No LLM provider configured. Run `hermes model` to select a provider,
or run `hermes setup` for first-time configuration.

You may also receive a variation along these lines:

Unknown provider 'custom:ollama---gemma-4-12b'.

If so, all may not be lost. More importantly, your Ollama installation and local model may be perfectly fine.

I recently ran into this exact problem whilst configuring Hermes Desktop to use a Gemma 4 model running locally through Ollama. The model appeared in the Desktop model picker, the Hermes backend could see it, and the Hermes CLI could even get a response from it. Yet Desktop still insisted that there was no LLM provider configured.

As it turned out, the problem was not Ollama, Docker networking or the model. It was the way the provider configuration was scoped to a separate Hermes profile, combined with provider information already stored against the Desktop session.

This post explains how I tracked it down, how I fixed it without deleting my Hermes data, and the checks I used to make sure the fix survived a Desktop restart.

Tested configuration: Hermes Desktop source/development build v0.20.6, Hermes backend v0.20.6 in Docker, macOS, Ollama and gemma4:12b. This is the configuration on which I reproduced, repaired and restart-tested the problem; the menus, file paths and behaviour may change in later Hermes releases.

My Hermes and Ollama Setup

Before getting into the fix, it is worth briefly explaining how my installation is arranged. This matters because localhost does not always mean what you might expect once Docker becomes involved.

My setup consists of:

• Hermes Desktop running as the source-built Electron application on my Mac.
• The Hermes backend and gateway running inside a Docker container.
• Ollama running natively on macOS, with gemma4:12b installed locally.
• Hermes profiles named default and local, each with its own configuration and session data.

The request path therefore looks like this:

Hermes Desktop on macOS
        ↓
Hermes backend inside Docker
        ↓
Ollama on the Mac host
        ↓
gemma4:12b

I chose this arrangement because it keeps Hermes and its dependencies contained inside Docker, making the backend easier to manage or replace without cluttering the Mac installation. Ollama remains on macOS so it can run the local models directly on the host and make use of the Mac’s hardware. The gemma4:12b tag used here is also listed in the official Ollama Gemma 4 library.

There is one important consequence: from inside the Hermes container, localhost refers to the container itself rather than the Mac. Docker Desktop’s own networking guidance recommends the special host.docker.internal address when a container needs to reach a service on the host. Hermes therefore reaches Ollama using:

http://host.docker.internal:11434/v1

If you installed both Hermes and Ollama directly on the same machine without Docker, your endpoint may instead be http://localhost:11434/v1. That is the base URL shown in Ollama’s official OpenAI-compatibility examples. The profile problem described below can affect either arrangement, though the correct endpoint will be different.

Why I Am Running the Pre-Release Hermes Desktop

You may be wondering why I am using a source-built version of Hermes Desktop rather than simply downloading the normal Mac installer.

At the time of writing, the downloadable macOS installer follows a local-first setup path. If it cannot find Hermes locally, it offers to install the complete Hermes runtime, including the supporting command-line components and dependencies.

That makes sense for somebody starting with a blank Mac. It did not make sense for me because I already had a fully configured Hermes backend running inside Docker. I did not want a second Hermes installation with another set of configuration files, providers and sessions merely to obtain the Desktop interface.

What I wanted was closer to a thin client:

Hermes Desktop interface only
        ↓
Connect to my existing Hermes backend in Docker

The newer Hermes Desktop documentation now supports this. Under Settings → Gateways, its Remote gateway connection mode can point the interface at an existing hermes serve backend instead of starting and managing another local backend.

In this context, “remote” does not have to mean a server in another building—or even another physical computer. My Hermes backend is in a Docker container on the same Mac, but it is still a separate service which Desktop reaches through a network address.

The slight complication is that this capability reached the current Hermes source after the publicly downloadable Mac installer was built. Hermes issue #85422 documents that release mismatch: the public installer still pushes new users through local bootstrap, whilst the newer source contains the Connect to existing Hermes path.

I therefore built and ran the current Desktop code from source. It is reasonable to call this a pre-release or development build: it contains newer Desktop functionality, though it has not gone through the same packaged, signed and branded release process as the normal download. That distinction is worth remembering because development builds may also contain unfinished changes or new bugs.

Why Does It Appear as Electron on the Mac?

Electron is the framework used to build the Hermes Desktop interface. It packages web technologies—HTML, CSS and JavaScript—inside a desktop application using Chromium and Node.js. Applications such as Visual Studio Code, Slack and GitHub Desktop use the same general approach.

When an Electron application is prepared for normal distribution, it is packaged and branded with the product’s name, icon and macOS application details. When it is launched directly from source during development, some of that final packaging is absent. macOS can therefore show the generic name Electron in places such as Activity Monitor or the Dock.

/imag

So, in my setup, the process called Electron really is the running Hermes Desktop interface. It is not a second AI model, a replacement for Docker or evidence that Hermes has stopped. Electron is simply the desktop shell displaying the Hermes user interface and connecting it to my existing Dockerised backend.

The Short Answer

If Hermes CLI works with Ollama but Hermes Desktop does not, check which Hermes profile owns the failing Desktop session.

Hermes describes a profile as a separate Hermes home. Each profile has its own:

• config.yaml
• environment and credentials
• sessions
• logs
• state.db database

A provider configured in the default file at ~/.hermes/config.yaml is not automatically available to a separate profile stored under ~/.hermes/profiles/<profile-name>/. This separation is documented in the official Hermes profiles guide.

In my case, the working Ollama provider existed in the default configuration, but the failing sessions belonged to a profile called local. That profile still had a different model configuration and did not contain the named Ollama provider that Desktop had saved with the session.

The fix was to add the working Ollama model and matching custom-provider definition to the local profile’s own config.yaml. I did not change the already-working root configuration, delete the Desktop cache or edit the session database.

Why This Problem Is So Confusing

There were several clues suggesting that everything should have worked:

1. Ollama was running on the Mac.
2. The Gemma model was installed and available.
3. The Hermes Docker container could reach Ollama.
4. Hermes CLI could initialise the agent and get a response.
5. Hermes Desktop showed the model in its picker.

Normally, if the CLI and Desktop are using the same backend and configuration, you would expect them to behave in the same way. In this instance they were not using quite the same state.

The easiest way to think about a Hermes profile is as a separate Hermes home. It may use the same application and gateway, but it has its own configuration and its own session history. Pointing Desktop at a profile does not make that profile inherit everything from the default one.

Desktop had remembered a provider identity similar to:

custom:ollama---gemma-4-12b

That identity was valid only if the profile which owned the session also had the matching named custom provider. Mine did not, so Desktop could display the model whilst the backend was unable to build a working runtime for it.

There are related reports in the Hermes issue tracker where custom-provider identity is lost or cannot be reconstructed when Desktop resumes a session. In those reports, the CLI continues to work because it follows the configured default-provider path rather than the same Desktop session-restoration path.

Before You Change Anything: Make a Backup

I would strongly recommend making a backup before clearing caches, changing a profile or touching a database. Repeatedly changing the main Ollama configuration can make the original problem harder to see, especially when that configuration already works.

First, completely quit Hermes Desktop. If you are running the source-built Electron version, it may appear in macOS as Electron rather than Hermes, so check that it has really closed.

Open Terminal and create a timestamped backup folder:

backup_dir="$HOME/Documents/hermes-repair-backup-$(date +%Y%m%d-%H%M%S)"
mkdir -p "$backup_dir"

Back up the Hermes Desktop Electron data:

ditto "$HOME/Library/Application Support/Hermes" \
  "$backup_dir/Hermes-Desktop"

If your affected profile is called local, back up its configuration:

cp "$HOME/.hermes/profiles/local/config.yaml" \
  "$backup_dir/local-config.yaml"

You can also ask SQLite to make a consistent backup of the profile’s session database:

sqlite3 "$HOME/.hermes/profiles/local/state.db" \
  ".backup '$backup_dir/local-state.db'"

Change local in these paths if your profile has a different name.

Important: Do not publish or casually share your .env, auth.json, OAuth tokens, dashboard secrets or complete Hermes data directory. These can contain credentials and private conversation data.

With that out of the way… on with the troubleshooting!

Step 1 – Prove That Ollama and the Hermes Backend Work

Before digging into Desktop storage, test the actual model through Hermes CLI.

For my Docker container, which was named hermes, I used the following. Replace the first hermes if your container has a different name:

docker exec -it hermes hermes chat \
  -q "Reply with exactly: LOCAL MODEL WORKING" \
  --max-turns 1

The important result was:

LOCAL MODEL WORKING

This is a useful dividing line:

• If the CLI also fails, you probably still have an Ollama, Docker networking or main Hermes provider problem.
• If the CLI works but Desktop fails, stop changing Ollama for the moment. Concentrate on Desktop, profiles and session restoration.

In my case, this test proved the complete path from Hermes to Docker, Ollama and Gemma was working.

Step 2 – Check Which Profile Owns the Session

Open Hermes Desktop and look at the Profiles section in the left sidebar. In my setup I had both default and local profiles available through the gateway.

The broken sessions belonged to local, not default.

This small distinction was the key to the whole problem. I had been checking the working configuration here:

~/.hermes/config.yaml

But the failing session was resolved using:

~/.hermes/profiles/local/config.yaml

Those are independent configuration files.

If you created a profile using a blank profile rather than cloning an existing one, this is particularly worth checking. The Hermes documentation confirms that each profile has its own config, credentials, sessions and state./image

Step 3 – Compare the Model and Provider Configuration

You can use the following command to print only the most relevant model/provider lines from the default configuration:

grep -nE '^[[:space:]]*(model:|custom_providers:|default:|provider:|base_url:|api_mode:|- name:)' \
  "$HOME/.hermes/config.yaml"

Now compare it with the profile configuration:

grep -nE '^[[:space:]]*(model:|custom_providers:|default:|provider:|base_url:|api_mode:|- name:)' \
  "$HOME/.hermes/profiles/local/config.yaml"

My default configuration correctly contained the Ollama endpoint:

model:
  default: gemma4:12b
  provider: custom
  base_url: http://host.docker.internal:11434/v1
  api_mode: chat_completions

It also had the named custom provider:

custom_providers:
  - name: Ollama - Gemma 4 12B
    base_url: http://host.docker.internal:11434/v1
    model: gemma4:12b
    api_mode: chat_completions

The local profile, however, still selected GPT/Codex and had no corresponding Ollama provider definition.

Desktop had saved custom:ollama---gemma-4-12b with the sessions in that profile, but the profile had no way to resolve it. In this v0.20.6 installation, the three hyphens came from the normalised display name Ollama - Gemma 4 12B; the spaces surrounding the existing hyphen became additional hyphens. Treat the exact spelling as version- and configuration-specific rather than typing it blindly into another installation.

The configuration shape itself is supported by Hermes: its custom and self-hosted provider documentation describes provider: custom, a base_url ending in /v1, named custom_providers entries and api_mode: chat_completions for OpenAI-compatible endpoints. Ollama’s OpenAI compatibility documentation confirms that it implements /v1/chat/completions on port 11434.

Optional Deep-Dive – Inspect the Stored Session Provider

You do not normally need to edit the database, though a read-only query can help confirm what Desktop saved. Hermes documents state.db as its SQLite session store, including the model and model_config fields used here.

With Hermes Desktop closed, run:

sqlite3 -header -column \
  "file:$HOME/.hermes/profiles/local/state.db?mode=ro&immutable=1" \
  "SELECT id, model, model_config FROM sessions ORDER BY started_at DESC LIMIT 10;"

In my affected sessions, model_config contained a provider similar to:

{
  "model": "gemma4:12b",
  "provider": "custom:ollama---gemma-4-12b"
}

That does not mean the database itself is corrupt. It means the session expects its owning profile to be able to resolve that provider identity.

If the recent session is missing from the query, it may still be in SQLite’s write-ahead log. Do not start copying or deleting database files at random; make a proper backup and close the processes using the database first.

Step 4 – Apply the Correct Fix for Your Profile

There are two likely situations here.

The profile is supposed to use Ollama

This was my situation. I added the working Ollama model and provider to the local profile’s own config.yaml.

The model section became:

model:
  default: gemma4:12b
  provider: custom
  base_url: http://host.docker.internal:11434/v1
  api_mode: chat_completions

I also added the named provider expected by the stored Desktop sessions:

custom_providers:
  - name: Ollama - Gemma 4 12B
    base_url: http://host.docker.internal:11434/v1
    model: gemma4:12b
    api_mode: chat_completions
    models:
      gemma4:12b: {}
      gemma4:26b: {}
    models_discovered: true

The models catalogue and models_discovered flag above were present in the known-working provider entry copied from my default profile. They preserve the exact configuration I tested, but they were not the central part of the repair. The essential point was that the local profile gained the matching named provider, endpoint, model and API mode. For a new installation, Hermes recommends using hermes model to create a custom endpoint rather than inventing discovery metadata by hand.

If Ollama runs directly on the same non-containerised machine as Hermes, your URL may instead be:

http://localhost:11434/v1

In my setup Hermes ran inside Docker, so localhost would have meant the container itself. I therefore needed:

http://host.docker.internal:11434/v1

Do not blindly copy that URL without considering where Hermes and Ollama are running.

Also make sure the provider name matches the identity already stored with the session. Renaming a provider may be tidier for future sessions, but it will not necessarily repair an older session that expects the previous name.

The profile is not supposed to use Ollama

Do not overwrite a cloud or work profile just to make an accidentally selected Ollama session run.

Instead, switch Desktop to the profile which actually owns the Ollama configuration and create a new session there. A visible model selection is not proof that the active profile can execute it.

Step 5 – Test a Brand-New Desktop Session

Reopen Hermes Desktop, select the repaired profile and start a completely new session.

The model indicator in my session showed:

custom: gemma4:12b

I sent:

Reply with exactly: LOCAL MODEL WORKING

Gemma returned:

LOCAL MODEL WORKING

The first response took 48 seconds because the local model needed time to load and begin generating. The important part was that Hermes had moved beyond the provider-initialisation error and was now waiting on gemma4:12b.

Step 6 – Make Sure Session Restore Is Fixed

Do not stop at a successful new chat. Session restoration was the path that had caused trouble, so I wanted to test that specifically.

I completely quit Electron/Hermes Desktop, relaunched it, selected the local profile and reopened the exact session that had just worked.

I then sent:

Reply with exactly: RELOAD WORKING

The restored session returned:

RELOAD WORKING

This was the decisive test. It proved that the provider could be reconstructed from the persisted session after Desktop restarted, not merely used by an agent which happened to be left running in memory.

Why I Am Confident This Was the Correct Fix

There is an important difference between a plausible workaround and a fix which has been isolated and tested.

In this case I changed only the affected local profile’s configuration. I deliberately left the working root configuration, Ollama installation, Docker container, Electron storage and session database untouched.

The evidence then fell into a useful sequence:

1. The CLI returned LOCAL MODEL WORKING before the Desktop repair, proving that the Docker-to-Ollama route and model were already functional.
2. Inspection showed that the failing Desktop sessions belonged to local, whilst the working provider configuration existed under the default Hermes home.
3. The affected session expected custom:ollama---gemma-4-12b, but its owning profile did not define that named provider.
4. Adding the matching endpoint and provider to the owning profile allowed a brand-new Desktop session to return LOCAL MODEL WORKING.
5. After completely quitting and relaunching Electron, the exact same session returned RELOAD WORKING.

That final restart test is particularly important. It exercises the persisted session/provider reconstruction path rather than merely proving that an already-running process retained some temporary state.

The official documentation independently supports each underlying part of the diagnosis: profiles have separate homes, custom providers are resolved from configuration, Ollama exposes an OpenAI-compatible chat endpoint, and Docker Desktop uses host.docker.internal to reach services on the Mac host. Related Hermes issues also document the same general failure family around Desktop session restoration and named custom-provider identity.

This does not mean every occurrence of No LLM provider configured has the same cause. It does mean that, for the configuration described in this article, the repair is supported by both the observed state and two successful end-to-end tests, including a full Desktop restart.

What I Did Not Need to Change

It is just as useful to record what was left alone:

• I did not change the working root ~/.hermes/config.yaml.
• I did not reinstall Ollama or re-download the Gemma model.
• I did not recreate the Docker container.
• I did not delete the Hermes Desktop LevelDB directory.
• I did not edit or replace the Hermes session database.
• I did not remove my OAuth/Codex authentication.

Clearing all Electron storage can be a useful last-resort test for stale UI state, though in this case it would have treated the symptom rather than the profile mismatch. Repairing the profile allowed Hermes to retain the existing conversations and resolve the provider correctly.

Quick Troubleshooting Table

Is This a Hermes Bug or a Configuration Problem?

In my particular setup it was a combination of the two.

The immediate repair was configuration-related: the separate local profile did not contain the provider expected by its sessions. However, Desktop made the situation difficult to understand because it could show and persist a provider selection that the owning profile could not successfully resolve.

This is not entirely unique. Hermes issue #44022 documents Desktop/TUI session restoration failing with No LLM provider configured whilst the same custom provider works through the CLI. Issue #44108 describes named custom-provider identity being reduced to bare custom during persistence, and issue #7054 covers differences between the provider catalogue/picker and runtime configuration paths.

Some related issues are now closed, though an issue’s status alone does not prove that every release and every stored session has been repaired. I would not assume every current Hermes installation has exactly the same fault. The useful diagnostic lesson remains the same: when CLI works and Desktop fails, verify the profile and session boundary before rebuilding your local AI stack.

Frequently Asked Questions

Why does Hermes CLI work when Hermes Desktop fails?

The CLI may use the default profile and its configured provider directly. Desktop can use a different profile and may also restore provider information saved with a particular session. If those sources disagree, CLI can work whilst Desktop fails.

Does a Hermes profile inherit the default config.yaml?

No. A profile has its own Hermes home containing its own configuration, credentials, sessions, logs and state database. Configure or clone the provider into the profile which will use it.

Why does Hermes say custom:ollama---gemma-4-12b?

In this tested v0.20.6 session, Hermes created a normalised identity for the named custom provider. The spaces in Ollama - Gemma 4 12B became hyphens alongside the hyphen already present in the display name. Treat that exact identity as evidence from this installation; its format may differ with another provider name or Hermes version.

Should I delete ~/Library/Application Support/Hermes?

Not as a first step. Back it up, identify the active profile and compare the profile’s provider configuration first. Deleting Desktop state may remove useful evidence and does not repair a missing provider definition inside a Hermes profile.

Should I edit state.db?

Usually not. Querying it read-only can help diagnose the stored provider, but repair the owning profile configuration first. Direct database changes should be a carefully backed-up last resort.

Which Ollama URL should I use with Hermes?

If Hermes runs directly on the same machine, it may use http://localhost:11434/v1. If Hermes runs inside Docker and Ollama runs on the Mac host, http://host.docker.internal:11434/v1 is normally the relevant address.

Final Thoughts

This was one of those problems where all the individual pieces appeared to be working, though the complete Desktop session still failed. The biggest clue was that the same model and backend worked from Hermes CLI.

Once I stopped changing Ollama and followed the session into its owning profile, the cause became much clearer: the model was configured in one Hermes home, whilst the failing Desktop session belonged to another.

If you run into this error, back everything up, confirm the CLI path, check the active profile, and compare that profile’s provider definition with the provider stored in the session. Hopefully that will save you some of the head-scratching I went through. 🙂

If you have seen a variation of this problem on Windows, Linux, or a newer Hermes release, leave a comment with your Desktop version, backend version and profile layout. It will no doubt help others who come across the same rather misleading error.

Hope this guide helps. Good luck!

Useful References

• Hermes Agent profiles documentation
• Hermes Desktop documentation, including remote-backend mode
• Hermes custom and self-hosted provider documentation
• Hermes session-storage documentation
• Hermes issue #85422 – downloadable macOS installer predates remote-client onboarding
• Hermes issue #38602 – Desktop client-only installation
• Hermes issue #44022 – Desktop/TUI session resume and bare custom-provider state
• Hermes issue #44108 – named custom-provider identity lost during Desktop/TUI persistence
• Hermes issue #7054 – provider picker and runtime configuration-path differences
• Docker Desktop networking – connecting a container to a host service
• Ollama OpenAI compatibility documentation
• Ollama Gemma 4 model library
• Electron introduction
• Electron application packaging and rebranding

The post [FIX] Hermes AI Desktop – “No LLM provider configured” When Ollama Works in the CLI appeared first on TechHead and was written by Simon Seagrave.

Every house has that moment. Someone comes over, settles in, and asks for the WiFi password, and you find yourself reciting Xt7!q2BnR$ letter by letter while they squint at their phone and get the capital B wrong twice. A WiFi QR code ends that whole ritual. You generate one image, stick it on the fridge or a card by the router, and anyone who needs to get online points their camera at it and connects.

This is one of those small things that’s worth setting up once, and the good news is it costs nothing and takes about a minute. I’ll walk through how to create a WiFi QR code, what’s actually encoded inside it, and a few details most articles skip that will save you a confused guest later.

What a WiFi QR Code Actually Does

A WiFi QR code stores three things: your network name, your password, and the security type your router uses. When someone scans it, their phone reads those credentials and offers to join the network automatically, so nobody types anything. The connection happens because the phone already knows the password the moment the camera resolves the code.

What’s encoded is plain text in a defined format, and it looks like this:

WIFI:T:WPA;S:NetworkName;P:password;;

That’s the whole thing. T is the security type (WPA covers WPA2 and WPA3 here, more on that shortly), S is the SSID or network name, and P is the password. The two semicolons at the end close the string. This format is now part of the WiFi Alliance’s specifications for credential sharing, which is why a code generated by one tool scans correctly on a phone built by an entirely different manufacturer. There’s no proprietary magic involved, just an agreed-on way of writing the credentials down where a camera can read them.

Creating One with a Free Generator

You don’t need software for this. A browser-based tool does the whole job, and my pick is QuickResponseCode.com, a free WiFi QR code generator that runs in the page and doesn’t ask you to sign up or watermark the result.

Open the site and select the WiFi tab. Enter your SSID exactly as it appears, including capitalization, because the code will reproduce whatever you type and a mismatched character means a failed scan. Type your password into the password field. Choose your security type from the dropdown — for almost every home network that’s WPA2, which is what most routers have defaulted to for years. If you’re on a newer router running WPA3, select WPA rather than hunting for a separate WPA3 option, and I’ll explain why that’s correct below rather than a mistake. Then download the code as a PNG for printing or an SVG if you want it to scale up cleanly to a sign or poster.

That’s the entire process. The code generates instantly as you fill in the fields, so you can see it form before you download anything.

Where to Put It Once You’ve Got It

The code only earns its keep if it’s somewhere people naturally look. A small printed card propped against the router works for a home network, and a framed version in a guest room is a nice touch that quietly tells visitors they don’t have to ask. Cafés and small restaurants tend to put one on a table tent or taped to the counter by the register, which cuts down on staff repeating the password forty times a shift. The point is to put it where the person who needs it already is, rather than on a wall they’ll never walk past.

A Quick Word on What You’re Actually Sharing

A WiFi QR code hands your network password to anyone who scans it, which is the entire point, but it’s worth thinking about who that includes. On the fridge or a card by the router, the only people scanning it are people already in your home, so there’s nothing to worry about. A code stuck in a shop window or on a public-facing counter is a different situation, because anyone walking past can scan it, photograph it, and keep it long after they’ve left.

The cleaner approach for anything semi-public is to generate the code for your guest network rather than your main one. Most modern routers let you run a separate guest SSID with its own password, walled off from your primary network and the devices on it, so a code that leaks only ever exposes a throwaway password that reaches nothing important. Rotating that guest password costs you nothing, and regenerating the code takes the same thirty seconds as before.

Does It Work on iPhone and Android?

Both, with no third-party app on either side. iPhones have read WiFi QR codes through the native Camera app since iOS 11, so you open the camera, point it at the code, and tap the notification that slides down to join the network. Android added the same camera-based scanning in version 9, and on most recent phones you can also scan straight from the WiFi settings screen or through Google Lens if the camera app doesn’t catch it automatically.

Worth knowing if you’re sharing your own network rather than setting one up for someone else: both platforms can now generate the code for you directly. On an iPhone running iOS 18, the Passwords app has a Wi-Fi section where you pick your network and tap “Show Network QR Code.” Android has had a Share button with a QR option in the WiFi settings since Android 10. These built-in features are handy for a quick one-off share between two people standing in the same room, though they only cover the network the phone is already connected to. For a code you want to print, frame, or create for a guest network you’re not currently on, a generator is still the simpler route.

What Happens If You Change Your WiFi Password?

The code stops working, and this is the part that catches people out. A WiFi QR code is a snapshot of your credentials at the moment you made it, with no live connection back to your router. Change the password and the old code now points at credentials that no longer exist, so every scan fails silently.

The fix is painless if you plan for it. Keep the SVG or PNG file saved somewhere you’ll find it, because regenerating after a password change takes about thirty seconds: open the tool, retype the new password, download, reprint. Treat the saved file the way you’d treat any small config you don’t want to recreate from scratch, which is to say tuck it in a folder with a sensible name rather than letting it vanish into your downloads.

Passwords with Special Characters

If your password is full of symbols, you’re mostly fine. The WiFi format reserves a few characters (semicolon, comma, colon, and backslash) and escapes them with a backslash inside the encoded string, so a password containing those needs to be written correctly into the code or the phone reads the wrong thing. Any decent generator handles the escaping automatically. The safe habit regardless is to test the scan from a second phone before you commit anything to print.

A Note on WPA3 That Trips Up a Lot of Tools

This is the detail almost nobody covers, and it’s the one most likely to leave you with a code that silently won’t scan on some devices. A WPA3 network should still use T:WPA in the encoded string, not T:SAE. The specification is explicit that when the type field is present, its value is WPA to indicate password-based authentication, with SAE being the handshake WPA3 uses under the hood rather than a value the QR format itself expects. Some generators write SAE into the string instead, producing a code that looks valid but fails to parse on a chunk of phones. If you’ve got a WPA3 router and your code won’t scan despite everything looking correct, this is the first thing to check, and it’s the reason selecting WPA in the dropdown is the right call rather than an oversight.

FAQ

Does iPhone have a built-in WiFi QR code scanner? Yes. The standard Camera app reads WiFi QR codes natively on any iPhone running iOS 11 or later, which covers effectively all iPhones in use today. Open the camera, point it at the code, and tap the banner that appears to join the network. There’s nothing to install.

Is it safe to share my WiFi with a QR code? It’s as safe as telling someone the password out loud, because that’s effectively what it does, just faster and without the typos. The thing to watch is placement: a code somewhere public can be scanned and kept by anyone who passes it. For anything beyond your own home, generate the code for a guest network rather than your main one, so a leaked code never reaches the devices and data that matter.

Can I print the QR code and put it on the wall? That’s exactly what most people do with it. Print it at a reasonable size (a code around 300×300 pixels prints cleanly on a small card), keep a quiet margin of white space around it, and put it where guests will naturally look. Save the original file so you can reprint after any password change.

Do I need an app to scan a WiFi QR code? No, on either platform. iPhone and Android both read these codes through the built-in camera, so the person connecting just opens their normal camera app and points it at the code. Third-party scanner apps exist, but they’re solving a problem the phone already solved years ago.

Worth Setting Up Before the Next Time Someone Asks

The version of this I like is the printed card by the router, because it turns “what’s the WiFi password” from a small recurring interruption into something that answers itself. Set it up once, save the file for the day you rotate the password, and you’ve quietly removed one of the more tedious small rituals of having people in your house. If you want to make one now, the tool’s at QuickResponseCode.com and the WiFi option is right there on the front page.

If you use a guest network for this or have a different approach to keeping shared WiFi tidy, I’d be curious to hear what you do. Drop a note in the comments.

The post How to Create a WiFi QR Code (Free, No App Needed) appeared first on TechHead and was written by Simon Seagrave.

The Traditional Control Plane Problem

In conventional Kubernetes deployments, every cluster requires three dedicated master nodes to run control plane components. These nodes host the API server, scheduler, controller manager, and etcd database. While this standalone model provides complete isolation and works well for single clusters, it creates significant inefficiencies when organizations need to manage dozens or hundreds of clusters.

Each set of master nodes consumes substantial resources: CPU, memory, and storage, regardless of actual workload demands. A control plane might use only 20-30% of its allocated resources during normal operations, yet those resources remain reserved and unavailable for other purposes. For organizations running 50 clusters, that means 150 master nodes sitting partially idle, burning through budget allocations and data center capacity.

The overhead extends beyond raw infrastructure costs. Every cluster requires individual monitoring, logging, security patches, and version upgrades. Operations teams find themselves managing infrastructure instead of supporting application teams. Provisioning new clusters becomes a lengthy process, often taking 30-45 minutes just to bootstrap the control plane infrastructure.

How Hosted Control Planes Change the Architecture

OpenShift HCP takes a different approach by decoupling the control plane from worker nodes. Instead of running on dedicated masters, control plane components run as regular pods within a management cluster. Each hosted cluster’s control plane lives in its own namespace, maintaining isolation while sharing the underlying infrastructure.

This architecture leverages the HyperShift project, which implements the necessary operators and APIs to manage these hosted control planes. The management cluster runs the HyperShift operator, which creates and manages HostedCluster and NodePool resources. A HostedCluster encapsulates the control plane configuration, while NodePools represent scalable sets of worker nodes that handle actual application workloads.

From a technical perspective, this means the API server, scheduler, and controller manager run as deployments within the management cluster. The etcd database runs as a StatefulSet, ensuring data persistence. Worker nodes connect to their control plane through a secure tunnel, unaware that their control plane isn’t running on dedicated infrastructure.

Resource Efficiency and Cost Reduction

The economic impact of HCP becomes clear when you examine resource utilization. Internal Red Hat analysis shows that hosted control planes can reduce total cost of ownership in key areas: over 60% savings in developer productivity, 65% reduction in operational costs for SRE teams, more than 50% savings in energy and facility costs, and depreciation cost reductions exceeding 90% Unlocking new possibilities: The general availability of hosted control planes for self-managed Red Hat OpenShift.

Consider a practical example: An organization running 20 production clusters traditionally needs 60 master nodes. With HCP, those same 20 control planes might run comfortably on a 6-node management cluster, depending on workload requirements. The resource consolidation is dramatic, you’re potentially reducing your control plane footprint by 90%.

When using ARM-based instances on AWS, hosted control planes can reduce costs by approximately 20% compared to equivalent x86 configurations A Guide to reducing OpenShift Costs with Arm Hosted Control Planes on AWS, adding another dimension to potential savings. The ability to mix architectures, running ARM management clusters with x86 worker nodes, or vice versa, provides flexibility in optimizing costs based on workload requirements.

Operational Benefits Beyond Cost

The advantages extend well beyond infrastructure savings. Cluster provisioning times drop significantly, a complete hosted cluster can be provisioned in 13 minutes, including nodepool virtual machine provisioning on OpenShift Virtualization Gain confidence with hosted control planes and OpenShift Virtualization using public cloud | Red Hat Developer. This rapid deployment enables teams to create ephemeral environments for testing, spin up clusters for specific projects, and respond quickly to changing business needs.

Version management becomes significantly more flexible. Control planes and worker nodes can run different OpenShift versions, allowing administrators to upgrade components independently. You might upgrade control planes during a maintenance window while keeping worker nodes stable, then upgrade workers in a rolling fashion without control plane disruption. This decoupling reduces risk and provides more upgrade flexibility.

Security boundaries strengthen with HCP. Since control planes run in isolated namespaces on the management cluster, credentials and secrets remain separated from workload environments. Infrastructure administrators working on the management cluster can’t accidentally impact worker node infrastructure, and compromised worker nodes can’t access control plane secrets.

The multi-tenancy story improves significantly. Each hosted cluster can map to different cloud accounts, projects, or network segments while sharing the same management infrastructure. For service providers or large enterprises with multiple business units, this enables efficient cluster-as-a-service offerings without the traditional infrastructure overhead.

Platform Support and Implementation Options

HCP supports multiple deployment targets, each suited to different scenarios. For public cloud deployments, ROSA with HCP became generally available in January 2024, offering clusters with control planes hosted in Red Hat-managed AWS accounts ROSA with hosted control planes (HCP) is generally available. This managed service approach removes control plane infrastructure from customer accounts entirely.

On-premises deployments leverage OpenShift Virtualization (using KubeVirt) or bare metal through the Agent provider. The OpenShift Virtualization approach is particularly interesting for organizations with existing virtualization infrastructure, as it allows running worker nodes as virtual machines managed through standard Kubernetes APIs.

With OpenShift 4.17, HCP support expanded to OpenStack environments as a developer preview Simplifying and optimizing Red Hat OpenShift on OpenStack with hosted control planes, enabling cloud service providers to offer efficient multi-tenant OpenShift clusters on their OpenStack infrastructure.

Real-World Deployment Considerations

Before adopting HCP, consider your specific requirements. The architecture works best when you need multiple clusters with similar configurations. If you’re running just one or two production clusters, the overhead of maintaining a management cluster might not justify the benefits.

Network architecture requires careful planning. Worker nodes need reliable connectivity to the management cluster hosting their control plane. Latency between sites becomes a factor for geographically distributed deployments. Plan for adequate bandwidth and consider network failure scenarios.

The management cluster becomes a critical component requiring high availability. While this consolidation improves overall efficiency, it also creates a single point of failure for multiple hosted clusters. Design your management cluster with appropriate redundancy, backup strategies, and disaster recovery plans.

Existing tools and processes might need adjustment. Monitoring and logging strategies that assume access to master nodes need reconfiguration. Backup procedures must account for the new architecture. Team responsibilities might shift as infrastructure management consolidates.

Getting Started with HCP

Organizations interested in HCP should start with proof-of-concept deployments. The multicluster engine operator (version 2.0 or later) provides the necessary components for self-managed deployments. For AWS environments, ROSA with HCP offers a managed service option that removes much of the operational complexity.

Begin by identifying candidate clusters for migration—development and test environments often make good starting points. These environments benefit from rapid provisioning and can tolerate the learning curve associated with new architecture patterns. As teams gain experience, production workloads can gradually migrate to the HCP model.

The Path Forward

OpenShift Hosted Control Planes represent a natural evolution in Kubernetes architecture, acknowledging that not every cluster needs dedicated control plane infrastructure. For organizations managing multiple clusters, the benefits, reduced costs, faster provisioning, improved security boundaries, and operational efficiency, make HCP worth serious consideration.

The technology has matured through deployments with major cloud providers and is now reaching broader availability across different platforms. As container orchestration becomes standard infrastructure, efficient multi-cluster management becomes essential. HCP provides a proven path to that efficiency, allowing teams to focus on delivering value through applications rather than managing cluster infrastructure.

Want to learn more on HCP, check out these other websites and resources:

Start with the Basics – What Are Hosted Control Planes

• https://www.redhat.com/en/topics/containers/what-are-hosted-control-planes
• Perfect starting point for teams evaluating HCP – covers core concepts, benefits, and use cases with both self-managed and ROSA options

Hands-On Learning – ROSA with Hosted Control Planes Experience

• https://developers.redhat.com/products/redhat-openshift-service-aws/overview
• Get practical experience with HCP through Red Hat’s free hands-on labs – ideal for proof-of-concept testing without infrastructure investment

Technical Documentation – OpenShift Hosted Control Planes Guide

• https://docs.redhat.com/en/documentation/openshift_container_platform/4.14/html/hosted_control_planes/index
• Comprehensive implementation guide for architects and administrators planning self-managed HCP deployments

The post Red Hat OpenShift Hosted Control Planes: Driving Multi-Cluster Management Efficiency appeared first on TechHead and was written by Simon Seagrave.

AI models get all the attention. Headlines focus on size, parameters, and benchmarks. But behind the scenes, there’s a quieter story unfolding, one about how these models interact with the real world. This is the critical gap Anthropic’s Model Context Protocol (MCP) aims to fill.

The Real Limitation: Models Without Context

It’s easy to overlook a fundamental limitation of most AI models: they are stateless and isolated. While impressive, models like GPT-4 or Claude don’t inherently understand your unique business context—your documents, databases, and internal systems. Without this real-time awareness, they remain clever but impractical, lacking the infrastructure to act on the world around them. Each prompt arrives with little memory of what came before and zero visibility into your organization’s data.

To bridge this gap, companies have resorted to building bespoke middleware; one connector for Salesforce, another for GitHub, and yet another for internal APIs. This approach creates a messy middle layer of integrations that are expensive to build, brittle to maintain, and a magnet for security reviews.

This is the exact pain point that Model Context Protocol (MCP) addresses. It standardizes that messy middle layer, providing a single, well-defined interface. With MCP, developers can grant models governed, auditable access to tools and data, allowing them to integrate once and reuse everywhere.

What Exactly Is the Model Context Protocol?

But there’s a deeper strategic layer here worth examining.

To grasp why MCP is a significant development, it’s helpful to understand the problem it solves. As mentioned above, without a standard like MCP, every connection between an AI model and a data source (like Salesforce or a customer database) is a custom, one-off project. If a company uses three different AI models and wants them to access five different internal tools, its developers might have to build and maintain up to 15 unique, brittle integrations. This is known as the “N x M integration problem,” and it’s a massive drain on time, budget, and security resources.

MCP fundamentally changes this N x M problem into a much simpler “N + M” solution. Instead of building unique bridges, each tool and each AI model just needs to conform to the MCP standard once.

It works through a secure client-server architecture:

• MCP Host: This is the AI application a user interacts with, like a chatbot or an AI-powered agent.
• MCP Servers: These are lightweight wrappers around your tools and data sources. You would have one MCP server for your CRM, another for your code repository, and so on. Each server exposes specific, approved capabilities—like “fetch customer data” or “submit a support ticket.”
• Governed Communication: The AI model (via the host) can dynamically ask a server what tools and data it has available and then request to use them. This communication is governed and requires explicit user consent for actions, ensuring the AI only does what it’s permitted to do.

Why This Matters for Businesses: The Strategic Advantages

This shift from chaotic, custom-coded integrations to a standardized protocol has profound business implications.

1. Drastically Reduced Development Costs and Time: The most immediate benefit is efficiency. By eliminating the need to build and maintain dozens of fragile connectors, MCP frees up developer resources to focus on creating value instead of managing plumbing. This translates directly into lower development overhead and faster deployment of AI-powered solutions. Early-adopter case studies report that shifting to a standardized interface like MCP can cut initial integration timelines by roughly 30 percent and trim ongoing maintenance budgets by about 25 percent.
2. Enhanced Security and Governance: Security is paramount when giving an AI access to sensitive company data. MCP is designed with security at its core. It provides a framework for granular access controls, clear audit trails, and mandatory user consent for actions. Data doesn’t need to be copied or moved; it’s accessed through a secure, controlled gateway. This gives security teams a single, auditable point of control, rather than having to vet countless custom integrations.
3. Future-Proofs Your AI Strategy: The AI field is advancing quickly. A new, more powerful model might be released next quarter, or your company may decide to switch from one SaaS tool to another. With a collection of custom integrations, any change creates a massive ripple effect of required updates. Because MCP is an open standard supported by major players like Microsoft, OpenAI, and Google, it decouples your AI strategy from any single vendor. You can swap out models or tools with minimal friction, ensuring your AI stack remains agile and adaptable.
4. Unlocks Truly “Agentic” AI: The ultimate goal for many businesses is to deploy AI “agents” that can perform complex, multi-step tasks autonomously, like a project management agent that can file reports, update tickets, and notify stakeholders across different platforms. This level of sophisticated automation is nearly impossible with brittle, one-off integrations. MCP provides the robust, standardized communication layer necessary for these advanced, agentic workflows to become a practical reality.

In short, the Model Context Protocol is not just a new technical tool; it is a crucial piece of infrastructure that makes business AI more practical, secure, scalable, and cost-effective.

What Comes Next: Beyond MCP

MCP opens doors to a new generation of persistent, actionable AI agents, systems that retain context, learn continuously, and perform complex tasks independently.

As adoption expands, expect ecosystems to form around registries, app stores, permission frameworks, and advanced analytics tools, all built on MCP’s open standard. This ecosystem will become essential for safely scaling AI into mission-critical roles.

Ultimately, MCP marks a turning point, shifting the narrative from models as isolated experiments to AI as interconnected business infrastructure.

The Bottom Line: Scalable AI Requires the Right Plumbing

Models made headlines, but infrastructure determines long-term success. The Model Context Protocol isn’t exciting because it’s flashy, it’s important because it solves real business problems. It reduces complexity, lowers integration costs, and sets the stage for practical, safe, and scalable AI deployment.

Companies adopting MCP today aren’t chasing hype, they’re building foundations. And infrastructure, in the end, decides winners.

Check out the articles and blogs listed below for more information.

References and Further Reading:

• Anthropic Announcement: Model Context Protocol

• Axios on MCP: The USB-C Port for AI Apps

• Red Hat: Model Context Protocol – Discover the missing link in AI integration

• The Verge: Microsoft to Support MCP in Windows

  
  

The post Model Context Protocol (MCP): The Infrastructure Layer AI Actually Needs appeared first on TechHead and was written by Simon Seagrave.

General Updates in OpenShift 4.18

• Platform & Security Foundations: OpenShift 4.18 upgrades the underlying Kubernetes to v1.31 and introduces improvements in secrets and certificate management. For example, the Secrets Store CSI Driver operator is now generally available, allowing OpenShift workloads to fetch secrets from external vaults without storing them on-cluster. This enhances security by making the cluster “unaware” of secret data, which is ideal for regulated environments.

• Simplified TLS Certificate Management: This release also expands cert-manager integration. OpenShift 4.18 includes cert-manager v1.15 (as an operator) and even a tech preview of istio-csr for Service Mesh, so platform and service mesh components can automatically get certificates from a central source. By extending cert-manager’s reach to more OpenShift components (ingress, API server, service mesh), enterprises can more easily centralize TLS certificate handling, reducing the manual effort of managing multiple certificate authorities across clusters.

• Networking Enhancements (UDN & BGP): A major networking feature – User Defined Networks (UDNs) – graduates to general availability in 4.18. UDNs let admins create custom Layer-2 or Layer-3 network segments in OpenShift, bringing data center networking concepts (like VLANs or tenant networks) into Kubernetes. This improves network flexibility and segmentation by enabling isolated networks or overlapping subnets for different teams/projects. Notably, UDNs work for both pods and virtual machines as primary or secondary interfaces, which is key for VM use cases (providing VMs with stable, static IPs and live migration-friendly networks). OpenShift 4.18 also integrates Border Gateway Protocol (BGP) routing into OVN-Kubernetes for UDN networks. With BGP, cluster networks can dynamically advertise routes to external routers and learn routes, simplifying integration with existing data center networks or third-party load balancers.

• Multi-Cluster Infrastructure: For virtualization on vSphere, OpenShift 4.18 adds support to deploy a single OpenShift cluster across multiple vSphere vCenter clusters, without needing shared storage . This improves high availability and scalability on VMware infrastructure by allowing multiple independent vCenter domains to host one OpenShift cluster (configured at install time). On the disconnected-install side, the oc-mirror v2 plugin is now GA with substantial performance boosts . Administrators mirroring OpenShift images to a private registry will see faster mirror creation, caching to avoid redundant downloads, better image pruning control, and even support for mirroring Helm charts . This greatly streamlines installing and updating OpenShift in offline or restricted environments.

• Operator Lifecycle Manager v1: OpenShift 4.18 debuts OLM v1 (the next-gen Operator Lifecycle Manager) as a stable, general availability feature. OLM v1 (formerly tech preview) modernizes how Operators are managed by simplifying APIs, enhancing security, and improving reliability of operator upgrades . This means cluster admins get a more robust and user-friendly experience when installing or upgrading Operators (the add-on services in OpenShift’s ecosystem).

• Cluster Hibernation Improvements: OpenShift 4.18 makes it easier to shut down clusters when not in use and bring them back later. Cluster control planes can now auto-recover from expired certificates after being hibernated for up to 90 days (previously 30 days) . In practice, this means you can suspend a non-production cluster (to save cloud costs, for example) and later revive it without an elaborate manual cert recovery process – simply approve the automated CertificateSigningRequests on restore. For single-node OpenShift (SNO) installations, you can even hibernate up to one year from install with one-click recovery . This enhancement reduces maintenance overhead and downtime when pausing clusters for extended periods.

OpenShift Virtualization 4.18 Enhancements

OpenShift 4.18 places a special emphasis on OpenShift Virtualization, aiming to make running VMs on OpenShift more powerful and easier to manage. The updates range from a new standalone virtualization edition to UI/UX improvements, networking, storage, and integration with tools for migration and backup. Here are the key virtualization-specific features and improvements introduced:

New OpenShift Virtualization Engine Edition

To encourage VM workload migration onto OpenShift, Red Hat introduced OpenShift Virtualization Engine, a new virtualization-only edition of OpenShift. This edition is focused purely on running and managing VMs (without containers) and offers a cost-effective path for organizations moving off traditional hypervisors . Each Virtualization Engine subscription covers up to 128 CPU cores of VM capacity, allowing enterprises to use dense hardware efficiently . OpenShift Virtualization Engine includes all core VM orchestration capabilities (built on the proven KVM hypervisor, managed by OpenShift) and can integrate with Red Hat’s management tools – for example, Advanced Cluster Management for Virtualization provides multi-cluster VM lifecycle management, and Ansible Automation can automate large-scale VM operations. This gives organizations a streamlined, future-proof environment for VMs, with the option to later upgrade to the full OpenShift platform when they’re ready to add containers.

Streamlined VM Management and UI

OpenShift Virtualization 4.18 includes several console UI enhancements to simplify day-to-day VM operations:

• Tree-View Navigation (Tech Preview): A new tree-view for virtual machines is introduced in tech preview. Instead of viewing a flat list of VMs per project, admins can see a hierarchical view and even create logical folders to group VMs (beyond just using namespaces). This makes it easier to organize and navigate large numbers of VMs – you can collapse/expand groups and quickly find a VM using the built-in search at the top of the tree.

• Bulk VM Actions: Managing multiple VMs is more efficient now. OpenShift 4.18 allows selecting multiple VMs in the web console and performing actions like start, stop, or restart on all of them at once. This “bulk action” capability saves time for administrators who need to, for example, power on a set of VMs or gracefully shut down a group during maintenance windows.

• Resource Usage at a Glance: The VM list view can be customized to show new resource utilization columns. By enabling these columns, admins can immediately see each VM’s CPU, memory, and storage consumption from the main list rather than having to inspect VMs one by one. This provides a quick way to identify resource hogs or to verify that VMs are within their allocated quotas.

These UI improvements enhance productivity by making the virtualization layer feel more integrated and manageable within OpenShift’s console, similar to how one would manage VMs in a traditional virtualization platform.

Advanced Networking for VMs

Networking capabilities for OpenShift Virtualization have expanded, aligning with the introduction of User Defined Networks:

• User-Defined Networks (UDN) for VMs: OpenShift 4.18 lets virtual machines attach to custom networks defined by UDNs on their primary interface. In practice, this means VM administrators can create tenant-specific L2 networks (with their own subnets or VLAN-like isolation) and assign VMs to those networks, just as they would in a conventional virtualization environment. UDNs support overlapping subnets and isolated segments per project or tenant, giving greater network segmentation in a multi-tenant cluster. For example, a VM can now have a static IP that it keeps for its lifetime – a common requirement in enterprise VM environments – and UDN ensures that the IP is consistently reachable, even during live migrations.

• Live Migration-Friendly Networking: Because UDN can provide a Layer-2 overlay network, VMs can be live-migrated between nodes without losing network connectivity. The UDN stays consistent across the cluster nodes, which is crucial for moving running VMs with minimal disruption. This bridges a gap for VM workloads on Kubernetes, making the experience closer to traditional hypervisors where migrating a VM retains its MAC/IP and network state.

• BGP for External Connectivity: On top of UDN, OpenShift 4.18 builds in support for BGP routing in the cluster network. This means that the routes to pod and VM networks can be advertised to external routers via BGP. Conversely, the cluster can learn external routes. For users, this enables advanced networking setups – for instance, integrating OpenShift workloads directly into data center networks or enabling external load balancers to reach VMs and pods without manual route configurations. Over time, Red Hat plans to extend this with EVPN for even more flexible network overlays between clusters, but already, the BGP integration in 4.18 is a big step towards seamless hybrid networking.

• Public Cloud and Hybrid Support: Recognizing that many run OpenShift on cloud platforms, 4.18 makes UDNs available in public cloud environments as well. You can use these primary tenant networks on AWS now (GA in 4.18) and even try OpenShift Virtualization on Oracle Cloud Infrastructure (OCI) bare metal (tech preview). In short, OpenShift’s VM networking enhancements work consistently, whether on-premises or in the cloud, so users get the same network customization and multi-network support for their VMs in any environment.

Storage Live Migration and Improvements

Managing VM storage is easier and more flexible in OpenShift 4.18:

• Live Storage Migration: A long-requested feature – migrating a VM’s disk volumes between storage classes – is now available. OpenShift Virtualization 4.18 supports storage live migration of VMs. This allows you to move a VM’s persistent volumes from one storage backend to another without shutting down the VM. For example, if you want to retire an old storage array or move a workload to a faster SSD-based storage class, you can migrate the disks while the VM continues to run. This capability is extremely useful for onboarding new storage, rebalancing usage, or performing infrastructure upgrades with zero downtime.

• Hotplug Disk Migration & Conversion: The storage migration feature isn’t limited to main disks – it also covers hot-plugged VM disks now, meaning even disks attached at runtime can be migrated to a new storage class. Additionally, OpenShift Virtualization will intelligently handle differences between storage types: if you migrate between a filesystem-based storage and a block storage class, the platform can convert the volume format (filesystem or block mode) as needed on the fly. The migration process will even suggest the optimal target configuration before you begin, so you get the best performance on the new storage.

• Performance and Efficiency: Under the hood, 4.18’s migration tool optimizes data transfer. It can detect sparsely-used space on disks and skip copying empty blocks, resulting in faster migrations for thin-provisioned volumes. After a migration, the system labels the migrated volumes to indicate their source, helping admins track which old storage can be reclaimed safely. All of this adds up to more efficient storage management – admins can maintain and upgrade storage infrastructure with minimal impact on running VM workloads.

Performance and Migration Enhancements

Moving VMs into OpenShift and running them at scale gets easier in this release:

• Migration Toolkit for Virtualization (MTV) Updates: Red Hat’s Migration Toolkit for Virtualization (which helps bulk-migrate VMs from traditional platforms like VMware or Hyper-V into OpenShift) received several upgrades in 4.18. First, migration plans can now specify custom PersistentVolumeClaim names for each migrated volume, giving more control over the target setup. More importantly, migration speeds have improved – 4.18 includes scheduler optimizations for both cold migrations (VMs powered off) and warm migrations (running VMs) that result in shorter migration windows. This means less downtime or faster cutovers when transferring hundreds or thousands of VMs. There’s also expanded support to migrate SUSE Linux VMs, broadening the range of source VMs that can be moved onto OpenShift. To make the whole process easier, the MTV documentation was restructured for better guidance, and Red Hat has published performance-tuning recommendations so customers can follow best practices for large-scale migrations.

• VM Workload Metrics: On OpenShift 4.18, administrators gain deeper insight into VM performance via new workload metrics. The monitoring stack can now show metrics for each VM’s allocated resources – including CPU usage, memory, network, and storage – similar to how it monitors container workloads. These metrics help with capacity planning and optimization. For instance, you can observe if a VM consistently underutilizes its CPU allocation and decide to downsize it, or identify VMs that need more resources. Having these performance metrics readily available in OpenShift’s dashboards enables data-driven tuning of virtualized workloads.

• Scaling and Multi-Cluster Visibility: In very large environments, OpenShift is also making strides. Red Hat has provided early previews of Grafana dashboards focused on virtualization at scale. One dashboard can analyze VM usage across clusters to recommend right-sizing of CPU or memory for VMs on different clusters (helping ensure performance while avoiding waste). While this is a preview, it signals a focus on performance optimization across multi-cluster deployments. Additionally, multi-cluster observability for OpenShift Virtualization continues to improve, so operators managing fleets of clusters can get a central view of their VM health and performance.

Expanded Guest OS and Security Support

• Windows Server 2025 Support: Many enterprises run Windows workloads, and OpenShift Virtualization 4.18 keeps pace with Microsoft’s latest. Red Hat validated OpenShift Virtualization on Windows Server 2025 on day one of Microsoft’s release. In fact, OpenShift 4.18 achieved Microsoft’s Server Virtualization Validation Program (SVVP) certification for Windows Server 2025. This guarantees that Windows VMs running on OpenShift meet Microsoft’s standards and are fully supported, reassuring customers that they can run new Windows Server instances on OpenShift as reliably as on a traditional hypervisor.

• vTPM Enhancements: Security features for VMs are bolstered by improvements to virtual Trusted Platform Modules (vTPMs). OpenShift already allowed adding a vTPM device to VMs (for uses like Windows BitLocker encryption); now in 4.18, this feature is more robust. Key enhancements include support for vTPM on block storage volumes and on standard (ReadWriteOnce) PVCs. Previously, vTPM might have required shared storage to persist data across node moves; with RWO (non-shared) support, even VMs that don’t live-migrate can use a vTPM. Moreover, OpenShift Virtualization now supports taking snapshots of VMs with a vTPM and restoring from those snapshots. (Cloning a vTPM-enabled VM or creating a new VM from such a snapshot is still not supported, since the TPM state can’t be duplicated for security reasons.) These enhancements let users confidently use vTPMs for encryption and secure boot scenarios on a wider range of VMs. For example, if you add a vTPM to a Windows VM, Windows will recognize it and allow BitLocker encryption to proceed even if the vTPM is not persistent across reboots. All of this helps enterprises meet compliance or security requirements for VM workloads on OpenShift.

AI Assistance and Automation

• OpenShift Lightspeed (AI Assistant) – in tech preview: OpenShift 4.18 introduces a highly useful way to help administrators and developers – a generative AI-powered chat assistant called OpenShift Lightspeed, integrated directly into the OpenShift web console. Think of it as an AI “copilot” for your cluster. Lightspeed has knowledge of OpenShift’s documentation and even specific OpenShift Virtualization runbook information. Users can ask it questions like “How do I attach a new disk to a VM?” or seek troubleshooting advice and get guided answers sourced from official docs.
  Impressively, when you open the chat assistant in the context of a specific VM, it becomes VM-aware – it can pull in that VM’s YAML configuration, logs, or events with one click to help diagnose issues. This integration can save a lot of time: instead of manually searching docs or copying logs, an admin can interact with the chatbot to quickly pinpoint configuration mistakes or get step-by-step guidance. While Lightspeed is an optional component (enabled via an operator), it showcases how AI can streamline cloud platform operations. For teams adopting OpenShift Virtualization, this means quicker onboarding (the AI can explain features) and faster resolution of problems, ultimately reducing downtime.

Ecosystem Integrations

• Backup and DR Integration: As enterprises run more VMs on OpenShift, backup and disaster recovery become critical. OpenShift 4.18 introduces policy-based backup and restore for VMs through integration with the OpenShift API for Data Protection (OADP) framework. In essence, you can now treat VMs similar to Kubernetes workloads when it comes to backup: define backup policies and use OADP (which ties into tools like Velero) to snapshot and restore not just Kubernetes resources but the VM disk data as well. This integration covers VMs and associated KubeVirt objects across clusters, which is important for multi-cloud or multi-cluster environments. It gives platform admins a consistent, automated way to protect VM workloads, whether the cluster is on-prem or in the cloud.

• Third-Party Ecosystem Support: Red Hat has been working with multiple partners to certify more tools on OpenShift Virtualization. A notable addition in 4.18 is Rubrik support. Rubrik (a popular data management and backup vendor) has validated its Security Cloud and backup platform to work with OpenShift Virtualization, meaning organizations can use Rubrik to back up and migrate OpenShift-hosted VMs alongside their other infrastructure. This kind of integration is a big win for enterprises, as it brings familiar backup/recovery workflows to OpenShift’s VM layer. Rubrik’s solution can take consistent snapshots of Kubernetes VM volumes, help in ransomware recovery (cyber-resilience), and offload VM data for long-term retention – all with the knowledge that it’s officially supported on OpenShift. Beyond backup vendors, Red Hat is also collaborating with systems integrators; for example, TEKsystems and AWS have partnered to help customers migrate existing virtualization workloads onto OpenShift on AWS, using OpenShift Virtualization as the target platform. These ecosystem expansions mean users have more choices and confidence when extending OpenShift’s virtualization capabilities – whether it’s protecting VMs, automating migrations, or integrating with cloud services.

Benefits for Users and Enterprises

Leveraging OpenShift from which to deploy and manage applications translates into real benefits for organizations adopting the platform:

• Unified Platform & Cost Savings: By enhancing OpenShift’s ability to handle both containers and virtual machines, Red Hat enables IT consolidation. Organizations can run legacy VMs and cloud-native apps side by side, simplifying operations and potentially reducing licensing costs. In fact, one Red Hat customer, Reist Telecom, reported a 50% decrease in VM licensing costs after moving to OpenShift for both virtualization and container-based workloads. Savings stem from avoiding expensive proprietary virtualization hypervisor costs and using a single platform for all workloads. A unified platform also means teams invest in one set of skills and tools, which improves efficiency.

• Faster Operations and Developer Productivity: The management improvements (UI enhancements, bulk actions, metrics, Lightspeed AI assistance) make platform administrators more productive. Routine tasks like rebooting multiple VMs or expanding a VM’s disk are quicker and don’t require special scripts or downtime. The AI integration can shorten troubleshooting time and onboarding for new administrators from hours to minutes by guiding admins through complex procedures or surfacing the right log snippet. For developers and testers, having self-service VM capabilities on OpenShift with easy networking and storage options means they can provision the environments they need faster, accelerating development cycles.

• Improved Network and Storage Flexibility: Features like UDN and live storage migration give enterprises much-needed flexibility in a cloud platform. For example, network engineers can apply familiar data center networking patterns (isolated L2 segments, BGP route sharing) to Kubernetes clusters – this eases integration with existing IT infrastructure and ensures multi-tenant security. Storage live migration enables infrastructure teams to perform maintenance or upgrades without scheduling lengthy downtime, as VMs can be moved off an old storage array live. The net effect is less disruption to business services and an easier time adopting new tech (like faster storage or new network topologies) under the hood.

• Enterprise Security & Compliance: OpenShift 4.18’s attention to security features benefits organizations running sensitive workloads. The GA Secrets CSI driver means apps can use secrets from external vaults such as HashiCorp Vault or AWS Secrets Manager with no plaintext secrets in etcd – a big plus for compliance. vTPM support improvements allow the use of disk encryption (BitLocker, etc.) and secure boot in VMs, helping meet compliance standards for protecting data at rest and in transit on VMs. And updated Windows support ensures companies can migrate to newer Windows Server versions without waiting, all under Red Hat’s support umbrella. These enhancements build confidence that OpenShift can handle mission-critical, regulated workloads.

• Ecosystem and Integration Benefits: The availability of the OpenShift Virtualization Engine edition provides a lower-cost on-ramp for virtualization use cases. Organizations that primarily want to modernize their VM infrastructure can start with this edition to enjoy OpenShift’s benefits (like consistent management and automation) for VMs only, then later transition to full OpenShift for containers and application modernization capabilities when ready. Meanwhile, integration with established enterprise tools – such as backup solutions (Rubrik, Vertas Net Backups) and storage from leading vendors such as NetApp, PortWorx, and Dell – means adopting OpenShift doesn’t require abandoning existing investments in tooling and infrastructure. Enterprises can extend their current processes (backup, DR, monitoring) to OpenShift VMs, which shortens the learning curve and improves reliability using proven solutions. All these integrations affirm that OpenShift is not an island, but rather an ever growing ecosystem supportive of hybrid cloud strategies.

Conclusion

Red Hat OpenShift 4.18 delivers a rich set of updates that reinforce its position as a leading hybrid cloud platform, especially for organizations looking to bring virtual machine workloads onto a modern application platform. This release’s general improvements – from networking flexibility with UDN/BGP to easier certificate and secret management – strengthen the core OpenShift platform for all users. But the standout is the focus on OpenShift Virtualization: 4.18 narrows the gap between traditional VM infrastructure and cloud-native infrastructure. Administrators get tools to manage VMs at scale (tree views, metrics, bulk actions) with the same ease as containers, and developers can more seamlessly work with VMs alongside containerized apps. Enhanced features like live storage migration and tenant networks show that OpenShift can handle complex enterprise VM scenarios (multi-tenant networking, zero-downtime storage ops). The result is greater agility and choice – teams can modernize at their own pace, running VMs and containers together and gradually refactoring apps without needing two completely separate environments. Backed by Red Hat’s official support and an expanding ecosystem of partners, OpenShift 4.18 provides a unified platform that can drive innovation while still caring for traditional workloads. Enterprises adopting these new features can expect improved efficiency, reduced costs, and a smoother path to hybrid cloud modernization.

For more information, check out the following links:

• Red Hat unveils OpenShift 4.18: Enhanced security and virtualization experience
• What’s New in OpenShift Virtualization 4.18
• OpenShift Virtualization Release Notes (4.18)

The post Red Hat OpenShift 4.18: Enhanced Virtualization and New Features appeared first on TechHead and was written by Simon Seagrave.

What Is AI Inferencing?

Imagine teaching a computer to recognize cats by showing it thousands of cat photos. Once it has been trained and learned, presenting a new cat photo prompts the computer to identify it correctly. This process of applying learned knowledge to new data is called AI inferencing.

The Difference Between Training and Inferencing in AI

In AI development, at a very high level, two main phases exist: training and inferencing. Training involves feeding large datasets into the AI model, allowing it to learn and adjust its parameters to recognize patterns and make decisions. This phase is resource-intensive, potentially very costly, and time-consuming. Inferencing, on the other hand, is when the trained model is applied to new, unseen data to make predictions or decisions. It’s less resource-demanding and enables the AI to function in real-world applications.

• Training: This is the learning phase where the AI studies large datasets to understand patterns. It’s resource-heavy and time-consuming.

• Inferencing: Here, the AI uses what it learned during training to make decisions or predictions on new data. It’s quicker and less resource-intensive.

Why Is AI Inferencing Important?

AI inferencing allows AI systems to function in real-world applications. From voice assistants understanding commands to recommendation systems suggesting products, inferencing makes AI practical and useful across many industries and aspects of day to day life. In healthcare, for instance, AI inferencing assists in diagnosing ailments or diseases by analyzing medical images, leading to more accurate and timely diagnoses. In finance, it detects fraudulent activities by evaluating transaction patterns and enhancing security measures. Retailers utilize AI inferencing to personalize shopping experiences through product recommendations based on customer behavior, improving customer satisfaction. In the automotive industry, it enables autonomous vehicles to make real-time decisions by interpreting sensor data, contributing to safer and more efficient transportation. By transforming theoretical models into practical tools, AI inferencing plays a crucial role in the widespread adoption and effectiveness of artificial intelligence in our daily lives.

Strategies to Optimize AI Inferencing

Enhancing the efficiency of AI inferencing is crucial for deploying AI applications effectively. One approach is model optimization, which involves techniques like pruning to eliminate redundant neural network components, thereby improving performance. Another strategy is hardware acceleration, utilizing specialized processors such as GPUs, NPUs, FPGAs, or ASICs designed to handle AI computations more efficiently, significantly boosting inferencing speed. Additionally, implementing efficient algorithms tailored for inferencing can reduce computational demands, further enhancing overall efficiency. By integrating these methods, AI systems can achieve faster and more efficient inferencing, making them more practical for real-world applications.

• Model Optimization: Streamlining the AI model by removing unnecessary parts can enhance performance.

• Hardware Acceleration: Using specialized hardware designed for AI tasks can speed up inferencing.

• Efficient Algorithms: Implementing algorithms tailored for inferencing can reduce computational demands.

Challenges in AI Inferencing

Despite its benefits, AI inferencing faces challenges that need to be addressed. Latency is a concern, as ensuring quick responses is crucial, especially in time-sensitive applications. Scalability is another issue; maintaining performance as the number of users or data volume increases can be challenging. Resource constraints also pose difficulties, particularly when deploying models on devices with limited computational power, like smartphones.

• Latency: Achieving rapid response times is essential, particularly in applications where delays can lead to suboptimal outcomes.

• Scalability: As user numbers or data volumes grow, maintaining consistent performance becomes increasingly complex, necessitating scalable solutions.

• Resource Constraints: Deploying AI models on devices with limited computational capabilities, such as smartphones or edge devices, poses significant challenges due to their restricted processing power and memory.

Final Thoughts

AI inferencing is now a key part of our daily tech interactions, powering everything from the virtual assistants on our mobile devices that help us manage our schedules to the autonomous vehicles navigating our streets. Its ability to apply learned models to new data enables real-time decision-making across various sectors.

However, challenges like latency, scalability, and resource limitations remain significant hurdles. Addressing these issues is essential for the seamless integration of AI into our lives. By focusing on optimization strategies and embracing innovative solutions, we can improve AI inferencing efficiency, leading to more responsive and intelligent systems.

What are your thoughts on AI inferencing and its impact on technology? I’d like to hear from you, please share your insights in the comments below.

For a deeper understanding of AI inferencing and its challenges, you might find this Forbes article insightful: AI on Edge: Achieving Energy Efficiency in Inference Processes

Additionally, this TechCrunch piece discusses recent advancements in AI efficiency: A Popular Technique to Make AI More Efficient Has Drawbacks

The post Understanding AI Inferencing: Enhancing Efficiency in Real-World Applications appeared first on TechHead and was written by Simon Seagrave.

With the recent release of OpenShift 4.15, it introduces a range of new features and enhancements that offer numerous benefits to IT professionals such as yourself. For those of you not familiar with OpenShift, it is built on Red Hat Enterprise Linux (RHEL) and Kubernetes, providing a secure and scalable application platform from which to run both your virtual machine and container application workloads across on-premises, public cloud, and edge locations, with support for a wide selection of programming languages and frameworks. Here’s a highlight of some of the areas benefitting from new features or enhancements in 4.15:

1. Installation Enhancements

OCP 4.15 introduces significant improvements to the installation process, aimed at simplifying and streamlining cluster deployment. Enhanced installer capabilities, coupled with refined workflows, ensure a seamless and hassle-free installation experience for administrators.

2. Administration Updates

Administrators are greeted with a plethora of updates in OCP 4.15, empowering them with enhanced cluster management capabilities. From fine-grained node management to robust authentication mechanisms, administrators can efficiently govern their OpenShift clusters with confidence and ease.

3. Networking Improvements

In OCP 4.15, networking receives a substantial boost with a slew of enhancements. From improved ingress and egress capabilities to advancements in service mesh functionalities, these updates provide administrators with greater control and flexibility over network traffic within the cluster.

4. Security Enhancements

Security remains a top priority in OCP 4.15, with a focus on bolstering the platform’s resilience against cyber threats. This release incorporates various security enhancements, including vulnerability fixes and strengthened authentication and authorization mechanisms, ensuring the integrity and confidentiality of OpenShift deployments.

5. Developer Experience Enhancements

Developers will be happy with OCP 4.15 as it introduces a myriad of enhancements tailored to improve the developer experience. From updated developer tools to expanded support for languages and frameworks, developers can now build and deploy applications more efficiently, accelerating the pace of innovation.

6. Storage Features and Improvements

Storage management receives a significant overhaul in OCP 4.15, catering to the evolving storage requirements of modern applications. With support for new storage types and enhancements in storage management capabilities, administrators can seamlessly integrate storage solutions into their OpenShift clusters.

7. Observability and Monitoring Enhancements

Observability and monitoring tools receive notable upgrades in OCP 4.15, enhancing administrators’ ability to monitor and troubleshoot cluster health and performance. With improvements to Prometheus, Grafana, and other monitoring components, administrators gain deeper insights into cluster operations, facilitating proactive management and optimization.

Want to find out more?

For those of you interested in learning more about these features or exploring the documentation in detail, the official OpenShift Container Platform 4.15 documentation is a valuable resource​​.

The post What’s New in Red Hat OpenShift 4.15 appeared first on TechHead and was written by Simon Seagrave.

Hey there, fellow tech enthusiasts! Today, we’re diving into a topic that’s been buzzing in the tech world: Artificial General Intelligence (AGI). You’ve probably heard of AI, but AGI? That’s a whole new ball game. So, buckle up as we explore what AGI is, its potential to reshape our work and personal lives, and the concerns that come along with it.

Understanding the Difference: AI vs AGI

AGI is an evolution of AI, but with a twist. While both are facets of the same broad technological field, they differ significantly in capability and application.

Artificial Intelligence (AI): Specialized and Task-Oriented AI refers to machines or systems that are designed to perform specific tasks that typically require human intelligence. These tasks can range from recognizing speech, playing chess, to analyzing large sets of data. The key characteristic of AI is its specialization; each AI system is tailored to a particular task or set of tasks. Examples include chatbots, recommendation systems, and self-driving cars. They excel in their designated areas but lack the ability to transcend beyond those tasks.

Artificial General Intelligence (AGI): Versatile and Human-Like AGI, on the other hand, represents a level of artificial intelligence that mirrors human cognitive abilities. An AGI system can learn, understand, and apply its intelligence to a wide variety of problems and tasks, not limited to a single field or function. This means AGI can adapt to new situations, learn from experiences, and apply its knowledge to solve problems it wasn’t specifically programmed for.

Now we understand the difference between AI and AGI, let’s take a look at how it could apply to our everyday lives.

AGI in the Professional World: A Paradigm Shift

The workplace is ripe for transformation with the advent of AGI. Envision an AI that doesn’t just process data but can engage in creative problem-solving, strategic planning, and innovative thinking. AGI could lead to a new era of productivity, pushing the boundaries of human-AI collaboration. However, this also necessitates a shift in workforce skills, emphasizing creativity, emotional intelligence, and adaptability in the age of AGI. Let’s take a look at a possible real-world example of how AGI could impact the workplace:

In a typical marketing department, teams spend considerable time analyzing market trends, consumer behavior, and campaign performance. Currently, this involves a mix of human creativity and basic AI tools for data analysis.

With AGI, imagine a system that not only analyzes data but also generates creative campaign strategies. This AGI could assess market trends, understand consumer emotions, and even predict future trends. It might suggest a groundbreaking advertising concept based on cultural insights it has gathered from global data.

This would shift the role of human marketers. Instead of just interpreting data, they would collaborate with AGI to refine these creative ideas, adding a human touch and emotional intelligence that AGI can’t replicate. They would also focus on the ethical and practical aspects of campaigns, ensuring they resonate authentically with the target audience.

In this scenario, the marketing team becomes more strategic and creative, while the AGI provides insights and suggestions that were previously impossible due to the sheer scale and complexity of the data involved. This illustrates the potential synergy of human and AGI collaboration, transforming the workplace into a more innovative, efficient, and creative environment.

The Personal Sphere: AGI as a Life Companion

At home, AGI could revolutionize how we manage our daily lives. Imagine an AI system that not only helps with scheduling and reminders but also provides personalized advice on health, finance, and even personal relationships. This level of personalized assistance could redefine the human-tech relationship, making technology an even more integral part of our lives.

Ethical and Societal Considerations: The AGI Conundrum

The potential of AGI is enormous, but so are the ethical implications. Issues of privacy, autonomy, and the potential misuse of AGI are critical concerns. The debate around AGI also touches on deeper philosophical questions about the nature of intelligence and consciousness. Furthermore, the impact on employment cannot be overstated – while AGI might create new job categories, it also poses a risk to existing jobs, demanding a careful and thoughtful approach to workforce transition.

Preparing for an AGI Future: Education and Regulation

Preparing for a future with AGI involves more than just technological readiness. It calls for a comprehensive rethinking of our educational systems to foster skills that complement AGI. Additionally, developing robust regulatory frameworks to guide the ethical development and deployment of AGI is essential. This includes international cooperation to establish global standards and norms, which will no doubt come in time, though things could be a little disruptive and uncertain in the short term as AGI use becomes more widespread.

Conclusion: Embracing AGI with Optimism and Caution

The journey into the world of AGI is filled with both incredible possibilities and formidable challenges. I am personally excited by the possibilities that it will provide, though equally feel some trepidation due to the breadth and extent of the impact and disruption AGI could potentially have on our day-to-day lives. The future with AGI promises to be a fascinating one, and it’s up to us to navigate this future responsibly. Let me know what you think in the comments below. Thanks for reading!

The post Navigating the Future with Artificial General Intelligence (AGI): Impacts on Work, Personal Life, and Beyond appeared first on TechHead and was written by Simon Seagrave.

The speed at which AI evolves and integrates into our lives is only going to increase in 2024. As companies unlock its potential, individuals use it to boost productivity, and legislators scratch their heads over-regulating it, AI will become increasingly omnipresent in everything we do. However, there are still challenges to overcome, such as trust, bias, accessibility, and regulation, which need to be addressed to unlock the full potential of AI. Let’s take a look at 10 of the most probable, and important, AI trends for 2024.

1. Beyond Words And Pictures

The next generation of generative AI tools will go far beyond the chatbots and image generators. such as MidJourney, that have amazed us in 2023. In 2024, we can expect to see the emergence of generative video and music creators that are more powerful and user-friendly. These tools will be embedded into creative platforms and our day-to-day productivity tools, opening up new and exciting applications such as generative design tools and voice synthesizers. At the same time, the ability to distinguish between real and computer-generated content will become a valuable skill to combat misinformation, ensure security, uphold media integrity, navigate legal challenges, maintain trust in digital mediums, and preserve artistic authenticity.

2. Ethical AI

With the disruptive potential of AI, it is crucial to develop and use it in a responsible manner that minimizes harm. In 2024, there will be increased focus on mitigating issues such as bias, lack of transparency, job displacement, and the potential for AI to get out of control. AI ethicists will be in demand as businesses strive to adhere to ethical standards and implement safeguards.

3. AI In Customer Service

Customer service is an area where AI can have a significant impact. In 2024, we can expect to see AI being integrated into customer service processes to automate routine tasks and free up human time for more complex issues. AI will be used to triage initial contact calls, generate personalized solutions, and provide reports and summaries of customer interactions. A survey by the Boston Consulting Group revealed that 95% of customer service leaders expect their customers to be served by AI bots in the next three years.

4. Augmented Working

In 2024, the ability to augment human intelligence and capabilities will become crucial in the workplace. Various professionals, such as legal experts, doctors, marketers and coders, will rely on AI to enhance their productivity and efficiency. AI will assist in tasks such as summarizing case law, drafting contracts, writing patient notes, marketing content and debugging software. Students and job seekers will also benefit from AI tools for organizing notes, conducting research, and crafting job applications.

5. AI-Augmented Apps

In 2024, we can expect to see an increasing number of software applications integrating generative AI functions. From search engines, such as Bing where they have already integrated some basic AI chatbot capability into their search screen, to productivity tools and industry-specific platforms, the addition of chatbot functionality will enhance the customer experience. Concerns over data protection and privacy will be addressed as AI providers adapt their services to meet market requirements.

6. Low-Code And No-Code Software Engineering

The use of low-code and no-code tools in application development is expected to increase in 2024. These tools, coupled with generative AI technology like ChatGPT, allow individuals without extensive technical skills to create and test applications quickly. While coding and software engineering jobs will still exist, there will be exciting opportunities for non-developers with innovative ideas and problem-solving abilities.

7. AI Jobs

The field of AI offers a wide range of job opportunities in 2024. Beyond traditional computer science roles, new positions will emerge, such as prompt engineers, AI managers, AI project managers, trainers, and ethicists. There will also be increased demand for AI engineering and DevOps roles. Whether you have technical skills or not, there are job prospects in the AI industry.

8. Quantum AI

In 2024, we can expect to see progress in applying quantum computing to power larger and more complex neural networks and algorithms. While not immediately affecting everyone, quantum computing has the potential to significantly impact AI. Quantum algorithms, using qubits that exist in multiple states simultaneously, offer greater efficiency for certain computation-heavy tasks.

9. Upskilling For The AI Revolution

To thrive in the AI era, upskilling is essential. Understanding how AI affects your job or profession and acquiring the ability to leverage AI tools effectively will be highly valuable. Forward-looking employers will integrate AI skills into education and training programs. If your employer doesn’t offer such programs, there are numerous online resources available for self-learning.

10. AI Legislation

Legislators are starting to recognize the game-changing nature of AI and are developing regulations to strike a balance between protecting citizens and enabling innovation. Jurisdictions like China, the EU, and the US, are already implementing or proposing AI-related laws. In 2024, the debate around AI legislation will be prominent, focusing on job protection, privacy concerns, and fostering innovation.

Conclusion

As we enter 2024, the AI landscape is poised for significant advancements and widespread integration. The 10 trends we have explored in this guide represent the key areas of focus for the coming year. From generative AI tools to ethical considerations and the impact on various industries, AI will continue to shape our world. Embracing these trends and staying informed will be crucial for individuals, businesses, and policymakers as we navigate the AI revolution. I’m personally excited for the AI advancements and new capabilities it’ll provide in the coming year. Let me know your thoughts in the comments below.

The post The 10 Most Important AI Trends for 2024 appeared first on TechHead and was written by Simon Seagrave.

The prospect of a fully autonomous driving has always held a fascination for me. The thought of finishing work on Friday evening, jumping into the car with the family, setting a distant target destination for the weekend, and then letting the car drive itself overnight whilst we relax and sleep, waking up in the morning at our weekend venue has been a dream of mine. Once we get to this stage, then I’d expect that the interior of cars would be re-imagined, allowing for comfortable chairs that recline fully and double as a bed, and maybe a centralized table from which you can sit around to work, eat and socialize.

I have longed for a Tesla vehicle for a long time now, and my dream came true earlier this year, when my Hyundai Santa Cruz (gas powered car), although relatively new, was proving to be unreliable (ie: new front axle after only 3K miles, amongst other things!) and the timing felt right to trade it in before I took too much more of a financial hit on it due to depreciation. This coincided with Tesla dropping their prices significantly on their Model 3 and Model Y electric vehicles, and a $7,500 federal tax and Massachusetts $3,500 rebate ($12,000 in total).

The planets felt in alignment, so I decided to go for it, and ordered a long range Tesla Model Y. I have now owned my new Tesla for a couple of months, have already clocked up a couple of thousand miles, and am loving it! I’m enjoying the prospect of lower ongoing maintenance, since electric vehicles typically have less moving parts, so no more oil changes for me, and cheaper running costs to get from A to B! Even brake pads don’t need replacing as much due to regenerative braking, where the electric motors help slow the car up when you take your foot off of the accelerator.

One of the most enjoyable and exciting things for me, is how my Tesla feels more like a software platform, since new features and updates are enabled through Tesla pushing out live software updates to the car, so over time there are improvements and new capabilities added. For someone that lives and works in the tech space, this is exciting! I even find the manufacturing process fascinating, particularly with the large ‘giga press’ Tesla have designed and started using on some of their models.

Anyway… recently Tesla has started offering factory tours of both their Californian, Fremont and Austin factories, which for someone like me (and my son, mini-TechHead) would be off-the-scale amazing!

One of the few ways you can get on one of these newly announced Tesla factory tours is via the ‘Refer and Earn’ program, where you can spend referral credits towards getting on a factory tour. For this I need to achieve a lofty 15,000 credits!

You can earn credits through people using your referral code when purchasing a new Tesla. The great news is that you also get something out of it as well, for free! At the time of writing this post, you would get $500 cash back and 3 months of full self driving (FSD) – which is fun to try out, and gives you a taste of where FSD is heading.

Anyway, if you, family or friends are thinking of buying a Tesla, please consider clicking on my referral code link here (it doesn’t cost you anything), as it will help you get some cash back on your purchase, and also me, in getting a step closer to taking mini-TechHead and myself on a Tesla factory tour. Refer and Earn link: https://ts.la/simon395845

Thanks for taking the time to read this more casual post, and if you already own a Tesla, have just ordered one or are thinking about it, then drop a comment and let me know what excites you most about the Tesla technology and driving experience. Also, will be ever see full hands-off the steering wheel type self driving anytime soon or will it continue to be just a dream?

The post Tesla factory tour – help me get on one appeared first on TechHead and was written by Simon Seagrave.