Owen Barford @SQLOB1

I just completed three Microsoft AI Agent modules: • Memory, State, and Evaluation • Multi-Agent Systems and Orchestration • Governance, Guardrails, and Operations One thing stood out. We've spent years talking about prompts. The real challenge is systems. 🧵

Check this out 🙌

Mads Kristensen @mkristensen

19 hours ago

If you missed some of the Visual Studio Copilot announcements from the Build conference, here's a short summary: devblogs.microsoft.com/visualstudio/w…

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🚀 Windows + NVIDIA/GB300 bringing DGX Station to the masses. A deskside AI supercomputer with: ✦ 252 GB HBM3e VRAM ✦ 748 GB total unified memory 💰 At an estimated $80 - $300K, who is this actually for? Enterprise devs? Researchers? Power users? Big AI teams? 👀 Capable of running trillion-parameter models and always-on AI agents locally on Windows. Built for building, running & connecting powerful AI agents to enterprise apps & workflows.

The OG Visual Studio Toolbox producer and host, Robert Green, is retiring and we'll miss him! Join Leslie and Robert for a walk down memory lane as we wish him a very long and happy retirement ⛷️🚵‍♂️🏔️ 🎥 buff.ly/VIFj79Z

I may have finally found the Python-in-a-sandbox solution I've been looking for... here's my latest experiment, this time running MicroPython in WebAssembly inside my Python applications simonwillison.net/2026/Jun/6/mic…

Building Agents should be easy and it is with Microsoft Agent Framework and Microsoft SQL! Check out Chris's reasons and the demo youtu.be/KrqYxSZsPBE?si…

CodeQL 2.25.6 adds support for Swift 6.3.2, full coverage for C# 14 and.NET 10, and improves sensitive data detection across multiple languages. • Updated GitHub Actions queries to reduce false positives and reopen some alerts github.blog/changelog/2026…

Hermes Agent v0.16.0 - “The Surface Release” Changelog below:

email integration with chatgpt

ChatGPT @ChatGPTapp

23 hours ago

Draft it. Tweak it. Send it. You can now send emails directly from writing blocks in ChatGPT on the web, without leaving the conversation.

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Have you thought about the future of Agentic AI? We have too, so AWS and @OpenAI leaders recently came together to discuss where agents are working today, and what your organization needs next to lead with AI. Check out the full stream below.

Prompt Engineering Master Tree PROMPT ENGINEERING — MASTER TREE 🌲 Prompt Engineering │ ├── 01. Foundations │ ├── What is a Prompt? │ ├── Tokens │ ├── Context Windows │ ├── Temperature │ ├── Top-P Sampling │ └── Model Behavior │ ├── 02. Core Prompting │ ├── Zero-Shot │ ├── One-Shot │ ├── Few-Shot │ ├── Role Prompting │ ├── Delimiters │ └── Output Formatting │ ├── 03. Reasoning Techniques │ ├── Chain of Thought │ ├── Tree of Thoughts │ ├── ReAct │ ├── Self-Reflection │ ├── Step-by-Step Reasoning │ └── Verification Loops │ ├── 04. Structured Outputs │ ├── JSON │ ├── XML │ ├── Markdown │ ├── Tables │ ├── Schemas │ └── Function Calling │ ├── 05. Context Engineering │ ├── RAG │ ├── Memory │ ├── Knowledge Bases │ ├── Vector Databases │ ├── Context Compression │ └── Prompt Chaining │ ├── 06. Agent Prompting │ ├── Tool Use │ ├── Planning │ ├── Task Decomposition │ ├── Multi-Agent Workflows │ ├── Reflection │ └── Autonomous Execution │ ├── 07. Prompt Optimization │ ├── Evaluation │ ├── A/B Testing │ ├── Prompt Versioning │ ├── Guardrails │ ├── Hallucination Reduction │ └── Reliability Testing │ ├── 08. AI Coding Prompts │ ├── Code Generation │ ├── Refactoring │ ├── Debugging │ ├── Test Creation │ ├── Documentation │ └── Code Reviews │ ├── 09. Business Applications │ ├── Content Creation │ ├── Research │ ├── Marketing │ ├── Sales │ ├── Customer Support │ └── Automation │ └── 10. Future of Prompting ├── Context Engineering ├── AI Agents ├── MCP ├── Agentic Workflows ├── Multimodal Prompting └── Natural Language Programming Most people write prompts. The top 1% engineer systems around prompts. 🚀

MICROSOFT JUST LAUNCHED THEIR OWN CODING AI INSIDE GITHUB COPILOT — AND IT IS COMPLETELY FREE STARTING TODAY. Not rented from OpenAI. Not built on OpenAI data. Built by Microsoft. Trained inside Copilot's production environment. Rolled out to every Copilot tier right now. It is called MAI Code 1 Flash. Here is what makes it different from every other coding model in the picker: - 60% fewer tokens on complex tasks versus comparable models. - 85.8% on Microsoft's adversarial coding benchmark. - 256K context window. - Trained inside the environment it ships in rather than externally tested then handed over. Here is the full setup in 6 steps: Step 1: Create a free GitHub account at github.com/signup Step 2: Enable GitHub Copilot at github.com/github-copilot… and select the free plan. No credit card required. Step 3: Install the VS Code extension. Open VS Code, press Ctrl+Shift+X on Windows or Cmd+Shift+X on Mac, search GitHub Copilot, install the extension by GitHub. Step 4: Sign in. VS Code will show a Sign in to GitHub prompt. Click it, approve in the browser, return to VS Code. Step 5: Open Copilot Chat. Press Ctrl+Shift+I on Windows or Cmd+Shift+I on Mac. Step 6: Select MAI Code 1 Flash from the model dropdown at the bottom of the chat panel. Already have Copilot? Skip straight to Step 5. Free coding model wars just got serious. Bookmark this before you pay for another coding subscription. Follow @cyrilXBT for every free AI coding tool the moment it drops.

6 API architecture styles every developer should know: 1. REST ↳ lucode.co/rest-api-proto… 2. gRPC ↳ lucode.co/grpc-api-proto… 3. WebSockets ↳ lucode.co/websockets-api… 4. GraphQL ↳ lucode.co/rest-vs-graphq… 5. SOAP ↳ lucode.co/soap-api-proto… 6. MQTT ↳ lucode.co/soap-api-proto… What else would you add? 👋 PS: Want to improve at system design? Download my free System Design Handbook and join 34,000+ engineers who get my free weekly newsletter → lucode.co/system-design-… 🔖 Save for later ♻️ Repost to help others learn system APIs. ➕ Follow me ( Nikki Siapno ) + turn on notifications.

Level Up Coding @LevelUpCoding_

a day ago

6 API Architecture Styles Developers Should Know

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Excited to share how Anthropic's data team has automated 95% of business analytics queries with Claude. Blog post covers how we approach evals, ablations, and online validation!

ClaudeDevs @ClaudeDevs

3 days ago

How do we automate business analytics with Claude? New blog post covering our best practices for skills, data foundations, and evaluations when building agents to perform data analysis: claude.com/blog/how-anthr…

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I spent 1000 hours figuring out Claude the hard way. Here are guides I wish someone had sent me earlier: Claude for Dummies: ruben.substack.com/p/claude-for-d… Claude certificates: ruben.substack.com/p/im-claude-ce… How to prompt Claude: ruben.substack.com/p/prompt-47 Claude Cowork: claude-co.work Claude for teams: how-claude.team Cowork + Projects: ruben.substack.com/p/claude-cowor… Claude for slides: how-to-gamma.ai Claude Skills: claude-skills.free Claude connectors: ruben.substack.com/p/claude-conne… Stop hitting Claude's limits: ruben.substack.com/p/how-to-stop-… Upload yourself in Claude: ruben.substack.com/p/youre-just-a… Stop prompting Claude: ruben.substack.com/p/stop-prompti… Claude Code: claudecode.free ___ 1. Save this list for later (three dots, top right). 2. Share it with a friend by ♻️ reposting this image. 3. Subscribe to my free newsletter: how-to-ai.guide.

Ruben Hassid @rubenhassid

3 days ago

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As an AI Engineer. Please learn: Harness engineering, not just prompt engineering Context engineering, not just long prompts Prompt caching vs. semantic caching tradeoffs KV cache management, eviction, reuse, and memory pressure at scale Prefill vs. decode latency and why they optimize differently Continuous batching, paged attention, and throughput optimization Speculative decoding vs. quantization vs. distillation tradeoffs INT8, INT4, FP8, AWQ, GPTQ, and when quantization hurts quality Structured output failures, schema validation, repair loops, and fallback chains Function calling reliability, tool contracts, argument validation, and idempotency Agent guardrails, loop budgets, tool budgets, and termination conditions Model routing, graceful fallback logic, and degraded-mode UX RAG architecture: chunking, embeddings, hybrid search, reranking, and freshness Retrieval evals: recall, precision, grounding, attribution, and citation quality Evals: golden sets, regression tests, adversarial tests, LLM-as-judge, and human evals LLM observability as a first-class discipline: traces, spans, tokens, latency, errors, and drift Cost attribution per feature, workflow, tenant, and user journey not just per model Safety engineering: prompt injection defense, data leakage prevention, and permission boundaries Multi-tenant isolation, cache safety, and cross-user context contamination prevention Fine-tuning vs. in-context learning vs. RAG vs. distillation and when each is the wrong tool Latency, quality, cost, and reliability tradeoffs across the full inference stack Production failure modes: hallucinated tool calls, malformed JSON, stale retrieval, runaway agents, and silent eval regressions Shipping LLM systems as reliable infrastructure, not demos wrapped around prompts aiengineeringfromscratch.com

@NVIDIAGeForce #RTXPowersPlay 5090 required for summer cooling!

Introducing our new agentic RAG framework. A collab with Google Cloud, our multi-agent workflow goes beyond standard RAG by breaking down complex enterprise queries & iteratively searching for sufficient context before generating dependable responses. 📜→goo.gle/4oeg94G

Intel Core Series 3 laptops from the Intel Showcase at Computex 2026: MSI Modern 16S, Asus ExpertBook B5 Flip, Acer Swift Air 14, HP Omnibook 5 14, Lenovo IdeaPad Slim 5, and Dell XPS 13. Learn more about Wildcat Lake in this Talking Tech deep dive: ms.spr.ly/6019vd0kB