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Models, breakthroughs, and the race to AGI

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Why Owl Post covers AI & Machine Learning

AI moves faster than any single feed can keep up with. Frontier model releases, new benchmarks, capability scares, regulation moves, and the steady drip of papers that actually matter — the signal-to-noise ratio is brutal, and most coverage is either uncritical hype or reflexive doomerism. Owl Post reads across hundreds of sources every day, filters out the takes that don't pass smell tests, and surfaces what genuinely shifted: model releases worth paying attention to, capability jumps with real-world implications, and policy moves with teeth.

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How hard can it be to build a CI/CD system?

How hard can it be to build a CI/CD system? That question stuck with me long enough that I actually started building one. Not because someone asked me to. Not because I spotted a market gap. Just because the question wouldn't go away. The trigger was Concourse CI. I've been using it for a while and what I love about it is the resource abstraction, an interface that anything external has to follow. Check for new versions, pull them, push back. As a Go developer this kind of clean interface resonates with me. Everything in the pipeline is just something that implements that contract. But the operational overhead is significant. And I needed CI for my own side projects anyway, games and open source tools that require custom environments GitHub Actions can't provide. So I started building. A single binary that could also scale horizontally when needed. Start with nothing, grow when you need to. You start like this: ./pikoci server \ --db-system mem \ --pubsub-system mem \ --run-worker \ --pipeline-config pipeline.hcl That's a complete CI/CD system. In memory, no files, no external services. When you want persistence, add --db-system sqlite. When you need distributed workers, add NATS and start workers on other machines. The pipeline config never changes. Four pluggable abstractions PikoCI has four concepts you define in HCL and can source from a URL: resource types, runners, service types, and secret types. Each follows the same pattern: define the type once, instantiate it with params. A resource_type defines how to watch something for changes and fetch it. A resource is an instance of it: resource_type "git" { source = "pikoci://git" # built-in } resource "git" "my-app" { params { url = "https://github.com/org/app" name = "app" } check_interval = "@every 1m" } A runner_type defines where tasks execute. The docker and exec runners are built-in, no declaration needed. Here's what the docker runner looks like under the hood, in case you want to define your own: # this is

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How hard can it be to build a CI/CD system?
Is it common to crave for a Super Accessible ASI without giving up Human Creativity?

Is it common to crave for a Super Accessible ASI without giving up Human Creativity?

Am I one of the rare developers who: Secretly wants AI to finally succeed doing all the things AI CEOs continuously promise AI will be able to do, but fail to deliver - so that I can implement all those ten thousand ideas I thought I would never have enough time to implement; or will finally be able implement my unique Open Source Everything App that will do everything from note taking, to time management, to publish blogs on my personal websites and/or social media, to email management, to password management ... everything in a deterministic way (not like the AI agent way) - all while everything will be end to end encrypted, secure and private! At the same time, will still write code, poetry and articles by hand even if such a super accessible ASI (Artificial Super Intelligence) exists, even if it's 100% free with unlimited tokens, and even if it can be run locally on cheap hardware? Or is this seemingly contradictory craving very common among developers? Just wanted to know! Note: No AI was harmed used to write this post. All the grammatical mistakes and the lack there of, are mine alone 😁 FayazFollow A Software Engineer who is not afraid of being replaced by AI, loves coding and writing with and without using AI, and values human life and human dignity far more than technological advancements.

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I Built a Delhi Metro Route Planner In React with GSAP

I recently built a free Delhi Metro route planner: https://metro.coolhead.in/ The first version worked well as an interactive React app. You could pick a source station, pick a destination station, and see the route, estimated fare, stop count, travel time, interchanges, and line-color guidance on the metro map. But there was a problem. Most people do not search for "Delhi Metro route planner app". They search for very specific routes: Rajiv Chowk to Kashmere Gate metro route New Delhi to Airport T3 metro fare Hauz Khas to Botanical Garden metro travel time Dwarka Sector 21 to Rithala metro route That meant a single-page app homepage was leaving a lot of useful search demand uncovered. So I added programmatic SEO pages for every station-to-station combination. I wanted both of these URLs to show the same useful route: https://metro.coolhead.in/routes/rajiv-chowk-to-kashmere-gate/ https://metro.coolhead.in/?from=RCK&to=KG The first URL is clean and search-friendly. The second URL is simple for app state and sharing. Both should hydrate into the same React planner state with: from auto-filled to auto-filled route searched automatically fare/time/stops/interchanges visible The app already had two useful datasets: labels.json: station IDs and names edge.json: graph edges between stations, with line colors and SVG paths Each station has a compact ID: { "id": "RCK", "text": "Rajiv Chowk" } The route page slug comes from the station names: Rajiv Chowk -> rajiv-chowk Kashmere Gate -> kashmere-gate So this: RCK -> KG becomes: /routes/rajiv-chowk-to-kashmere-gate/ There are 241 stations in the app data. For every ordered source/destination pair, excluding same-station routes, the generator creates a static page: 241 * 240 = 57,840 route pages Each generated route page includes: a route-specific meta description Open Graph metadata Twitter metadata canonical URL JSON-LD prerendered route text in the HTML a hydration payload for the React app If you liked this project, want to

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173 Undocumented Security Findings in TerraGoat: What Standard IaC Scanners Miss (and Why Post-Quantum Matters)

TerraGoat is the canonical vulnerable Terraform repository maintained by Bridgecrew (now Prisma Cloud). It has over 5,000 GitHub stars and is used by security teams worldwide as the benchmark for validating IaC scanners. The premise is straightforward: run your tool against TerraGoat, check how many of the known vulnerabilities it catches. The problem is that the "known vulnerabilities" reference list is incomplete by design — or by oversight. This research quantifies that gap for the first time. Three tools were run against TerraGoat in isolation, with no tuning or custom rules: Checkov — the official Bridgecrew scanner, the tool TerraGoat was originally built to test Trivy (Aqua Security) — the industry-standard open source vulnerability scanner with IaC support pq-audit — an open source post-quantum cryptography audit framework built to detect cryptographic exposure that standard scanners do not model Each tool produced its raw JSON output. Results were deduplicated per finding identifier and cross-referenced against Bridgecrew's official TerraGoat documentation to determine which findings had been acknowledged by the maintainers and which had not. Raw data, gap matrix, and per-tool JSON outputs are available in the research repository. Checkov produced 56 findings. Every single one maps to documented behavior in Bridgecrew's official documentation. Checkov does exactly what it says. Trivy produced 125 findings against the same codebase. AVD-AWS-* and aws-* identifiers covering real misconfigurations across S3, IAM, EC2, RDS, and networking resources — critical and high severity. None of these 125 findings appear in Bridgecrew's TerraGoat documentation. Total undocumented findings: 173 out of 243. That is 70% of the actual security surface. The implication is direct: if your team selected Checkov as your primary IaC scanner because it is the "official" tool for TerraGoat and Terraform — you are currently seeing 23% of your exposure. Not because Checkov is broken,

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The Dark Art of Veltrix Configuration: How I Learned to Stop Worrying and Love the Metrics

The Problem We Were Actually Solving I was tasked with taking our event-driven system from a default configuration to a production-ready state, with a focus on optimizing the Treasure Hunt Engine, a critical component of our application. As a Veltrix operator, I knew that getting this right would mean the difference between a system that hummed along smoothly and one that would be plagued by errors and performance issues. The parameters that mattered most were not immediately clear, and I knew that mistakes could compound quickly. I had to navigate the complex implementation sequence to avoid common pitfalls. My initial approach was to follow the standard configuration guidelines, which emphasized the importance of setting optimal values for batch size, concurrency, and timeout thresholds. However, after deploying these changes to our staging environment, we began to see a significant increase in latency, with average response times ballooning from 50ms to over 200ms. Upon further investigation, I discovered that our database connection pool was being exhausted due to the increased concurrency, resulting in a cascade of errors and timeouts. It became clear that a more nuanced approach was needed, one that took into account the specific requirements of our system and the characteristics of our workload. After careful consideration, I decided to adopt a more metrics-driven approach to configuring the Treasure Hunt Engine. I began by instrumenting our system with Prometheus and Grafana, allowing us to collect and visualize key metrics such as request latency, error rates, and resource utilization. With this data in hand, I was able to identify the most critical parameters and adjust them accordingly. For example, I reduced the batch size to minimize memory usage and adjusted the concurrency level to prevent database connection pool exhaustion. I also implemented a circuit breaker pattern to detect and prevent cascading failures. This approach allowed us to optimize the

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AI May Do for FOSS What 30 Years of Idealism Couldn't

Free and Open-Source Software (FOSS) is a cheat code for AI development. Thirty years of idealism couldn't get it into the mainstream, but a year of coding agents may just do it. For three decades, the open-source pitch ran like this: it's technically great, it's free forever, and you own it completely. The response from corporate IT: who do we call at 2am when payroll breaks? Nobody had a fully convincing answer. With AI, that is all changing. The Free Software Foundation spent decades arguing that proprietary software was philosophically indefensible. Many developers agreed. The code seemed to agree. Linux conquered server infrastructure. PostgreSQL has more recently replaced Oracle at hundreds of serious enterprises that find it mature enough to replace Oracle and its high annual licensing fees. The open-source stack underneath most of the internet is vast, deep, and excellent. But the desktop never tipped. Enterprise productivity software never tipped. The industries where software has to work the way non-engineers expect it to work -- accounting, legal, healthcare administration, graphic design -- stayed commercial, stubbornly, decade after decade. This was not a failure of idealism. The idealists were right about most of the technical arguments. It was a failure of practical infrastructure: the expensive scaffolding that enterprises actually need and that nobody provides for free. Eric Raymond made the ideological case most clearly in The Cathedral and the Bazaar 1 -- the essay that gave distributed open-source development its intellectual framework. His central argument: the bazaar model (public code, distributed contributors, rapid iteration) produces better software than the cathedral (centralized, controlled, released complete). There are a lot of supporting examples: Linux on servers, PostgreSQL replacing Oracle, the entire open-source infrastructure stack. What Raymond didn't solve -- what no amount of idealism could solve -- was the consumption problem.

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I Built an AI-Powered PC Monitor in Python. 28 Strangers Shaped Its Brain. PC Workman 1.7.6

I Built an AI-Powered PC Monitor in Python. 28 Strangers Shaped Its Brain. PC Workman 1.7.6

I built an AI-powered PC monitor in Python. 28 strangers shaped its brain. PC Workman has created, when I live and work in Nederlands by Agency. Not because I had energy. Because I couldn't stop. PC Workman started from one problem: my PC would slow down and I had no idea why. Task Manager said "CPU: 87%." But which process? Since when? How long? Nothing. Every monitoring tool was either outdated, required installing 3 extra libraries, or looked like it was designed in 2009 and nobody touched the code since. So I thought: I'll build my own. Classic mistake. Best decision I ever made. Current state: Python files: 96 Total lines: 48,081 Response builder: 255,065 chars (yes, one file) Process library: 241 entries AI intents: 82 Response handlers: 65 None of these numbers are what I'm most proud of. The most technically interesting part of PC Workman is hck_GPT — an AI assistant built directly into the app. No external API. Doesn't send your data anywhere. Works locally through a hybrid engine: if the intent gets recognized by the 82-intent parser, it responds deterministically from real data. If not, it delegates to a local Ollama LLM. user input → IntentParser (vocab match + ML classifier) → HybridEngine (rule dispatch vs LLM fallback) → ResponseBuilder (65 handlers, real sensor data) → bilingual response (PL/EN auto-detected) 82 intents cover everything from "why is CPU high" to "can I run Cyberpunk" to "what's eating my battery." Each intent has dozens of patterns — because users ask the same thing 15 different ways. The bilingual helper looks almost embarrassingly simple: def _t(lang: str, pl: str, en: str) -> str: return en if lang == "en" else pl Behind that sits 854 lines of vocabulary patterns in two languages. This is one of my favorite bugs because it was so non-obvious. The parser normalizes text before matching — strips diacritics so "dlaczego" (why) with or without accents hits the same intent: def _ascii_fold(self, text: str) -> str: import unicodedata re

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I Deploy to Docker Swarm from GitHub Actions — Here's the Setup That Actually Works

If you've ever tried to set up continuous deployment to a remote Docker host, you know the pain. GitHub Actions is great for CI — build, test, done. But deploying to a remote server? That's where things get messy. Most tutorials hand you a 200-line shell script with ssh hacks, scp gymnastics, and prayer. I got tired of that, so I packaged it into a reusable GitHub Action that handles Docker Compose and Docker Swarm deployments over SSH. Here's how it works and how to set it up in under 10 minutes. You have a VPS (or a bare-metal server) running Docker. You want GitHub Actions to: Build your images (or pull them from a registry) SSH into your server Deploy using docker-compose up or docker stack deploy Clean up old files Without leaking SSH keys everywhere or writing bespoke deployment scripts per project. docker-remote-deployment-action github.com/sulthonzh/docker-remote-deployment-action It's a GitHub Action available on the Marketplace that does exactly this. It supports two deployment modes: docker-compose — runs docker-compose on the remote host docker-swarm — runs docker stack deploy for Swarm services Both via SSH, both from your existing docker-compose.yml. In your repo → Settings → Secrets and variables → Actions, add: SSH_PRIVATE_KEY — your private key for the server SSH_PUBLIC_KEY — the corresponding public key # .github/workflows/deploy.yml name: Deploy on: push: branches: [main] jobs: deploy: runs-on: ubuntu-latest steps: - name: Checkout uses: actions/checkout@v4 - name: Deploy to server uses: sulthonzh/docker-remote-deployment-action@v1 with: remote_docker_host: deploy@your-server.com ssh_private_key: ${{ secrets.SSH_PRIVATE_KEY }} ssh_public_key: ${{ secrets.SSH_PUBLIC_KEY }} deployment_mode: docker-compose stack_file_name: docker-compose.yml args: up -d Push to main → your service deploys. That's it. If you're running a Swarm cluster, switch the mode and add the stack name: - name: Deploy to Swarm uses: sulthonzh/docker-remote-deployment-action@v1 wi

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I gave up on making my AI builder write good media queries

Every site my AI website builder produced looked great on a phone and weak on a desktop. The hero stretched edge-to-edge in a single anemic column. Features grids stayed at one column on a 27" monitor. Section padding that felt generous on mobile felt empty at desktop widths. I spent two weeks trying to fix this from the prompt side. None of it worked the way I wanted. Then I gave up on the approach entirely and switched the generation to Tailwind via CDN. The desktop problem disappeared. This is the writeup of why the original approach was wrong, what I tried first, and the specific change that mattered. The system prompt told the model to write mobile-first CSS: Default CSS targets mobile (≤480px); layer up with @media (min-width: 768px) and (min-width: 1024px). Models followed the instruction. They wrote good mobile rules. Then they wrote thin, half-hearted desktop overrides — a max-width here, a media query for two-column layout there. Critical things were missing: bounded container widths, real multi-column grids, typography that genuinely scaled up, deliberate section padding rhythm. Desktop wasn't broken — it was underdesigned. The mobile rules absorbed most of the model's attention budget. This wasn't a one-model problem. I tested four (Cerebras GPT-OSS 120B, Groq Llama 4 Scout, Cloudflare Qwen3 30B, OpenRouter free auto-router). All four showed the same pattern. The strongest model produced the most polished mobile experience and still had thin desktop. Round 1: explicit desktop rules in the prompt. Desktop must be a designed experience, not a stretched mobile view: - Every major section uses a bounded container (max-width 1100–1280px). - Multi-column grids on desktop (3-col features, not 1-col). - Typography scales up: hero ≥ clamp(2rem, 6vw, 5rem), body 16–18px. - Section padding visibly larger on desktop. - Real horizontal nav on desktop (no permanent hamburger). Helped, maybe 20% better. Not enough. Models still hand-wrote breakpoints one at a time, oft

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How I Automated My Obsidian Vault with Claude — It Now Works the Night Shift

I was drowning in my own notes For about two years, I lived inside Obsidian. Daily notes, fleeting thoughts, meeting takeaways, half-formed ideas at 2am, voice memos I'd transcribe by hand. My vault had over 3,000 notes. And I remembered almost none of it. Every morning I'd open my laptop and stare at yesterday's daily note, trying to reconstruct where I was. The vault was full — full of captures that were never synthesized, tasks that were never carried forward, ideas that died in a folder called _inbox. I was doing the work of a knowledge worker but getting none of the compounding returns. Notes went in. Nothing came out. What made it worse: I knew I was supposed to do the synthesis. Review your notes. Write a morning brief. Connect the dots. But after a full day of work, the last thing I wanted to do was sit down and play editor. So I didn't. And the vault stayed a graveyard. Here's what I eventually figured out: I wasn't failing at note-taking. I was failing at synthesis — and I was expecting myself to do it at the worst possible time. Synthesis requires cognitive distance. You need to look at what you captured with fresh eyes. The problem is that "fresh eyes" happen in the morning, and the synthesis work needs to happen after the day is over. That's a structural mismatch, and no productivity system fixes it because it's not a productivity problem. It's a timing problem. And timing problems are exactly what automation solves. The thought that changed everything: Claude doesn't get tired at 11pm. It doesn't need cognitive distance — it can read the whole day in one pass and pull out what matters. So I built vault-os to hand that job off. vault-os has two main jobs: capture anything, anywhere, and synthesize everything while you sleep. Capture via Telegram bot. I wanted to send notes from my phone without opening Obsidian. The bot uses tag-based routing — anything you send gets sorted into the right section of your daily note automatically. #idea goes into Content

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The hidden cost of cloud GPU training: egress, idle time, and lock-in

The GPU hourly rate is the number everyone compares. It is also the number that tells you the least about what a training run actually costs. The sticker price, say $2 to $3.50 an hour for an H100 on a specialized cloud, is the visible tip. The real bill is built from three things almost nobody puts on the comparison spreadsheet: the GPU sitting idle, the data you have to move, and the cost of ever leaving. This post breaks down each one, with 2026 numbers, and what you can actually do about it. The most expensive line item in most setups is not compute. It is compute you pay for but never use. The 5 percent problem A 2026 Cast AI report found average GPU utilization across Kubernetes clusters on major clouds sits around 5 percent. Other analyses are kinder, Anyscale puts sustained production utilization below 50 percent, FinOps studies land at 20 to 30 percent, but the conclusion holds: most of every GPU-hour you pay for produces no useful work. Why it happens It is structural, not laziness. Workloads bounce between CPU preprocessing, GPU training, and CPU postprocessing. Python dataloaders on the GPU node starve the accelerator. Teams overprovision to dodge out-of-memory errors and default to the biggest instance "just in case." Why it hurts more than CPU waste An idle CPU costs cents per hour. An idle GPU costs dollars per hour. A single AWS p4d.24xlarge left idle over one weekend burns about $1,573 for nothing. A month of overnight and weekend idling typically wastes $3,000 to $8,000 per instance. What to do Add idle detection. A script watching nvidia-smi that scales down an instance after utilization stays below ~5 percent for 30 minutes is the highest-ROI thing most teams can ship. Commonly cuts 20 to 35 percent off GPU spend. Right-size the hardware. Not every job needs an H100. Running on the biggest card when a smaller one delivers the same result is pure burn. Fix the pipeline first. If dataloaders are starving the GPU, a bigger GPU does not help. Profile

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Control SwiftUI and Compose State Synchronously with Worklets in Expo UI

React Native developers have long dealt with the friction of bridging JavaScript with native UI threads. Every time you need to update native state, you send a message across the bridge, wait for the round-trip, and hope the user doesn't notice the delay. Expo UI in SDK 56 changes this with worklet integration. You can now control SwiftUI and Compose state directly on the UI thread, with zero JavaScript round-trips. Here's what that looks like: import { Host, TextInput, useNativeState } from '@expo/ui'; export default function Screen() { const value = useNativeState(''); return ( { 'worklet'; // Runs synchronously on the UI thread, on every keystroke. console.log('[UI thread] typed:', value); }} /> ); } Note: you'll need react-native-reanimated and react-native-worklets installed in your project for this to work. Two pieces make this possible: useNativeState creates an ObservableState - a SharedObject that lives in native code and gets observed by both SwiftUI and Compose. On iOS, it maps to an ObservableObject. On Android, it's a MutableState. Both platforms watch this state and re-render when it changes. Worklet callbacks like onTextChange run directly on the UI thread when the native view fires its event. No bridge crossing required. Put them together: each keystroke in the TextField updates the shared text state, executes your worklet, and triggers SwiftUI and Compose to re-render. All on the UI thread, all in the same frame. If you know SwiftUI, this pattern should click immediately. The TypeScript above translates almost directly: struct Screen: View { @State var text = "" var body: some View { TextField("Type something", text: $text) .onChange(of: text) { _, newValue in print("[UI thread] typed:", newValue) } } } useNativeState acts like @State, text={text} works like TextField(text: $text), and the worklet onTextChange behaves like .onChange(of:). Compose developers will recognize the same shape with mutableStateOf and onValueChange. The immediate win here i

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AI Tools & Products Radar — May 28, 2026

A weekly snapshot of new AI tools, products, and platform launches that matter for builders. This week in one sentence: AI coding tools command billion-dollar valuations, agentic AI moves from demos to enterprise, and Google's AI search is driving users to DuckDuckGo. I've been tracking AI product launches through Firecrawl and TechCrunch feeds over the past week. The signal is clear: the AI race has shifted from models to distribution. Here's what caught my attention. Cognition raised $1 billion at a $25 billion pre-money valuation. That's a coding assistant startup valued higher than many public SaaS companies. OpenRouter doubled its valuation to $1.3 billion in just one year — turns out model routing and API gateway infrastructure is a real business. Meanwhile, Microsoft shipped Copilot Cowork — an autonomous multi-step AI agent built directly into Microsoft 365, in collaboration with Anthropic using Claude technology. It can execute complex workflows across Office apps without hand-holding. Figma Make now edits production codebases. You can visually tweak a UI and it modifies the actual source code, not just mockups. Google AI Studio launched "vibe coding" mode with a Google AI subscription, and Colab Learn Mode shipped with Gemma 4 — an open model that is byte-for-byte the most capable open model right now. Google's Cloud Next 2026 was all about agents. The Gemini Enterprise Agent Platform lets organizations build, deploy, and manage fleets of AI agents. Google also rolled out their 8th-gen TPUs — custom silicon purpose-built for the agentic era, not just traditional ML training. Meta launched "Plus" tier subscriptions across Instagram, Facebook, and WhatsApp, with AI plan trials bundled in. The social giant is betting you'll pay for AI features alongside ad-free browsing. Robinhood now lets AI agents trade stocks autonomously. Users can delegate trading decisions to bots. The risks are obvious — but so is the signal: every platform is becoming an AI agent plat

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