We’re all drowning in content. Every week there are new podcast episodes, conference talks, and YouTube videos that feel essential to keeping up.

The problem is not finding good content. The problem is that a 3-hour podcast demands 3 hours of your life before you know whether it was worth it.

I built Audio Notes to solve this.

You paste a YouTube or podcast URL, and it produces an AI-generated summary with key takeaways, action items, and topic tags.

Version 1 did a solid job. Version 2 does it significantly better.

I had neglected the version 1 prototype but have gave it a reboot.  There were some things I wasn’t happy about.  I leveraged Claude Code to extend some of the features -another productivity boost.

In this post I will walk through what has changed in Audio Notes 2.0, why I made these changes, and how the new features help you extract more value from long-form content in less time.

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What Was Missing in Version 1

The original Audio Notes relied heavily on Azure AI Language for summarisation. It worked, but the summaries were extractive. The system pulled key sentences directly from the transcript rather than synthesising new ones.

For a 30-minute video this was adequate. For a 3-hour podcast, the output often felt thin.

Key takeaways were the biggest gap.

A 3-hour conversation covering half a dozen topics would produce 3 to 5 bullet points. That is not enough to capture the breadth of a long discussion. I was losing information that mattered.

I also had no way to see at a glance how much time a summary was saving me. The numbers were impressive, but they were invisible.

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What Is New in Audio Notes 2.0

LLM-Powered Summary Generation.  The biggest change is the integration of OpenAI via Semantic Kernel.

Instead of relying solely on extractive summarisation, Audio Notes now sends the full transcript to a large language model with a structured prompt. The model returns:

• A written summary of the content
• Key takeaways covering the breadth of topics discussed
• Action items mentioned in the conversation
• Topic tags for quick categorisation

The difference in quality is immediately noticeable. Summaries read like something a person wrote, not a collection of sentences pulled from a transcript.  The generated summaries are much better,

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Smarter Key Takeaways That Scale with Content Length

This was a specific frustration. A 15-minute video and a 3-hour podcast were producing roughly the same number of key takeaways. That made no sense.

Audio Notes 2.0 scales the number of requested takeaways based on the transcript length:

• Short content (under ~20 minutes): 5 takeaways
• Medium content (up to ~1 hour): 8 takeaways
• Longer content (up to ~2 hours): 10 takeaways
• Extended content (3+ hours): 15 takeaways

The prompt also instructs the model to cover the breadth of topics discussed, rather than clustering around a single theme.

The result is a set of takeaways that actually represents the full conversation.

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Time Savings Dashboard

Every summary page now displays three stat cards at the top:

• Original Duration showing the length of the source content in hours and minutes
• Reading Time estimating how long it takes to read the summary at an average reading pace
• Time Saved showing the minutes reclaimed and a percentage with a visual progress bar

For a typical 60-minute podcast, the reading time is around 3 to 4 minutes. That is a 90%+ saving displayed right on the page. It is a small addition, but seeing the concrete numbers reinforces that the tool is doing its job.

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Improved YouTube Processing

Audio extraction from YouTube has been overhauled. The underlying tooling now handles a wider range of video formats and edge cases. The processing pipeline is more reliable, and failures that previously required manual intervention are handled automatically.

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Topic Badges

Topics extracted from the content are displayed as visual badges on the summary page. This gives you an instant signal about what a piece of content covers before you read any of the summary text.

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How It Works

The workflow is still three steps and hasn’t changed.

1. Paste a URL. Drop in any YouTube video or podcast URL from the dashboard.
2. Generate Summary. Audio Notes extracts the audio, sends it to Azure transcription, then passes the transcript to OpenAI for structured analysis. The entire pipeline runs asynchronously so you can submit a URL and come back to it later.
3. Review Your Insights. The summary page presents the time savings dashboard, the full summary with markdown formatting, key takeaways, action items, and topic badges. A link back to the original source is included if you decide the content warrants a full listen.

I have removed the voice note feature for now.

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The Technology Behind It

Audio Notes 2.0 is built on .NET with the following services:

• Azure Speech Services for batch transcription of audio content
• OpenAI via Semantic Kernel for LLM-powered summary generation, key takeaway extraction, action item identification, and topic classification
• Azure Blob Storage for persisting transcripts and generated summaries
• SQL Server with Entity Framework Core for job tracking, user management, and encrypted settings storage

The background processing middleware monitors the database for new jobs, submits them to Azure Speech Services, polls for completion, downloads the transcript, extracts the duration, and triggers summary generation.

The web application handles the user interface and job management.

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A Real Example

I recently processed a 6-hour podcast episode. In version 1, I would have received a handful of takeaways that barely scratched the surface.

With version 2, the summary page showed:

• Original Duration: 6h 9m
• Reading Time: 2 min
• Time Saved: 367 min (99%)

The key takeaways section listed many distinct points covering the full range of topics discussed across the episode.

The action items flagged specific tools and frameworks mentioned that were worth investigating further. The topic badges gave me an instant overview before I read a single line.

Total time from paste to insight: under 10 minutes including processing time.

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What Is Next

I am continuing to refine the summarisation prompts and exploring the integration of Audio Notes with my AI Researcher agent.

The goal is a fully automated pipeline where the researcher agent discovers noteworthy content and Audio Notes summarises it without manual intervention.

I am also looking at supporting batch submission of multiple URLs in a single operation, making it practical to process an entire week of podcast episodes in one go.

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Wrapping Up

Audio Notes 2.0 is a good step forward from the original. The move to LLM-powered summarisation, the dynamic scaling of key takeaways, and the time savings dashboard make the tool more useful for processing long-form content.

If you are spending hours each week consuming podcasts and YouTube videos, this is the kind of tool that gives you those hours back.

The summaries are better, the insights are deeper, and the time savings are now visible on every page.  You can then decide if you want to make the time investment.

Audio Notes 2.0 is available here.

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When to use Prototype pattern in C#: decision guide with examples, use cases, and scenarios where cloning objects is better than creating new instances.

The first feature release after htmxRazor hit 1.1 is here, and it targets the three complaints I hear most from .NET developers building server-rendered apps with htmx: “I need toast notifications,” “I need pagination that works with htmx from the start,” and “your CSS keeps fighting with mine.”

Version 1.2.0 addresses all three. Here is what shipped.

Toast Notifications That Actually Work with htmx

Every htmx-powered app needs toast notifications. A user submits a form, the server processes it, and you need to tell them what happened. Until now, your options in the ASP.NET Core world were to wire up a JavaScript toast library by hand or build your own partial-view-plus-htmx-oob-swap plumbing.

htmxRazor 1.2.0 ships a complete toast notification system. Drop a <rhx-toast-container> on your layout, then trigger toasts from the server using the HxToast() or HxToastOob() extension methods. The component handles auto-dismiss timers, severity variants (success, warning, danger, info), stacking when multiple toasts fire at once, and aria-live announcements so screen readers pick up every notification automatically.

No JavaScript. No third-party library. One Tag Helper and a server-side method call.

Pagination Built for htmx

Pagination is another pattern that shows up in nearly every production app, yet nobody had shipped a .NET Tag Helper that wires up htmx navigation correctly. The new <rhx-pagination> component gives you page buttons, ellipsis for large ranges, first/last/prev/next controls, and size variants. All page transitions happen through htmx, so you get partial page updates without full reloads.

If you have been hand-coding pagination partials on every project, this replaces all of that with a single component.

CSS Cascade Layers: No More Specificity Wars

This is the change that will matter most to teams adopting htmxRazor in existing applications.

Every component library ships CSS that eventually collides with your own styles. You write a rule, the library’s rule wins because of higher specificity, and you start sprinkling !important everywhere. It is a familiar and miserable cycle.

Version 1.2.0 wraps all htmxRazor component CSS inside @layer declarations. Cascade layers let the browser resolve specificity in a predictable order: any CSS you write outside a layer will always beat CSS inside one. That means your application styles win by default, with zero specificity hacks needed.

This single change makes htmxRazor significantly easier to adopt in brownfield projects that already have their own stylesheets.

Accessibility: ARIA Live Region Manager

The new <rhx-live-region> component solves a problem that most developers do not realize they have until an accessibility audit flags it. When htmx swaps content on the page, screen readers do not automatically announce the change. Users who rely on assistive technology can miss critical updates entirely.

The live region manager listens for htmx swaps and pushes announcements to screen readers with configurable politeness levels (polite or assertive) and atomic update control. If you care about building applications that work for all of your users, this component closes a real gap.

View Transitions and hx-on:* Support

Two smaller additions round out the release. The new rhx-transition and rhx-transition-name attributes let you wire up the View Transitions API for animated page transitions with no custom JavaScript. And the hx-on:* dictionary attribute on the base Tag Helper class brings full support for htmx 2.x event handler attributes across every component in the library.

Upgrade Now

dotnet add package htmxRazor --version 1.2.0

Browse the full docs and live demos at https://htmxRazor.com, and check the source at https://github.com/cwoodruff/htmxRazor.

htmxRazor is MIT licensed and accepting contributions. If toast notifications, proper pagination, or cascade layers solve a problem you have been working around, give 1.2.0 a try.

The post htmxRazor 1.2.0: Toast Notifications, Pagination, and the End of CSS Specificity Fights first appeared on Chris Woody Woodruff | Fractional Architect.

Apple released iOS 26.4 Beta 3 on March 2, 2026, continuing the testing cycle for the upcoming platform update. While this beta primarily focuses on stability improvements, the broader iOS 26.4 release introduces several changes that developers should be aware of across media experiences, system behavior, and platform capabilities.

Below is a breakdown of the most relevant updates in the iOS 26.4 cycle.

Native video podcasts support

iOS 26.4 introduces native video podcast support in the Apple Podcasts ecosystem. Users can now seamlessly switch between audio and video versions of podcast episodes within the Podcasts app without losing playback position.

The implementation relies on Apple’s HTTP Live Streaming (HLS) technology, enabling adaptive video streaming, full screen playback, and the ability to download video podcast episodes for offline viewing.

For media platforms and podcast creators, this update signals Apple’s push toward richer multimedia podcast formats. Video podcasting has become a major distribution channel across platforms such as YouTube and Spotify, and Apple’s native support positions the Podcasts ecosystem to compete more directly in this space.

Although iOS does not expose new developer APIs specifically for podcast video, applications that distribute or aggregate podcast content may increasingly need to support both audio and video podcast formats.

Encrypted RCS messaging improvements

iOS 26.4 expands Apple’s messaging capabilities with support for encrypted RCS messaging, improving security for conversations that use the Rich Communication Services protocol.

RCS enables more modern messaging features compared to traditional SMS, including improved media sharing and richer conversation capabilities. The addition of encryption strengthens the privacy model of these communications.

For developers working with messaging integrations, notification processing, or communication features inside apps, this change reflects Apple’s continued movement toward more secure messaging infrastructure across platforms.

Apple Intelligence expansion and AI powered media features

iOS 26.4 continues Apple’s integration of Apple Intelligence, with new AI powered capabilities appearing in system applications such as Apple Music.

One example is Playlist Playground, which allows users to generate music playlists using natural language prompts. The system can interpret descriptions and automatically create playlists based on mood, activity, or context.

This feature demonstrates Apple’s broader strategy of embedding generative AI capabilities directly into system apps and services. For developers, the growing presence of Apple Intelligence across the system may influence user expectations around AI assisted content creation and automation within applications.

Ambient media and system widget additions

The iOS 26.4 beta introduces new ambient music widgets that allow users to quickly start background sound environments directly from the home screen or lock screen.

These widgets provide quick access to soundscapes designed for focus, relaxation, or background listening. While primarily a user facing feature, it reflects Apple’s continued expansion of system level media experiences and widget based interactions.

Applications that provide media or background audio experiences may need to consider how users interact with these features alongside system level controls.

System improvements and platform stability

Later beta builds such as iOS 26.4 Beta 3 focus heavily on platform stability and bug fixes. Apple continues refining system behavior, background execution handling, and application lifecycle interactions as the update moves closer to public release.

Developers testing applications on the iOS 26.4 beta should validate compatibility against the latest SDK and verify behavior across updated frameworks and system services.

On the one hand, I feel a bit conflicted pointing out the recognised security issues with OpenClaw, even as serious AI thought leaders are naming their agents “Arnold” and shouting orders at them. I feel duty-bound to take their enthusiasm seriously, while also stressing that this whole area remains problematic.

Enter NanoClaw. And it’s more than just a very small claw.

Firstly, NanoClaw can isolate each agent in its own container. So the agentic loop starts with no knowledge of other agents, and only knows about the resources you tell it about.

The other intriguing thing is that the app isn’t a large configuration file talking to a monolith; it is just the code you need (and the appropriate Claude skills) — with Claude hacking itself as needed. Given that code is “cheap” now, and Claude editing is reliable, this does make sense. And it does keep the code size down.

WhatsApp? No thanks.

My first question was… how does a bot get access to WhatsApp? This is the preferred contact choice of most OpenClaw users (and NanoClaw). The problem here is that unless you have a business account, you surely don’t own enough stake in the platform to host an arbitrary new user. On closer inspection, it appears that the WhatsApp connection relies on a module called Baileys that scans WhatsApp Web’s WebSocket-based data, which Meta strongly discourages. In fact, accounts using unauthorised methods to connect to WhatsApp are actively monitored and restricted.

I’m hardly going to encourage using such a method, but fortunately, we don’t have to. I do pay for a Slack workspace, and while connecting to Slack is a little painful, it is at least fully accounted for.

Installing

I have Claude installed, of course, connected to a “Pro” account. With the instructions, I do the usual thing:

git clone https://github.com/qwibitai/nanoclaw.git

Then I ran Claude within the new directory with /setup:

I have Docker Desktop installed, as this part requires:

On a Mac, you will see the familiar Docker icon in the Menu Bar if you didn’t start it yourself.

Then we move to how you are connecting to Claude:

Usually, I have to remember to turn the API key off because it’s more expensive than a subscription. This is the first time I’ve seen the two options mentioned side by side—a good sign.

Then we get to the contentious bit:

As I’ve indicated, I don’t think WhatsApp is appropriate, so I’ll be using Slack.

Then we were given the great Slack sidequest:

I now have to find two tokens, but not with my sword and trusty shield, but with the Slack API. I only recommend this campaign for an experienced adventurer. Onwards.

Generating the tokens in Slack

Fortunately, there are some good instructions on the Slack skill, and Claude is patient. First, we need to generate the tokens and scopes.

On Slack, I found the appropriate dialog:

We need to turn on Socket Mode:

Then we need to subscribe to a set of bot events:

And add scopes for OAuth – these limit what the NanoClaw App can do in the account:

And finally, you get to install your new app and fetch the final dungeon key token:

I have slain the dragon / found the treasure / defeated the Rocketeer. Well, not quite.

Claude crashed. But I quickly got back to where I was, and Claude appears to fix the errant Slack script, and accept my two tokens for its .env file:

Then it was a case of introducing NanoClaw into my Slack channel.

I suspected we were done with Slack itself, but we needed to give it access to my server folders. Remember, this is what we did with Claude Cowork to give it real power:

A nicer way to select the folders would be cool, but I added the folders I was happy for NanoClaw to see:

And then I was able to communicate with NanoClaw on my Slack channel, after getting the correct Claude auth token:

My initial attempt to confirm that NanoClaw could see my folders on my Mac failed:

This is both good and bad. It proved that the agent is sitting in a container and not part of a single app. And of course, I asked Claude to fix itself. I had been tailing the log, so I could relay all the problems back to Claude, which eventually mapped the folders in a way that the NanoClaw agent could understand:

Note how it refers to “the agent” as a separate entity. So I had a back-and-forth between the NanoClaw agent and Claude. I’m still very much the engineer here – but the separation of control is fine. The errors were the ones we all make, not understanding what Linux wants. No one understands what Linux wants.

Eventually, it fixed its internal database and restarted what it needed to for the container. And with the new mapping, I could see my Documents folder:

To check, I added a new file to check it really was mapped live to the directory. Eventually, it did reflect that the file was there.

“I like the fact that Claude thought of the agent sitting in the container as quite separate from itself, and overall this is certainly a much more sensible and secure setup if you really want to be a ‘power user’ who really just needs a secretary to yell at.”

Now this isn’t running on a Mac Mini under my cupboard, but on my laptop. So I won’t be asking for a research document based on a report in my inbox at 2 a.m. while out for a jog, but if I were into that sort of thing, NanoClaw can clearly provide this fairly safely.

While I did need to play engineer to get everything working, in reality, I was telling Claude my problems, and Claude fixed them. For that, I got a direct connection from my mobile Slack app to my server. I like the fact that Claude thought of the agent sitting in the container as quite separate from itself, and overall, this is certainly a much more sensible and secure setup if you really want to be a “power user” who really needs a secretary to yell at.

The post NanoClaw can stuff each AI agent into its own Docker container to deal with OpenClaw’s security mess appeared first on The New Stack.