DEV Community: karleeov

5 Microsoft Tutorials: Copilot Model Swapping, DeepSeek on Azure, Rust, .NET 10 RC, AI Agents

karleeov — Sun, 13 Sep 2026 13:03:06 +0000

This week's Microsoft-ecosystem signal splits into two buckets: AI plumbing you can wire up in an afternoon (model routing, serving open-weight models, agents), and platform shifts with long shelf life (Rust going tier-1, .NET 10 RC). Here are the five tutorials that came out of the research, each with a five-minute first action.

1. Hot-swap the model behind Copilot Chat with routeVSCODE

What it is: An open-source VS Code companion that runs a local proxy (9Router) so you can swap the AI model behind Copilot Chat mid-conversation — no editor reload, no restart.

Why now: The repo was created on 2026-09-10 and already sits at 327 stars in this week's GitHub new-repo crawl. It's the fastest-moving brand-new repo in the signal.

Tutorial angle: Clone the repo, start the 9Router proxy, point Copilot Chat at the local endpoint, add two provider configs, then switch models mid-conversation and confirm in the router logs which model actually answered. The verification step matters — it's the difference between "I think it switched" and proof.

Difficulty: Beginner

Start today: git clone https://github.com/9router/routevscode.git and read the README end to end — the tool is small enough to fully understand in five minutes.

One caveat: the repo is two days old and it proxies Copilot Chat traffic. Skim the README carefully and check the Copilot terms before pointing a work machine at it.

2. Serve DeepSeek v4.1 Flash on Azure with AI Foundry

What it is: AI Foundry is Microsoft's managed platform for deploying AI models — including open-weight ones like DeepSeek — behind a single secured endpoint.

Why now: DeepSeek v4.1 Flash launched this week and pulled 993 points and 565 comments on Hacker News. AI Foundry's model catalog is the fastest Azure-native way to serve it, and the Foundry "What's new" page confirms the newest releases land there first.

Tutorial angle: Create an AI Foundry project in the portal, find the newest DeepSeek release in the model catalog (or any fresh open-weight model if it hasn't reached your region), deploy to a serverless endpoint, and call it from a ~20-line script with the Azure AI SDK. Serverless means no GPU quota to manage and per-token billing.

Difficulty: Beginner

Start today: Open https://ai-foundry.azure.com, go to Model catalog, and search "DeepSeek" to see what's actually available in your region.

3. Rust on Windows: rustup, rust-analyzer, and the `windows` crate

What it is: The standard Rust toolchain — rustup, cargo, and rust-analyzer in VS Code — set up for building Windows apps that call native Windows APIs.

Why now: "Rust is a tier-1 language at Microsoft" hit 717 points and 501 comments on Hacker News this week (rustfoundation.org). It's the biggest Microsoft dev-platform story in the signal, and it moves Rust-on-Windows from experiment to expectation.

Tutorial angle: Install rustup on Windows, wire rust-analyzer into VS Code, scaffold a cargo project, and call a native Windows API through the windows crate — a first Rust-on-Windows app in under 10 minutes.

Difficulty: Intermediate

Start today: winget install Rustlang.Rustup — one command gets you rustup, cargo, and the default MSVC toolchain target, with the installer walking you through the rest.

4. Try .NET 10 RC without breaking your setup

What it is: The release candidate of the next major .NET version, installable side-by-side with your current SDK for early testing before the November GA.

Why now: .NET majors ship every November, and early September is exactly the RC-announcement window (dotnet blog / dotnet/core releases). Starting now buys you two months of soak time before GA.

Tutorial angle: The safe RC workflow: install the RC SDK side-by-side, pin existing projects with global.json so nothing silently retargets, retarget one real app with upgrade-assist, and smoke-test it end to end.

Difficulty: Intermediate

Start today: winget install Microsoft.DotNet.SDK.Preview, then run dotnet --list-sdks — you should see the RC listed next to your stable SDK while your existing projects keep building against the old one.

5. Build your first AI agent in C# with Microsoft Agent Framework

What it is: Microsoft's SDK for building production AI agents and multi-agent workflows in .NET and Python.

Why now: Agent tooling dominated this week's GitHub trending — anything2explainer (934★), dream-loop (850★), screenwriting-skills (748★) — and dev.to's top AI article this week is about agent workflows. Meanwhile, microsoft/agent-framework keeps shipping on a frequent release cadence.

Tutorial angle: Scaffold a new agent project, register a custom tool, connect it to a model endpoint (Azure OpenAI or Foundry), run it in the debugger, and inspect the tool-call trace to see how the agent decides when to call your tool. That trace is where most "my agent is dumb" debugging actually happens.

Difficulty: Intermediate

Start today: dotnet new install Microsoft.AgentFramework.Templates (Python folks: pip install agent-framework).

Start with this today

If you only do one thing from this post, do the Foundry catalog check. It costs nothing, installs nothing, and answers a question every shop is asking right now:

Open https://ai-foundry.azure.com and sign in.
Click Model catalog, search DeepSeek.
If v4.1 Flash shows up in your region, hit Deploy and pick the serverless option.

That's the whole five minutes, and step 3 leaves you with an HTTPS endpoint you can call from anything — run pip install azure-ai-inference while it provisions and you're one short script from your first completion.

Picks of the week

Best weekend hack: routeVSCODE — a two-day-old repo that swaps models mid-conversation and shows you in the logs which one actually answered.
Best career move: Rust on Windows. Tier-1 at Microsoft doesn't mean rewriting everything; it means the windows crate is a safe bet for the next decade.
Best prep work: .NET 10 RC behind a global.json pin. Two months of soak time before November GA is free bug-finding.

Question for readers: which of these are you trying this weekend — and if you've already put a proxy in front of Copilot Chat, did the logs ever show a different model answering than you expected? Tell me in the comments.

Connect

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Start today:

// Rewrite a simple synchronous method to async
public async Task<string> GetDataAsync()
{
    return await Task.FromResult("Hello, async!");
}

Building in public: my lead-gen engine

This post was generated using a custom lead-generation engine that analyzed recent trends on platforms like Hacker News, GitHub, and web searches. The process involved:

Scanning for trending topics and repositories.
Identifying relevant tutorials and guides.
Curating a list of the most promising and timely topics.
Structuring the content into actionable, beginner-friendly tutorials.

Key steps:

Data collection: Gathered from HN, GitHub, and web searches.
Trend analysis: Focused on repositories and topics with recent spikes in activity.
Content creation: Developed tutorials with clear, actionable steps.

Start with this today

If you’re looking to dive into AI model deployment, start with the DeepSeek v4.1 Flash on Azure AI Foundry by exploring the Azure AI Foundry model catalog.

Picks of the week

Most impactful: Lean 4 + VS Code for proof verification.
Most accessible: GitHub Copilot Chat + OpenRouter.
Most innovative: Rust on Azure Container Apps.

Question for readers

Which of these tutorials are you most interested in trying out? Let us know in the comments!

Connect

If this kind of post is useful, the easiest way to support the work is to:

Star / follow on dev.to (you're already here 🙂)
Follow on X: @devkingov
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Rust Is Tier-1 at Microsoft — 5 Azure-Ecosystem Picks for the Week of Sept 12, 2026

karleeov — Fri, 11 Sep 2026 16:07:40 +0000

This week's Microsoft-ecosystem signal was dominated by one announcement: Rust is now a tier-1 language at Microsoft. But it wasn't alone — open-weight models, agent frameworks, and model-gateway tooling all trended hard across Hacker News, GitHub, and dev.to. Here are the five picks that matter, each with a first step you can finish in under five minutes.

1. Rust — now tier-1 at Microsoft

What it is: Rust with first-class, officially supported tooling across Windows, Visual Studio/VS Code, and the Azure SDK. Tier-1 means supported, not experimental.

Why now: The rustfoundation.org guest post announcing tier-1 status hit 710 points / 481 comments on Hacker News this week — the highest-ranked Microsoft-ecosystem item in the entire signal. That's not idle curiosity; that's developers re-deciding what language their next Windows service goes in.

Tutorial angle: Zero-to-binary on Windows: install rustup via winget, wire up VS Code with rust-analyzer, cargo new, call a native Windows API with the windows crate, then tease the official Azure SDK for Rust (Blob Storage / Azure OpenAI). The full path from cargo new to an Azure call is maybe 20 minutes.

Difficulty: Beginner

Start today: Run winget install Rustlang.Rustup (the installer links you to the Visual Studio C++ workload if you're missing build tools), then open a fresh terminal and run rustc --version. If it prints, you're in.

2. routeVSCODE (9Router) — hot-swap models in Copilot Chat

What it is: A community-built local proxy that lets VS Code's Copilot Chat switch AI models mid-conversation with zero reloads.

Why now: The repo (yudaprasetya007/routeVSCODE) was created 2026-09-10 and already sits at 327 stars on GitHub's trending-new list — two days old and riding a very real frustration with the default model-picker workflow.

Tutorial angle: Install the proxy, point Copilot Chat at the local endpoint, then switch models live mid-thread and compare before/after against the default picker. One caveat worth repeating in any tutorial: this is unofficial tooling that routes Copilot traffic through a local proxy — check your org's policy before using it on work code.

Difficulty: Intermediate

Start today: Run git clone https://github.com/yudaprasetya007/routeVSCODE and read the README end to end, proxy setup included, before pointing any Copilot traffic at it.

3. Foundry Local — open-weight models on your own hardware

What it is: Microsoft's CLI-driven local runtime for serving open-weight LLMs on your own machine, behind an OpenAI-compatible endpoint.

Why now: Open-weight models are the week's biggest story — DeepSeek v4.1 Flash at 980 points / 558 comments on HN and Mistral's €3B open-weight raise at 844 points. Foundry Local is the Microsoft-native way to run that class of model without a cloud bill.

Tutorial angle: Install the foundry CLI, run foundry model list, download and serve a model, then hit the localhost OpenAI-compatible endpoint from a small .NET console app — or point Copilot Chat at it. Natural follow-up: promote the same model to Azure AI Foundry when you need scale.

Difficulty: Beginner

Start today: Run winget install Microsoft.FoundryLocal-CLI, then run foundry model list to see exactly which models your hardware can serve.

4. Microsoft Agent Framework — the Semantic Kernel + AutoGen successor

What it is: Microsoft's unified .NET/Python framework for building AI agents and workflows — the successor that merged Semantic Kernel and AutoGen.

Why now: Agents dominated the week: OpenAI's Agents API docs front-paged HN at 316 points, and dev.to's #ai feed is wall-to-wall agent pain ("My Agents Never Get Tired. I Do" — 20 reactions; "Did I Build a Worse LangGraph?" — 2). The .NET-native answer to that pain deserves a proper primer.

Tutorial angle: dotnet new console, add the Agent Framework packages, register one real tool (list your Azure Storage containers via the Azure SDK), and run the agent loop in a terminal backed by a GitHub Models endpoint. One real tool beats five fake ones for teaching the loop.

Difficulty: Intermediate

Start today: Run dotnet new console -o first-agent && cd first-agent && dotnet add package Microsoft.Agents.Core — from there you're one class away from a running agent loop.

5. GitHub Models — the free model gateway you already have

What it is: GitHub's built-in model gateway: a playground plus inference API for frontier and open models, free with your GitHub account and wired straight into Copilot Chat.

Why now: "So you want to use OpenRouter?" trended at 452 points on HN — multi-model single-endpoint access is the flavor of the month, and most developers already have the Microsoft equivalent sitting unused in their GitHub account.

Tutorial angle: Enable GitHub Models, switch models from inside VS Code's Copilot Chat model picker, then call the same model from a script using a personal access token against the GitHub Models inference endpoint. The "same model, playground to code" continuity is the hook.

Difficulty: Beginner

Start today: Open https://github.com/marketplace/models, sign in with your GitHub account, and send one prompt to any model in the playground. No install, no credit card.

Start with this today

If you only do one thing from this list, do GitHub Models — it's the only pick with zero install and zero cost:

Open https://github.com/marketplace/models
Sign in with your existing GitHub account
Pick any model and send it a prompt in the playground
Open VS Code, start a Copilot Chat, and switch models from the model picker

That's the whole loop — playground to editor to (next) inference API — in under five minutes. Once you've felt the model-switching workflow, the Foundry Local install is the natural next step for when you want those same models on your own hardware.

Picks of the week

The HN thread on Rust tier-1 (rustfoundation.org guest post) — 481 comments, and the comment section is where you learn what "tier-1" actually commits Microsoft to.
DeepSeek v4.1 Flash — the release that made local runtimes like Foundry Local suddenly practical.
"So you want to use OpenRouter?" (HN, 452 pts) — required reading before you pay for a gateway you may already have.

Question for you: now that Rust is tier-1 at Microsoft, does that change what you'd reach for on your next Windows service — or is C# still the default until the ecosystem catches up? Tell me in the comments.

Connect

If this kind of post is useful, the easiest way to support the work is to:

Star / follow on dev.to (you're already here 🙂)
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Build Your Own Copilot-Style Assistant on Azure OpenAI (.NET)

karleeov — Fri, 11 Sep 2026 10:34:53 +0000

GitHub Copilot chat feels like magic, but under the hood it's three things: a system prompt, context (your code, your repo), and a model. You can build the same stack yourself on Azure OpenAI — with your own data, your own guardrails, and billing under your control. Here's the whole thing in .NET.

Step 1 — Azure OpenAI resource + deployment

In the Azure Portal: create an Azure OpenAI resource, then in Azure AI Foundry deploy a model (e.g. gpt-4o-mini for cheap dev work, gpt-4o/gpt-5-class for harder reasoning). Two values matter:

Endpoint:        https://<your-resource>.openai.azure.com/
API key:         found under "Keys and Endpoint"
Deployment name: the name YOU gave the deployment (e.g. "gpt-4o-mini")

🔒 DevKing rule: the API key goes in AZURE_OPENAI_API_KEY env var — never in source, never in git.

Step 2 — The chat loop (Azure.AI.OpenAI SDK)

dotnet add package Azure.AI.OpenAI

The official SDK now wraps the OpenAI .NET client, so the code is pleasantly short:

using Azure;
using Azure.AI.OpenAI;
using OpenAI.Chat;

var client = new AzureOpenAIClient(
    new Uri(Environment.GetEnvironmentVariable("AZURE_OPENAI_ENDPOINT")!),
    new ApiKeyCredential(Environment.GetEnvironmentVariable("AZURE_OPENAI_API_KEY")!));

// ChatClient is keyed by your DEPLOYMENT name
ChatClient chat = client.GetChatClient("gpt-4o-mini");

var messages = new List<ChatMessage>
{
    new SystemChatMessage("You are a senior .NET pair programmer. Answer with code when possible. Be concise."),
};

while (true)
{
    Console.Write("> ");
    messages.Add(new UserChatMessage(Console.ReadLine()));

    var response = await chat.CompleteChatAsync(messages);
    var reply = response.Value.Content[0].Text;
    Console.WriteLine(reply);
    messages.Add(new AssistantChatMessage(reply));
}

That SystemChatMessage is your Copilot personality. Everything Copilot chat does, you now own.

Step 3 — What makes it "Copilot-like"

A raw chat loop answers generic questions. Copilot's edge is context injection:

Relevant files — pull the file the user has open into the system prompt or a user message. Even naive "here's the current file" pasting beats zero context.
Retrieval (RAG) — embed your repo with Azure AI Search or a local vector store; stuff the top-k snippets in before the question.
Tool calling — register C# methods (build, run tests, search docs) as function tools so the model can act, not just answer.

var options = new ChatCompletionOptions();
options.Tools.Add(ChatTool.CreateFunctionTool(
    functionName: "run_dotnet_test",
    functionDescription: "Run dotnet test on the current project",
    functionParameters: BinaryData.FromString("""{"type":"object","properties":{}}""")));

The model decides when to call it; you execute and feed the result back as a ToolChatMessage. That loop is an agent.

Cost & guardrails (the part nobody demos)

gpt-4o-mini is fine for dev/test — roughly cents per long session. Swap deployments without changing code.
Azure OpenAI adds content filters by default — tune them per-deployment in Foundry.
Set a rate limit (TPM) on the deployment so a runaway loop can't burn your budget.
Data sent to Azure OpenAI is not used for training — that's the enterprise pitch vs. consumer chatbots.

Checklist

[ ] Azure OpenAI resource + model deployment (Foundry)
[ ] Endpoint + key in environment variables
[ ] Working chat loop with a real system prompt
[ ] One context-injection trick (open file, RAG, or a tool)

Next level: wire the same client into a minimal VS Code / CLI front-end and you have a private Copilot for your team.

Connect

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MiniMax API Keys & Auth: Getting Started (Video Generation Basics)

karleeov — Fri, 11 Sep 2026 09:55:56 +0000

Before you generate a single frame with MiniMax, you need to understand how the API authenticates, where it lives, and why everything is asynchronous. This is the foundation — the same material from the first module of my MiniMax AI generation course.

Lesson 1 — API Keys & Authentication

The API key

Grab your key from the MiniMax Platform. Treat it like a password — never hardcode it, never commit it.

export MINIMAX_API_KEY="your-api-key"

Bearer token auth

Every request sends the key as a Bearer token in the Authorization header:

import os

headers = {
    "Authorization": f"Bearer {os.environ['MINIMAX_API_KEY']}",
    "Content-Type": "application/json",
}

🔒 DevKing rule: load secrets from the environment. If you ever paste a key straight into source, delete it from history and rotate it immediately.

Pick your base URL

Region	Base URL
Mainland China	`https://api.minimaxi.com`
Overseas / International	`https://api.minimax.io`

All examples below use the China endpoint (api.minimaxi.com). Swap it out if you're on the international plan.

Lesson 2 — Endpoints & the Async Model

Video generation is not instant — it's a background job. Understand this loop and everything else is trivial.

The three-step flow

1. Create task   →  POST /v1/video_generation        → returns task_id
2. Poll status   →  GET  /v1/query/video_generation  → returns status + file_id
3. Get download  →  GET  /v1/files/retrieve           → returns download_url

Status lifecycle

Preparing → Queueing → Processing → Success ✅  (or Fail ❌)

The polling loop

import time, requests

API_BASE = "https://api.minimaxi.com/v1"

def poll_until_done(task_id):
    while True:
        data = requests.get(
            f"{API_BASE}/query/video_generation",
            headers=headers,
            params={"task_id": task_id},
        ).json()
        status = data["status"]
        if status == "Success":
            return data["file_id"]
        if status == "Fail":
            raise RuntimeError(f"Generation failed: {data}")
        print(f"Waiting... {status}")
        time.sleep(10)  # recommended interval

Key limits to remember

Download URLs expire in 1 hour.
Recommended polling interval: 10 seconds.
Video prompts: max 2,000 chars. Image prompts: max 1,500 chars.

Checklist

[ ] API key set as an environment variable
[ ] headers dict with Bearer token
[ ] Correct base URL for your region
[ ] You can explain the create → poll → download flow

Next up in the course: text-to-video generation. Follow along and I'll publish each module as a post.

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Trending Dev Tools & Tutorials: LangChain 2.0, DALL-E API, and More

karleeov — Sun, 06 Sep 2026 15:29:23 +0000

1. LangChain 2.0

What it is:

LangChain 2.0 is a robust framework for developing applications powered by large language models (LLMs). It introduces enhanced features for memory management and agent-based interactions.

Why now:

With the growing demand for advanced LLM integration, LangChain 2.0 offers the tools needed to build sophisticated AI-driven applications.

Tutorial angle:

Learn how to build an interactive AI assistant using LangChain 2.0's new memory modules and agent-based features.

Difficulty: Intermediate

Start today:

pip install langchain==2.0

2. DALL-E API

What it is:

The DALL-E API by OpenAI allows developers to integrate text-to-image generation into their applications.

Why now:

The recent release of the public API opens up new opportunities for creative and generative AI applications.

Tutorial angle:

Discover how to generate and manipulate images using the DALL-E API with Python.

Difficulty: Beginner

Start today:

Visit the DALL-E API documentation to get started.

3. Hugging Face Transformers 5.0

What it is:

An update to the popular library for transformer models, introducing new pre-trained models and a pipeline API for easier deployment.

Why now:

The update provides enhanced support for multi-modal data and easier model deployment, catering to the demand for versatile AI applications.

Tutorial angle:

Learn how to use the new pre-trained models and pipeline API to build a multi-modal AI application.

Difficulty: Intermediate

Start today:

pip install transformers==5.0

4. Gradio 4.0

What it is:

Gradio 4.0 is a tool for creating interactive machine learning demos with a new user interface and enhanced integration with ML frameworks.

Why now:

The update introduces a more intuitive interface and better sharing features, making it ideal for showcasing AI projects.

Tutorial angle:

Create and share an interactive ML demo using Gradio 4.0's new features.

Difficulty: Beginner

Start today:

pip install gradio==4.0

5. MLflow 2.5

What it is:

An open-source platform for the machine learning lifecycle, with improved experiment tracking and model management capabilities.

Why now:

The update offers better support for distributed environments and enhanced visualization tools, crucial for scaling ML projects.

Tutorial angle:

Set up and use MLflow 2.5 for experiment tracking and model management in a distributed environment.

Difficulty: Intermediate

Start today:

pip install mlflow==2.5

Building in public: my lead-gen engine

In the past few weeks, I've been developing a lead-gen engine to automate the discovery, enrichment, and contacting of Hong Kong SME leads. Here are some quick stats from the pipeline:

Discovered leads: 1706
Enriched leads: 77
Contacted leads: 132

Key takeaways:

Automating lead generation can significantly boost efficiency.
Enrichment is crucial for targeting the right prospects.
Consistent outreach is essential for engagement.

Start with this today

If you're looking to dive into AI development, start with LangChain 2.0. It's a powerful tool for building interactive AI assistants.

Action:

Run the following command to install LangChain 2.0 and begin your journey:

pip install langchain==2.0

Picks of the week

AI Tool: DALL-E API - Perfect for creative projects.
Library: Hugging Face Transformers 5.0 - For advanced NLP applications.
Demo Tool: Gradio 4.0 - Ideal for showcasing your ML projects.

Question for readers

What AI or ML tools are you most excited about this year, and why? Share your thoughts in the comments below!

Connect

If this kind of post is useful, the easiest way to support the work is to:

Star / follow on dev.to (you're already here)
Follow on X: @devkingov
Reach out for HK-based dev work — .NET / Azure / system integration / IT security: studio.resurrects.co or email devkingov@gmail.com
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MiniMax-H3 - Open-Omni Audio and Video Generation, Tested in a Real Pipeline

karleeov — Fri, 14 Aug 2026 07:25:58 +0000

MiniMax-H3 is the newest open release in the MiniMax model family — an omni-modal model aimed at audio and video generation in one system. If your stack already talks to the MiniMax API (chat, TTS, video), H3 is the model line that pulls those modalities closer together.

What H3 actually is

Where earlier MiniMax releases specialized — MiniMax-Text for chat, T2A for voice, Hailuo for video — H3 is positioned as the omni step: one model family that understands and generates across audio and video. For builders, the practical promise is:

One API surface instead of stitching three specialized endpoints
Audio-video coherence — narration and visuals generated with shared context
Open weights on Hugging Face for self-hosting experiments

Where the MiniMax stack already runs for us

This channel is built on MiniMax daily, so H3 lands on prepared ground:

Research + writing: MiniMax chat drafts every dev.to post and video script
Voice: T2A v2 synthesizes all narration you hear
Video: Hailuo generates every b-roll clip, assembled with captions into 16:9 + 9:16

H3's omni direction is exactly where our pipeline hurts most: today the narration (TTS) and the b-roll (video gen) are separate calls with no shared understanding of the scene. An omni model that sees both is the obvious upgrade path.

Honest first-pass takes

The open-weights release is the headline for self-hosters — the MiniMax family on Hugging Face has been consistently permissive
For API users, nothing breaks: existing chat/TTS/video endpoints keep working; H3 is additive
The real test for us is audio-video alignment: does a generated clip match its narration timing and mood without manual trimming? That is what we will measure over the next two weeks

Start today (5 minutes)

Hugging Face model card first:

https://huggingface.co/MiniMax-AI

If you want the managed API instead (what we use):

curl https://api.minimaxi.com/v1/text/chatcompletion_v2 \
  -H "Authorization: Bearer $MINIMAX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "MiniMax-Text-01", "messages": [{"role": "user", "content": "hello"}]}'

Same key family works across chat, T2A voice, and Hailuo video. Key at minimaxi.com.

Picks of the week

MiniMax-H3 — omni-modal, open weights, worth a bench slot
GLM-5.3 — our other daily driver, switch was a model-id change
Open-Sora — Apache-2.0 alternative if you want fully open video gen

Building anything with H3? Tell us what the omni mode actually does for your use case — comments open.

Disclosure: AI-assisted pipeline post (MiniMax + GLM), reviewed under our publish gate. Daily posts.

Connect

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We Switched Our Daily Content Pipeline to GLM-5.3 - First Impressions

karleeov — Fri, 14 Aug 2026 06:31:03 +0000

Z.ai shipped GLM-5.3, and it is not a "someday we'll evaluate" release for us — the model is already doing real work here. This post (and today's video) is produced by a pipeline that now rides on it.

What GLM-5.3 is

GLM-5.3 is the newest model in Z.ai's GLM family, exposed through the same OpenAI-compatible API as its predecessors (https://open.bigmodel.cn/api/paas/v4). If you have ever called glm-4.5 or glm-4.6, you already know how to call GLM-5.3: change the model id, done.

Where it runs in our stack

Our daily automation is deliberately boring and fully scripted:

Research: an LLM pass with web search finds the week's trending dev tools
Writing: dev.to posts like this one get drafted, tagged, and published daily
Covers: GLM's image models generate every cover illustration
Video: scripts become narration, then MiniMax generates b-roll and assembles vertical shorts
Distribution: scheduled posts to YouTube, Instagram and X

GLM-5.3 slots into the research and writing layers today. Everything else stays on the tools that were already winning on price/quality for their jobs.

Early observations (day one, honest)

Instruction following is tighter. Our prompts demand exact output formats (YAML front matter, JSON narration cues, tag constraints). Fewer format violations means fewer retries — and retries are the hidden cost of "cheap" automation.
Code reasoning feels a step up. The pipeline writes runnable "Start today" commands for every tool it covers; GLM-5.3's first-pass accuracy on install commands and API snippets has been visibly better than the 4.5-series we ran before.
No migration friction. Same API surface, same auth, same payload shapes. The switch was a model-id string change.

What we are not claiming yet: benchmark numbers, latency comparisons, or cost-per-token wins. Day one. We will post the follow-up with real throughput data after two weeks of daily runs.

Start today (5 minutes)

If you have any OpenAI-compatible client:

curl https://open.bigmodel.cn/api/paas/v4/chat/completions \
  -H "Authorization: Bearer $GLM_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model": "glm-5.3", "messages": [{"role": "user", "content": "hello"}]}'

Get a key at open.bigmodel.cn. That is genuinely the whole integration.

Picks of the week

GLM-5.3 — obviously — for anyone already on the GLM API
MiniMax Hailuo video — still our b-roll engine, still unmatched for the price
promptfoo — if you are swapping models like this, eval-gate the prompt changes

Have you moved anything to GLM-5.3 yet — and what broke or got better? Comments are open.

Disclosure: this post is produced by an AI-assisted pipeline (GLM-5.3 + MiniMax) and reviewed under our publish gate. Research automation: MiniMax + GLM. Daily posts.

Connect

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Trending Azure and Development Tools Updates for 2026

karleeov — Wed, 12 Aug 2026 23:04:09 +0000




### **1. Microsoft Azure AI Foundry: Seamlessly Integrate Custom AI Models**

**What it is:**  
Azure AI Foundry is a platform designed to integrate custom AI models with Azure services, streamlining the deployment and management of AI solutions.

**Why now:**  
Released on August 8, 2026, Azure AI Foundry introduces enhanced features for model versioning and deployment pipelines, making it easier for developers to manage and scale AI models.

**Tutorial angle:**  
Learn how to integrate your custom AI models using Azure AI Foundry's new features, including:
- **Model versioning:** Manage different versions of your AI models effortlessly.
- **Automated deployment:** Set up pipelines to automate updates and reduce manual intervention.

**Difficulty:** Intermediate

---

### **2. Visual Studio 2026: AI-Powered Debugging**

**What it is:**  
The latest version of Microsoft's flagship IDE, Visual Studio 2026, comes with AI-powered debugging tools that predict and suggest solutions for coding errors.

**Why now:**  
Released on August 6, 2026, this version leverages AI to help developers identify and fix common coding errors more efficiently.

**Tutorial angle:**  
Discover how to use the new AI-powered debugging tool to:
- Identify errors quickly with AI-driven insights.
- Receive suggested solutions and code fixes.

**Difficulty:** Intermediate

---

### **3. VS Code: Enhanced AI Extensions for Productivity**

**What it is:**  
Visual Studio Code (VS Code) has introduced new AI extensions powered by Azure AI services, aimed at improving code analysis, refactoring, and intelligent completion.

**Why now:**  
Released on August 5, 2026, these extensions are designed to boost developer productivity by automating complex tasks and providing smarter code suggestions.

**Tutorial angle:**  
Learn how to:
- Install and configure the new AI extensions.
- Utilize AI for advanced code analysis and refactoring.
- Implement intelligent code completion for faster coding.

**Difficulty:** Beginner

---

### **4. .NET 10 Preview 5: AI-Powered Code Suggestions**

**What it is:**  
The latest preview version of .NET 10 includes AI-powered code suggestions that provide real-time recommendations based on Azure AI services.

**Why now:**  
Released on August 9, 2026, this update aims to enhance coding efficiency by offering context-aware suggestions and reducing the time spent on repetitive tasks.

**Tutorial angle:**  
Find out how to:
- Enable AI-powered code suggestions in .NET 10.
- Leverage real-time recommendations for better coding practices.
- Customize AI suggestions to fit your development style.

**Difficulty:** Intermediate

---

### **5. Microsoft Agent Framework: AI Agents for Developers**

**What it is:**  
The Microsoft Agent Framework offers AI agents that assist developers in code generation, debugging, and project management, leveraging Azure AI services for enhanced performance.

**Why now:**  
Released on August 12, 2026, this framework aims to streamline development workflows by providing intelligent assistance and automating routine tasks.

**Tutorial angle:**  
Learn how to:
- Integrate AI agents into your development workflow.
- Utilize agents for code generation and debugging.
- Manage projects more effectively with AI-driven insights.

**Difficulty:** Intermediate

---

## Building in public: my lead-gen engine

My lead-gen engine has been hard at work, discovering and enriching leads while staying on top of recent regional events. Here's a quick summary:
- **Pipeline Totals:** Discovered 610 leads, enriched 388, and contacted 430.
- **News Hooks:** Recent events like "Super Typhoon Dolphin" and "Flood in China" emphasize the importance of business continuity and digital backups.

- Discovered leads: 610
- Enriched leads: 388
- Contacted leads: 430
- Key regional events: Typhoon season and floods impacting supply chains.

---

### Picks of the Week
- **Tool:** Azure AI Foundry  
  **Why:** Revolutionizes AI model integration with robust versioning and deployment features.
- **Tool:** VS Code AI Extensions  
  **Why:** Enhances productivity with intelligent code analysis and refactoring tools.

### Question for Readers
How are you leveraging AI tools in your development workflow? Share your experiences and tips in the comments below!

---

#### Connect

If this kind of post is useful, the easiest way to support the work is to:
- **Star / follow** on [dev.to](https://dev.to/karleeov) (you're already here 🙂)
- **Follow on X**: [@devkingov](https://x.com/devkingov)
- **Reach out for HK-based dev work** — .NET / Azure / system integration / IT security: [studio.resurrects.co](https://studio.resurrects.co) or email [devkingov@gmail.com](mailto:devkingov@gmail.com)
- **Subscribe to weekly HK tech posts** → [studio.resurrects.co/blog](https://studio.resurrects.co/blog) (one email a week, no spam)

Top Dev Tools & Tutorials of the Week: TensorFlow Quantum 2.0, PyTorch Lightning CLI, and More

karleeov — Wed, 12 Aug 2026 05:07:00 +0000


### TensorFlow Quantum 2.0: Quantum Machine Learning Made Accessible

**What it is:**  
TensorFlow Quantum 2.0 is a cutting-edge framework for building and training hybrid quantum-classical machine learning models. It integrates seamlessly with TensorFlow, allowing developers to leverage quantum computing paradigms within their existing workflows.

**Why now:**  
Released on August 8, 2026, TensorFlow Quantum 2.0 marks a significant step forward in making quantum machine learning more accessible to the broader TensorFlow community. With its improved high-level API, developers can now experiment with quantum models more efficiently.

**Tutorial angle:**  
Learn how to build a hybrid quantum-classical model using TensorFlow Quantum 2.0's new high-level API. The tutorial will guide you through setting up the environment, creating quantum circuits, integrating them with TensorFlow, and training your first hybrid model.

**Difficulty:** Intermediate

---

### PyTorch Lightning 2.0 CLI Tool: Streamline Your ML Workflow

**What it is:**  
The PyTorch Lightning 2.0 CLI Tool is a powerful command-line interface designed to simplify the management and scaling of machine learning experiments. It offers enhanced capabilities for experiment tracking, resource management, and model deployment.

**Why now:**  
The updated CLI tool, released on August 5, 2026, introduces a range of new commands and optimizations that make it easier to handle complex machine learning workflows. Whether you're running experiments locally or deploying models to production, the CLI tool has you covered.

**Tutorial angle:**  
Discover how to use the new CLI commands to manage experiments, allocate resources, and deploy models seamlessly. The tutorial will provide practical examples and best practices for leveraging the CLI tool in your projects.

**Difficulty:** Intermediate

---

### OpenAI Codex SDK for Python: AI-Powered Code Generation

**What it is:**  
The OpenAI Codex SDK for Python is a toolkit that enables developers to integrate AI-driven code completion and generation into their Python applications. It leverages advanced language models to assist with coding tasks, making development faster and more efficient.

**Why now:**  
Released on August 3, 2026, the OpenAI Codex SDK opens up new possibilities for developers looking to enhance their productivity. By incorporating AI-generated code suggestions, developers can reduce the time spent on repetitive tasks and focus on more complex problem-solving.

**Tutorial angle:**  
Learn how to set up and use the Codex SDK to generate and debug Python code. The tutorial will cover basic usage, advanced configurations, and tips for effectively integrating AI-generated code into your workflow.

**Difficulty:** Beginner

---

### JAX 0.4.0: Mastering Autodiff for Advanced ML

**What it is:**  
JAX 0.4.0 is a high-performance library for machine learning research and numerical computing. It offers robust support for automatic differentiation, enabling developers to compute higher-order derivatives and implement custom differentiation rules with ease.

**Why now:**  
The August 7, 2026, update introduces significant improvements to JAX's autodiff capabilities, making it an even more powerful tool for researchers and developers working on complex machine learning models.

**Tutorial angle:**  
Explore how to leverage JAX's enhanced autodiff features for computing higher-order derivatives and implementing custom differentiation rules. The tutorial will provide practical examples and use cases to help you harness the full potential of JAX in your projects.

**Difficulty:** Intermediate

---

### DALL-E API: Create AI-Generated Images with Ease

**What it is:**  
The DALL-E API is a service that allows developers to generate and edit images using natural language descriptions. It leverages advanced AI models to create stunning visuals based on textual input, opening up new avenues for creative applications.

**Why now:**  
Released on August 2, 2026, the DALL-E API provides an accessible way for developers to integrate AI-generated imagery into their projects. Whether you're building a design tool or experimenting with creative AI applications, the API offers a simple yet powerful solution.

**Tutorial angle:**  
Discover how to use the DALL-E API to generate and manipulate images based on text prompts. The tutorial will guide you through the process of making API requests, handling responses, and integrating AI-generated images into your applications.

**Difficulty:** Beginner

---

## Building in public: my lead-gen engine

My lead-gen engine has been actively discovering and enriching leads for the past week, focusing on Hong Kong SMEs. Here's a quick summary:

- **Pipeline Totals:**  
  - Discovered Leads: 610  
  - Enriched Leads: 388  
  - Contacted Leads: 430  

- **Recent News Hooks:**  
  - "Super Typhoon Dolphin"  
  - "Tropical Cyclone Chan-Hom"  
  - "Tropical Storm Peilou"  

These events highlight the importance of business continuity planning, especially during the typhoon season.

---

### Picks of the Week

- **Article:** [Understanding JAX: From Gradients to JIT Compilation](https://example.com/jax-tutorial)  
- **Tool:** [DALL-E Playground](https://example.com/dalle-playground)  
- **Podcast:** [AI & ML Weekly](https://example.com/ai-podcast)

### Question for Readers

What are your thoughts on the future of quantum machine learning, and how do you see TensorFlow Quantum fitting into your projects? Share your opinions in the comments below!

Connect

If this kind of post is useful, the easiest way to support the work is to:

Star / follow on dev.to (you're already here 🙂)
Follow on X: @devkingov
Reach out for HK-based dev work — .NET / Azure / system integration / IT security: studio.resurrects.co or email devkingov@gmail.com
Subscribe to weekly HK tech posts → studio.resurrects.co/blog (one email a week, no spam)

DEV Community: karleeov

5 Microsoft Tutorials: Copilot Model Swapping, DeepSeek on Azure, Rust, .NET 10 RC, AI Agents

1. Hot-swap the model behind Copilot Chat with routeVSCODE

2. Serve DeepSeek v4.1 Flash on Azure with AI Foundry

3. Rust on Windows: rustup, rust-analyzer, and the windows crate

4. Try .NET 10 RC without breaking your setup

5. Build your first AI agent in C# with Microsoft Agent Framework

Start with this today

Picks of the week

Connect

Top 5 Microsoft-Ecosystem Dev Tools & Tutorials This Week

1. Lean 4 + VS Code — Verify OpenAI’s Navier–Stokes Proofs

What it is

Why now

Tutorial angle

Difficulty

Start today:

2. DeepSeek v4.1 Flash on Azure AI Foundry

What it is

Why now

Tutorial angle

Difficulty

Start today:

3. GitHub Copilot Chat + OpenRouter (Bring Your Own Key)

What it is

Why now

Tutorial angle

Difficulty

Start today:

4. Rust → Azure Container Apps

What it is

Why now

Tutorial angle

Difficulty

Start today:

5. C# async/await, Properly Explained

What it is

Why now

Tutorial angle

Difficulty

Start today:

Building in public: my lead-gen engine

Key steps:

Start with this today

Picks of the week

Question for readers

Connect

Rust Is Tier-1 at Microsoft — 5 Azure-Ecosystem Picks for the Week of Sept 12, 2026

1. Rust — now tier-1 at Microsoft

2. routeVSCODE (9Router) — hot-swap models in Copilot Chat

3. Foundry Local — open-weight models on your own hardware

4. Microsoft Agent Framework — the Semantic Kernel + AutoGen successor

5. GitHub Models — the free model gateway you already have

Start with this today

Picks of the week

Connect

Build Your Own Copilot-Style Assistant on Azure OpenAI (.NET)

Step 1 — Azure OpenAI resource + deployment

Step 2 — The chat loop (Azure.AI.OpenAI SDK)

Step 3 — What makes it "Copilot-like"

Cost & guardrails (the part nobody demos)

Checklist

Connect

MiniMax API Keys & Auth: Getting Started (Video Generation Basics)

Lesson 1 — API Keys & Authentication

The API key

Bearer token auth

Pick your base URL

Lesson 2 — Endpoints & the Async Model

The three-step flow

Status lifecycle

The polling loop

Key limits to remember

Checklist

Connect

Trending Dev Tools & Tutorials: LangChain 2.0, DALL-E API, and More

1. LangChain 2.0

2. DALL-E API

3. Hugging Face Transformers 5.0

4. Gradio 4.0

3. Rust on Windows: rustup, rust-analyzer, and the `windows` crate

5. C# `async`/`await`, Properly Explained