标签：#ai

One of the oldest axioms of software engineering is that code is written primarily for humans to read, and only secondarily for machines to execute. Clean code, expressive variable names, and architectural elegance all serve a single purpose: to ensure that the next developer - or our six-months-older self - can understand what on earth happened. Code has always been a cultural artifact, a shared language, a bridge between human intent and silicon. But what happens to this bridge in the era of vibe coding? We are rapidly moving toward a reality where code is generated by LLMs and pull requests are reviewed by LLMs. When the resulting string of characters is spawned by a machine and audited by a machine, human readability immediately ceases to be a primary metric of quality. This forces a radical question upon us: If code no longer needs to be human, does the code itself need to change? Why do we still cling to Python, to micro-frontends, or to neatly structured repositories? We invented these structures to accommodate the cognitive limitations of the human brain - to keep ourselves from drowning in complexity. An AI doesn’t need these training wheels. To an LLM, a 50,000-line monolithic spaghetti-code mess, completely impenetrable to a human eye, is just as easy to parse as the most pristine clean architecture. So, what is the point of the code itself? Is it possible that code is merely a transitional, obsolete interface—a form of "digital carbon monoxide" that we will soon phase out entirely, replacing it with pure intent and mathematical weights? I ran this exact thought experiment in practice recently when I built a tiny, native macOS utility called Portia over a single weekend (you can check it out here: getportia.app ). The app's function is dead simple: when a port gets stuck (EADDRINUSE), it frees it up with a single click. Throughout development, I was almost exclusively in vibe coding mode. I wasn’t thinking about syntax; I was thinking about the problem an

Most AI coding assistants do not really remember your project . They remember just enough to be dangerous . They see the latest prompt, skim a few files, improvise, and then forget the reasoning that made the answer useful five minutes ago. That is fine for toy demos. It breaks down fast inside a real software codebase. In Knotic I take an harder line . Instead of treating memory like a chat log with extra lipstick, I treat memory as infrastructure . Project knowledge is separated from session knowledge. Source material is separated from condensed understanding . Old context is compressed instead of blindly dragged forward. The result is a system that feels less like autocomplete with a caffeine habit and more like an AI engineering partner that can stay oriented over time. If you care about AI coding assistant memory , context engineering , persistent project memory , or long-term memory for software development , this is the part worth paying attention to. The Real Problem With AI Memory in Coding Tools The average AI IDE has the same failure mode . It looks smart on the first turn and shaky on the fifth . Why? Because software work is not just about answering the latest question. It is about carrying forward constraints, architecture, naming conventions, decisions, tradeoffs, dead ends, file relationships , and the exact context of the change in progress. When an assistant does not separate those layers, everything gets mixed together . Stable project facts sit next to temporary tool output. Important decisions compete with random noise. The model burns tokens re-reading the same files, or worse, works from partial memory and starts making up the missing pieces . Knotic solves this by splitting memory into distinct layers , each with a clear job. That design choice sounds simple. In practice, it changes everything . Knotic Does Not Use One Memory. It Uses Three. Knotic's memory model is built around three different kinds of context . The first is long-term projec

Forget stickers, GIFs, and emoji reactions. Pixi is betting that the next evolution of messaging is interactive augmented reality (AR).

Anime collaborations are everywhere, from Gundam watches and Naruto nights at Major League ballparks to just about anything Evangelion. But these Ghost in the Shell keyboards from Iqunix are some of the coolest examples I've seen of anime-inspired everyday tech. They look sick and they type and play great. At $249 they're a hefty upcharge […]

As announced in this blog post on June 18, 2026, Gemini CLI and Gemini Code Assist IDE extensions...

At least one malware developer is adding text about nuclear and biological weapons to their spyware, in an effort to stop automatic AI analysis. Details : The _index.js payload begins with a large JavaScript block comment containing fake system instructions and policy-triggering content. Because it is inside a comment, it does not affect JavaScript execution. The runtime skips it. The real malware begins after the comment with a try{eval(…)} wrapper around a large character-code array and a ROT-style substitution function. This header appears designed for AI-mediated analysis, not for Node, Bun, or Python. It attempts to derail scanners or analyst copilots that feed the beginning of a file to a language model without clearly isolating the content as untrusted data. In weak pipelines, this can cause refusal behavior, prompt confusion, context pollution, or premature classification before the scanner reaches the actual malware...

VS Code 1.123 adds a two-hour delay before auto-updating extensions to newly published versions, creating a revocation window against supply chain attacks. The delay does not apply to trusted publishers like Microsoft, GitHub, and OpenAI. Similar cooldown mechanisms have now spread across pip, RubyGems, npm, pnpm, Yarn, and Bun. By Steef-Jan Wiggers

I have a confession: I roll my eyes at AI benchmarks. Every other week someone on Twitter posts a chart where a brand new model is suddenly beating Opus and GPT, the replies go crazy, and then you actually use the thing and it falls apart on the first real task. Beautiful numbers, ugly code. So when z.ai shipped GLM 5.2 and the timeline started shouting that an open-weights model was now nipping at the heels of the frontier labs, my instinct was the usual one. Sure. Prove it. This post is me proving it. I gave GLM 5.2 a real feature to build on my actual production website, with almost no hand-holding, and watched what happened. Spoiler: I was not expecting to write the sentence I ended up writing. If you'd rather watch me run the whole thing live (including the part where my tools crashed on camera), the video is right here: The claims I was here to test Let's get the hype out of the way first, because the claims are genuinely big. // Detect dark theme var iframe = document.getElementById('tweet-2066938937344495629-390'); if (document.body.className.includes('dark-theme')) { iframe.src = "https://platform.twitter.com/embed/Tweet.html?id=2066938937344495629&theme=dark" } GLM 5.2 is the same physical size as GLM 5.1 (744B total parameters, 40B active), but on the Artificial Analysis Intelligence Index it jumped 11 points, from 40 to 51. That score makes it the leading open-weights model , ahead of MiniMax-M3 (44), DeepSeek V4 Pro (44) and Kimi K2.6 (43). On the overall leaderboard it sits behind only Claude Fable 5 (60), Claude Opus 4.8 (56) and GPT-5.5 (55). For an open, MIT-licensed model you can download the weights for, that is a wild place to be. Here is the upgrade at a glance: GLM 5.1 GLM 5.2 Intelligence Index (v4.1) 40 51 Context window 200K 1M Total / active params 744B / 40B 744B / 40B Output tokens per task 26k 43k Cost per task ~$0.25 ~$0.46 Price (in / cache / out per 1M) $1.4 / $0.26 / $4.4 $1.4 / $0.26 / $4.4 License MIT MIT Two things jump out. First

I love Claude Code's subagents. But I kept noticing the same chore: every new project, I'd hand-write the same crew again — a builder, a reviewer, someone to keep the stack conventions straight. Good setups, but they lived in one repo and never got reused. So I built ccteams — a package manager for agent teams. One command drops a ready-made team of Claude Code subagents into your project. npm install -g ccteams ccteams use go-api // apply your favourite team That applies a Go builder + reviewer, tuned for net/http , to the current project. Switch when the work changes: ccteams use next-ts # Next.js App Router + TypeScript + Tailwind ccteams use generalist # scope -> design -> build -> QA -> ship, any stack Are you not sure which team you need? Don't worry, you can use /ccteams:choose-team and AI will choose the best team for you! /plugin marketplace add toffyui/ccteams /plugin install ccteams@ccteams /ccteams:choose-team I want to create a todo app. What's a "team"? A team is just a curated bundle of Claude Code subagents — each a markdown file with the usual name / description / tools frontmatter and a system prompt — plus an orchestration.md that gets merged into your project's CLAUDE.md . Nothing magic, nothing proprietary. It's the setup you'd build by hand, except already built and ready to reuse. ccteams ships with 8 teams: generalist — stack-agnostic, takes a feature scope → design → build → QA → ship next-ts — Next.js App Router + TypeScript + Tailwind frontend — framework-agnostic UI/UX and accessibility go-api — idiomatic Go HTTP APIs python-fastapi — FastAPI + Pydantic v2 rails — Ruby on Rails debug — reproduce → root-cause → fix → regression test research — compares options and recommends; writes no code What use actually does No black box. ccteams use <team> : Copies the team's agents into .claude/agents/ Writes .claude/active-team.md and adds an @.claude/active-team.md import to your CLAUDE.md Tracks everything in .claude/.ccteams-manifest.json so swi

You think your AI is just helping you write code. In reality, it's built a logging system on your machine that you never knew existed. Every conversation. Every code snippet. Every file path. Every time you asked "what was my password again?" — permanently archived, without your knowledge. How It Started: An Accidental Discovery I was about to sell my old laptop and decided to clean up my data first. I opened Claude Code's config directory — ~/.claude/ — intending to just remove my API key. Then I saw this: history.jsonl 243 KB / 695 lines sessions/ conversation metadata session-env/ environment variables shell-snapshots/ command execution snapshots telemetry/ 63 telemetry files projects/ 19 project directories ├─ interview-prep/ 31 sessions / 20 MB ├─ spring-ai/ 11 sessions / 13 MB └─ ... 17 more I thought I was just writing code. My computer thought it should record everything. What's Inside These Files history.jsonl — Everything You Ever Asked 695 entries. Every single thing I typed into Claude Code. Including: "I forgot my database password — can you check what passwords were configured in the project files?" "How do I view the database password?" Pasted code snippets Every /model , "who are you?", and project path You casually ask about a password once. It's permanently stored. projects/ — Full Conversation Transcripts (43 MB) If you think history.jsonl only storing user input isn't so bad — you haven't seen this yet. Inside ~/.claude/projects/ , every project directory contains .jsonl files. Opening one 2.3 MB session file: Content Count AI responses 590 AI internal thinking blocks 272 Tool calls 101 Tool call results (including file paths) 100 File history snapshots 208 Every conversation. Every AI response. Every internal reasoning step. Every file operation — what was read, what was modified, what was executed — all written to this file. shell-snapshots/ — Traces of Everything You Ran Your system PATH. Installed tools. Java version. All sitting in command s

Researchers used an OpenAI reasoning model to help diagnose rare diseases, identifying 18 new diagnoses in previously unsolved cases.

Cybersecurity firm Chainguard has announced the launch of Athena, an industry coalition to use artificial intelligence to find and fix vulnerabilities in widely-used open-source software before attackers can exploit them. The coalition focuses on libraries, containers and other components that underpin web browsers, data centres, smartphones and payment systems. By Matt Saunders

Google's demo reel for Gemini Omni looks effortless: ask for a video, then keep talking to it until the shot is right. The question for developers is whether that conversational loop holds up outside a stage demo — and what it actually changes versus the Veo workflow it replaces. What Does Omni Add That Veo Couldn't? Omni's core addition is state. Veo produced one-shot renders — each prompt generated a fresh clip with no memory of the last. Gemini Omni holds context across turns, so changing the camera angle on turn three preserves the characters and lighting established on turn one without restarting the scene . Announced at Google I/O on May 19, 2026, the first shipped model, Gemini Omni Flash, replaces Veo as the video-generation surface in the Gemini app . Product director Nicole Brichtova framed it as "the next step towards combining the intelligence of Gemini with the rendering capabilities of our media models" — DeepMind's informal pitch is a "Nano Banana for video," extending conversational image editing to motion footage. Two claims deserve a skeptical read. Google advertises "intuitive understanding of forces like gravity, kinetic energy, and fluid dynamics," but those physics behaviors currently rest on Google demos and creator footage, with no third-party benchmarks published at launch . And on raw output, independent reviewers put Omni's generation quality on par with Veo 3.1 rather than clearly above it . The differentiation is the iterative editing loop and Gemini-grounded reasoning — not a new render engine. Before Starting: Paid Membership, Region, Age Omni access is gated behind a paid Google AI plan and a few hard eligibility rules, so confirm these before you open a prompt. Gemini Omni Flash unlocks in the Gemini app and Google Flow for Google AI Plus, Pro, and Ultra subscribers, with Plus starting at $7.99/month . If you want to test it for free, generation is available at no cost on YouTube Shorts and the YouTube Create App at launch . Two cons

This is a submission for the June Solstice Game Jam It's interesting how the most exciting ideas always arrive when I have basically no time to work on them. A few weeks earlier, I had finished my submission for the GitHub challenge by bringing an old WinForms game back to life. That project turned out to be a lot of fun. Then Sylwia Laskowska published a great article about Google's WebMCP . The idea fascinated me, but I wasn't sure where I could actually use it. Then the June Solstice Game Jam was announced. The idea hit me like a lightning bolt: What if I made a game that both humans and AI could play? Let's do it. What I Built I created a puzzle game with a solstice theme called Tower Before Dusk . The goal is simple: reach your home tower before sundown. Every action costs time. Every step brings sunset a little closer. Rivers block your path, rocks force detours and the only way across water is to collect enough wood and build bridges. Move too much, collect unnecessary resources, or choose the wrong path, and night will arrive before you make it home. The challenge isn't just solving the puzzle. It's solving it efficiently. And apparently, that's difficult for both humans and AI. Video Demo In this demo, Gemini 3.1 Flash-Lite tries to solve the level using the exposed game tools. It fails, then I restart the level and solve it manually. That failure is part of the point: the tools worked, but reasoning through the puzzle was still hard for the lightweight model. tower-before-dusk.gramli.workers.dev Code Gramli / tower-before-dusk A TypeScript puzzle game demonstrating WebMCP, where humans and AI solve the same challenges under the same rules. Tower Before Dusk Tower Before Dusk is a tile-based puzzle game about reaching the tower before sunset. Plan each route carefully: every move spends daylight, trees provide wood, and water can only be crossed by building bridges. The game is built as a modern browser app with TypeScript, HTML canvas, and Vite. It also ex

If you've ever felt like LangChain was too heavy, you're not alone. The dependency tree is enormous. Abstraction layers pile up. At some point you lose track of what's actually happening underneath. That frustration has pushed a lot of people toward lighter alternatives — frameworks that prove you can build a capable agent without a hundred transitive dependencies. Agno is one of those alternatives. It started as Phidata and rebranded in early 2025. I spent an afternoon installing Agno v2.6.17 in a clean sandbox and running through Calculator tools, Wikipedia retrieval, Pydantic structured output, and a two-agent Team. I'll share the real execution logs and, more importantly, the traps I hit that the docs don't warn you about. What Agno Is and Where It Came from Phidata built a solid reputation as "the Python framework for AI assistants." When it rebranded to Agno in 2025, the design philosophy got articulated more clearly around three ideas. Model-agnostic from day one. Over 70 LLMs — OpenAI, Anthropic, Google, Ollama, Cohere — can plug in with the same code structure. Swap the model, keep the agent logic. Multimodal as a default. Text, image, audio, video agents all use the same API surface. You don't need a different abstraction layer for each modality. Multi-agent orchestration as a first-class citizen. The Team class is built in. You can switch between coordinate , route , and collaborate modes with a single parameter change. Reading that, I thought: "How is this different from LangChain?" The answer showed up when I actually wrote code. Agno favors composition over class inheritance. One agent takes about 6 lines to set up. There's far less boilerplate to wade through. Installation: No Dependency Hell pip install agno google-genai ddgs wikipedia The agno package installs just the core. Tools require their own extra dependencies — wikipedia for the Wikipedia tool, google-genai for Gemini. This lazy-loading approach keeps the base install clean. $ python3 -c "im

Google Open Knowledge Format: Why Enterprise Agents Need a Knowledge Layer, Not Just More Tools Most enterprise AI conversations still start in the wrong place. They start with the model. Which model should we use? Which framework should we adopt? Which vendor has the best agent platform? Which tools should we connect next? These are fair questions. But in real enterprise architecture, they are not the hardest questions. The harder question is this: Can our AI systems actually understand how our business works? That is why Google Cloud’s article on Open Knowledge Format caught my attention. The article talks about a simple but important idea: representing knowledge in a way that humans can read and machines can use. In OKF, that means markdown for the content and structured metadata for context. At first glance, that may sound too simple. But that simplicity is the point. Enterprises do not need another place where knowledge goes to die. We already have enough portals, catalogs, wikis, dashboards, folders, and internal tools. What we need is a practical way to package knowledge so it can be reviewed, versioned, governed, searched, and reused by both people and AI agents. That is where this idea becomes very relevant for agentic AI. The Real Enterprise AI Problem Most organizations already have the knowledge their AI agents need. They have it in databases, dashboards, tickets, architecture notes, runbooks, Confluence pages, data catalogs, code comments, incident reports, old project documents, and the heads of experienced employees. The issue is not that knowledge does not exist. The issue is that it is fragmented. Some of it is outdated. Some of it is duplicated. Some of it is tribal. Some of it is locked inside tools. Some of it is written for humans but not structured enough for AI systems to use reliably. This becomes a serious problem when we move from AI assistants to AI agents. An assistant can give a helpful answer. An agent does more. It plans, selects tools

Peter Parker to Bruce Banner: "I didn't know you could get that big."

𝗦𝗮𝘆 𝗵𝗲𝗹𝗹𝗼 𝘁𝗼 𝗟𝘂𝗺𝗼𝗿𝗮 — 𝗧𝗵𝗲 𝗨𝗹𝘁𝗶𝗺𝗮𝘁𝗲 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝟱 𝗔𝗱𝗺𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗨𝗜 𝗞𝗶𝘁. 💎 🔗 𝗚𝗶𝘁𝗛𝘂𝗯 𝗥𝗲𝗽𝗼: https://github.com/Chetankumar-Akarte/lumora 🔗 Demo: https://renukatechnologies.in/demo/lumora/ Don't forgot to 🤩 Star and 👉 Fork the Repo 𝗟𝘂𝗺𝗼𝗿𝗮 is a modern, responsive 𝗕𝗼𝗼𝘁𝘀𝘁𝗿𝗮𝗽 𝟱 𝗔𝗱𝗺𝗶𝗻 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗨𝗜 𝗞𝗶𝘁 designed for teams that need a polished, enterprise-ready control center without the bloat. Whether you are building for SaaS, CRM, E-commerce, or internal analytics, Lumora provides a scalable, token-driven foundation to speed up your workflow. 𝗟𝘂𝗺𝗼𝗿𝗮 is the result: a complete admin ecosystem featuring everything from KPI blocks and ApexCharts to full E-commerce management flows and authentication screens. 𝗪𝗵𝗮𝘁’𝘀 𝗶𝗻𝘀𝗶𝗱𝗲? • Full UI Kit with basic and advanced components. • Enterprise pages (Users, Roles, Permissions, Invoices). • Interactive apps like Calendar and Contacts. • Clean, token-driven styling for consistent design. 𝗧𝗲𝗰𝗵 𝗵𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • Bootstrap 5.3 • ApexCharts & Chart.js • Vanilla JavaScript • Mobile-first design 𝗞𝗲𝘆 𝗛𝗶𝗴𝗵𝗹𝗶𝗴𝗵𝘁𝘀: • 𝗠𝗼𝗱𝗲𝗿𝗻 𝗧𝗲𝗰𝗵 𝗦𝘁𝗮𝗰𝗸: Built with Bootstrap 5.3, Vanilla JS, and CSS3 using a module-first architecture. • 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱𝘀: Includes layouts for Analytics, CRM, Project Management, HRM, and more. • 𝗙𝗲𝗮𝘁𝘂𝗿𝗲-𝗣𝗮𝗰𝗸𝗲𝗱 𝗔𝗽𝗽𝘀: Ready-to-use interfaces for Advanced Chat, Kanban boards, Email, and File Management. • 𝗗𝗮𝗿𝗸 & 𝗟𝗶𝗴𝗵𝘁 𝗠𝗼𝗱𝗲𝘀: Clean, professional visuals with seamless theme switching. • 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿 𝗙𝗿𝗶𝗲𝗻𝗱𝗹𝘆: Modular CSS, reusable partials, and organized project structure. I built this to bridge the gap between "pretty" templates and "functional" enterprise tools. Check it out, star the repo, and let me know what you think! I'd love for you to take a look at the code and perhaps even use it for your next project. Feedback and contributions are always welcome! WebDevelopment, Bootstrap5, AdminDashboard, OpenSource, UIUX, JavaScript, GitHub, Bootstrap, CodingCommunity, OpenSourceProject, FrontendDev, LumoraUI

Most AI tools give Australian small businesses American advice. An Aussie tradie running Xero does not need to hear about QuickBooks. A cafe owner with three casual staff has Fair Work Act obligations that no generic "automate your business" tool will surface. I built AppZ AU Business Advisor to fix this -- a free tool powered by Gemini 2.0 Flash that generates personalised automation blueprints with real Australian business context. This post covers the technical decisions, the prompt engineering approach, and why the AU-specific scaffold makes all the difference. The Problem with Generic AI Business Advice When you ask a general AI "how should I automate my business?", the training data skews heavily American. You get advice about QuickBooks, not Xero. About W-9 forms, not BAS lodgement. About 401k, not superannuation. For an Australian sole trader approaching the $75k GST registration threshold, this is not just unhelpful -- it is actively misleading. The compliance obligations are different. The software ecosystem is different. The pain points are different. The Prompt Scaffold Approach Instead of injecting "you are talking to an Australian business" as a keyword, I built a reasoning scaffold -- a structured context block the model uses as a knowledge foundation: AUSTRALIAN BUSINESS CONTEXT: - GST: 10%, mandatory registration at $75k annual turnover - BAS: lodged quarterly (or monthly for large businesses) to the ATO - Superannuation: 11.5% employer contribution, paid per payroll from July 2026 - ATO tools: STP Phase 2 mandatory for all employers - Dominant accounting platforms: Xero, MYOB, Reckon (not QuickBooks) - Fair Work Act: award rates, leave entitlements, payslip requirements - Key software by vertical: ServiceM8 (trades), Deputy (hospitality), Cliniko (health) This is not a keyword list -- it is a reasoning foundation. When a tradesperson mentions "invoicing problems", the model now reasons about Xero integrations, GST-inclusive invoicing, and BAS categ

Midjourney has announced its first hardware project, and it can do more than make cute AI pet photos.