AI feels like it "suddenly" got smart in the last few years. It didn't. It's been evolving in distinct stages for over 70 years — each one building on the limits of the last. Here's the journey, broken down simply. Stage 1: Rule-Based AI (1950s-1980s) The earliest AI wasn't "intelligent" — it was a giant pile of if-else logic written by humans. How it worked: Programmers manually coded rules. "If symptom X and symptom Y, then diagnose Z." Chess engines, expert systems, early chatbots like ELIZA — all rule-based. The limit: These systems couldn't learn. Every scenario had to be explicitly programmed. Show it something outside its rules, and it broke. Stage 2: Machine Learning (1990s-2000s) Instead of hand-coding every rule, engineers started teaching systems to find patterns in data themselves. How it worked: Algorithms like decision trees, support vector machines, and linear regression learned relationships from labeled examples — spam vs. not spam, fraud vs. not fraud. The limit: These models needed carefully hand-engineered "features" (inputs) prepared by humans. They also struggled with messy, unstructured data like raw images or audio. Stage 3: Deep Learning (2010s) This is where things accelerated. Neural networks with many layers ("deep" networks) could learn features automatically from raw data, given enough compute and data. How it worked: Instead of a human deciding "look at edges, then shapes, then objects" in an image, the network learned that hierarchy itself. This powered breakthroughs in image recognition, speech-to-text, and translation. The limit: Deep learning was narrow. A model trained to recognize cats couldn't write an email. Each task needed its own model trained from scratch. Stage 4: Generative AI & LLMs (2018-Present) The current stage. Large Language Models like GPT and Claude are trained on massive amounts of text to predict "what comes next" — and in doing so, they pick up grammar, facts, reasoning patterns, and coding ability, all from o

Hello everyone! After publishing my Machine Learning with ML.NET series, I decided to turn the recommendation model into a complete application. In this new series, we build RockPlayer, a rock music player that combines modern software architecture, ASP.NET Core, Angular 22, Redis, and YouTube integration. Each article focuses on a different part of the project: 🎵 1. Introducing RockPlayer An overview of the project, its goals, and the overall architecture. https://devfullstack.net/blog/introducing-rockplayer 🔌 2. Adapters: Isolating the YouTube Provider Using the Adapter pattern to decouple the application from the YouTube integration. https://devfullstack.net/blog/adapters-isolating-the-youtube-provider ⚡ 3. No Database: Caching Lookups with Redis Using Redis to cache search results instead of storing external data in a database. https://devfullstack.net/blog/no-database-caching-lookups-with-redis 🅰️ 4. Angular 22 in Practice Applying modern Angular 22 features to build the user interface. https://devfullstack.net/blog/angular-22-in-practice 🚀 5. Building the RockPlayer API Building the API that orchestrates the application. https://devfullstack.net/blog/building-the-rockplayer-api ▶️ 6. The YouTube Adapter: Finding and Playing the Song Implementing the YouTube integration to search for and play songs. https://devfullstack.net/blog/the-youtube-adapter-finding-and-playing-the-song 🎧 7. RockPlayer in Angular 22: Onboarding Setting up the Angular application and organizing the project structure. https://devfullstack.net/blog/rockplayer-in-angular-22-onboarding 🎸 8. RockPlayer: Putting It All Together Bringing all the components together into a complete application. https://devfullstack.net/blog/rockplayer-putting-it-all-together I hope this series is useful for developers interested in software architecture, .NET, and Angular. See you there!

Gemini Prompt for Google AI Studio Image Generation Prompt (paste into Gemini image generator): A futuristic cityscape at sunset with a swirling vortex of neon lights and flying cars; multiple translucent tetrahedron bubbles forming a luminous word-and-light matrix suspended above a skyline of glass spires; warm magenta and orange sunset on the horizon blending into electric cyan and violet neon; reflective wet streets below mirroring the tetrahedra; dynamic motion blur on flying vehicles; volumetric fog and light shafts; high-detail, cinematic wide-angle, ultra-detailed textures, rim lighting on edges, subtle lens flares, 8k, photorealistic + stylized neon cyberpunk aesthetic. Suggested Generation Settings: Model: Gemini multimodal image model Aspect Ratio: 16:9 (wide cinematic) Quality / Resolution: High / 8k or max available Style: Cyberpunk photoreal + neon stylized Guidance / Creativity: Medium-high (to keep structure but allow creative tetrahedron arrangements) Seed: Leave blank for variety or set a fixed seed for reproducible results Safety / Content Filters: Default on Image Variations to Request Close-up: Single tetrahedron bubble with internal micro-lights forming a single glowing word fragment. Aerial: Bird’s-eye view of the vortex and traffic lanes of flying cars. Night variant: Same scene fully after dark with intensified neon contrast. Motion study: Long-exposure streaks from flying cars and rotating tetrahedra. Export & Integration Notes Export images as PNG for transparency-friendly assets and MP4 or animated WebP for short looping demos. Generate a short 10–15s video loop from Gemini if available to show the vortex animation for your demo. Use the image as background and the video loop as a hero demo in your CodePen prototype. DEV Submission (Ready-to-publish Markdown) Title Multiple Tetrahedron Bubble Word and Light Matrix — A Neon Vortex Cityscape What I Built What I built: a generative visual piece that layers geometric tetrahedron bubbles into a

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry. The...

I judged three hackathons over about ten days this July: MLH x DigitalOcean "AI for Social Good" on July 11, the Sports World Cup Hackathon in San Francisco on July 17, and Aethera Hacks, an online event on Devpost, across July 19 to 21. I came in from the sports technology side, building athlete monetization tools, so I was usually the judge asking who pays for this rather than the judge asking what is your bundle size. That turned out to be a useful seat, because the questions that decide scores are mostly not technical ones. Here is the part builders rarely get told: a judge is scoring under a hard constraint. Some number of teams, a fixed window, and by the middle of the block the demos start blurring together. Judges are not evaluating your project against an ideal. They are ranking it against the six they just saw while trying to remember which one had the map. Everything below follows from that. The rubric is real, but it is not what separates teams Most events hand judges four or five categories with numbers next to them. Technical difficulty, originality, design, impact, something about use of a sponsor API. Those categories are real and I filled them in honestly. But they compress. Almost every team lands mid-range on most of them, and the spread that produces a winner comes from two or three things the rubric does not name directly. 1. Whether the demo ran This sounds too obvious to write down. It is the single largest score differentiator I saw. A working demo, live, on the judge's screen or the team's laptop, beats a more ambitious project shown as slides almost every time. Not because judges are impressed by working software as such, but because a live demo removes doubt, and doubt is what a judge is actually managing under time pressure. The practical version: cut scope until something end-to-end runs. One complete path through the product beats four half-built paths. If your architecture diagram has six boxes and two of them work, demo the two and de

Hi HN community, As a busy techie with kids and other family obligations and my recent health diagnosis, I wanted an efficient way to integrate workouts in my daily routine and my research indicated that HIIT/Tabata style routines fit that requirement much better than a dedicated long running/planned workout routine. I made a simple PWA to help me with that endeavor that I have found useful and hoping some of you may like it as well. No sign ups needed but you can sign in if you'd like to save c

GitOps for AI Agents: Treating Tool Configs and Memory Like Production Infrastructure Stop managing AI agent configurations as fragile scripts. Adopt GitOps principles for AI, treating your tool configs and memory as version-controlled, auditable infrastructure-as-code. Learn to implement mcp.jsonc, PR-reviewed workflows, and CI validation for reliable, reproducible AI. The Configuration Chaos in Modern AI Agents Today's AI agents are powerful orchestrators, not just chatbots. They connect to dozens of external tools, databases, and APIs via configurations that define their capabilities, permissions, and memory pathways. But this configuration—often scattered across JSON files, environment variables, or proprietary dashboards—becomes a critical vulnerability. A single typo in a tool's endpoint URL or an incorrect memory namespace can cause silent failures, security leaks, or non-reproducible agent behavior across development and production. Consider a common scenario: Your team updates an AI agent's access to a vector database for its long-term memory. The change is made directly in a production dashboard by an engineer. A week later, the agent starts hallucinating corrupted context. Reverting is guesswork because there's no change log, no PR review, and no record of the previous state. This is the classic "configuration drift" problem that plagued traditional infrastructure, and it's now crippling advanced AI systems. The Infrastructure-as-Code Paradigm for AI The solution lies in applying mature DevOps practices to AI management. We must stop treating AI configurations as special snowflakes and start treating them as infrastructure as code . This means storing all defining components—tool endpoints, authentication scopes, memory indexes, and even behavioral guardrails—in a version-controlled repository. The industry-standard format for this is emerging as mcp.jsonc , a JSONC (JSON with Comments) file that defines an agent's Model Context Protocol tools and memory

Forget YouTube videos—frontier physical AI models need multiple camera angles, dense annotation, and soon, brain wave readings.

I wrote about the Electricity Planning Engine a little while back, including a timezone bug that made a correct price look "not found" after a database round trip. A few days later, Alex Shev left this comment: Timezone bugs are brutal in planning engines because the result can look mathematically correct while being operationally wrong. Energy workflows especially need tests around boundaries, not just averages. That is a genuinely sharp way to put it, and it is not just a comment about the bug I already wrote about. It is a comment about how I test the project in general, and I did not like how well it applied once I went and checked. The part that stung a little "Looks mathematically correct while being operationally wrong" is exactly what the original timezone bug was. PriceSeries::priceAt() threw a clean "price not found" error, which is arguably the good version of that failure mode: loud, easy to catch, hard to ship. A quieter version of the same class of mistake, off by one hour instead of missing entirely, would not throw anything. It would just return a plan that looks completely reasonable and is wrong the entire time it runs. Alex's second point, boundaries over averages, is the one I actually had to go check rather than just agree with in the abstract. So I opened tests/Unit/Domain/Contract/PricingStrategyTest.php and looked at every hour used in every peak/off-peak assertion: new DateTimeImmutable ( '2026-07-18 14:00:00' ) // peak new DateTimeImmutable ( '2026-07-18 23:00:00' ) // off-peak new DateTimeImmutable ( '2026-07-18 05:00:00' ) // off-peak 14:00, 23:00, 05:00. Every single one comfortably inside its window. None of them anywhere near the actual transition. The off-peak slot in the config is 22:00 to 06:00 , and the comparison behind that lives in TimeSlot::contains() : // wraparound slot, e.g. 22:00 -> 06:00 return $minuteOfDay >= $this -> startMinuteOfDay || $minuteOfDay < $this -> endMinuteOfDay ; That >= versus < is exactly the kind of one-

I spent the last while building a prompt-injection detector trained on 78,000+ attack samples. Here's what surprised me, and why I ended up going the unfashionable route. The trendy approach is to use an LLM. I didn't. The default move in 2026 is "use an LLM to judge whether input is an attack." It's appealing because models understand nuance. But once you try to run it inline on every request, the problems pile up fast: Latency. You've added a full model round-trip to every single call. Hundreds of milliseconds, minimum. Cost. Your security bill now scales with your traffic. Every request pays the token tax. Non-determinism. The same input can get a different verdict tomorrow. Try explaining that in an incident review. It's jailbreakable itself. Your security model is an LLM, which means it's vulnerable to the exact attacks it's supposed to catch. So I built the boring version instead: deterministic regex plus classical ML (TF-IDF character n-grams into logistic regression). No LLM in the detection path. It runs in about 7ms, costs nothing per call, and is fully deterministic. What the data actually showed Here's the part I want to be honest about, because most vendors quote one number and hide the rest. Measured on public benchmarks the model was not trained on (held out, non-circular): Real-world, in-the-wild jailbreaks: 0.895 recall at 1.00 precision Obfuscated / evasion attacks: 0.799 at 1.00 precision A frozen external split: 0.804 recall, 0.48% false-positive rate Subtle roleplay-framed jailbreaks: 0.324 That last number is bad, and it's the most important one on the list. The honest read is that deterministic detection is excellent on real-world and obfuscated attacks and weak on subtle roleplay framing. That's a real gap, and pretending otherwise just means someone finds it later and trusts you less. The false-positive rate is a moving target One thing I didn't appreciate going in: FPR is completely traffic-dependent. The same model reads roughly: ~0.4% fal

I've built dozens of dashboards. Most have been ignored. A few have been used constantly. The difference isn't the graphs. It's the design. The 3-second test A useful dashboard answers 'is everything OK?' in 3 seconds. Not 'let me scroll through 40 graphs to find out.' Big colored header at the top: green = healthy, yellow = watching, red = broken. That's the 3-second answer. Everything else is drill-down. The hierarchy rule Three layers, no more: Overview — one line per service, status color, key SLI Service detail — one dashboard per service, 6-12 graphs max Deep dive — triggered from service detail, domain-specific Anything beyond 3 layers is 'please get lost in my dashboard tree.' The on-call test Imagine you're on-call at 3 AM. You get paged for 'service X is slow.' Can you, in 30 seconds, use this dashboard to tell if the problem is the service itself, its database, its upstream dependency, or its downstream consumers? If yes, the dashboard works. If no, redesign. What to cut Graphs with no baseline (flat line or spiky forever — how do you know if it's bad?) Metrics you've never used in an actual incident Vanity metrics (total requests ever) Graphs where the y-axis is in units nobody understands The hidden metric The real measure of a dashboard's value: does the on-call engineer open it before or after the paging tool? If they open it first — it's their compass. If they open it only after being paged — it's a reference, not a dashboard. Aim for the first. Written by Dr. Samson Tanimawo BSc · MSc · MBA · PhD Founder & CEO, Nova AI Ops. https://novaaiops.com

Steam Forum ClickFix: Fake Repair Commands Lead to XMRig SYSTEM Persistence 1. Basic Information Article Title : Steam forum ClickFix attacks infect gamers with XMRig cryptominers Publisher : BleepingComputer Publication Date : July 25, 2026, 18:37 EST / July 26, 2026, 07:37 JST Original Source : https://www.bleepingcomputer.com/news/security/steam-forum-clickfix-attacks-infect-gamers-with-xmrig-cryptominers/ Related Information Sources : At the time of publication, no alternative primary sources providing a detailed analysis of this campaign were identified. Related Malware, Threat Groups, CVEs, and Products : XMRig ClickFix PowerShell Microsoft Defender Windows Firewall Windows Task Scheduler Steam Discussion Forums Unknown Threat Group No CVE Related IOCs : Domain: msfconfig[.]icu URL: https://msfconfig[.]icu:443/tmp/system.txt Directory: C:\Windows\Background Executable: C:\Windows\Background\system.exe Scheduled Task: XMRig-[computer name] Defender Exclusion: C:\Windows\Background Severity : Medium Reason for Selection : The attack chain is highly specific and realistic: a ClickFix scenario where a user pastes a command into an administrator PowerShell, progressing from disabled certificate validation, Defender exclusions, Firewall allowances, and XMRig retrieval, to a SYSTEM-privileged Scheduled Task. Such behavior can easily translate to enterprise settings where employees copy commands from forums or generative AI responses. 2. Executive Summary Attackers reply to Steam technical support threads disguised as "repair methods," instructing users to run administrator PowerShell commands. Upon execution, the script adds Microsoft Defender exclusions and Firewall rules on the victim's machine, downloads XMRig, and establishes persistence via a SYSTEM-privileged Scheduled Task in a classic ClickFix attack. 3. Attack Flow Chain A: XMRig Infection via Steam Forums The attacker creates a randomized Steam account. They search for existing threads discussing game crash

A few months ago, I submitted a talk proposal to WordCamp US without really knowing what to expect. Today, I'm happy to say that it was accepted, and I'll be speaking at one of the largest WordPress conferences in the world. WordCamp US has always been one of the events I've looked up to in the WordPress ecosystem. As someone who has spent over a decade building content platforms with WordPress, contributing to open source, and working with teams across different countries, having the opportunity to share my experience on that stage is something I don't take for granted. My session is: Stop Blaming WordPress: Building a Real Editorial Workflow Without Leaving the Ecosystem Throughout my time working with WordPress, I've noticed a recurring pattern. When editorial teams struggle to publish content efficiently, WordPress often gets the blame. But after working with organizations of different sizes, I've learned that the CMS is rarely the real problem. The real challenges are usually: disconnected editorial processes; unclear content ownership; missing approval workflows; inconsistent governance; too much reliance on manual work. In this session, I'll share practical strategies for building scalable editorial workflows while keeping WordPress at the center of the ecosystem. The goal isn't to introduce another platform, it's to make the existing one work better. Speaking at WordCamp US is especially meaningful because I've been part of the WordPress ecosystem for many years. Being able to give something back to this community is an opportunity I'm genuinely grateful for. If you'll be at WordCamp US 2026 in Phoenix, I'd love to connect. 🎟️ Get your ticket: https://us.wordcamp.org/2026/tickets/ 💸 Use my speaker discount: speaker-friend20 during checkout for a discount on your ticket. See you at WCUS! 🚀