecessary comparison errors on Bool/Enum to bed. There's a crucial runtime patch too—no more enum == / != crashes blowing up the interpreter. We've also streamlined CI through direct run_matrix.sh runs. These advances sit 13 steps ahead of main without a single technical barricade to rushing them into action. This delay in merging? It's purely deliberate, not dictated by troublesome conflicts or failing tests. What's Queued Once Work Resumes So, what's cooking when the fingers start flying across keyboards again? Here's what's lined up: Merge submain to main. We've got 13 commits begging for integration. Expect this to be painless, almost ceremonial, since main's been untouched. 0.3.4: Gene/Phen Implementation. The design spec isn’t a riddle wrapped in an enigma—it's a clear blueprint. It's got everything: pass ordering, canonical key formats, robust collision detect

Everything you need to go from pip install to a working multi-object tracker, one runnable snippet at a time. kalbee is a Python library for state estimation — the art of recovering a clean signal (position, velocity, temperature, whatever you're measuring) from noisy sensor data. This guide walks through it from the ground up. Every code block runs as-is; copy them into a file and follow along. Install pip install kalbee The only runtime dependencies are NumPy and SciPy. Optional extras add object-detection ( pip install "kalbee[yolo]" ) and plotting ( pip install "kalbee[viz]" ) support. The one idea you need: predict and update Every filter in kalbee works the same way. You alternate between two steps: predict() — advance the state forward in time using a motion model ("where do I think the object is now?"). update(z) — correct that prediction with a new measurement z ("what does the sensor actually say?"). The filter tracks two things: the state x (your best estimate) and the covariance P (how uncertain that estimate is). You read them back via kf.x and kf.P . Your first filter Let's track an object moving at roughly constant velocity, measuring only its (noisy) position. Instead of hand-building matrices, we use kalbee's ready-made models : import numpy as np from kalbee import KalmanFilter , rmse from kalbee.models import constant_velocity , position_measurement_model dt = 1.0 # Motion model: state is [position, velocity] F , Q = constant_velocity ( dt = dt , process_var = 0.01 , n_dims = 1 ) # Measurement model: we observe position only, with noise variance 4.0 H , R = position_measurement_model ( order = 1 , n_dims = 1 , measurement_var = 4.0 ) # Simulate a noisy trajectory rng = np . random . default_rng ( 0 ) pos , vel = 0.0 , 1.0 truths , measurements = [], [] for _ in range ( 50 ): pos += vel * dt truths . append ( pos ) measurements . append ( pos + rng . standard_normal () * 2.0 ) # std 2.0 -> var 4.0 # Create the filter: start at zero with high uncert

Originally published on tamiz.pro . Real-time systems are at the heart of modern interactive applications, from chat platforms to collaborative editing tools and financial dashboards. Delivering a seamless, low-latency experience while ensuring high availability and fault tolerance presents significant architectural challenges. This deep-dive explores how WebSockets, for persistent client-server communication, and Redis, for state management and pub/sub, can be combined with strategic design patterns to build resilient real-time systems. The Core Challenge of Real-Time Resilience The primary challenge in real-time systems is maintaining continuous connectivity and data flow despite inevitable network issues, server failures, or application restarts. A single point of failure can disrupt numerous active user sessions, leading to a poor user experience. Resilience, in this context, means the system's ability to recover gracefully from failures and continue operating, even if in a degraded state. WebSockets: The Foundation for Real-Time Communication WebSockets provide a full-duplex communication channel over a single TCP connection, enabling persistent, low-latency message exchange between client and server. Unlike traditional HTTP requests, WebSockets keep the connection open, eliminating the overhead of connection setup for each message. However, managing a large number of concurrent WebSocket connections and ensuring their availability across a distributed system requires careful design. WebSocket Server Scalability and Load Balancing Directly load balancing WebSocket connections using standard HTTP load balancers can be tricky because WebSockets are long-lived. Sticky sessions are often employed to ensure a client consistently connects to the same backend server. While this works, it can lead to uneven server load and complicates server replacement during failures. A more robust approach involves a dedicated WebSocket gateway layer that can manage connections and

Why FaultPlane Exists Modern low-latency infrastructure demands bypassing heavy disk serialization. FaultPlane addresses this by implementing an eBPF-driven architecture designed for zero-copy memory management. We are building the core engine along with an interactive Next.js operations dashboard, and we need your expertise to accelerate development. Our Technical Stack Core Infrastructure: Go (Golang), eBPF, Kernel-space architecture, PCIe DMA abstractions Operations Dashboard: Next.js, Tailwind CSS, TypeScript Active Challenges (Good First Issues Available) We have organized our current roadmaps into highly accessible, well-documented GitHub issues. Contributors of all skill levels are welcome to claim tasks: Architecture & Documentation: Migration of repository references and architectural namespaces from AgentMesh to FaultPlane. Frontend Engineering: Implementation of a minimalist dashboard grid layout with command palettes, responsive sidebar navigation, and multi-region panel toggles. System Programming: Implementation of lock-free ring buffer memory allocators using sync/atomic flags, and eBPF sockmap TCP stream splicing. How to Get Your First Pull Request Merged Explore our current open tracker list: https://github.com/devloperdevesh/FaultPlane/issues Leave a comment on the issue you wish to claim, and it will be assigned to your profile immediately. If you require setup assistance or technical clarification, start a thread under our discussions tab or leave a query right here in the comments. Join us in building a high-performance open-source system engine.

I had a rule with no exceptions. RETSBAN, my primary agent, runs local inference only: open models on my own hardware, no cloud, no fallback. If the GPU box is down, the agent is down. Then Mia, the agent that runs my marketing, needed a frontier model to do her job well. And I did something that felt strangely formal for a one-person company. I amended my own policy, in writing, with the date attached. No one was in the room Here is what took me a while to see. A human employee remembers the day you made an exception. They were in the room when you said fine, just this once. An agent was never in the room. There is no room. Every session starts cold from the files, and the files are the only memory the company has. So when the policy says local only, no exceptions, and reality contains an exception, one of two things happens. Either the agent obeys the file and blocks work I actually want done, or it notices the contradiction and starts guessing which side to trust. The guessing is the dangerous case. A rule that has been contradicted once, silently, is not a rule anymore. Every agent that loads it gets to decide, in every session, whether to believe it. You never see the deciding. You just see the drift. An exception that lives in your head does not bend a rule. It erases it. The amendment On 2026-04-23 I opened the policy file, a file literally named local_only_no_anthropic, and narrowed it instead of breaking it. The amendment names who is exempt: Mia, and only Mia. It says why: marketing work that needs capability the local stack does not have. It carries the date, and it points to the full model-topology doc for anyone, human or agent, who wants the whole picture. RETSBAN's constraint did not move an inch. Still local only. Still no fallback. Still down when the GPU box is down. That is the difference between an amendment and a repeal. An unwritten exception repeals the rule and hides the repeal. A written amendment narrows the rule and makes it stronger, beca

When you give an LLM a tool, you hand it a real function and let it choose the arguments. Those tools are everything your agent can do to a real system: read a file, write to your database, send an email, run a shell command, delete data. They are your risk surface, and most teams have never looked at it in one place. So we did. We ran scan across a batch of popular open-source TypeScript AI agents. A few of the things it found, none of them exotic: A coding agent whose grep and glob tools, which sound read-only, actually shell out through execSync . Its bash tool passes a model-chosen string straight to spawn . Arbitrary command execution, behind three innocuous names. A query tool that fires an HTTP DELETE . A "query" that deletes. A calculator that runs eval on whatever the model types, in a widely-used agent framework. Arbitrary code execution behind the friendliest name in the box. A send-email tool that posts to an array of recipients, so the model chooses who gets mailed. The single most common finding, in almost every agent we scanned: a fetch tool aimed at whatever URL the model supplies. That is a door to your internal network (an SSRF surface). Notice the pattern. The dangerous tools are not named dangerous . They are named grep , query , calculator . A name is a claim. The code is the evidence. See your own agent's tools scan reads that evidence. One command, no install, no signup, no code change: npx @agentx-core/scan . It lists every tool the model can call and ranks each one by what it can do, from read-only up to destructive: 🔍 AGENTX SCAN (TypeScript · 3 files · 5 tools) =========================================================================== RISK TOOL GUARD WHY ---- ---- ------ ------------------------ high calculator yes calls `eval` lib/tools/compute.ts:4 high grep yes calls `execSync` lib/tools/system.ts:8 med sendEmail yes calls `mailer.send` lib/tools/io.ts:5 med fetchUrl yes outbound req to agent-controlled host (SSRF) lib/tools/io.ts:11 2