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Octopus and CATL are pledging to roll out a network of battery swap stations for heavy trucks across Europe.

In this article, the author explores data poisoning as a threat to machine learning systems, covering techniques such as label flipping, backdoors, clean-label poisoning, and gradient manipulation. The article reviews real-world incidents, discusses the challenges of detecting poisoned data, and presents practical defenses, tools, and operational practices for securing ML training pipelines. By Igor Maljkovic

We just shipped the Forgelab PDF API — a fast, affordable REST API for developers who need to handle PDF files without the hassle. What it does: Merge multiple PDFs into one Split PDFs by page ranges Compress PDFs to reduce file size Convert PDFs to images (PNG/JPEG) Pricing: Starts at $5/month for 100 calls/month. No hidden fees. Quick start: curl -X POST https://www.forgelab.africa/api/pdf/merge \ -H "X-API-Key: your_key" \ -F "files=@doc1.pdf" -F "files=@doc2.pdf" Sign up at forgelab.africa

We asked Johan Ejdemo to list which Ikea items populate his home. He also tells us about his all-time favorite. (No, it's not a Billy bookcase.)

Every AI developer has been here: your app is throwing 503s, users are pinging you, and you have 12 browser tabs open — OpenAI status page, Anthropic status page, the GitHub Copilot health page, three different Discord servers — trying to figure out is this me or is it them? That's the problem we set out to solve. Prismix aggregates status from 77 AI services in one place. Six weeks of running it in production taught us some things that might save you time. The problem is worse than you think AI APIs don't fail like traditional infrastructure. They fail in weird, partial ways: Degraded performance that passes your health checks but makes your product feel broken Regional outages — OpenAI US-East is down while EU is fine, so half your users are affected Silent rate-limit cascades — the API returns 429s but their status page says "operational" for another 20 minutes Incident lag — providers often post status updates 10–30 minutes after engineers are already aware The official status pages are optimistic by design. They're customer-facing communications tools, not real-time engineering dashboards. There's nothing wrong with this — but it means you need a different mental model for "is this service down?" What 77 status pages look like in aggregate When you watch 77 AI services simultaneously, patterns emerge fast. OpenAI is the most-watched service (and has the most incidents to watch). The pattern is almost always the same: investigating → identified → monitoring → resolved , typically in 45–90 minutes. The investigating phase is where most developers panic — it looks bad but usually resolves without action on your end. Anthropic runs noticeably clean compared to its API usage growth. Incidents are rarer and shorter. When they do happen, updates arrive faster than most providers. The long tail is interesting. Services like Replicate, Runway, ElevenLabs, and Suno have incident patterns that don't correlate with OpenAI at all. If you're routing across multiple providers

Every World Cup there's a moment. Some player nobody outside their domestic league had heard of scores an absolute screamer in a knockout match, and by the time they've finished celebrating, their follower count is climbing like a rocket. I always found that fascinating, but I could never see it happening. By the time the "X gained 3M followers!" tweets show up, the surge is already over. So this tournament I built a little tracker that snapshots player follower counts on a schedule and shows me the growth curve in near real-time. Here's how it works. The problem with doing this "properly" My first instinct was the official APIs. That died fast. Instagram's Graph API won't give you follower counts for accounts you don't own. TikTok's Research API is academics-only and takes weeks of applications. X's API now starts at $100/month and climbs steeply from there. I just wanted public follower counts — numbers anyone can see by opening the app. I didn't want a data partnership and a legal review. I ended up using the SociaVault API , which wraps public profile data from each platform behind one key. One request, one credit, JSON back. The shared client Everything runs through one tiny helper: // Node 18+ has fetch built in const API_KEY = process . env . SOCIAVAULT_API_KEY ; const BASE = " https://api.sociavault.com " ; async function sv ( path , params ) { const url = new URL ( BASE + path ); Object . entries ( params ). forEach (([ k , v ]) => url . searchParams . set ( k , v )); const res = await fetch ( url , { headers : { " X-API-Key " : API_KEY } }); if ( ! res . ok ) throw new Error ( ` ${ res . status } ${ await res . text ()} ` ); return res . json (); } Grabbing follower counts across platforms Each platform nests the count slightly differently, so I use fallback chains to stay defensive: async function instagramFollowers ( username ) { const data = await sv ( " /v1/scrape/instagram/profile " , { username }); const p = data . data ?. user ?? data . data ?? data

Code reviews, configuration changes, and debugging sessions demand precise understanding of what changed between two versions of text. Manual comparison of large blocks of code or configuration files is error-prone, and version control diffs don’t always provide a quick, focused view for sharing or verifying changes outside a repository. What it is Diff Checker is a browser-based text comparison tool that performs line-by-line analysis of two text blocks and highlights differences with color-coded visual indicators. It processes text entirely in the browser—part of the 200+ free tools on DevTools—meaning no data is uploaded or stored, a privacy‑first design. The interface uses a split‑pane layout: original text on the left, modified text on the right. As you paste or type, the comparison engine recalculates the diff in real time, marking added, removed, and changed segments so differences are immediately clear. Several configuration options tailor the analysis. Toggling whitespace sensitivity ignores differences in indentation or blank lines, useful when comparing code from teams with different formatting conventions. Case sensitivity can be turned off for text where capitalization inconsistencies are irrelevant. A swap button reverses the comparison direction with a single click, handy when the assignment of “original” and “modified” is accidentally reversed. How to use it Paste the original text into the left panel and the modified version into the right panel. The diff view updates instantly, so you don’t need to press a button to see changes. For code, the process is straightforward. Drop a baseline function on the left: function calculateTotal ( items ) { let total = 0 ; for ( let item of items ) { total += item . price ; } return total ; } And the updated version on the right: function calculateTotal ( items , taxRate = 0 ) { let total = 0 ; for ( let item of items ) { total += item . price * ( 1 + taxRate ); } return Math . round ( total * 100 ) / 100 ; } The

Placeholder text is necessary scaffolding in web development, but ubiquitous Lorem ipsum can lead to design monotony and disconnect from project context. Developers building mockups, prototypes, or content-heavy interfaces often need filler text that matches the tone of the target application without introducing distracting Latin. What it is The Lorem Ipsum Generator is a browser-based tool that produces placeholder text in multiple styles, moving beyond classical Latin pseudo-text. It offers distinct variants: traditional Lorem ipsum, Hipster Ipsum with artisanal terminology, Corporate Speak filled with business jargon, and Pirate Ipsum with nautical themes. Each style maintains readability while providing vocabulary that aligns with the spirit of a given project. The generator is part of DevTools, a privacy-first collection of 200+ free browser tools where all processing happens locally—no signup, no tracking. Developers can configure generation parameters to specify the number of paragraphs, total word count, and whether to start with the familiar “Lorem ipsum dolor sit amet” opening. The output is plain text ready for pasting into HTML, design files, or CMS entries. How to use it The interface is a straightforward form: select a text style from the dropdown, then set the number of paragraphs or words you need. The tool generates the text instantly and provides a one-click copy button. <!-- Example output structure when pasting into HTML --> <div class= "content-area" > <p> Leverage agile frameworks to provide a robust synopsis for high level overviews... </p> <p> Iterative approaches to corporate strategy foster collaborative thinking... </p> </div> For typical workflows, 1–3 paragraphs suffice for article previews or body content. Headlines work well with 5–15 words, while navigation elements often need only 2–5 words. The quick copy functionality streamlines populating multiple content areas. Different styles suit different contexts: Corporate Speak makes busi

Por muito tempo eu acreditei que programação e desenvolvimento de software como sinônimos. Na...

A few months ago I was demoing my RAG-powered support bot to a colleague, feeling pretty confident about it. Then it confidently told her our refund policy was “30 days, no questions asked.” Our actual policy is 14 days, with conditions. The bot didn’t hedge. It didn’t say “I’m not sure.” It just made it up and said it with the same calm tone it uses for everything else. That demo stung. RAG was supposed to fix hallucinations, not just relocate them. Here’s what I learned debugging it, roughly in the order I learned it. 1. My chunks were too big, and too dumb I was splitting documents by character count, 1000 chars with slight overlap. It felt efficient. It wasn’t. A single chunk often contained unrelated sections. For example, the end of a “Shipping Policy” and the start of a “Returns Policy” could sit together in the same block. So when the retriever saw a query about returns, it would grab that chunk and the model would blend both sections into one confident but wrong answer. Fix: I switched to semantic chunking based on headings and paragraphs instead of raw character limits. More work upfront, but it stopped feeding the model Frankenstein context. 2. I trusted top-k similarity way too much My retriever was pulling the top 3 chunks by cosine similarity and passing them straight into the prompt. The problem: “similar” is not the same as “relevant.” A chunk can be semantically close to the query but still not actually contain the answer. The model doesn’t know that, it just assumes everything in context is true. Fix: I added a reranking step using a cross-encoder and started logging retrieval scores properly. That alone made it obvious when the system had no real answer but was still trying to act confident. 3. I never told the model it was allowed to say “I don’t know” My prompt was basically: “Use the context to answer the question.” That’s it. No instruction on what to do when the context is insufficient. So the model did what LLMs do when under-specified: it f

Software has become the backbone of modern business operations, powering everything from customer-facing applications and e-commerce platforms to enterprise systems and cloud-based services. Behind every successful software product is a well-structured development process designed to ensure quality, scalability, security, and long-term maintainability. The Software Development Process, commonly referred to as the Software Development Life Cycle (SDLC), provides a systematic framework for transforming ideas into reliable software solutions. By following a defined methodology, organizations can reduce risks, optimize resources, improve collaboration, and deliver products that align with business objectives. This article explores the key stages of the software development process and highlights why each phase is essential to successful project delivery. What Is the Software Development Process? The software development process is a structured sequence of activities involved in designing, building, testing, deploying, and maintaining software applications. It serves as a roadmap that guides development teams from initial requirements gathering to ongoing support after deployment. A well-defined development process helps organizations: Improve project predictability and delivery timelines Reduce development and maintenance costs Enhance software quality and reliability Strengthen security and compliance Increase customer satisfaction Facilitate collaboration across teams Whether developing a small business application or a large-scale enterprise platform, a structured process is critical for achieving sustainable success. _ Phase 1: Requirements Gathering and Analysis_ Every successful software project begins with a clear understanding of business needs and user expectations. During this phase, stakeholders, business analysts, project managers, and development teams collaborate to identify: Business objectives Functional requirements Non-functional requirements User expe

Chrome has had scroll restoration support since 2015. You can even control it: history.scrollRestoration = 'manual' . But if you've ever tried to reliably restore a user's position on a React or Next.js app, you know it doesn't work the way you'd expect. Here's what breaks, why it breaks, and how a browser extension can sidestep the entire problem. What the Browser Actually Does The default behavior is history.scrollRestoration = 'auto' . When you navigate back to a page, the browser tries to scroll to where you were. This works fine for static pages. It falls apart for: SPAs where content is injected into the DOM after navigation Infinite scroll pages where the content at a given Y position changes depending on what was previously loaded Lazy-loaded images that push content down after the scroll restore fires The fundamental problem: the browser fires scroll restoration when the page HTML is parsed, not when the page content is fully rendered. A React app that loads a skeleton → fetches data → renders actual content will restore scroll into a partially-rendered DOM. The history.scrollRestoration = 'manual' Trap If you set manual , you own scroll restoration completely. Most Next.js apps do this. The typical approach: // Save position before navigation router . beforeEach (( to , from ) => { savedPositions [ from . path ] = window . scrollY ; }); // Restore after navigation router . afterEach (( to ) => { const position = savedPositions [ to . path ]; if ( position !== undefined ) { nextTick (() => window . scrollTo ( 0 , position )); } }); The nextTick is the problem. It fires after the Vue/React render cycle, but before async data fetching completes. The page renders empty containers, scroll restores to Y=800, then data loads and pushes everything down. User ends up at Y=800 in a now-different page position. The correct fix is to wait until the content that was at Y=800 actually exists. There's no clean hook for this — you'd need to observe the DOM until the expec

This is a submission for the June Solstice Game Jam I've been in online queer communities for a long time, and one thing that's always stood out is the endearing obsession with pop culture. The artists, the music, the fashion, the references. Every form of art gets appreciated, deeply analyzed, and celebrated. Diva Academy is an attempt to reflect that energy and honor Pride month and the pop culture that comes with it. What I Built Diva Academy is a pop culture trivia adventure. You play as a fresh face entering a campus where the currency is knowledge. The questions cover everything from ballroom culture and drag history to Beyoncé's discography and the origins of the Pride flag. The game runs in sessions: NPCs challenge you to timed trivia battles. Reach your REP(utation) goal to win, or hit zero and you're out. Earned REP converts to permanent currency between sessions, making it a rogue-lite-lite-lite experience where you gradually get stronger even when you lose. The game is built with vanilla HTML5 Canvas, CSS, and JavaScript. It features: 6 explorable rooms 4 NPC tiers - Starlet , Diva , DJ , and Mother - each with distinct personalities and increasing difficulty A rival system where a recurring NPC named Vex Vivienne spawns across the map and hunts you down A permanent perk system where REP earned in each run converts to permanent currency for buying perks like Grace (forgive one wrong answer), Clutch (survive at 0 REP once), and Haste (extra time on the timer) A Spotlight mechanic - defeat a Diva-tier or higher NPC and you earn a one-time 1.5x REP buff for your next face-off Two minigames - Hangman (guess the pop star name) and Pop Connect (link two artists through a mathematically perfect, AI-grounded collaboration graph with look-ahead validation) The Turing Challenge - Archivist Alan tests your ability to distinguish real pop culture quotes from AI-generated fabrications A customization system that unlocks new dress and hair colors as you defeat NPCs An

I did not choose DeepSeek because I think GPT-4 is bad. I chose it because I was building a free app, and free apps teach you what actually matters pretty fast. The question was simple: how do I keep sessions cheap enough that people can practice a lot without me lighting money on fire? The answer pushed me toward DeepSeek-V3 (and later R1 for specific tasks). The real constraint was volume The app is a conversation practice tool. People come in to rehearse hard talks, not to admire the model. A single practice session runs 8-15 turns. Each turn is roughly 300-600 tokens in, 100-300 out. Multiply that by five sessions a week per active user and the costs start compounding. Here is what the math looked like when I was choosing (mid-2026 pricing): Model Input cost (per 1M tokens) Output cost (per 1M tokens) Cost per 10-turn session (est.) GPT-4o $2.50 $10.00 ~$0.04-0.06 GPT-4 Turbo $10.00 $30.00 ~$0.12-0.18 DeepSeek-V3 $0.27 $1.10 ~$0.004-0.007 DeepSeek-R1 $0.55 $2.19 ~$0.008-0.012 At scale, the difference between $0.005 and $0.05 per session is the difference between running a free product and needing a paywall after three conversations. I wanted people to come back daily without hitting a wall. What DeepSeek handled well It stayed in character for 10-15 turns. It pushed back when the user got vague. It followed persona heuristics (numbered if/then rules in the system prompt) about as reliably as GPT-4o did for our use case. For salary negotiation rehearsal, the model needs to say "that's not in the budget" and hold that position for three more turns while the user tries different approaches. DeepSeek-V3 did this. Not perfectly, but reliably enough that sessions felt real. It also made the app easier to run as a free product. People can try, fail, reset, and try again without me worrying about per-session cost. Where GPT-4 was still better GPT-4 (and 4o) is smoother with nuanced emotional wording. When a conversation gets subtle, loaded with subtext, or requires pick

Debugging authentication in web apps is painful. You need to test the same flow as five different user types — new visitor, returning user, admin, expired session, logged-out — and the easiest way is to constantly create new accounts or clear all your cookies and start over. There's a faster way. These five techniques use direct cookie manipulation to simulate any auth state without touching your database or creating dummy accounts. I use CookieJar for most of this — a free Chrome extension built natively on MV3 that gives you a proper UI for cookie editing. But I'll show you the underlying Chrome DevTools method too, so you understand what's actually happening. 1. Simulate a Logged-Out State Without Clearing Everything The naive approach: clear all cookies and reload. The problem: you just nuked your dev server session token, your local storage flags, your Stripe test mode cookie, and everything else you carefully set up. The targeted approach : identify and delete only the session/auth cookie. Most session cookies are named session , sid , auth_token , _session_id , or something close. In DevTools: Application → Cookies → [your domain] → find the session cookie → right-click → Delete With CookieJar: open the extension, search session , click the trash icon next to just that cookie. Your dev environment stays intact. The user state resets to logged-out. 2. Test the "Returning User" vs "New User" Path Without a Second Account Session cookies tell the server you're authenticated. But many apps use separate cookies to track whether a user has seen the onboarding flow, completed setup, or visited before. Look for cookies like onboarding_complete , setup_done , first_visit , or custom flags in your app code. To test the new user experience: Export your current cookies (CookieJar → Export → JSON format, or copy from DevTools) Delete the specific onboarding/first-visit flag cookie Reload and test the new user path Re-import or re-set the cookie to restore your state This

Over the past year, I've noticed something interesting in conversations about enterprise AI. Most...

TL;DR: I built a .NET library that renders Helm charts and drives Kubernetes releases without shelling out to the helm CLI. 129/129 templates across ingress-nginx, cert-manager, external-dns, podinfo, and metrics-server now render successfully. The main entry point is HelmSharp.Action, with lower-level packages available for chart loading, rendering, Kubernetes operations, and release storage. MIT licensed, looking for feedback and early adopters. Why I Built This At work, our .NET services deploy to Kubernetes through Helm. Every Docker image had to bundle the helm binary — another dependency to manage, another layer in the image, another surface for CVEs. I wanted to cut that out entirely and do Helm-style rendering directly in-process. The .NET ecosystem doesn't really have this. There are YAML libraries. There are Kubernetes client libraries. There are template engines. But nothing ties them together the way helm template does — values merging, named templates, include , range , toYaml , the whole Sprig function set, all wired into a single render pipeline. So I started building one. (This is also my first real open source project — I'd spent years consuming OSS without contributing back, and HelmSharp is what came out of deciding to change that.) What HelmSharp Does HelmSharp is a multi-package .NET SDK (net8.0 / net9.0 / net10.0) that covers: Package What it does HelmSharp.Action High-level Helm client — TemplateAsync , UpgradeInstallAsync , RollbackAsync HelmSharp.Chart Chart loading from directories and .tgz , values merging, --set / --set-json style overrides HelmSharp.Engine Helm-style template rendering — 100+ Sprig/Helm functions HelmSharp.Kube Kubernetes apply, delete, and wait (no kubectl needed) HelmSharp.Release Release history stored in Kubernetes Secrets (Helm-compatible) HelmSharp.Repo Chart repository index, pull, and search Plus Registry , Storage , PostRenderer extension points Here's the lower-level rendering API — no result objects, no stdout

Let me confess something a little creepy. I have a habit of peeking at other people's dev posts. Not stealing the writing — relax. I run a tiny read-only job that fetches the public pages on dev.to, Zenn, and Qiita and counts only the boring parts: titles, post times, like counts. Who published what, at what hour, and how far it traveled. Then it tallies the lot. The reason is petty: my own posts weren't landing. The content is already in my hands — so I wanted to know how much the rest, the when and how you publish , actually moves the needle. By the numbers, not by gut. So I counted across three platforms. And the conditions that make a post fly turned out to be roughly mirror images between Japan (Zenn / Qiita) and the English-speaking world (dev.to). Here's the story. First, my most important disclaimer This post is full of numbers, so let me put up a guardrail before any of them. This is correlation, not causation . A result like "weekend posts don't do well" could mean the weekend itself is bad — or it could mean people who post on weekends are just dashing something off on the side. The data can't separate those. Please read it that way. Also, I only keep aggregate numbers I computed myself . I don't store or reuse anyone's article body (read-only GET, count the features, throw the page away). I peek, but only at the overall shape . Nobody gets singled out here. With that out of the way — four findings I enjoyed. 1. The best hour to publish is just your readers' time zone This one came out cleanest. On Qiita , posts published in the morning win (+32pt in the GOOD group). Midday is +14pt. Evening is -32pt, late night -14pt. Zenn likes midday too (+27pt). Late night is -15pt. dev.to is the exact opposite. Late night Japan time scores +7pt — Japanese evening is actually weak. The trick is obvious once you see it. dev.to's readers are English-speaking, mostly US. Late night in Japan is the US working day. Zenn and Qiita readers are in Japan, so the Japanese morni

I run a paid infrastructure service. Alone. No co-founder, no on-call rotation, no senior engineer to escalate to. My only collaborator is Claude Code, and after about a year, my persistent memory has grown to 60+ entries. Those entries have become more valuable than any runbook I've written. They've also taught me — painfully — what makes memory architecture work and what makes it quietly fail. If you're running anything solo with an AI agent, here are five lessons I wish I'd burned into my brain on day one. 1. Write the why , not the what The first instinct when you start using persistent memory is to log what you did. "Migrated service X from tool A to tool B." "Switched protocol from X to Y." Six months later, when something breaks, that information is worthless . You don't need to know what you did — git log and git blame already tell you that. You need to know why you made that choice. What constraint forced it. What you ruled out. Real example. The bad version of an entry I once wrote: Switched the worker pool from Docker containers to systemd units on host. Tells me nothing my git history doesn't. The rewritten version: systemd units on the host instead of Docker containers on this VPS provider. Why: the provider runs aggressive kernel-wide OOM scoring across tenants; containers were getting reaped by oom-killer triggered by other customers' workloads. systemd processes survive because they're scored as system processes. How to apply: any VPS where dmesg | grep -i oom shows kills from PIDs you don't recognize — don't run containers there, run systemd. That one entry has saved me three rebuilds. Because the next time I'm tempted to "just dockerize it, it'll be cleaner," the memory entry says: no, you already learned this, you'll be back here in a week. The pattern: always include Why: and How to apply: lines. If a memory entry can't answer those two questions, delete it. 2. Memory rots — prune or pay About six months in, I did a memory audit. Of 60 entries, 1

This is a submission for the June Solstice Game Jam Archive — The Last Historian of Humanity What if history wasn't discovered... but selected? History is often treated as something permanent—something waiting to be uncovered. Archive asks a different question: What happens when humanity loses the ability to tell the difference between truth, memory, and fabrication? You play as the final Archivist after the collapse of civilization. Humanity's knowledge survives, but it has become fragmented, contradictory, and corrupted. Your responsibility is no longer to preserve everything—you must decide what deserves to be remembered. Every decision changes the civilization that will inherit your version of history. What I Built Archive is a narrative investigation game where players reconstruct humanity's past by examining historical memories, investigating evidence, resolving contradictions, and deciding what becomes official history. Unlike traditional mystery games, there is rarely a perfect answer. Instead, every investigation asks questions such as: Should conflicting memories be preserved? Is stability more important than truth? Can compassion justify rewriting history? If no one can verify the past, what does "truth" even mean? Each recovered memory is presented as a historical article. Players investigate through classified documents, research papers, witness testimonies, forensic reports, personal journals, and government archives before making irreversible decisions. Those decisions reshape the civilization that follows. By the end of the game, players don't simply receive a score—they discover the kind of society they created. Why It Fits the Theme The June Solstice represents a turning point. It is the moment when one season gives way to another, when light begins yielding to darkness, or darkness begins yielding to light. Archive explores a similar transition. Not between seasons... but between certainty and uncertainty. Human civilization reaches a moment where