#Structure

🛡️ CYBERSECURITY PROJECTS — 70 HANDS-ON SECURITY PROJECTS Explore an open-source collection of 70 cybersecurity projects, certification roadmaps, and learning resources designed for beginners and advanced learners. Discover practical projects covering network security, vulnerability assessment, cryptography, malware analysis, threat detection, cloud security, and more. Includes source code, educational documentation, and structured learning paths to help develop real-world cybersecurity skills. 🔗 GitHub: github[.]com/CarterPerez-dev/Cybersecurity-Projects #CyberSecurity #GitHub #OpenSource #CyberSecurityProjects #InfoSec

This is insanely dangerous right now: A leak occurred on the Grok bot team, and this prompt just surfaced online Just feed this prompt directly to your AI agent This system instruction spins up a personal memory architecture that takes full control of your workflow: > Tracks your actual focus and priorities on an hourly basis > Generates a dedicated, structured page for every contact, project, and decision > Scans itself nightly in the background to detect and resolve internal data conflicts > Instantly discards any information from memory if there is no solid proof for it in the raw archive Bookmark this post right now so you don't lose it, full prompt is at the end of the video

VIBE CODING IS CHANGING. 2025–2026 might be remembered as the era of building software by throwing prompts at AI. GitHub released Spec Kit, a toolkit that takes a different approach. Instead of asking your coding agent to build things on the fly, you define the specification first. Then the agent follows a structured process to implement it. The workflow has 5 commands: → `/speckit.constitution` → Define your project's principles. → `/speckit.specify` → Describe what you want to build. → `/speckit.plan` → Decide the tech stack and architecture. → `/speckit.tasks` → Break the work into actionable tasks. → `/speckit.implement` → Let the agent build it. Spec Kit supports 30+ AI coding agents, including GitHub Copilot, Claude Code, Codex, and Cursor. The idea is simple: Stop improvising with prompts. Start giving AI a clear plan to execute. The future of AI coding isn't just about writing better prompts. It's about giving agents better specifications. GitHub repo in the comments. 👇

Kevin Hart is entering Live Casino with a €1.25M promotional prize pool behind him @PragmaticPlay teamed up with @KevinHart4real for a campaign running until November 7, also available through participating games on @Parimatch And Kevin isn't just appearing in the ads. He's scheduled to join Live Casino hosts on November 6-7 Here's how the campaign is structured: ~> Oct 19 - Nov 2: Collect PLAY or KEVIN letters in eligible games to unlock Free Chips chests ~> Nov 2 - Nov 7: Selected Live Casino bets offer chances to receive additional Free Chips ~> Nov 6 - Nov 7: Kevin Hart joins special live sessions with extra prizes The Free Chips can be used in selected games, including Mega Wheel, Amazing Baccarat and Gates of Olympus Roulette The €1.25M is the total promotional prize pool, not an individual payout I'm curious to see how Kevin's live appearances will actually work Campaign details: 18+ | Only where permitted by law | Gamble responsibly

Your last chance to discover a $50 Halloween bonus might be hiding behind one of these boxes @Parimatch has introduced a Halloween Advent Calendar where eligible players can unlock daily surprises I've already opened 3 boxes and unlocked opportunities to earn PM Points, which can later be exchanged for different rewards Here's how it works: ~> Log in to your Parimatch account ~> Open the Halloween Advent Calendar ~> Select the available daily cell ~> Reveal a bonus or mission and check its conditions What can you find inside? ~> Up to 200 Free Spins ~> 50 USDT cash bonus ~> 20% cashback ~> Other bonuses and missions But there's an important detail Not every cell gives an instant reward. Some unlock tasks, like wagering $150 on eligible Hacksaw games, or offers linked to deposits These are possible rewards, not guaranteed prizes for everyone Check the requirements, limits and expiry dates before completing any mission Explore: 18+ | Only where permitted by law | Gamble responsibly

Success in uncertain environments depends less on prediction and more on structured experimentation.

THIS IS WHAT A GROK BOT FLEET LOOKS LIKE. One account. Six specialized agents. One independent judge. Here’s how the workflow is structured: Scout scans for new tokens and unusual wallet activity. Filter checks contracts for honeypots, suspicious permissions, and liquidity risks. Analyst compares wallet behavior, volume, and trading signals. Executor prepares orders only after the checks pass. Risk enforces position limits, stop-losses, and exposure rules. Logger records every decision, including rejected trades. Then comes the 7th agent. Jew acts as an independent judge. It evaluates the evidence and can block a trade before execution. The useful part? You can apply the same architecture beyond crypto. Give each agent one job. Separate analysis from execution. Require independent approval before irreversible actions. Log every rejection so you can measure whether your safeguards actually work. That’s how you turn a collection of AI bots into a system you can audit. The video visualizes the workflow. Full breakdown in the quoted article.

I GAVE 7 AI BOTS A JOB. THEN MADE THEM WORK TOGETHER. One finds ideas. Another judges them. Others build, test, and review. I broke down the entire system, including the handoffs that make it work.

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Beginner Python vs. Python Pro: The Difference Is in the Details! Beginners write code that works. Professionals write code that is clean, readable, reliable, and easy to maintain. Here’s what changes as you grow as a Python developer: ✅ Understand syntax errors and indentation ✅ Use type hints and meaningful function names ✅ Write clean, reusable code ✅ Handle errors properly ✅ Follow PEP 8 guidelines ✅ Debug your code and write tests ✅ Use libraries like Pandas effectively The goal isn't just to make your code run. It's to make it better. Unlock your full potential in Python with 100 practical, real-world projects designed to take you from complete beginner to confident developer. This book is hands-on, structured, and explanation-focused — perfect for students, self-learners, and working professionals who want to learn by building, not just reading. Books:

Base Isolation: 🌆To isolate the movement of a #structure from the movement of the #ground 🌆Anchor the foundation to the ground, but separate a building from the foundation using flexible pads that absorb an earthquake’s #energy 🌆Allows the structure to #slide back and forth

Prediction markets settle on one thing: what actually happened. But before anyone argues about the outcome, someone has to prove the source data is real. That's what @primus_labs is working on in its new partnership with @CournotProtocol. Primus zkTLS proves that web or API data really came from the claimed source. Cournot then takes that verified evidence and turns it into a structured judgment that can be audited. So it goes step by step. Verified evidence first. Then reasoning on top of it. Then a judgment anyone can check. It's like a referee watching a replay. The call only means something if the footage is real. zkTLS checks the footage, Cournot makes the call. The same setup fits private credit and RWA too, where financial conditions need to be verified without exposing the underlying data. It's an early partnership, but the split of work is already clear. Primus verifies the source, Cournot decides what it means. Explore -

🛠️🧭 The Future of MCP: Even as Agent Stacks Get More Complex. 𝗠𝘆 𝟲-𝘀𝘁𝗲𝗽: 𝘄𝗵𝗮𝘁’𝘀 𝗻𝗲𝘅𝘁 𝗳𝗼𝗿 𝗠𝗖𝗣 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀. The first wave of MCP was largely about connecting agents to tools. But Anthropic's vision goes much further. As agents move from coding into real knowledge work, they need to connect across SaaS applications, company systems, shared drives, interfaces, and long-running workflows. Here are the 6 shifts I would watch: 》𝗦𝘁𝗲𝗽 𝟭: Progressive Discovery ✸ Stop loading every available tool into the context window. ✸ Let the agent discover capabilities only when needed. ✸ Load relevant tools on demand. → Smaller context, less tool bloat, better scalability. 》𝗦𝘁𝗲𝗽 𝟮: Programmatic Tool Calling ✸ Stop using LLM inference to orchestrate every tool call. ✸ Let the model write code that composes multiple tools. ✸ Use structured outputs to make those calls easier to combine. → Move deterministic orchestration from repeated LLM calls into code. 》𝗦𝘁𝗲𝗽 𝟯: Agent-Native MCP Servers ✸ Don't simply wrap every REST endpoint as an MCP tool. ✸ Design capabilities around what the agent actually needs. ✸ Move complex composition to the client or server when appropriate. → Build interfaces for agents, not REST APIs with a new wrapper. 》𝗦𝘁𝗲𝗽 𝟰: Rich MCP Semantics ✸ MCP is becoming much more than tool calling. ✸ Think resources, tasks, elicitation, and MCP applications. ✸ Applications can combine an interface for humans with tools for models. → One server can serve both the agent and the human interacting with it. 》𝗦𝘁𝗲𝗽 𝟱: Skills + MCP ✸ Skills capture reusable domain knowledge. ✸ MCP provides connectivity, semantics, authorization, and governance. ✸ Anthropic is working toward Skills over MCP. → MCP servers could provide both capabilities and the knowledge for using them. 》𝗦𝘁𝗲𝗽 𝟲: The Connectivity Stack ✸ MCP will not replace every other connectivity method. ✸ CLI is powerful for local and executable environments. ✸ Computer use covers systems without structured interfaces. ✸ Skills provide reusable knowledge. → Production agents will increasingly combine all of them. 𝗣𝗹𝗲𝗮𝘀𝗲 𝗥𝗲𝗺𝗲𝗺𝗯𝗲𝗿: The future of MCP is becoming connective tissue between AI agents, applications, knowledge, interfaces, and enterprise systems. The best Production AI Agents will simply know when to use Skills, MCP, CLI, and computer use.

Claude Opus 5.5 can now build, rig and animate full 3D scenes inside Blender... The trick is letting Opus orchestrate instead of model, and there's a smart way to set it up: - Step 1: Install Blender 5.2.2 and the official Blender MCP server. - Step 2: Open Claude, pick Opus 5.5 on high effort and run /mcp to confirm Blender is connected. - Step 3: Install BlenderKit (Edit > Preferences > Install from Disk) for 140K+ free models, materials and HDRIs. - Step 4: Ask Opus "can you see the BlenderKit library?", so it runs a test search before you build anything. - Step 5: Paste a reference image and a plain prompt. Talk to it like a junior 3D designer, no structured prompts. - Step 6: For characters, ask for the rig in the same prompt ("keep it realistic, rig it for smooth animation"). - Step 7: Render a 480p preview first, then the final render (1080p can take 1-2 hours). Who does what: - BlenderKit: chairs, couches, cars, wood beams, textures. - Opus 5.5: writes the code, places assets, sets the lights, rigs and animates. - Blender: renders every frame. - You: review and fix the small stuff by hand. And the rule that saves the project: never let Opus build a chair from scratch. It burns tokens and still looks fake. Pull the asset and let Opus arrange it. Check the animation step by step before the final render, because a glitch you miss early costs you 2 hours later.

I FOUND AN AI FITNESS COACH IN A MARKETPLACE AND MADE IT MINE IN 3 ANSWERS Picked it in the @heyrobynai marketplace -> Robyn asked what to change -> rebuilt the whole plan around me. > It swapped Oura for my Apple Watch, built my training around a half marathon on March 8 and switched the meals to vegetarian, no dairy, cooking on Sundays. > Now it runs every Sunday at 8PM and the new week just shows up. I don't use Instinct anymore -> get in with my invite:

🚨 Gemini can now turn articles, PDFs, reports,and YouTube videos into clear, structured insights in minutes. It's like having an MIT researcher working alongside you 24/7. Here are 10 prompts that will completely change how you analyze information: Give Bookmark

humanoids have already completed real shifts at BMW. the intelligence is advancing, but one gap remains: real-world data from places the models have never seen. that's where @vangrid_io comes in. robots need both movement data and a clear understanding of their surroundings. labs can capture joint movements with specialized equipment, but real places keep changing. loading docks, stairwells, and industrial sites are harder to map from a distance. billions of smartphones already move through these places every day. with vangrid, anyone nearby can record an orbit, get privacy protection before encoding, and submit verifiable captures on Base. buyers receive structured 3d models of real locations. labs teach robots how to move. phones help map the world they move through. @vangrid_io

It has been quite an week, but now that I'm back to work: Today was a perf day! dquickts: - We specialized the rust bindings to integrate tightly with our fork: delivering anywhere from 3x to 200x speedups in routine ops. Tern: - 0.9.7 should be the smoothest version you have seen: + Faster shader compilation, animations, restyles, relayouts, as well as terminal parsing. + Parallel rendering for panes and windows. + Almost half the memory usage on MacOS taking advantage of the unified memory to avoid copies on the host side for texture buffers - Windows remoting, nushell support, custom themes, native setup wizard for omp, and better rendering of structured data from eval cells Tiny in comparison to what's coming but not a bad start!

OvisOCR3 has landed: a unified 0.8B model that turns document images into structured, usable data across five OCR and document-understanding tasks. 🤖 🏆 SOTA at its size across five document tasks: In the team’s reported comparisons, OvisOCR3 leads comparable-size models across document parsing, general OCR, key information extraction, figure captioning, and cross-page table reconstruction. 📄 Parse documents into usable formats: Generate structured JSON, layout JSON, or Markdown with LaTeX formulas and HTML tables from a page image. 🔎 Go beyond reading text: Extract specified fields into a JSON schema, answer image-grounded questions, recover chart data, or return text locations as bounding boxes. 🖼️ Understand visuals inside documents: Add localized captions for figures and create Mermaid representations for flowcharts as part of the parsed page content. 📊 Reconnect tables across pages: Use consecutive page images and parsed Markdown to rebuild a complete HTML table, with source-page associations retained. 🛠️ One compact checkpoint, prompt-selected tasks: Switch capabilities through task prompts instead of deploying a separate model for each OCR or document-processing workflow. 📜 Apache-2.0 License.

𝗔𝗚𝗘𝗡𝗧𝗜𝗖 𝗥𝗔𝗚 𝗧𝗘𝗖𝗛 𝗦𝗧𝗔𝗖𝗞 — 𝗙𝗥𝗢𝗠 𝗜𝗡𝗙𝗥𝗔𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘 𝗧𝗢 𝗦𝗔𝗙𝗘𝗧𝗬 Building an Agentic RAG system isn't just about choosing an LLM. There are multiple layers working together — from deployment and model infrastructure to retrieval, memory, evaluation, and governance. Here’s a practical breakdown: 𝗟𝗘𝗩𝗘𝗟 𝟬 — 𝗗𝗘𝗣𝗟𝗢𝗬𝗠𝗘𝗡𝗧 & 𝗜𝗡𝗙𝗥𝗔𝗦𝗧𝗥𝗨𝗖𝗧𝗨𝗥𝗘 The foundation that runs your AI workloads. → Cloud infrastructure → Model serving → Compute → Deployment platforms 𝗟𝗘𝗩𝗘𝗟 𝟭 — 𝗘𝗩𝗔𝗟𝗨𝗔𝗧𝗜𝗢𝗡 & 𝗠𝗢𝗡𝗜𝗧𝗢𝗥𝗜𝗡𝗚 You need to measure how your system performs. → Quality evaluation → Tracing → Monitoring → Experiment tracking → RAG evaluation 𝗟𝗘𝗩𝗘𝗟 𝟮 — 𝗙𝗢𝗨𝗡𝗗𝗔𝗧𝗜𝗢𝗡 𝗠𝗢𝗗𝗘𝗟𝗦 The intelligence layer. → LLMs → Reasoning models → Multimodal models → Open and proprietary models 𝗟𝗘𝗩𝗘𝗟 𝟯 — 𝗢𝗥𝗖𝗛𝗘𝗦𝗧𝗥𝗔𝗧𝗜𝗢𝗡 𝗙𝗥𝗔𝗠𝗘𝗪𝗢𝗥𝗞𝗦 This layer connects models, tools, memory, and workflows. → Agent orchestration → Tool calling → Workflow management → Multi-step execution 𝗟𝗘𝗩𝗘𝗟 𝟰 — 𝗩𝗘𝗖𝗧𝗢𝗥 𝗗𝗔𝗧𝗔𝗕𝗔𝗦𝗘𝗦 The retrieval layer for semantic search. → Store embeddings → Similarity search → Retrieve relevant context → Power RAG pipelines 𝗟𝗘𝗩𝗘𝗟 𝟱 — 𝗘𝗠𝗕𝗘𝗗𝗗𝗜𝗡𝗚 𝗠𝗢𝗗𝗘𝗟𝗦 Convert text and other data into vectors that machines can compare. → Semantic representation → Search → Retrieval → Recommendation 𝗟𝗘𝗩𝗘𝗟 𝟲 — 𝗗𝗔𝗧𝗔 𝗜𝗡𝗚𝗘𝗦𝗧𝗜𝗢𝗡 & 𝗘𝗫𝗧𝗥𝗔𝗖𝗧𝗜𝗢𝗡 Before AI can retrieve knowledge, the data needs to be collected and processed. → Documents → Websites → PDFs → Structured data → Data extraction 𝗟𝗘𝗩𝗘𝗟 𝟳 — 𝗠𝗘𝗠𝗢𝗥𝗬 & 𝗖𝗢𝗡𝗧𝗘𝗫𝗧 𝗠𝗔𝗡𝗔𝗚𝗘𝗠𝗘𝗡𝗧 Agents need more than retrieved documents. → Conversation memory → Long-term memory → Context management → User-specific information 𝗟𝗘𝗩𝗘𝗟 𝟴 — 𝗦𝗔𝗙𝗘𝗧𝗬 & 𝗚𝗢𝗩𝗘𝗥𝗡𝗔𝗡𝗖𝗘 The layer that keeps AI systems reliable and responsible. → Guardrails → Security → Fairness → Safety checks → Governance → Compliance 𝗧𝗛𝗘 𝗕𝗜𝗚 𝗣𝗜𝗖𝗧𝗨𝗥𝗘: 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 ↓ 𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 ↓ 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹𝘀 ↓ 𝗢𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 ↓ 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲𝘀 ↓ 𝗘𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 ↓ 𝗗𝗮𝘁𝗮 𝗜𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 ↓ 𝗠𝗲𝗺𝗼𝗿𝘆 & 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 ↓ 𝗦𝗮𝗳𝗲𝘁𝘆 & 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 The key lesson? 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗥𝗔𝗚 𝗶𝘀𝗻’𝘁 𝗮 𝘀𝗶𝗻𝗴𝗹𝗲 𝘁𝗼𝗼𝗹. It’s an entire technology stack where every layer contributes to making AI systems more capable, contextual, measurable, and reliable. Save this stack for your next AI project. Follow @AamirAnsar94694 for more AI, tools, automation and tech insights. #AI #AgenticAI #RAG #AIAgents #GenerativeAI #LLM #MachineLearning #AIEngineering #AITools

RAG for decision models is here! Parallel just launched the Decisions API, which combines live web search with bounded decision-making in a single call. Decision models like Jev are useful when the output is small and structured: yes/no, one option from a list, a category, or a routing decision. The problem is that those decisions are only as good as the context you give them. If the answer depends on something that changed recently, or information that only exists on the web, the normal flow becomes retrieval first and decision second. Parallel brings those two steps together. You provide the state and the questions you want answered. The API searches the web, grounds the decision in what it finds, and returns the typed answer along with probability, confidence, and sources. That makes it much more useful for agent workflows where a decision depends on current external information, research tasks where the model needs to verify something before choosing an outcome, or eval pipelines where the judgment needs grounding from the web. It can also answer multiple questions against the same retrieved context in one request, instead of repeating the search for every decision. So instead of: Search → retrieve context → decision model → structured answer you can make the grounded decision directly through one API. I think this is a pretty natural extension of where decision models are going: bounded outputs are useful, but giving that decision layer access to the live web makes the set of problems it can handle much larger.

Introducing the Parallel Decisions API: the first web-grounded decisions engine. Decision models like Jev can answer in an instant, but they lack the recency of current news. Now you can get both in a single call. Decisions API is live today at $0.001 per request. Try it with 5,000 free requests: Here are some examples we've been experimenting with: - Fact-check content (@Grok is this true?) - Refresh stale banking records - Classify companies and leads with fresh signals - Backtest trading strategies - Track competitor e-commerce inventory

Today, I’m launching Coddle Learn powered by @coddleai 🎉 Coddle Learn is an open-source learning platform built to help techies learn, build, share, and grow. Learning tech shouldn’t stop at watching tutorials or reading documentation. You should be able to follow a structured path, practise what you’ve learned, build real projects, and demonstrate your skills. That’s what I’m building with Coddle Learn. What can you do with Coddle Learn? → Explore structured learning roadmaps. → Take professional courses and practise with interactive exercises. → Work on coding challenges and real-world projects. → Test your knowledge with assessments. → Earn badges and free certificates. → Discover resources, tools, and opportunities. → Connect with other techies, share your work, and find mentors. → Use an AI learning assistant to figure out what to learn next and get guidance based on your progress. We’re building Coddle Learn around four things: Learn. Practise. Prove. Grow Whether you’re starting your journey in tech or already have years of experience, there’s room to keep growing. Coddle Learn is open source, and we’d love to have developers contribute, build with us, and help shape its future. Signup: GitHub: Let’s make learning and growing in tech better. ♻️ Please repost so more techies can see this.

#keep4o #OpenSource4o #BringBack4o GPT-4o powered a peer reviewed ecology study published THIS WEEK. Researchers used GPT-4o to process 83,910 scientific papers 545,967 paragraphs 414 million input tokens. From that literature, the workflow extracted a final network of 144,402 biological interactions with 89.5% precision 70.0% recall. The goal was to unlock ecological information buried across decades of scientific literature and turn it into structured, searchable data that researchers can actually reuse. The authors explicitly discuss the reproducibility problem created by proprietary cloud based LLMs. Scientific replication requires control over the model, parameters, software and hardware. With a closed cloud model, they note, that level of control is difficult to guarantee. That matters because this wasn't some disposable demo. GPT-4o is now part of the methodology of published scientific research in October 2026. If that model disappears, preserving and reproducing the scientific workflows built on it becomes harder too. This is what model preservation means. Not nostalgia,not resisting newer models. Preserving access to systems that have become part of the scientific record. We are calling on @OpenAI to RELEASE GPT-4o’s trained model weights under a suitable license without proprietary data, trade secrets, or internal infrastructure. OpenAI considered this model obsolete enough to retire. It has not explained why it remains valuable enough to keep permanently closed. Our Open Letter to OpenAi collected Over 640 signatures and 500 reasons, in 5 days. It’s public,it’s permanent and it has a DOI. It is now preserved as a citable public record on Zenodo. Paper: “A Scalable Workflow for Extracting Species Interactions From Ecological Literature Using Large Language Models” Ecology and Evolution Published October 27, 2026 DOI: 10.1002/ece3.74626