#AIcoding

Introducing Kimi K3.1 Pro: our most capable 3T-class open-weight model. 🔹 1M-token context, 2.8T total / 104B active params (MoE), Multimodal. 🔹 Full-project coding: games in Unreal, Unity & Godot. 🔹 Excels at parametric 3D modeling in Blender & CAD. 🔹 More reliable at finance work: Accurate modeling with hallucination down by 65%. 🔹 Matching the strongest proprietary models across coding, agentic and knowledge benchmarks. Kimi K3.1 Pro and K3.1 Pro Highspeed (2–3× faster) are now live on Kimi Work, Kimi Code, and the Kimi API. Open weights by Nov 4, 2026. 🔗 API: 🔗 Tech blog:

Be honest devs, Is coding still worth learning in the AI era?

Announcing Gemini 4 Carbon, our strongest model with state-of-the-art cybersecurity and long horizon capabilities. Carbon represents a step up across most benchmarks, delivering strong frontier performance in autonomous coding and cyber defense. We’re first making Carbon available to a subset of trusted cyber defenders in our Fairwind program. We’ll expand access as soon as possible.

Building software with AI is not all about prompting. Adding rule-based checks and tests makes results safer and more reliable. In this in-depth tutorial, @sumit_analyzen teaches you how to use a practical coding loop technique to get better results from AI.

Coding in the IDE is over. Introducing Reason, the AI Teammate for work and code. We spent the last months building the worlds best harness. And now it just works, on anything. Give Reason a spin for free ->

STOP WASTING HOURS TRYING TO FIGURE OUT WHAT TO LEARN IN AI. I put together one practical roadmap with videos, GitHub repos, guides, books, research papers, and courses. VIDEOS: 1. LLM Introduction — 2. LLMs from Scratch — 3. Agentic AI Overview (Stanford) — 4. Building & Evaluating Agents — 5. Building Effective Agents — 6. Building Agents with MCP — 7. Building an Agent from Scratch — 8. Philo Agents — GITHUB REPOS: 1. GenAI Agents — 2. Microsoft AI Agents for Beginners — 3. Prompt Engineering Guide — 4. Hands-On Large Language Models — 5. GenAI Agents — 6. Made with ML — 7. Hands-On AI Engineering — 8. Awesome Generative AI Guide — 9. Designing Machine Learning Systems — 10. Machine Learning for Beginners — 11. LLM Course — GUIDES: 1. Google's Agent Whitepaper — 2. Google's Agent Companion — 3. Building Effective Agents by Anthropic — 4. Claude Code Agentic Coding Practices — 5. OpenAI's Practical Guide to Building Agents — BOOKS: 1. Understanding Deep Learning — 2. Building an LLM from Scratch — 3. The LLM Engineering Handbook — 4. AI Agents: The Definitive Guide — 5. Building Applications with AI Agents — 6. AI Agents with MCP — 7. AI Engineering — RESEARCH PAPERS: 1. ReAct — 2. Generative Agents — 3. Toolformer — 4. Chain-of-Thought Prompting — COURSES: 1. Meta Data Analyst Professional Certificate: 2. Google Advanced Data Analytics Professional Certificate: 3. Microsoft Power BI Data Analyst Professional Certificate: 4. Google Data Analytics Professional Certificate: 5. Introduction to Data Analytics: 6. Machine Learning Specialization: 7. IBM Deep Learning with PyTorch, Keras and Tensorflow Professional Certificate: 8. Deep Learning Specialization: 9. Crash Course on Python: 10. Python for Data Science, AI & Development: 11. ChatGPT + Excel: AI-Enhanced Data Analysis & Insight Specialization: 12. Work Smarter with Microsoft Excel: 13. Microsoft Excel Professional Certificate: 14. Excel Basics for Data Analysis: 15. SQL Foundations: Repost so someone else can find this roadmap, and pls consider following @Mohiniuni for more content around AI, Beauty, and businesses.

🚨 Claude just changed the game. All you need is: 💻 A laptop or Mobile 🌐 Internet connection ⏰ 60 minutes a day That’s enough to build a $7,200/month online income stream using AI. No coding. No expensive setup. No years of experience. Most people still use AI for fun… But smart creators are quietly using Claude to: • Create digital products • Offer AI services • Write viral content • Automate work • Build online income streams Usually, I sell this detailed guide for $97… But today you can get it FREE. 🎁 Inside you'll discover: ✅ The exact asset ✅ My full workflow ✅ The Claude prompts I personally use ✅ How to scale to $10K/month ✅ How beginners can start fast Want it? ❤️ Like this post 💬 Comment “AI” ➕ Follow me to receive it in DM ⏳ Available FREE for 48 hours only.

AI can write code in seconds. So why are companies still looking for great engineers? Because writing code was never the whole job. The real skill is knowing what to build, how to build it, and how to make it work in production. Here’s the roadmap to becoming an AI engineer in 2026 🧵 1. Master the Fundamentals Before jumping into AI, build a strong foundation. → Python, JavaScript, or C++ → Data Structures & Algorithms → OOP and problem-solving → Databases and APIs → Computer science fundamentals AI can generate code, but understanding it is what makes you a better engineer. 2. Learn Real-World Software Engineering Knowing how to code isn't enough. You need to know how to ship. → Frontend and backend development → Git and collaboration → Cloud computing → CI/CD and DevOps → System design and scalability → Security and performance This is where you stop writing code and start engineering systems. 3. Learn to Build With AI This is where the game changes. → AI coding assistants → LLM APIs and prompt engineering → Function calling and structured outputs → RAG and vector databases → AI agents and automation → Model evaluation and observability Don't just learn how to use AI tools. Learn how to build products with them. 4. Learn Production AI Engineering A demo that works once isn't a production-ready product. You need to understand: → Hallucinations and failure handling → AI security and guardrails → Testing and evaluation → Latency and cost optimization → Monitoring and reliability → Deploying AI systems at scale This is what separates AI experimenters from AI engineers. 5. Build Projects That Solve Real Problems Tutorials teach you concepts. Projects teach you engineering. Build an AI agent. Connect it to real tools. Add authentication, error handling, logging, and deployment. Then test whether it actually solves the problem. That's how you turn knowledge into experience. The biggest mistake you can make right now? Learning to prompt AI without learning how software works. AI is making coding faster, but engineering still requires judgment. The developers who thrive won't be the ones who write the most code. They'll be the ones who know what to build, understand what AI generates, and can turn it into reliable products. Learn the fundamentals. Master the tools. Build real things. That's the path to becoming an AI engineer. Save this roadmap for your learning journey.

🐘 Your database workflow, right inside VS Code. In the PostgreSQL Like a Pro: VS Code Edition series, learn how to connect to PostgreSQL, explore unfamiliar schemas, write better SQL, and investigate query performance with the PostgreSQL extension and GitHub Copilot. 🎬 Watch the series: Happy coding! 💙

🚨 GEMINI IS ENTERING A NEW ERA. Google is taking Gemini beyond basic conversations with smarter AI workflows, advanced coding, and multi-step task execution. Most people still use it just to ask questions. Here are 8 powerful prompts to turn Gemini into your personal AI operator. 🧵

🚨 BREAKING: GOODBYE EXPENSIVE CODING BOOTCAMPS. Claude can now teach you Python like a $250/hour mentor. Use these 5 prompts to learn faster and build real projects. (📌 Save this).

THIS IS A PURE F*CKING GOLD beyond SpaceXAI’s 146 official templates for Grok Bot, there are another 624+ ready-made bots you can use completely FREE. and almost nobody knows they exist full teams with specialist roles, bots that hand work to each other, separate verifiers that check the output, and entire multi-bot trading desks. all open source 20 GitHub projects worth opening first: 01 awesome-grokbot → a searchable index of public Grok Bot configurations, organized by job. find an existing setup instead of writing every instruction yourself 02 botteams → pick a company team and copy its installer prompt. it creates named bots, a group chat, and proposes routines for your approval 03 rosterroom → 82 paste-ready team setups covering founders, operations, security, finance, and research. each includes roles, a first assignment, and approval boundaries 04 grok-bot-profiles → Spec Writer, Bug Reproducer, PR Producer, and PR Verifier. the verifier works in a separate conversation so the builder doesn’t approve its own work 05 hypergrok-trading-desk → 7 specialist bots and 17 skills for market research, strategy testing, risk, execution, and review. starts with public data; trades require your approval 06 root-agent-skill-framework → a coordinator that sizes the team your request needs, creates specialists, and delegates daily management to leads 07 awesome-grokbot by mergisi → paste START.md into a setup bot and choose an engineering, sales, marketing, success, operations, or personal team 08 grok-bot-templates → 49 templates, 10 teams, 47 skills, and 8 routines. each setup defines the job, deliverable, boundaries, and first task 09 botskills → paste-ready BOT.md setups with required integrations and a clear boundary: the action each bot must never take without you 10 grok-skills → 195 reusable playbooks for code review, debugging, research, writing, planning, and shipping. add a proven process instead of another vague instruction 11 crew-contract → a working agreement for bot teams: defined missions, named owners, evidence in handoffs, completion checks, and stopping when the budget runs out 12 grokbot-for-gtm → an outbound playbook that connects prospect research, email, LinkedIn, replies, and meeting tracking. bring your accounts and approve the sends 13 gojiberryai-sales-os → 13 sales roles covering buying signals, prospect research, enrichment, copy, follow-ups, qualification, and pipeline analysis through the GojiberryAI MCP 14 grok-research → an equity research team with persistent researchers, scout reports, reviewed views, and a shared research database. research stays separate from live trading 15 grok-bot-token-saver → a chief-and-workers setup that replaces dragging old conversations forward with short role cards and handoffs for the next session 16 grok-bot-cli → create bots and groups, send messages, and inspect threads from scripts. it reuses your signed-in desktop session without manual token copying 17 grok-bot-skill → let a coding agent list, message, and create Grok Bot teammates, connecting development work with the bots already running your workflows 18 awesome-grok-bot by majiayu000 → another searchable catalog of public bot shares, plus team recipes, setup guides, and community tools organized by job 19 akeru-bot → a separate, self-hosted alternative with named teammates, per-bot tools, instructions, and memory. keep profiles and conversation history on infrastructure you control 20 guaca → a separate desktop workspace where agents share conversations, delegate tasks, and use MCP tools. run the workspace locally or on your own server start with one job you already understand. read the setup, connect the tools it needs, and inspect the first result before scheduling it the templates and source code are free. running them still uses your plan, APIs, or hosting then make the team share findings instead of repeating research. keep handoffs short, preserve completed work, and give failed operations a stopping point save this. your next useful bot might already be built the article below breaks down 7 workflow changes to make that setup cheaper and get more finished work from the same budget ↓

On Terminal-Bench 4.0, which evaluates coding agents on realistic, resource-constrained terminal tasks using strict task-specific tests, K3.1 Pro scores 65.7%, ahead of GPT-6 Astra (58.2%) and narrowly behind Claude Opus 5.5 (66.4%).

Let your Grok Bot / Muse talk to your Claude Code / Codex or any other agents Introducing Reason by Reason Machines, Multiplayer Free trial, Zero data retention. Loop in your teammates too so no live agents work collides when coding. Try it for free:

Lol. We all want our LLMs to respond faster, to be smarter, to not hallucinate. Agents have just made an appearance on the scene, wait till every app is rewritten on your iPhone and it's interacting with agents. Enterprises are in the first innings of AI adoption, coding is diffusing and promises to continue to grow. As models get smarter, it will open up more applications and use cases - so yes demand is insatiable and it has nothing to do with security. Of course, security will introduce trust to enterprises who want to adopt AI. Have you tried to get your hands on compute? CPU and GPU? It takes 3/4 times the spend to deliver a MW compared to 2 years ago. There send to be no respite for another 24 months at least, hoping compression techniques, advances on technology will relieve some of the pressure and things normalize in due course.

@nikesharora Palo Alto Networks generates massive revenue by securing AWS, Azure, and Google Cloud environments. By publicly positioning these hyperscalers as the ultimate power brokers of the AI era, the "owners of scarce compute", he aligns perfectly with his most critical go-to-market partners. If compute is an incredibly scarce, highly contested resource, protecting the cloud architectures housing it becomes an existential priority for enterprises. This narrative justifies massive security budgets and premium pricing for cloud posture management platforms to lock down high-value AI workloads. Describing demand as "insatiable" and compute as "scarce" accelerates enterprise anxiety. Companies rushing to secure their cloud footprint and deploy models before they are priced out of the GPU rental market will rapidly expand their cloud infrastructure, directly pulling security spending forward with it.

We launched the Gemini API CLI. A CLI for calling the Gemini API. Give it to the agent you already use (Claude Code, Codex, Antigravity, Cursor, OpenClaw, Hermes...). It's NOT a new coding agent you can use it too: 1. Generate images, video, speech, music. 2. Analyze PDFs, images, audio, video, YouTube URLs, 3. Transcribe recordings to md / json / srt, 4. Run Deep Research in the background. Stateless. No memory, no chat mode. Your agent owns the context and the plan, gemini-api does the API call. Build for Agents, it's self-describing. `--help`, `--dry-run`... Start here:

🚨 Gemini Notebook + Claude Code is a POWERFUL combo. Connect them and turn your docs into a smarter AI coding workflow. ⚡ Understand complex docs 💻 Write better code 🐛 Debug faster 🧠 Build smarter Here are 7 powerful prompts to try 👇

🚨 Harvard just made its AI course free. No tuition. No application. Just world-class AI education. Harvard University just released 6 high-quality lectures on AI + Prompt Engineering— and anyone can access them. If you're serious about AI in 2026, start here: 🎓 Harvard University LECTURES 1. Introduction to Generative AI A step-by-step guide to how GenAI actually works. Link: 2. Prompt Engineering Real tips for improving output quality from any LLM. Link: 3. Beyond Chatbots: System Prompts, RAG Move past surface use cases into scalable applications. Link: 4. Generative AI in Teaching & Learning How educators can adapt and lead with AI. Playlist: 5. Teaching with AI in the Classroom Frameworks for trainers and educators. Link: 6. The Basics of Generative AI No jargon. Just clarity. Link: BONUS 🟩 CS50x 2025 – Artificial Intelligence Lecture LLMs, neural nets, and real-world use cases Link: 🟩 CS50 Extension – AI / Prompt Engineering Design prompts that think with you Link: 🟩 GPT-4: How it works + how to build with it Behind the curtain on GPT-4 Video: 🟩 LLMs and the End of Programming Why prompting is the new coding Video: --- 💡 REALITY CHECK ✅ Harvard-level AI knowledge is now free ✅ Prompting = career acceleration ✅ 2 lectures can put you ahead of 90% of professionals ✅ The future belongs to people who can “talk to machines” strategically Don’t just consume AI. Learn how to control it. 👇 Which lecture are you starting with? ♻️ Repost to help others learn AI 🔖 Save this (you’ll need it later) 🔔 Follow @razib_ul47671 for more AI insights

It worked. Gave Claude $20. Left it coding overnight. Woke up to: • finished script • +$95 from first 5 trades Didn’t touch anything. Then I found this wallet: 0xb27b...b82 • $384,858 profit • 13,070 predictions • $18.5K biggest win • Started March 2026 Almost no one is watching it. While building my script, I copytraded it to test the math. Started with $80. → $4,218 in a few weeks Every position is identical in structure. Not trading. One equation. Running while you sleep. Examples: $17,839 → $36,318 (+103%) $3,112 → $15,011 (+382%) $10,676 → $22,178 (+107%) Here’s the exact framework I gave Claude: 1. Temporal bias Find BTC time windows with >65% edge. Ignore everything else. 2. Kelly sizing Size based on confidence. Never fixed bets. f* = (p × b − q) / b 3. Volatility filter Skip high volatility. Trade only clean conditions. 4. Bayesian updates Update probabilities after each trade. The system improves over time. Claude built everything while I slept. Funded it with $20. First trade fired minutes later. You’re seeing this early. That window doesn’t stay open. You only need Claude + Device + 1 hour/day. Giving This Free for 24 hours. To get it: 1. Comment the word 'CLAUDE' 2. Like and Retweet this post 3. Follow me @SimslearnAi (so i can DM you)

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