标签：#AI

French serial entrepreneur and open-source legend Jean-Baptiste Kempf has been building Kyber, an infrastructure layer to control remote devices in real time.

How AI Will Shape the Technology Industry in 2027 We're roughly 6 months out from 2027, and the signals are already converging: AI is not coming — it has arrived, and the next wave will be fundamentally different from everything that came before it. For developers and tech professionals, 2027 isn't a distant horizon. It's the next major inflection point to prepare for now. Here's what the research, analysts, and industry leaders are saying about what's ahead. From General-Purpose to Task-Specific: The Enterprise AI Shift One of the clearest signals comes from Gartner (April 2025): by 2027, organisations will use small, task-specific AI models three times more than general-purpose large language models. The era of "one model to rule them all" is already ending at the enterprise level. Companies are learning that a fine-tuned, domain-specific model trained on their proprietary data consistently outperforms a generic LLM on their specific workflows. Faster, cheaper, more accurate, and harder for competitors to replicate. For developers, this has real implications: Skills in fine-tuning, RAG (retrieval-augmented generation), and model evaluation become more valuable than prompt engineering alone The ability to build and maintain internal AI pipelines on private data will be a core engineering competency Generic API integrations to OpenAI or Anthropic get replaced — or layered under — proprietary model infrastructure The companies building and maintaining these specialised models will have durable competitive advantages. The ones that don't will be running on shared infrastructure that their competitors can access equally. The Macroeconomic Wake-Up Call: AI Hits GDP in 2027 Goldman Sachs projects that AI may start to meaningfully boost US GDP in 2027 — marking the first measurable macroeconomic signal of the current AI wave. Paired with estimates that ~25% of tasks in advanced economies could be automated by 2027 (10–20% in emerging markets), the scale of workforce restr

AI isn't coming for your job. It already showed up, merged its first PR, and doesn't need a code review. The question developers keep dancing around — but rarely say out loud — is this: If GitHub Copilot, Cursor, and Claude can do what a junior dev does in a fraction of the time, what happens to junior devs? And more uncomfortably: what happens to mid-level devs in three years? The Uncomfortable Data Points This isn't speculation. It's already showing up in hiring data. Entry-level developer roles are contracting. Stanford's Digital Economy Lab (2025) found measurable decline in entry-level employment in AI-exposed roles — and software development is one of the most exposed. One senior dev + AI tools = the output of a small team. Brynjolfsson, Li & Raymond (NBER, 2023) showed generative AI productivity gains that compress what used to require multiple headcount into one. Goldman Sachs (2023) estimated significant white-collar labour market exposure — knowledge workers, not factory workers, are the primary target this time. This isn't the loom replacing weavers. It's the IDE replacing the person using the IDE. The Counter-Argument (And It's Not Weak) Here's where it gets interesting — because the doomsayer take isn't the whole story either. Every major technology wave destroyed jobs and created more than anyone predicted: The ATM didn't eliminate bank tellers — it lowered branch costs, banks opened more branches, teller roles increased for a decade The spreadsheet didn't kill accountants — it created an entire industry of financial analysts The internet didn't destroy publishing — it exploded the number of people who could publish The argument: AI raises developer productivity so dramatically that it expands the total addressable market for software. More products get built. More tools get created. More companies can afford to build what previously required a $500k engineering team. More demand for developers, not less. Where It Gets Complicated for Devs Specifically

If you've been following the AI market lately, you already know the headline numbers: DeepSeek V4 costs about 3% of what GPT-4o charges per token. GLM-4 runs benchmarks competitive with GPT-4 at roughly one-twentieth the price. Qwen delivers multilingual performance that rivals Claude for a rounding error in your cloud bill. The spreadsheets look incredible. The problem is actually using these models. Signing up for each provider means navigating Chinese-language dashboards, topping up separate wallets, managing six different API key formats, and dealing with SDKs that don't follow any consistent convention. Most developers give up after the second integration. That friction is why, despite the economics being objectively absurd in 2026, most teams still default to a single Western provider and eat the cost. AIWave exists to kill that friction. One API key. One endpoint. Fifty-plus models across eight Chinese labs, all speaking standard OpenAI-compatible format. Zero code changes to switch between DeepSeek, GLM, Qwen, MiniMax, and everything else. This post covers how the platform works under the hood, what the request lifecycle looks like, and how to integrate it in any language that can speak HTTP. The Fragmentation Problem, Quantified Before getting into the solution, here's what the Chinese LLM landscape actually looks like as of June 2026: Provider Flagship Model API Format Auth Method SDK Language DeepSeek V4-Pro Custom (DS format) Bearer token + signature Python, JS Zhipu GLM-4.5 OpenAI-compatible-ish JWT with expiry Python, Java Alibaba Qwen-3-Max DashScope (Alibaba) AK/SK + HMAC Python, Java, Go MiniMax MiniMax-Text-01 Custom REST API Key + Group ID Python Moonshot Kimi-K2 OpenAI-compatible API Key Python, JS Baidu ERNIE 4.5 Qianfan (Baidu) OAuth 2.0 Client Cred Python ByteDance Doubao-Pro Ark (Volcengine) IAM AK/SK + SigV4 Python, Go 01.AI Yi-Lightning OpenAI-compatible API Key Python Eight providers, seven different authentication schemes, four distinct A

Supervised vs. Unsupervised Machine Learning: How to Choose the Right Approach Supervised learning trains a model on data that's already labeled with the correct answer, so it learns to predict outcomes for new, unseen examples. Unsupervised learning works on unlabeled data and finds patterns or groupings on its own, without being told what the "right answer" looks like. Use supervised learning when you have historical examples of the outcome you want to predict; use unsupervised learning when you're trying to discover structure in data you don't yet understand. That's the short version. Here's what it actually means in practice, and how to know which one your project needs. What is supervised learning? In supervised learning, every training example comes with a label — the "correct answer" the model is trying to learn to predict. Feed a model thousands of emails, each tagged "spam" or "not spam," and it learns the patterns that separate the two. Once trained, it can label emails it's never seen before. The defining trait: you already know the outcome for your training data. You're not asking the model to discover something new — you're asking it to learn a pattern well enough to apply it to fresh cases. Common supervised tasks: Classification — sorting things into categories (spam vs. not spam, fraudulent vs. legitimate transaction) Regression — predicting a number (home price, next month's revenue) What is unsupervised learning? Unsupervised learning gets raw, unlabeled data and is asked to find structure in it — without anyone telling it what to look for. There's no "correct answer" to check against during training. The defining trait: you don't know the outcome in advance — you're trying to find it. A retailer might feed customer purchase histories into an unsupervised model not because they have a label called "customer segment" already assigned, but because they want the model to discover natural groupings on its own. Common unsupervised tasks: Clustering — gr

If you've been following the AI market lately, you already know the headline numbers: DeepSeek V4 costs about 3% of what GPT-4o charges per token. GLM-4 runs benchmarks competitive with GPT-4 at roughly one-twentieth the price. Qwen delivers multilingual performance that rivals Claude for a rounding error in your cloud bill. The spreadsheets look incredible. The problem is actually using these models. Signing up for each provider means navigating Chinese-language dashboards, topping up separate wallets, managing six different API key formats, and dealing with SDKs that don't follow any consistent convention. Most developers give up after the second integration. That friction is why, despite the economics being objectively absurd in 2026, most teams still default to a single Western provider and eat the cost. AIWave exists to kill that friction. One API key. One endpoint. Fifty-plus models across eight Chinese labs, all speaking standard OpenAI-compatible format. Zero code changes to switch between DeepSeek, GLM, Qwen, MiniMax, and everything else. This post covers how the platform works under the hood, what the request lifecycle looks like, and how to integrate it in any language that can speak HTTP. The Fragmentation Problem, Quantified Before getting into the solution, here's what the Chinese LLM landscape actually looks like as of June 2026: Provider Flagship Model API Format Auth Method SDK Language DeepSeek V4-Pro Custom (DS format) Bearer token + signature Python, JS Zhipu GLM-4.5 OpenAI-compatible-ish JWT with expiry Python, Java Alibaba Qwen-3-Max DashScope (Alibaba) AK/SK + HMAC Python, Java, Go MiniMax MiniMax-Text-01 Custom REST API Key + Group ID Python Moonshot Kimi-K2 OpenAI-compatible API Key Python, JS Baidu ERNIE 4.5 Qianfan (Baidu) OAuth 2.0 Client Cred Python ByteDance Doubao-Pro Ark (Volcengine) IAM AK/SK + SigV4 Python, Go 01.AI Yi-Lightning OpenAI-compatible API Key Python Eight providers, seven different authentication schemes, four distinct A

When it comes to our deepest thoughts, secrets, and mental health struggles, "the cloud" can feel like a very crowded place. In an era where data privacy is paramount, sending your private journal entries to a central server for analysis feels... risky. But what if you could have the power of a world-class LLM like Llama 3 running entirely on your MacBook? Thanks to the Apple MLX framework, local LLM execution is no longer a pipe dream—it’s a high-performance reality. By leveraging privacy-preserving AI and advanced Llama 3 quantization , we can build a personal mental health assistant that provides Cognitive Behavioral Therapy (CBT) insights without a single byte ever leaving your machine. 🚀 Why Apple MLX? 🍏 Apple's MLX is an array framework designed specifically for machine learning on Apple Silicon. It’s essentially "NumPy meets PyTorch," but optimized to squeeze every drop of power out of your M1/M2/M3 chip's Unified Memory Architecture. The Architecture: 100% Local Data Flow Here is how our private assistant handles your data. Notice the absence of any "External API" or "Cloud Storage" blocks: graph TD A[User Private Journal Entry] --> B{Local Python App} B --> C[Apple MLX Framework] C --> D[Quantized Llama 3 - 4bit/8bit] D --> E[CBT Sentiment Analysis] E --> F[Empathetic CBT Feedback] F --> B B --> G[Local Encrypted Storage] subgraph MacBook Pro / Air C D E end Prerequisites 🛠️ To follow this advanced guide, you’ll need: An Apple Silicon Mac (M1, M2, M3 series). Python 3.10+ . mlx-lm : The high-level library for running LLMs with MLX. Step 1: Setting Up the Environment First, let's create a virtual environment and install our dependencies. We are using mlx-lm because it handles the complexities of quantization and model loading seamlessly. mkdir private-mental-health-ai && cd private-mental-health-ai python -m venv venv source venv/bin/activate pip install mlx-lm huggingface_hub Step 2: Downloading & Quantizing Llama 3 Llama 3 8B is a powerhouse, but it's a bi

Anthropic’s Fable/Mythos shutdown is the first real model export-control shock The important AI story this week is not just that Anthropic launched bigger Claude models. It is that the US government then told Anthropic to switch two of them off for foreign nationals — and Anthropic says the practical answer was to disable them for customers while it works through compliance. That is a very different kind of platform risk than rate limits or pricing changes. If you are building on frontier models, model access can now move because of export-control decisions, safety claims, and geopolitical pressure. What happened Anthropic announced Claude Fable 5 and Claude Mythos 5 on June 9. Fable 5 was described as Anthropic’s most capable generally available model, with stronger performance across software engineering, knowledge work, vision, scientific research, and longer complex tasks. Mythos 5 was positioned above that: an upgrade to Claude Mythos Preview, with Anthropic calling out cyber-defence and life-sciences use cases. Three days later, Anthropic published a blunt update: the US government had issued an export-control directive requiring Anthropic to suspend all access to Fable 5 and Mythos 5 by any foreign national, whether inside or outside the United States — including foreign-national Anthropic employees. Anthropic said the order arrived at 5:21pm ET on June 12, did not include detailed specifics, and that its understanding was that the government believed it had become aware of a jailbreaking method for Fable 5. Anthropic said access to other models was not affected, but the “net effect” was that it had to abruptly disable Fable 5 and Mythos 5 for customers to ensure compliance. Al Jazeera’s follow-up on June 19 frames the downstream effect clearly: allied countries and companies are now being forced to think harder about dependence on US frontier-model access. It also reports that Anthropic had granted roughly 200 institutions across 15 countries access to Claud

Written by Stephanie Dover, Software Engineer 10+ YOE, ex GitHub, Twitch, Microsoft. Creator of Klaussy. LinkedIn · GitHub · Klaussy Desktop · Klaussy Agents TL;DR klaussy-agents is a free, MIT-licensed CLI ( pip install klaussy-agents ) that makes the prose an AI coding agent writes, PR comments, review notes, commit messages, read like a person wrote them. It works in two layers: a humanization spec baked into the agent's skills so it writes clean prose up front, and a deterministic klaussy humanize pass that scrubs the output afterward. The scrubber is rule-based regex, not an LLM, and it never touches code. There's also a part I didn't expect going in: once the AI tells are gone, what's left can read curt and run long, so the spec also handles tone (don't be rude) and length (one sentence for a reply, one to five for a review comment). Repo: github.com/steph-dove/klaussy-agents. The problem You can spot AI-written text now. Everyone can. And the place it grates most is a code review comment or a commit message, where the prose sits next to your name in a thread your teammates read. The tells are consistent. The em-dash is the biggest one. Right behind it: filler openers like "It's worth noting that…" and "I wanted to point out that…", chatbot scaffolding like "Hope this helps!" and "Let me know if you have questions!", and stacked hedges like could potentially . An agent that leaves those in your PR reads like a bot, and people notice. The obvious fix is to tell the model not to do it. Add "don't sound like AI" to the prompt and move on. That helps, inconsistently, and it regresses silently the moment you change the model or the prompt drifts. Editing every comment by hand works too, but hand-editing every comment defeats the point of having an agent write them. I wanted something I could trust without rereading. Why "just tell the model" wasn't enough The honest answer to "why not just prompt for it" is: a prompt asks, it doesn't enforce. The model tries to com

Hi, it's Furkan. I'm a security professional prepping for the CompTIA SecAI+ (CY0-001) cert, and I couldn't find study material that actually clicked for me, so I built my own and structured it around the exam blueprint. This is me sharing it back. Each post maps to one objective, and I've leaned hard on real-world scenarios because that's what made it stick for me. If it helps you pass too, even better. CompTIA SecAI+ CY0-001 | Domain 1.0: Basic AI Concepts Related to Cybersecurity "Compare and contrast various AI types and techniques used in cybersecurity." 🧠 What's in This Section? This one breaks down into three big building blocks: AI Types — which kind of AI does what, and where it shows up in security Model Training Techniques — how a model actually gets trained and tuned Prompt Engineering — how to ask an AI the right question Ready? Let's get into it. 🔷 PART 1: Types of AI AI isn't one single thing. Different problems call for different AI approaches. Think of a carpenter's toolbox: there's a hammer, a screwdriver, a saw. They all do different jobs, but they're all "tools." AI types work the same way. 1. Generative AI What it does: Creates new content. Text, images, audio, code; whatever you ask for. How it works: Models trained on huge amounts of data use the patterns they've learned to produce outputs that never existed before. They don't memorize, they learn patterns and then build new things out of them. In security: Defense side: Building security awareness training by simulating phishing emails, auto-generating incident response playbooks, drafting security reports. Offense side: Attackers use generative AI to spin up convincing phishing emails, deepfake audio, or malicious code. 🍕 Real-World Example: A security team wants to test their employees, so they use generative AI to write phishing emails that nail the CEO's writing style. "Hey, I need you to push through an urgent payment...", the email is so realistic that 40% of staff click. That right the

The dangerous thing about CI agents is not that they can write code. It is that they run in the place where we already concentrate trust. CI has repository access. CI has tokens. CI has build logs. CI can fetch dependencies, publish artifacts, comment on pull requests, open issues, deploy previews, and sometimes touch production systems. It is the automation layer we taught ourselves to trust because the alternative was humans doing the same boring steps by hand. Now we are putting agents inside it. That is useful. It is also exactly where the security model gets weird. Microsoft published a write-up this month about a Claude Code GitHub Action case where untrusted GitHub content and file-reading capability could combine badly. The short version is that an agent operating in a CI/CD context had enough ambient access to read more than the user probably intended, including process environment data that could expose workflow secrets. Anthropic mitigated the issue in Claude Code 2.1.128. The specific bug matters. The pattern matters more. CI/CD agents are not chatbots with a build badge. They are automated actors running in a high-trust environment while reading untrusted instructions from pull requests, issues, comments, commit messages, files, logs, and whatever else the workflow feeds them. That combination deserves more fear than it is getting. prompts are now part of the attack surface We are used to thinking about CI security in terms of code and configuration. Who can modify the workflow file? Which secrets are available to pull requests? Do forks get privileged tokens? Are dependencies pinned? Are artifacts trusted? Can a build script publish something? Does the workflow run on pull_request or pull_request_target ? Those questions still matter. But agents add another layer: text becomes operational input. The agent may read a pull request description. It may read a comment asking it to fix a test. It may read source files changed by an untrusted contributor. It

For the last 30 years, stopping the flow of cybersecurity-related software has proven to be ineffective. It's unclear why it would work now with Anthropic’s cybersecurity model Mythos.

Every time I started a new AI chat session, it read my entire codebase. 50 files. Thousands of tokens. On every single message. Whether I was asking about authentication, database schema, or a single UI component — the AI read everything. I'm 16 and building AI-powered products. Token costs add up fast. Context windows fill up. The AI loses track of older files. Responses slow down. So I built something to fix it. The Problem When you work with AI on large projects, you face a choice: Give the AI too much context → burns tokens, hits context limits, slower responses Give it too little → AI misses important files, makes wrong assumptions There's no middle ground — or at least there wasn't. Introducing FolioDux FolioDux is a lightweight, open-source file-mapping standard for AI-assisted development. The idea is simple: instead of giving your AI every file, you give it a compact index that tells it where everything is and what it does . The AI reads the index first, identifies the relevant files, and reads only those. One file. Two rules. Any AI. It works with Claude, ChatGPT, Gemini, Cursor, Copilot — any tool that accepts a system prompt. How It Works You add one file — FOLIODUX.md — to your project root. # FOLIODUX · TaskFlow · v1.0 · 2026-06-18 · 17 files STACK: React19+TypeScript+Vite · Express+SQLite · JWT --- ## TASKS auth/login/register → AuthView.tsx, authService.ts, server.ts create/edit task → TaskForm.tsx, taskService.ts, server.ts, types.ts list/filter tasks → TaskList.tsx, taskService.ts database → db.ts, server.ts --- ## INDEX App.tsx | fe | root: routing, auth state, layout wrapper AuthView.tsx | fe | login + register forms, error display taskService.ts | svc | CRUD tasks, local cache, optimistic updates server.ts | be | Express: all routes — auth, tasks, projects, user db.ts | be | SQLite setup, schema creation, migrations on boot types.ts | typ | Task, Project, User, Status(todo|in-progress|done) --- ## GROUPS Frontend: App.tsx · AuthView.tsx · TaskLi

The AI agent had one job: pay approved vendor invoices, so the finance team could stop doing it by hand. On a Tuesday morning, it picked up invoice #4471 from a freight vendor Ksh48,000, stamped Approved in the company's ERP, cleanly matched to a valid purchase order. The agent checked the things it was told to check. They all passed. It paid the invoice. The invoice had already been paid. The previous Thursday. By a member of the finance team. Here is what the company's systems believed that morning and none of them was wrong. The ERP said: Approved. Unpaid. The reconciliation job that pulls in bank activity runs overnight, and last night it had failed silently. So the ERP's picture of the world was simply four days stale. The bank feed said: Paid. Last Thursday. It was right. Nobody had told the ERP. A Slack thread said: "hold everything to this vendor they double-billed us last quarter, I'm sorting it out with their AP team." Posted by the accounts-payable lead. Three days earlier. Resolved in her head, and nowhere else. The vendor's own email said: "Payment well received, thank you!" referring, of course, to Thursday's payment. The agent's inbox reader had seen it that morning, then set it aside, because email ranked below the ERP and the two disagreed. Every system was internally consistent. Every system was the authority on something . And there was no system anywhere not one that could answer the only question that actually mattered: has invoice #4471 been paid? A human clerk would almost certainly have caught it. Not because a clerk is smarter than the model they're not. Because a clerk would have felt the friction. They'd have half-remembered cutting the check. Or scrolled past the Slack message that morning and hesitated. Or simply had the reflex to ping someone before sending $48,000 out the door. Reconciling systems that quietly disagree is most of what operations people actually do all day so much of it that nobody files it under "work." It's just judgm

AI Agent Orchestration: Proxmox Automation, OpenAI Data Agents & Azure Serverless Runtime Today's Highlights Today's highlights focus on practical AI agent applications and robust deployment strategies. We delve into building a secure AI admin for Proxmox, explore OpenAI's internal data analyst agent, and examine Azure Functions' new serverless runtime for agents. I didn't trust an AI with my Proxmox cluster — so I built one that can't surprise me (Dev.to Top) Source: https://dev.to/john-broadway/i-didnt-trust-an-ai-with-my-proxmox-cluster-so-i-built-one-that-cant-surprise-me-2k9l This article details a practical, hands-on approach to building a reliable AI agent for managing a Proxmox virtual environment. The author sought an agent capable of performing critical tasks like creating VMs, fixing storage issues, and tailing container logs, but with an emphasis on predictable and safe operations. The core idea is to create an AI that operates within defined boundaries, ensuring it doesn't perform unexpected or destructive actions. This tackles a crucial challenge in AI agent development: achieving trust and control in automated workflows. The implementation likely involves careful prompt engineering, tool use, and possibly a custom execution environment or validation layers to ensure commands are executed as intended and within pre-approved parameters. This project exemplifies how developers can apply AI agent orchestration principles to real-world IT automation, moving beyond simple information retrieval to true task execution, while maintaining human oversight and preventing 'surprises' common with less constrained AI systems. It's a blueprint for anyone looking to build robust, trustworthy AI-powered RPA solutions for system administration. Comment: A brilliant take on building AI agents for critical infrastructure. The focus on 'can't surprise me' highlights the need for robust control and guardrails, crucial for production workflow automation. This is what practic

The Runbook That Lied to Me at 3am The pager went off at 3:14am for a wedged OpenStack Neutron agent. I did what any tired engineer does: I opened the runbook. It told me to restart a service that had been renamed eighteen months earlier, pointed at a Grafana dashboard that 404'd, and assumed a network topology we'd migrated off of two quarters back. The runbook wasn't just unhelpful. It was actively lying to me, and I burned twenty minutes trusting it before I gave up and went to read the source. That's the real problem with documentation. It isn't that we don't write it. It's that the moment we finish writing it, it starts rotting, and the cost of keeping it fresh is high enough that nobody pays it until the document has already betrayed someone at 3am. A runbook your team doesn't trust is worse than no runbook, because no runbook at least forces you to think. This is where AI actually earns its keep in a platform org, and not in the way the marketing decks suggest. AI is not going to own your documentation. It's going to do the tedious first-draft labor — turning a resolved incident, a chunk of shell history, or a deploy diff into a structured skeleton — so a human engineer can spend their scarce attention on the part that matters: verifying the commands, marking what's unproven, and editing the robotic tone out so the team actually reads it. AI drafts. You verify and sign off. That distinction is the whole game. Why "Humanizing" AI Is the Job, Not a Slogan Let me be precise about what I mean by "humanizing AI," because the phrase gets abused. I don't mean making AI sound human to fool a reader. I mean keeping a human in the loop as the editor and owner of record, and doing the unglamorous work of turning a competent-but-soulless machine draft into something a colleague trusts. Two things break trust in AI-drafted docs, and both are fixable by a human pass: Unverified claims stated with confidence. An LLM will happily tell you to run systemctl restart neutron-l3-

Modern collaboration needs more than documents and chat messages. Teams need a shared visual space where ideas can be created, organized, and refined together in real time. That's why I built SyncCanvas — an AI-powered collaborative whiteboard that combines real-time multiplayer collaboration, infinite canvas drawing, and AI-assisted brainstorming into one modern workspace. The Problem Most collaboration tools focus on only one aspect of teamwork. Some are great for drawing. Others are excellent for documentation. AI tools often live in separate windows, disconnected from the creative workflow. I wanted a platform where teams could brainstorm visually while AI actively helped generate and organize ideas directly on the canvas. Introducing SyncCanvas SyncCanvas is an infinite multiplayer whiteboard designed for students, developers, teams, and creators. Key features include: Real-time collaboration with live synchronization Infinite canvas with pan and zoom Drawing tools, shapes, text, and sticky notes AI-powered content generation using Gemini Private room sharing Guest mode access PNG export support WiFi Rooms for local collaboration Real-Time Collaboration Collaboration is at the heart of SyncCanvas. Using Yjs and WebSockets, multiple users can work on the same board simultaneously while seeing updates instantly. Users can: View live cursors Track online participants Join private rooms using secure room codes Collaborate anonymously through guest mode This creates a seamless experience similar to working together in the same room. Infinite Canvas Experience The whiteboard is designed to be limitless. Users can: Draw freehand sketches Create rectangles and circles Add text elements Organize ideas with sticky notes Move freely across an infinite workspace Export workspaces as images The canvas is powered by Fabric.js, providing smooth rendering and flexibility for future enhancements. AI-Powered Brainstorming One of the most exciting features is the integration of G

If you want to reproduce my current local Hermes Agent + Qwen3.6-27B setup, this is the shape I would start from. Target One local coding agent. One 24GB GPU. Long context. Tools enabled. Thinking enabled. No child agents fighting the main request. The goal is not peak tok/s on a short prompt. The goal is: can the same agent session keep working after hours of tool calls without losing prefix locality, timing out during prefill, or getting wrecked by auxiliary requests? Model This setup is intentionally text-only. I am not serving the multimodal GGUF variant here. The working configuration uses groxaxo/Qwen3.6-27B-GPTQ-Pro-4bit through vLLM with --language-model-only . That choice matters. On a 24GB RTX 3090, the text-only GPTQ-Marlin path gave the best balance I found between long context, prefix caching, stable agent behavior and usable decode speed. Vision should be handled by a separate service/model if needed. vLLM The useful shape: CUDA_VISIBLE_DEVICES = 0 vllm serve groxaxo/Qwen3.6-27B-GPTQ-Pro-4Bit \ --served-model-name qwen3.6-27b-gptq-pro-4bit \ --dtype float16 \ --quantization gptq_marlin \ --tensor-parallel-size 1 \ --max-model-len 131072 \ --max-num-seqs 1 \ --kv-cache-dtype fp8_e5m2 \ --enable-prefix-caching \ --reasoning-parser qwen3 \ --enable-auto-tool-choice \ --tool-call-parser qwen3_coder \ --gpu-memory-utilization 0.95 \ --max-cudagraph-capture-size 32 \ --language-model-only I used a recent vLLM nightly, not an old stable image ( 0.20.1rc1.dev16+g7a1eb8ac2 ). The two flags people will want to argue about: --max-num-seqs 1 --max-model-len 131072 I use max_num_seqs=1 deliberately. With an agent, parallelism is not free. Title generation, context compression, retries, browser checks, tool calls and side jobs can all steal KV/cache locality from the main request. On one 24GB GPU I prefer one useful request over two requests sabotaging each other. 131k context is tight, but workable here. If your service OOMs, reduce context before adding MTP or enf

Imagine a user trying to upload a 100MB video or a high-resolution photo to your app. If you use the standard Rails file upload, that file travels from the user's browser to your Rails server, and then your server sends it to S3 or Google Cloud. This is a terrible way to do it. While that 100MB file is transferring, your Rails worker (Puma) is frozen. It can't handle other users. If three people upload large files at once, your whole app will stop responding. In 2026, the professional way to handle this is Direct Uploads . With Direct Uploads, the file goes directly from the user's browser to your cloud storage (S3, R2, etc.). Your Rails server only handles a tiny bit of metadata. It is faster for the user and much safer for your server. Here is how to set it up in Rails 8. STEP 1: Configure Your Storage First, make sure you aren't using the local disk for production. You need a cloud provider like AWS S3 or Cloudflare R2. In your config/storage.yml : amazon : service : S3 access_key_id : <%= ENV['AWS_ACCESS_KEY_ID'] %> secret_access_key : <%= ENV['AWS_SECRET_ACCESS_KEY'] %> region : us-east-1 bucket : my-app-uploads # Crucial for Direct Uploads! public : true Note: You must configure CORS in your S3/R2 dashboard to allow requests from your domain. If you don't do this, the browser will block the upload. STEP 2: The Rails Form Rails makes the backend part incredibly easy. You just add one attribute to your file field: direct_upload: true . <!-- app/views/users/_form.html.erb --> <%= form_with ( model: user ) do | f | %> <div class= "field" > <%= f . label :avatar %> <%= f . file_field :avatar , direct_upload: true %> </div> <%= f . submit "Save Profile" %> <% end %> When you add direct_upload: true , Rails automatically includes a JavaScript library that handles the "handshake" with S3. STEP 3: Adding a Progress Bar (The UX Win) Direct uploads can take a few seconds. If nothing happens on the screen, the user will think your app is broken. We can use the built-in Ac

Norway will reportedly ban young kids from using AI in schools.