标签：#ai

After running AI coding agents in production for a while, one thing became clear: the failures aren't in the code the model writes. They're at the seams — git, CI, auth, the network. The boundaries with the outside world. The model itself is genuinely capable. It writes functions, writes tests, refactors. What breaks is everything around the work: pushing the result, waiting on CI, merging the PR, refreshing a token, calling another service. And the failures are often the kind a human would avoid without thinking. Here are five incidents we hit and fixed in Codens' Purple (the orchestration core) over the last few weeks. All real, with production task IDs and dates. Every fix is merged. There's a shared design lesson at the end that ties them together. Incident 1: a half-resolved merge nearly flooded a PR with 12,000 lines This was the scary one. A Purple task on opsguide-back opened a PR. I looked inside: +12,162 lines / 149 files changed, with literal <<<<<<< markers in 2 of them . The commit graph: e567ce67 (merge commit, "chore: Fix HYBRID_SEARCH...") ├ parent[0] = 0b069e5d (develop tip, +1468 commits over main) └ parent[1] = 2940de35 (the actual feature commit) What happened: in the fix step, the AI decided to git merge develop to backport some test fixes. The merge conflicted. The AI resolved it partially and drove git commit through anyway with markers still in the tree. What got pushed: develop's entire divergence plus unresolved conflict markers. If anyone had clicked merge, main would have been polluted by 1468 commits of develop drift in one shot. A human wouldn't do this. They wouldn't merge develop into a main-targeted PR in the first place, and if it conflicted they wouldn't commit until it was fully resolved. But the AI, optimizing locally to get one test passing, does it without hesitation. Fix: stop it at push time, in two layers A single git pre-push hook. This is where the AI's git push actually goes, so this is where the guard belongs. #!/bin/bas

Your agent nailed every test case. You shipped it. Within 48 hours, users report hallucinated outputs, silently dropped tool calls, and responses that bear zero resemblance to what worked on your machine. You reload the same prompt locally. It works perfectly. Welcome to the most predictable failure mode in AI engineering: the dev-to-prod gap. This is Crucible C01. We dissect the five failure modes that kill agents in production and give you the tools to catch them before your users do. The Idea (60 Seconds) Developers test agents in idealized conditions: deterministic inputs, warm context windows, generous API latency budgets, and sequential tool calls. Production exposes the opposite environment: cold starts strip context, rate limits compress timing, and parallel calls introduce race conditions. The agent that performed flawlessly at temperature 0 on a 2k-token context window collapses at temperature 0.7 on an 8k-token window. The five failure modes are temperature drift and context window overflow first; silent API errors and prompt drift follow; race conditions complete the set. Each one has a detection pattern and a fix, and this article delivers both plus the CLI tool to automate the detection. Why This Matters AI agent failures differ from traditional software failures in one critical way: they are stochastic. A web API either returns 200 or 500. An AI agent returns something that looks plausible 90% of the time and is catastrophically wrong 10% of the time. That 10% is invisible in manual testing and devastating in production. The economics compound fast because every failed agent interaction wastes tokens, and wasted tokens cost money. At scale, a subtly broken agent burns budget faster than a working one because it retries, loops, and rephrases instead of succeeding. A single temperature drift bug can double your API spend. Reliability is the differentiator. The market is flooding with AI wrappers. The ones that survive will be the ones that work consiste

I haven't written articles in quite a while and I recently decided to come back to it. At work I use AI daily, so a big part of my coding tasks are delegated to agents. I try not to do the same when it comes to writing text but I don't have a clear reason for that (or maybe I do and I don't want to admit it). I am sure people can take advantage of generating text with the help of AI but at the moment I feel like prompting would not save me any time and writing the text myself would be faster. What about you? Are you using AI to write your articles? Image credit - jessica olivella, on pexels

The Letter VCs Are Quietly Deleting from ARR Startups are reporting revenue they haven't earned yet. VCs know it. Investors are cheering anyway. We've seen this movie. You're evaluating an AI startup. The pitch deck shows $100 million in ARR. The growth curve is parabolic. The deck says they signed $100 million. What it doesn't say is that $70 million of that is "committed ARR": contracts signed but not yet invoiced, customers who haven't deployed yet, pilots that count toward the number if they convert. Subtract the gap and you've got $30 million in actual recognized revenue hiding under a $100 million headline. This is the ARR inflation playbook, and it's running at full speed right now. The Trick Has a Name "CARR" stands for Contracted or Committed ARR. It's a legitimate concept. In industries where revenue accrues slowly after contract signing (healthcare AI deployments, energy optimization platforms, multi-year enterprise integrations), the gap between signature and recognition can legitimately take months or years to close. Reporting CARR alongside ARR, properly labeled, is defensible. That's not what's happening. What's happening is simpler: founders strip the "C" and just call it ARR. One VC told TechCrunch the gap between CARR and actual ARR can run as high as 70% [1]. In some confirmed cases the spread is 3-5x. Another investor said flatly: "For sure they are reporting CARR as ARR" [1]. The article indicates that the investor community is not only aware, but many are actively complicit. The logic follows its own warped rationality. When one startup in a category inflates, the others have to follow to stay competitive for talent and headlines. "When one startup does it in a category, it is hard not to do it yourself just to keep up," as one investor put it [1]. Spellbook CEO Scott Stevenson, one of the few willing to call this in public, described the practice as a "huge scam," adding that major VC funds are not just watching it happen but actively supporti

LLM-powered Learning, Handwritten Digit Recognition, and AI Career Guidance Today's Highlights This week's top stories showcase practical AI applications: an LLM-powered tool for domain learning, a cloud-enhanced handwritten digit recognition system, and an AI-driven career guide. These projects demonstrate how AI frameworks are being applied to real-world workflows, from knowledge acquisition to personalized advice. Show HN: Lathe – Use LLMs to learn a new domain, not skip past it (Hacker News) Source: https://github.com/devenjarvis/lathe This project, Lathe, presents a novel approach to leveraging Large Language Models (LLMs) not just for quick answers, but for deep, structured learning within a new domain. Unlike traditional LLM interactions that might encourage skipping detailed research, Lathe aims to facilitate a more profound understanding by guiding users through a systematic learning process. It likely employs advanced retrieval augmentation generation (RAG) techniques, potentially combined with iterative prompting strategies and graph-based knowledge representation, to help users build a comprehensive knowledge base on a chosen topic. The framework focuses on transforming raw information into actionable insights and structured learning paths. This makes LLMs a powerful study aid, enabling domain experts or newcomers to grasp complex subjects more efficiently by providing tools for semantic search, concept mapping, and progressive knowledge acquisition, moving beyond simple question-answering into true assisted learning workflows. Comment: This is precisely what's needed for complex enterprise knowledge management – turning LLMs into an active learning partner, not just a summarizer. I'd explore how it structures knowledge graphs or progressive learning paths. Handwritten Digit Recognition System with Cloud and AI Enhancements (Dev.to Top) Source: https://dev.to/yohannesah/handwritten-digit-recognition-system-with-cloud-and-ai-enhancements-i4e This project

I'm starting a new project. It's the most ambitious thing I've attempted from a phone. The goal: an AI agent that controls a smartphone. It opens apps, navigates screens, taps buttons, types text, and completes multi-step tasks. All offline. All local. No cloud. This is Day 1 of a public build log. No fluff. Just what I'm building, how it works, and what breaks along the way. What I'm Building An autonomous AI agent that runs entirely on an Android phone. You give it a command in plain English: · "Open WhatsApp and message Mom I'll call later." · "Search for Kotlin jobs on Wellfound." · "Open my notes and summarize what I wrote yesterday." The agent parses the command, plans the steps, and executes them—opening apps, finding the right buttons, typing text, hitting send. No cloud. No API keys. Just a phone that acts on your behalf. The Stack Component Tool AI Brain Gemma 4 E4B (local, via Ollama) Runtime Termux (Linux on Android) Phone Control ADB + UI Automator Orchestration Python Why This Matters Most AI agents live in the cloud. They need internet, APIs, and someone else's server. A local agent that runs on a phone means: · Privacy: your data never leaves your device. · Offline: works even without internet. · Accessible: built for the device billions of people already own. The Hard Parts I Already See · The agent needs to "see" the screen to know where to tap. Text detection is doable. Image-based buttons are harder. · Multi-step tasks need verification. If one tap misses, the whole chain fails. · Android permissions. ADB requires developer mode. A user-facing version would need a workaround. What's Next · Day 2: Create the repo. Set up the project structure. Push the first working script. · Day 3: Get screen text detection working with OCR. · Day 4: Test a full 3-step task. This is Day 1. The repo goes live tomorrow. Follow along if you want to see something rare get built from scratch.

Launch week for a side project means the same chore every time: export the logo, open Figma, create artboards for LinkedIn (400×400 and 4200×700 cover), Google Business Profile (1200×1200 JPEG with a minimum file size or Google rejects it), YouTube (2560×1440 with a safe zone nobody remembers), Open Graph (1200×630), Instagram (circle crop), Discord, TikTok… Design tools are great at making brand assets. They are slow at batch-exporting exact platform specs — especially when encoding rules differ (PNG vs JPEG, max KB, min KB for GBP, center-right vs YouTube-safe positioning). So we built a small free tool: one SVG or PNG in → 21 platform files out. What it actually does Upload a logo. Pick categories (or take everything): 8 profile icons — mark-only, padded for circular crops (LinkedIn, X, GBP, Facebook, Instagram, YouTube, TikTok, Discord) 2 profile lockups — logo + wordmark for LinkedIn company and GBP 7 banners/covers — including LinkedIn 4200×700, YouTube safe-area channel art, Facebook cover under 100 KB target, Open Graph 1200×630 4 post sizes — Instagram square/portrait/story, Pinterest pin Outputs are PNG or JPEG per platform rules. White background. ZIP download. Why not Canva? Canva is excellent for templates and marketing creatives. For “I already have a logo, give me every official size,” you duplicate frames and export manually — often behind a paid tier for brand kits and bulk workflows. This tool does one job, free, no account: 👉 https://varnox.io/tools Longer write-up with platform gotchas (GBP min JPEG size, YouTube safe area, etc.): 👉 https://varnox.io/blog/free-social-media-logo-size-generator Privacy (because it’s your logo) SVG uploads sanitized before render Generated files expire after 30 minutes — nothing permanent stored No signup We use the same catalog internally when onboarding clients onto GBP + LinkedIn + OG tags. Shipping it publicly was easier than answering “what size is our Facebook cover again?” for the fifth time. Stack note (for

We're likely to see more price increases as the big AI companies plan to go public.

Head's true genius—and that of his character, Giles—lay in quietly filling in the gaps in every scene

The 2026 edition of Summer Game Fest just wrapped up, and it was surprisingly hectic. The nearly week-long event came at a challenging time for the games industry, and for the most part the big keynotes were used as a chance to show some strength through major announcements, while largely ignoring pesky details like hardware […]

A2A has become like a standard for enabling agent to agent communication, we could use the a2a-sdk for running and configuring the a2a server and its features such as agent card, agent skills, agent executor, request handler etc. However we are going to go with a simplified approach here with strands where the agent card will be fetched automatically. Let's get started! Server Initialize a uv project for the a2a server and switch to that directory. uv init ~/strands-a2a-server cd ~/strands-a2a-server Add the required packages. uv add python-dotenv == 1.2.2 strands-agents[a2a] == 1.42.0 Change the code in main.py to look like below. $ cat main . py from dotenv import load_dotenv from strands import Agent from strands.multiagent.a2a import A2AServer load_dotenv () def main (): agent = Agent ( callback_handler = None , description = " A sample strands agent " , model = " us.amazon.nova-micro-v1:0 " , ) a2a_server = A2AServer ( agent = agent ) a2a_server . serve () if __name__ == " __main__ " : main () I like the simplicity here, as you see above, it's quite simple to start a basic a2a server from with in strands, with just a couple of lines of code, we didn't have to install the a2a-sdk separately. Run the code, to start the a2a server. $ uv run main.py INFO: Started server process [18006] INFO: Waiting for application startup. INFO: Application startup complete. INFO: Uvicorn running on http://127.0.0.1:9000 (Press CTRL+C to quit) Client Let's now do the client part on a separate terminal. Initialize the project and switch the directory. uv init ~/strands-a2a-client cd ~/strands-a2a-client Modify main.py code to look as follows. import asyncio from strands.agent.a2a_agent import A2AAgent async def main (): agent = A2AAgent ( endpoint = " http://localhost:9000 " ) agent_card = await agent . get_agent_card () print ( " Invoking remote agent with agent card: " ) for key , value in agent_card : print ( key , " : " , value ) print ( ' - ' * 20 ) while True : prompt = input

Whenever I heard about harmonics, I thought they were only related to large substations, transmission systems, and industrial facilities. I assumed harmonics were something utility engineers dealt with and not something connected to everyday devices. Phone chargers can create harmonics. Laptop chargers can create harmonics. LED lights can create harmonics. Even a UPS sitting under a desk can create harmonics. Today, modern power systems use many power electronic devices such as EV chargers, solar inverters, battery energy storage systems (BESS), UPS systems, data centers, and Variable Frequency Drives (VFDs). While these technologies bring many benefits, they can also introduce harmonic distortion. The more power electronic devices we connect to the grid, the more important harmonic analysis becomes. In this article, I will explain what harmonics are, what causes them, how they affect power quality, how they can be analyzed using PSCAD, and why they are becoming more important in modern power systems. Before we talk about harmonics, let's first understand electrical loads, because this is where harmonics usually begin. What Is an Electrical Load? An electrical load is any device that uses electrical energy to perform useful work. For example, think about a typical evening at home. You turn on a ceiling fan, LED light, laptop, air conditioner, and phone charger. All of these devices use electricity, so they are called electrical loads. Examples of electrical loads include motors, heaters, fans, computers, air conditioners, lighting systems, and EV chargers. However, not all electrical loads use electricity in the same way. Some draw current smoothly, while others draw current in short pulses. This small difference is actually where the story of harmonics begins. Linear vs Non-Linear Loads To understand harmonics, we first need to understand the difference between linear and non-linear loads. Although both types of loads consume electricity, they draw current from the

A step-by-step, beginner-friendly guide to stripping out Bootstrap and setting up the blazing-fast Tailwind CSS v4 compiler in your .NET Blazor Hybrid or Web app. Tailwind CSS v4 is officially here, and it is a complete game-changer! It scraps the old, bulky JavaScript configuration files ( tailwind.config.js ) and moves your theme settings directly into standard CSS. Plus, its new native compiler is faster than ever. If you are building a .NET Blazor app (whether it's Blazor WebAssembly, Server, or a MAUI Blazor Hybrid app) and want to swap out the default Bootstrap styles for utility-first Tailwind bliss, you can do it all without ever leaving your IDE. Let's get this configured step-by-step using Visual Studio 2022 ! Prerequisites Before we start, make sure you have: Node.js installed on your development machine. A Blazor project opened in Visual Studio 2022 . Step 1: Say Goodbye to Bootstrap in Solution Explorer Blazor templates ship with Bootstrap by default. Let's use Visual Studio's Solution Explorer to clean that out so our styles don't conflict. In the Solution Explorer window, expand your wwwroot folder. Expand the css subfolder. Right-click the bootstrap folder and select Delete . Now, open your main HTML entry file inside wwwroot (this will be index.html for Blazor Hybrid/WASM or App.razor inside the Components folder for Blazor Web). Locate the <head> section and delete the line linking the Bootstrap stylesheet: <link rel= "stylesheet" href= "css/bootstrap/bootstrap.min.css" /> powershell Step 2: Open the Developer PowerShell & Install Tailwind v4 Tailwind v4 splits the design library from the command-line build tool, so we need to install both. We can use Visual Studio's built-in terminal for this. In the top menu of Visual Studio 2022, go to Tools > Command Line > Developer PowerShell . A terminal window will open at the bottom of your IDE, already navigated to your project folder. Paste and run the following commands: i. Initialize a package.json fil

A step-by-step, beginner-friendly guide to stripping out Bootstrap and setting up the blazing-fast Tailwind CSS v4 compiler in your .NET Blazor Hybrid or Web app. Tailwind CSS v4 is officially here, and it is a complete game-changer! It scraps the old, bulky JavaScript configuration files ( tailwind.config.js ) and moves your theme settings directly into standard CSS. Plus, its new native compiler is faster than ever. If you are building a .NET Blazor app (whether it's Blazor WebAssembly, Server, or a MAUI Blazor Hybrid app) and want to swap out the default Bootstrap styles for utility-first Tailwind bliss, you can do it all without ever leaving your IDE. Let's get this configured step-by-step using Visual Studio 2022 ! Prerequisites Before we start, make sure you have: Node.js installed on your development machine. A Blazor project opened in Visual Studio 2022 . Step 1: Say Goodbye to Bootstrap in Solution Explorer Blazor templates ship with Bootstrap by default. Let's use Visual Studio's Solution Explorer to clean that out so our styles don't conflict. In the Solution Explorer window, expand your wwwroot folder. Expand the css subfolder. Right-click the bootstrap folder and select Delete . Now, open your main HTML entry file inside wwwroot (this will be index.html for Blazor Hybrid/WASM or App.razor inside the Components folder for Blazor Web). Locate the <head> section and delete the line linking the Bootstrap stylesheet: <link rel= "stylesheet" href= "css/bootstrap/bootstrap.min.css" /> powershell Step 2: Open the Developer PowerShell & Install Tailwind v4 Tailwind v4 splits the design library from the command-line build tool, so we need to install both. We can use Visual Studio's built-in terminal for this. In the top menu of Visual Studio 2022, go to Tools > Command Line > Developer PowerShell . A terminal window will open at the bottom of your IDE, already navigated to your project folder. Paste and run the following commands: i. Initialize a package.json fil

There is a class of projects that teaches you more about a language than any tutorial ever could. Building a PDF engine from scratch in Go is one of them. It is not glamorous. It is not trendy. But it forces you to confront memory management, binary serialization, concurrency safety, interface design, and performance profiling all at once, in a domain where correctness is non-negotiable. This article walks through the lessons learned building GoPdfSuit (~500 Github ⭐), a production PDF engine written in Go that generates 1.5 million financial PDFs in roughly 45 minutes on a single node, achieves PDF/A-4 and PDF/UA-2 compliance, and exposes itself as a REST API, a Go library, and Python CGO bindings simultaneously. Note : While I have six years of overall experience including two years working specifically with Go, I rarely encountered these types of challenges in my day-to-day work, as my role focused primarily on implementing new features within an existing architecture. Working on gopdfsuit was an excellent learning experience; it allowed me to dive deep into performance optimization and taught me a great deal. Below are some of the key takeaways. Building GoPdfSuit from a blank editor to a production-grade PDF engine-one that ships PDF 2.0 , PDF/A-4 , PDF/UA-2 , PKCS#7 signing, merge/split, XFDF fill, secure redaction, and a public gopdflib API-forced a shift from “business logic” to “systems engineering.” When you chase ~2,000+ aggregate ops/s on a mixed financial workload (48 workers, PDF/A on) and sub ~10 ms PDF generation, you stop debating frameworks and start fighting the allocator, cache lines, and ISO 32000 semantics. These fifty lessons are drawn from the actual codebase ( internal/pdf , pkg/gopdflib , benchmark harnesses under sampledata/ , and documented optimization passes in guides/cursor/ ). They mix specification pain with Go runtime craft and production reality-not generic blog advice. Part 1: Structural Hurdles & PDF Specification Nightmares Deco

How I got here On principle, you will never catch me parading myself as a some sort of expert data scientist. Technically, that's what I do in my day job, but I know I still have so much to learn because the field is broad, and to truly become expert requires dangerously ambitious levels of work ethic. I think I'm a functional data scientist who learns more as I encounter new problems daily. I'm writing this piece because in the last week or two, precisely three people have asked me questions related to transitioning into data science. As such, I thought to unify my thoughts around the topic so that I can refer anyone else who asks here--if anyone else ever asks. This article assumes you're already familiar with some of the data science entails such as data analysis, model training, prediction, etc, so I will not be doing a lecture series, just addressing some of the disconnects I have observed in conversation with people looking to transition to the field. Initial Excitement In 2026, it's easy to see what claude or chatGPT is doing and go "What sorcery is this? I must learn this trick!" and then reach out to the closest person you know who has ever mentioned anything about data or machine learning to find out how you can transition into AI. First of all, transitioning into "AI" is such a broad way to look at it. It is analogous to saying "I want to emigrate to Africa, show me how". But that's forgivable too. To cut short your initial excitement, or maybe redirect it, playing with a locally hosted LLM or making API calls to the DeepSeek endpoint is not data science, or machine learning or "AI". It's coding. And if you want to go down that route, you're better of focusing on software engineering. I say this because when you work with LLMs, the finished models to be specific, it's like using any other SaaS API out there. The difference being that you're interacting with a much less deterministic interface. But the rest of the work you do around it is pretty much a det

Most merchants know they have unused files. Far fewer realize they're accumulating AI-generated media they never intended to keep. There's a problem quietly growing inside thousands of Shopify stores right now. It's not abandoned carts. It's not slow page speeds. It's not even the 400 unused product images you already know you should deal with. It's something newer, and most merchants have no idea it's happening. The Rise of AI-Generated Commerce Content Over the past two years, AI image tools have gone from novelty to routine. Shopify Magic. Canva AI. Midjourney. ChatGPT image generation. Adobe Firefly. Background removers. Lifestyle photo generators. Product shot enhancers. Merchants are using these tools constantly — to mock up new products, test background options, generate seasonal variants, create ad creatives, experiment with lifestyle photography. The workflow feels clean: generate a few options, pick the best one, move on. Here's what's actually happening on the backend. Every time you use Shopify's native AI tools to generate, edit, or enhance an image, Shopify quietly deposits files into your media library. Not just the one you kept. All of them. The rejected generations. The experimental edits. The "let me try one more variant" files. The abandoned attempts from six months ago when you were testing a new product that never launched. Every. Single. One. Most merchants assume the files they don't choose disappear. They don't. The lifecycle looks something like this: ┌─────────────────────┐ │ AI Image Generation │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Rejected Variants │ │ • Drafts │ │ • Test Images │ │ • AI Edits │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Hidden Media Files │ │ Accumulate Over Time│ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ AI Clutter │ │ Invisible Technical │ │ Debt │ └──────────┬──────────┘ │ ▼ ┌─────────────────────┐ │ Reduced Media │ │ Governance │ │ • More Noise │ │ • Less Visibility │ │ • Hard

This year's Summer Game Fest turned out to be a good one for fans of Japanese RPGs. First, the conclusion of the Final Fantasy VII remake trilogy was announced at SGF Live, and now we have the long-awaited return of the Persona series. Atlus confirmed Persona 6 exists with a brief teaser, but aside from […]

Notion's head of product said he was "astonished" at “the amount of people RT-ing this."

Minecraft Dungeons 2, a sequel to Microsoft's dungeon crawler spinoff Minecraft Dungeons, will be released on September 29th. The company originally revealed the game in a brief trailer in March, promising a fall 2026 release window. Here's how Microsoft described it at the time: Return to the world of Minecraft Dungeons in an all new […]