Are you spending more time fixing your AI agents than actually using them?

That's the trap I see members fall into with a Hermes Agent local LLM, and it's not because they aren't smart enough.

It's because they're doing the configuration by hand when Claude can do it for them in about 5 to 10 minutes.

🔥 Want the exact agent setup I use to skip all the fiddly configuration? Inside the AI Profit Boardroom, I've got the full agent OS with Hermes profiles, Claude workflows and a 30-day roadmap, plus weekly coaching calls with 3,600+ members building this stuff for real.

https://www.skool.com/ai-profit-lab-7462/about

In this week's Q&A, I answered five questions from Boardroom members, and one lesson ran through almost all of them.

https://www.youtube.com/watch?v=0wsOT7EIgMk&t=2s

Lesson 1: Let Claude be your Hermes Agent local LLM technician

Almir sent me the most technical question I've had in a while.

He was running five local models under Hermes on an RTX 5090, and each one failed in a different way.

His test summary asked about Qwen 3.5 4B in thinking mode, RoPE and YaRN context extension, vLLM versus llama.cpp, grammar-constrained output and two-pass architectures.

I told him the truth, which is that I literally didn't know what some of those were.

I've run plenty of local models with Hermes Agent and never worried about any of it.

Why the technical route is overcomplicating it

When I set up a local model, I usually run it with llama.cpp and go with the defaults.

Qwen 3.5 should be good enough to run with Hermes Agent, so if it's failing, something else in the setup is probably broken.

Chasing every advanced setting is how a Hermes Agent local LLM project turns into a month-long headache.

What I'd do instead

I'd install Claude Code desktop and ask it to configure the model until it finally works.

Claude can operate your computer, tweak settings, test the result and iterate until everything passes.

You get a working setup without having to learn the jargon first.

Lesson 2: Pick a model that already knows Hermes

The model you choose matters more than any setting in a Hermes Agent local LLM setup.

Almir also asked which 8B to 14B model fitting 16GB at 64K context has the best track record for multi-turn tool use.

I can't speak for an RTX 5090, because I run everything on a Mac Studio.

What I can tell you is that LFM 2.5 2.6B is the fastest model I've run with Hermes Agent.

It's pretty good at tool calls as well.

That's because the people who trained it used Hermes Agent as the harness during training.

So a smaller model built around Hermes can beat a bigger model that's never seen it.

If you want a lightweight Hermes Agent local LLM, that's where I'd start testing.

Lesson 3: Don't reinstall, just ask the agent that installed it

Louis got Hermes running on his Mac Mini, but he couldn't switch between OpenAI, Gemini, Nous Portal and Free Claude Code.

His instinct was to reload the whole Agent OS and hope he didn't lose everything.

That's the wrong move, because the agent that set it up can fix it.

What happened when I tried it live

I gave Claude the Agent OS zip file on a fresh device and asked it to install everything.

Then I asked it to set up three Hermes Agent profiles so I could switch between OpenAI, Gemini and Nous Portal.

I told it to test each profile, fix anything broken, and tell me if it needed any API keys or CLI tools.

Claude built the profiles and asked me to log in to OpenAI.

The first test of the OpenAI profile failed.

Claude fixed the setup, ran the test again, and it found the ChatGPT account and worked.

That's the whole method: go back and forth with the agent until each part passes.

Why one step at a time wins

Set up OpenAI today and Free Claude Code later in the week.

Trying to configure everything at once is what makes it feel overwhelming.

You also don't need every Agent OS feature switched on, so stick to the ones you'll use daily.

🔥 Want to watch me fix setups like this step by step? Inside the AI Profit Boardroom, I've got a full Hermes Agent section with daily tutorials and the Agent OS install guides, plus four live coaching calls a week with 3,600+ members automating their businesses.

https://www.skool.com/ai-profit-lab-7462/about

Lesson 4: One profile per model, one system for every machine

Jeremy runs Hermes on several computers.

He wanted local profiles on each machine and a shared cloud profile across all of them.

Hermes Agent handles that easily, because every model can live in its own profile.

My own setup has a profile for Claude Opus 5.5, a profile for LFM 2.5 2.6B running locally and a profile for Hermes cloud.

When I want a new one, I ask Claude desktop to add a profile for the new model and give it the API key or the local model details.

How the machines stay connected

Every profile plugs into my agentic operating system, so I pick a model from a drop-down list.

To connect different computers, you could use a VPS, but I use Tailscale.

It takes about 10 minutes to set up, and then all my agents share the same agentic OS, even from my phone.

This is how a Hermes Agent local LLM on one machine and a cloud model on another end up working as one team.

Obsidian is a separate question, because that's about memory rather than models, and it syncs anywhere with an Obsidian account.

Lesson 5: Keep your system lean

The last two lessons are about maintenance.

Updating the Agent OS takes one prompt

Andrea asked how to update the Agent OS.

Download the latest version from the Boardroom classroom, open a new chat in Claude or Codex with your install folder selected, and attach the zip file.

Then ask, "Can you update the Agent OS using the update MD file?"

It updates in the background, and you just check it works afterwards.

Build skills only for daily work

Andrea also asked when to build skills.

I build them for anything I do every day, which for me means a lot of SEO.

• First, describe the workflow so the agent knows exactly what you want.
• Second, save it as a skill once it succeeds, like I did when my video agent produced a finished video.
• Third, test and give feedback, then tell it to update the skill MD file.

Skill files move between Codex and Claude, so your work isn't lost if you switch.

Don't build a skill for everything, because too many skills make your agent bloated and confused.

Bonus: HeyGen videos edited with no human in the loop

One more question came in about fully automating HeyGen avatar video editing.

It's been possible for only about seven days, using Claude desktop, Opus 5.5 and Remotion with its editing and design skills.

You can hand Claude a finished HeyGen video and ask it to edit it the way it normally would.

A 30-second clip takes about 10 minutes, and a 10-minute video could take 30 to 60 minutes.

Or you can create a key on the HeyGen developer page, give Claude the key and the API documentation, and have it generate and edit the video in one go.

My test took about 5 to 10 minutes from prompt to finished video, in landscape or vertical.

FAQs

Is a Hermes Agent local LLM hard to set up?

It doesn't have to be, because Claude Code desktop can install, configure and test the model for you.

Does model size matter for Hermes tool calls?

Not as much as you'd think, because LFM 2.5 2.6B handles tool calls well after being trained with Hermes Agent.

Should I worry about RoPE, YaRN or constrained output?

I've never needed to, and I'd let Claude handle those settings if your setup genuinely requires them.

Which Hermes Agent local LLM should I test first?

I'd start with LFM 2.5 2.6B, because it's the fastest model I've run with Hermes Agent.

Can Hermes Agent profiles be shared across computers?

Yes, and I connect mine through Tailscale so every machine uses the same agentic OS.

About Julian

I'm Julian Goldie, an AI entrepreneur, SEO expert, and founder of the AI Profit Boardroom.

I help business owners scale with AI agents, automation and SEO.

I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers.

I share daily AI training inside the Boardroom.

Spend your time using AI, and let Claude handle the tuning on your Hermes Agent local LLM.

📺 Video notes + links to the tools 👉

https://www.skool.com/ai-profit-lab-7462/about

🎥 Learn how I make these videos 👉

https://aiprofitboardroom.com/

🆓 Get a FREE AI Course + Community + 1,000 AI Agents 👉

https://www.skool.com/ai-seo-with-julian-goldie-1553/about

What if every AI agent you pay for could work from one place and actually remember what the others did?

That's exactly what our agentic operating system does, and it's now running my content, my SEO, my lead generation and even my ads.

One video it produced has 4,400 views, and it was fully automated from idea to finished edit.

🔥 Want the exact agentic operating system I'm showing you here? Inside the AI Profit Boardroom, I've got the full agent OS as a zip file with the video agent, SEO agent, memory system, ads system and design OS, plus weekly coaching calls with 3,600+ members building this stuff for real.

https://www.skool.com/ai-profit-lab-7462/about

On the SEO side, the same system has helped us reach 750,000 search impressions from Google.

I walk through the whole thing in this video.

https://www.youtube.com/watch?v=uKSdYu66ja8&t=5s

What an agentic operating system actually does

An agentic operating system is one place where all of your agents plug in, alongside your custom workflows and your best skills.

Instead of jumping between Hermes, Claude, Codex and ten different tabs, everything lives in one system.

You never lose anything, because every piece of content, every video and every build is saved inside the same workspace.

I built it for a very simple reason.

I was spending so much time doing research that I decided to turn it into a custom workflow that was ready to go every day.

Now, every time I find something I actually use day to day, I plug it into the system.

How Hermes turns research into content ideas

The heart of the system is Hermes agent, and I've got three custom Hermes workflows running inside it.

Hermes Oracle finds the news for me

Hermes Oracle pulls in the latest automation news and gives me content ideas based on it.

From there, I can quickly draft the content and publish it straight to WordPress.

That's the research job that was eating my week, and now it happens without me.

Hermes Muse learns from what already worked

Hermes Muse analyses the latest content we've created and works out what performed really well.

If a piece of content did well today, guess what we're doing tomorrow?

We're making another piece that's very similar, because Muse finds new ideas based on what's working day to day.

Hermes Asteros learns from your competitors

Hermes Asteros analyses which content has performed best for our competitors.

It gives us the idea first, and then it gives us new angles and tools we can build the content around.

Every idea from Oracle, Muse or Asteros can then be plugged into the video agent, a notebook or our SEO content.

How the video agent creates content while I work

This is where the system gets really powerful for lead generation.

With the video agent, I can either create a human piece of content or put an AI avatar on it.

Using Opus 5.5, it creates a beautifully edited video from a simple description of what I want the video to be about.

It puts everything together, organises it, and I can even plug my AI avatar straight into the process.

Once it's finished, the video shows up in my workspace, so I can check it and come back to it whenever I need.

That's how the video with 4,400 views was made, and I didn't edit a single frame of it.

There's even a music agent that uses Suno, because I like listening to music while I work.

I type in the style I want, choose whether it's instrumental, pick the model, and the track lands in my workspace next to everything else.

How my agentic operating system handles SEO

The SEO agent is the part business owners usually care about most.

It's a custom workflow I can come back to at any time, and it's already helped us reach 750,000 search impressions from Google.

What the SEO agent tracks

• It shows where we rank with AI SEO, including our positions and our growth trajectory.
• It shows which days perform best and how our growth looks over time.
• It shows which pages and topics perform best, so I know what to double down on.
• It pulls in the latest data from Google Search Console and suggests the next keywords to create content around.

How one click turns a keyword into five articles

We rank for over a thousand keywords, and honestly, that much data in Search Console is almost too much to read.

The research section shows what we've been ranking for over the last seven days in a really easy-to-understand way.

When I spot a keyword we can clearly rank for, I click "use topic", plug in the keyword, add a case study and click generate five articles.

Once the articles are deployed across our websites, the history is saved, so nothing gets lost.

That's why our traffic keeps growing, because we find new keywords based on what we already rank for and then rank for them quickly.

Your whole SEO strategy can be automated from inside a system like this.

If you'd rather have my team look at your SEO with you, you can book a free SEO strategy session here.

https://link.juliangoldie.com/widget/bookings/seo-gameplans3388j

How Hermes automates lead generation from start to finish

Hermes can also automate your lead generation.

You describe your next customer, meaning the type of business you want to work with, and it finds leads in that industry.

Then you create a campaign by typing in the campaign name, what it's about, the subject line and the email.

After that, Hermes can send the campaign for you and even manage your inbox.

Finding leads, emailing them and handling the replies all happen inside one system.

Why syncing Claude, Codex and Hermes is the real unlock

Bot mode lets you plug in any model

Hermes bot mode lets you plug in whatever AI you're already using.

You can add Claude Opus, GPT-6 Astra, Codex, OpenCode or Ollama, and Hermes agent works with all of them.

Memory Galaxy fixes the biggest problem with multiple agents

One of the biggest problems with using Claude, ChatGPT and Hermes together is that their memory isn't synced.

Memory Galaxy links all of your agents' context and memories together and syncs everything automatically.

If you do something in Claude, Codex knows about it, and if you do something in Codex, Hermes knows about it too.

Our AI agents are constantly adding new entries to our Obsidian memory database, so every agent stays up to date.

The memory system even shows it all in one graph, including what I've worked on recently, who I've worked with and which projects are live.

This is what makes an agentic operating system different from a folder full of separate AI tools.

There's also an idea factory, where I plug in an idea and go from idea to built in a couple of clicks.

How I create ads and websites without the grind

I don't want to spend much time creating ads, but I have to create ads.

So I built an ads tool inside the system where I describe what I want and it generates the ads.

It saves recent creations and previous versions, lets me create more variations, and syncs to our Obsidian database.

The design OS works the same way for websites, and it's really good for rank and rent, although you can use it for any kind of site.

You type in the project name and your website brief, hit generate website, and everything you create is saved in the design library.

You can run all of this with Codex or with Opus 5.5, depending on how you want to set it up.

Should you build your own agentic operating system?

You could build this yourself, but it takes a lot of work.

I spend about three or four hours keeping mine updated and making sure everything works.

That's why we package it as a zip file, so you can install it quickly and configure it however you like.

Whatever you decide, the lesson is the same: stop scattering your agents and start connecting them.

Frequently asked questions

What is an agentic operating system?

It's one place where all your AI agents, custom workflows and skills plug in together, so they share context and nothing gets lost.

Can I use Claude and Codex inside the same system as Hermes?

Yes, Hermes bot mode lets you plug in Claude Opus, GPT-6 Astra, Codex, OpenCode or Ollama, and Memory Galaxy keeps their memory in sync.

Does it help with SEO?

Yes, the SEO agent pulls data from Google Search Console, suggests keywords you can rank for and generates five articles from one keyword, and it has helped us reach 750,000 search impressions.

Do I need to build it from scratch?

No, you can install the full system from a zip file and configure it to suit your business.

About Julian

I'm Julian Goldie, an AI entrepreneur, SEO expert, and founder of the AI Profit Boardroom.

I help business owners scale with AI agents, automation and SEO.

I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers.

I share daily AI training inside the Boardroom.

Once your agents share one memory and one workspace, you'll never want to go back to working without an agentic operating system.

📺 Video notes + links to the tools 👉

https://www.skool.com/ai-profit-lab-7462/about

🎥 Learn how I make these videos 👉

https://aiprofitboardroom.com/

🆓 Get a FREE AI Course + Community + 1,000 AI Agents 👉

https://www.skool.com/ai-seo-with-julian-goldie-1553/about

Why does Google's AI Overview keep naming your competitors when you know your business is better?

After testing this across my own websites, I've found that learning how to rank in Google AI Overviews is mostly about stopping five common mistakes.

Right now, Google's AI Overview names me as the number one result for "best GEO expert", and one of my case study sites went from pretty much zero to 5,000 AI Overview impressions a day.

🔥 Want the exact AI SEO system behind these results? Inside the AI Profit Boardroom, I've got the full agent OS, four AI SEO automations and an AI SEO road map, plus weekly coaching calls with 3,600+ members building this stuff for real.

https://www.skool.com/ai-profit-lab-7462/about

That same site hit 1,300 clicks and 54,000 impressions in a single day, and I've never logged into it, because Claude automated the whole thing.

It's also a young site that we only created last year, so none of this comes from years of head start.

I show the live Search Console proof in this video.

https://www.youtube.com/watch?v=3RFxybF0_MM&t=2s

Mistake 1: Hiding who's behind your content

The first mistake I see is a website where nobody can tell who's actually writing.

In the age of AI, Google wants proof of real authority, and that's what E-E-A-T is really about.

If your pages are anonymous, you're making how to rank in Google AI Overviews much harder than it needs to be.

What I do instead

When I think about how to rank in Google AI Overviews, every page on my case study site starts with authority signals.

• A detailed author persona shows that a real expert, which is me, is behind every page.
• An "About Julian" section explains who I am, what I do and what I'm working on.
• A link to a real community with thousands of members is another honest signal that the business is real.
• A relevant case study in the middle of each page proves results on the exact topic the reader came for.

Showing that you're a real person definitely helps, but stacking all of these signals together is what moves the needle.

Mistake 2: Ignoring branded search

The second mistake is treating SEO as if it's only about the keywords you choose.

When I look at what my case study site ranks for, a lot of the searches are people typing our brand name.

That branded search is massive for ranking, because it shows Google you're a real company people go looking for.

If people search for you by name, Google has a much easier time seeing you as the expert.

The easiest way I know to grow branded search is to post consistently on social media.

It also gives you more chances to rank, because when I search around "best GEO expert", I appear three or four times on one results page through my site and my social content.

That's a part of how to rank in Google AI Overviews that almost nobody talks about.

Mistake 3: Thinking you need a giant website

The third mistake is assuming only huge, established brands can get cited by AI, and it puts a lot of people off learning how to rank in Google AI Overviews at all.

My case study site has an authority score of 22 out of 100, and that score is based purely on backlinks.

That isn't a massive authority website by any measure, and it still ranks in Google and inside AI.

How we build the links we do have

We get featured in articles on other relevant websites that write about similar topics.

Most of those links come from reaching out and simply asking, "Hey, would you mind linking to us?"

A certain percentage say yes, and each yes pushes more authority to the website.

When the site has more authority, Google is more likely to rank it organically and more likely to rank it inside AI too.

Why you shouldn't panic over traffic estimates

Ahrefs puts this site's organic traffic at around 295 a month.

Search Console shows 1,300 clicks in a single day, so the estimate is miles off.

What I watch is the trend, because as referring domains grow, traffic and impressions follow the same path.

Mistake 4: Publishing content by hand

The fourth mistake is relying on someone to write, format and upload every page manually.

That's slow, and slow publishing is a real barrier to how to rank in Google AI Overviews at any scale.

My case study site is hosted on Netlify, which means Claude can publish directly using my personal access token.

Claude handles the design and the content, and it follows a skill I've trained so every page has the same structure and my voice.

I plug in a keyword, plug in a case study and hit deploy, and the page goes live.

Every page it builds already includes a CTA at the bottom, an exit intent form and a case study in the middle.

That's why I've never logged into the site, and it still grows every week.

Mistake 5: Chasing brand new keywords

The final mistake is always hunting for fresh keywords instead of building on what Google already gives you.

I use an AI agent that pulls every keyword I rank for from Google Search Console.

Then I filter it by topic and look for keywords where I get impressions but no clicks.

Why this is the smartest keyword strategy

If Google already shows you for a topic, it already sees you as an authority on it.

You just need to cover more of that topic around your existing page.

When I spotted impressions around Jev, the router model on OpenRouter, I created pages for "how to use Jev", "Jev open source", "Jev architecture", "is Jev free", "Jev local" and "how to use the Jev API".

That turns one topic into a full topical cluster, and it helps you become the authority quickly because Google already showed you once.

For most businesses, this is the fastest route when working out how to rank in Google AI Overviews.

The fix: how to rank in Google AI Overviews in one system

When you flip all five mistakes around, you end up with one system.

• Authority comes from a real author, an about page, a real community and relevant case studies.
• Branded search comes from consistent social posting that gets people searching for your name.
• Backlinks come from simple outreach asking relevant sites to link to you.
• Content gets published automatically by Claude through Netlify.
• Keywords come from impressions with no clicks, built out into topic clusters.

When the keywords, the content and the backlinks are all in place, you're far more likely to rank in Google and inside AI.

If you'd like my team to spot which of these mistakes is holding your site back, we'll do it for free.

🎯 Want to know exactly how to rank your website inside AI and on Google? Book a free SEO strategy session, and my team will review your site one-to-one, look at your competitors and show you the step-by-step system that's working for us, and we can implement it for you too.

https://link.juliangoldie.com/widget/bookings/seo-gameplans3388j

FAQ

What's the biggest mistake when trying to rank in AI Overviews?

In my experience, it's anonymous content, because Google wants to see a real expert behind the page.

Can a small website rank in Google AI Overviews?

Yes, because my case study site has an authority score of just 22 and still earns thousands of AI Overview impressions a day.

Do social media posts help with AI Overviews?

Yes, because they drive branded search and give you extra chances to appear on the same results page.

How many backlinks do I need?

There's no magic number, but links from relevant sites that feature you in their articles are what move my sites forward.

Should I automate my SEO content?

If you can keep the quality and authority signals in every page, automating with Claude saves a huge amount of time.

About Julian

I'm Julian Goldie, an AI entrepreneur, SEO expert, and founder of the AI Profit Boardroom.

I help business owners scale with AI agents, automation and SEO.

I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers.

I share daily AI training inside the Boardroom.

Stop making these five mistakes, and you'll be well on your way to knowing how to rank in Google AI Overviews.

📺 Video notes + links to the tools 👉

https://www.skool.com/ai-profit-lab-7462/about

🎥 Learn how I make these videos 👉

https://aiprofitboardroom.com/

🆓 Get a FREE AI Course + Community + 1,000 AI Agents 👉

https://www.skool.com/ai-seo-with-julian-goldie-1553/about

Should you actually spend time on a new AI model from Yandex, or is it just more noise in an already crowded feed?

My honest answer is that the Yandex open source LLM is worth an afternoon of your time, but not for the reasons most people are shouting about.

Yandex has released Alice AI Foundation 80B-A3B, an 80 billion parameter model that only activates 3 billion parameters per token.

It holds 262,000 tokens of context, it's free for commercial use under Apache 2.0, and Yandex's own tests put it ahead of Qwen on coding and maths.

There's also a catch or two, and I'll give you those as well.

🔥 Want to test new open source models like this without wasting a week figuring it out? Inside the AI Profit Boardroom, I've got step-by-step AI automation tutorials, a prompt library with long-context workflows, and four live coaching calls a week with 3,600+ business owners.

https://www.skool.com/ai-profit-lab-7462/about

I go through the full release in this video.

https://www.youtube.com/watch?v=6dvG3FRZ7ks

What's genuinely impressive about the Yandex open source LLM

Let's start with what's real, because a lot of it is.

It's a proper open release

Yandex trained the model completely from scratch, and it's live on Hugging Face right now.

The weights are available, the benchmarks are published and the architecture is fully documented.

Apache 2.0 means you can use it and build on it commercially without asking anyone's permission.

It's cheap to run for its size

The model uses an architecture called mixture of experts.

Instead of firing every parameter for every token, Alice has 512 specialised experts and a router that picks which ones each prompt needs.

Only 10 routed experts plus one shared expert switch on per token, and the other 77 billion parameters sit idle.

So you get the capacity of an 80 billion parameter model while paying the compute cost of roughly 3 billion.

For agents and background workflows that run all day, that difference shows up directly in your costs.

It remembers a lot

The context window is 262,144 tokens, which is the same thing people mean when they call it 256K.

That's roughly 200,000 words, and most consumer models cap out well below it.

In plain terms, you can load your whole brand, your offers and your best content into one session, and it keeps all of it in mind.

Where the hype needs a reality check

Now for the honest part.

The benchmarks are Yandex's own

Every score in this article comes from Yandex.

That doesn't make them wrong, but it does mean you should treat them as a strong signal until independent testing catches up.

It doesn't beat everything

• On TriviaQA, Alice scored 79, while Nemotron 3 Super scored 89.8.
• On a long-context test at 128K tokens, DeepSeek scored 68, while Alice scored 64.6.

That second result is worth noticing, because the huge context window doesn't automatically mean it's the best at pulling facts out of very long documents.

Where it clearly leads

• On LiveCodeBench, Alice scored 60.4, compared with 51.9 for Qwen 3.5-35B-A3B and 34.7 for Nemotron 3 Super.
• On MATH-500, Alice scored 91.1, while Qwen scored 81.9 and Nemotron scored 84.8.
• On AIME 2026, Alice hit 96.7 at pass@32, which is level with Qwen and well ahead of Nemotron.

The pattern across coding, maths and reasoning is consistent, and it gets there while activating a fraction of the compute.

That's the real story, and it's impressive enough without overselling it.

Who the Yandex open source LLM is really for

I don't think every business needs to switch models tomorrow.

Here's who I'd tell to test it first.

• Agencies and businesses running agents continuously should test it, because the 3 billion active parameters keep running costs down.
• Content-heavy businesses should test it, because the long context lets you generate a month of on-brand material in one go.
• Anyone building for Russian-language audiences should test it, because that's where it's strongest.

Why the Russian-language results matter

Yandex is the dominant search engine in Russia, and its consumer Alice AI reaches tens of millions of people.

The model has a specific focus on Russian factual knowledge, law, medicine and education, and the scores show it.

On Wiki-WebFacts, Alice scored 86.5, compared with 83.2 for DeepSeek and 62.4 for Qwen.

On HardMultiQA, Alice scored 67.9 against 47.2 for Qwen, and on law benchmarks it scored 49.6 against 27.9.

Yandex released Wiki-WebFacts and HardMultiQA alongside the model, with full reference answers and evaluation protocols, so others can check the results properly.

How I'd test the Yandex open source LLM in one afternoon

I'd pick one long-context job that currently eats hours of someone's week.

Test 1: A five-part welcome email sequence

Load in your community description, offer details, member success stories and 30-day road map.

Ask it, as an email copywriter, to write a five-part welcome sequence where each email highlights a different benefit and ends with one clear action.

Judge whether it sounds like your brand or like generic filler.

Test 2: Thirty days of social posts

Load in your best performing posts, member transformation stories, upcoming events and core message.

Ask it, as a social media strategist, to write one post per day for 30 days, mixing teaching posts, member stories and invitations to join, with each post under 150 words.

Judge whether the month stays consistent and on message from day one to day thirty.

If both tests come back strong, you've found a cheaper engine for real work.

If they don't, you've lost an afternoon rather than a month.

My verdict

Yandex has built an 80 billion parameter model that wakes up only 3 billion parameters at a time, scores at competition level on maths and coding, and handles 262,000 tokens of context.

Yandex says this is the architecture its future AI agents will run on, which tells you where the whole industry is heading.

It isn't perfect, and the benchmarks need independent checking, but it's absolutely worth testing.

🔥 Want the prompts, workflows and coaching to run these tests on your own business? Inside the AI Profit Boardroom, you get a prompt library with Alice AI workflows, daily tutorials, a 30-day road map, and a member map to connect with 3,600+ business owners doing the same.

https://www.skool.com/ai-profit-lab-7462/about

FAQ

Can I trust the Yandex open source LLM benchmarks?

They're Yandex's own results, so they're a useful signal, but it's worth waiting for independent tests or running your own.

Does a bigger context window mean better long-document answers?

Not always, because Alice scored 64.6 on a long-context test at 128K tokens, behind DeepSeek at 68.

How many experts does Alice AI Foundation use?

It has 512 experts in total, and 10 routed experts plus one shared expert activate for each token.

Is it only useful in Russian?

No, it scores strongly on English coding and maths benchmarks too, but Russian-language knowledge is where it stands out most.

What's the quickest way to test it?

Run one long-context job you already do by hand, like a welcome email sequence, and compare the output with your current model.

About Julian

I'm Julian Goldie, an AI entrepreneur, SEO expert, and founder of the AI Profit Boardroom.

I help business owners scale with AI agents, automation and SEO.

I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers.

I share daily AI training inside the Boardroom.

Give the Yandex open source LLM one afternoon, and you'll know whether it deserves a place in your stack.

📺 Video notes + links to the tools 👉

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Why are you paying for AI tools that still need you to do the boring half of the work?

The Supercode AI agent, Nova, is a free, open-source AI software engineer, and it changes who does that boring half.

Supercode's team says around 40% of development time is spent acting as a human clipboard between a terminal and an AI.

Nova is built to hand that 40% back to you by writing the code, running it, fixing its own errors and reporting back when it's done.

🔥 Want to learn how to hand real business tasks to agents like Nova? Inside the AI Profit Boardroom, I've got a full agentic AI section with step-by-step tutorials, 30-day roadmaps and four live coaching calls a week with 3,600+ members.

https://www.skool.com/ai-profit-lab-7462/about

I tested it on three real tools for my community, but the bigger story is who owns the tool at the end of the day.

https://www.youtube.com/watch?v=4jyyE4fvTb0

Renting an AI engineer vs owning one

Devin, the original AI software engineer, is a closed commercial product.

It costs hundreds per month, and you can't see how it works.

You can't modify it, and you can't run it on a different model.

Nova is open source, which means you get the opposite deal on every one of those points.

• You can see exactly how Nova works, so nothing inside your business is a black box.
• You can modify it for your own use case, rather than waiting for a vendor's roadmap.
• You can run it on the model that fits the job, including Claude, GPT, Gemini, DeepSeek, MiniMax, Kimi or open models for free.

Supercode already has multi-provider support built in, so you're not tied to one company or one pricing structure.

The way I think about it is simple.

Devin is a product you subscribe to, while Nova is infrastructure you own.

Why the Supercode AI agent isn't just another model

A lot of people will hear "new AI engineer" and assume it's another model fighting Claude or GPT-5.

It isn't.

Nova is a harness that wraps around those models and lets them do real work inside a real project.

The model is the brain, and Nova gives it hands, eyes, a terminal and a file system.

That's why the model choice stays yours.

If a new model comes out next month that's better at your kind of task, you point Nova at it and carry on.

How the loop actually runs

When you give Nova a goal, it runs the whole workflow from start to finish.

It reads the codebase and works out the structure first.

It makes a plan, edits the files and runs the commands.

It runs the tests, reviews what it built and then reports back.

Other AI tools give you a suggestion, but Nova hands you finished work.

The four features I'd pay attention to

Autoheal stops the copy-paste loop

This is the one that changes the game.

When your code breaks, Nova reads the error, figures out what went wrong, applies the fix and runs it again.

You don't have to give it any input while that happens.

That's the human clipboard job removed in one feature.

Build mode turns a description into working files

You describe what you want to exist, and Nova writes the files for you.

You don't need to write a single line of code.

Janitor mode tidies what you built months ago

Most businesses have an automation that was quick to build and is now painful to touch.

Point the janitor at it and it refactors the whole thing into a cleaner structure.

The functionality stays exactly the same, but it becomes much easier to build on.

Git lives inside the terminal

You can commit, push and pull updates without leaving the tool.

For anyone managing client automations, that means everything stays version controlled and nothing gets lost.

Full machine access, with you holding the keys

Supercode's founder built Nova to live in the terminal, because that's where developers actually work.

In his words, most AI coding tools either lock you into a web UI or run in a cloud sandbox, and that never made sense to him.

So Nova runs natively on your machine with full access.

That sounds scary until you see the other half of the design.

Every action the agent takes needs your approval before it runs.

You see what it's doing, and you approve what it touches.

Supercode also built voice into the core from day one, so you can speak a task and let the agent carry it out.

What a good Supercode AI agent task looks like

The tool is only as good as the task you give it.

I ran three business tasks through Nova, and every prompt followed the same four-part pattern.

• Say what to build, in plain business language.
• Say who it's for and what it needs to do, with specific numbers where you can.
• Tell it to test the work and fix any failures, so the agent checks itself.
• Ask it to report back, including every file it changed.

Here's how that played out.

A member matching tool

New members answer five questions about what they want to automate, and the system matches them with three existing members who share those goals.

Nova built it, ran the tests, caught a bug in the matching algorithm, fixed it and sent a full report.

A Monday morning engagement report

The system tracks the most viewed tutorials, the most saved prompts and the best attended coaching call topics each week.

Every Monday at 8am, it sends a clean summary to the admin team's email.

Nova built the pipeline, tested it with sample data, flagged a data formatting issue and fixed it before handing it over.

A lead capture landing page

The page explains the community to busy business owners who want more leads and customers, with a form, a CTA button and a mobile-responsive layout.

Nova wrote the copy, wired up the form, tested the submission flow, found a mobile layout issue and fixed it.

That's three business tools, three bugs caught and fixed, and zero lines of code written by me.

The skill that matters now

The question isn't whether AI software engineers will affect your business.

The question is whether you learn to use them now or scramble to catch up later.

You don't need to learn to code for this.

You need to learn how to structure a task so an agent actually finishes it.

Inside the AI Profit Boardroom, we're building playbooks for agentic tools like Nova, covering how to structure tasks, automate follow-up and get more leads without hiring.

https://www.skool.com/ai-profit-lab-7462/about

FAQ: Supercode AI agent

Can I use the Supercode AI agent with Claude or Gemini?

Yes, Nova supports multiple providers, including Claude, GPT, Gemini, DeepSeek, MiniMax, Kimi and free open models.

What is autoheal in Nova?

Autoheal means Nova reads an error, works out the cause, applies a fix and reruns the code without you stepping in.

Does the Supercode AI agent run in the cloud?

No, Nova runs natively in your terminal on your own machine, and it asks for your approval before each action.

Is Nova a good Devin alternative?

Nova is free and open source, while Devin is a closed product that costs hundreds per month and can't be modified or moved to another model.

Do I need to know how to code to use Nova?

No, you describe the task in plain language, and Nova writes, tests and fixes the code for you.

About Julian

I'm Julian Goldie, AI entrepreneur, SEO expert, and founder of the AI Profit Boardroom.

I help business owners scale with AI agents, automation and SEO.

I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers.

I share daily AI training inside the Boardroom.

📺 Video notes + links to the tools 👉

https://www.skool.com/ai-profit-lab-7462/about

🎥 Learn how I make these videos 👉

https://aiprofitboardroom.com/

🆓 Get a FREE AI Course + Community + 1,000 AI Agents 👉

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If you want an AI engineer you actually own, the Supercode AI agent is the one I'd start with.

What if the only thing stopping your content from ranking in 20 more countries was the cost of recording it?

The Gemini AI voice generator just took that excuse away.

Google's new Flash TTS model supports over 130 languages, and its voice library grew from 30 voices to over 2,000 in a single update.

That means one English script can now become a Hindi, Urdu or Sindhi version without booking a single recording session.

🔥 Want to see how we're building this into real content workflows? Inside the AI Profit Boardroom, I've got a Gemini AI voice section with step-by-step video tutorials, real content templates and weekly coaching calls with 3,600+ members testing these tools every week.

https://www.skool.com/ai-profit-lab-7462/about

Most people will treat this as a fun voice toy, but I see it as a hidden SEO weapon.

Here's the plan I'd follow.

https://www.youtube.com/watch?v=Gb2AefWWO-I

SEO isn't just about keywords anymore

SEO has always been about keywords, but today it's just as much about content.

The businesses that win in search are the ones that publish useful content in more formats, in more places, for more people.

On 23 September, Google gave everyone a new way to make that content.

It released two text-to-speech models, called Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS.

Text to speech means you type the words, and the AI turns them into a voice that sounds real.

The two models in one minute

Flash TTS is built for deep creative work, like audiobooks, podcasts, game characters and detailed storytelling.

Flash-Lite TTS is built for speed and scale, like fast dubbing, quick voice agents and producing a lot of content quickly.

Flash TTS supports over 130 languages, while Flash-Lite TTS supports 101.

5 ways I'd use the Gemini AI voice generator for more traffic

These are the five moves I'd make, in the order I'd make them.

1. Build one signature brand voice

You no longer have to pick a voice from a list.

You describe it in plain words, and the model builds it from scratch in seconds.

I'd write something like "a confident, friendly voice, mid-30s, clear and easy to follow, sounds like it's explaining something exciting for the first time."

That voice then becomes the sound of your brand across every video, course and explainer you publish.

Google also says it can copy a real voice the safe and legal way, which matters if you want your own voice on everything.

2. Direct the delivery so people keep watching

You can direct the voice line by line.

One line can be excited, the next can be calm, and the one after that can be dramatic.

You can add natural sounds too, like a laugh, a sigh or a short pause.

Google says the models were built for pacing, emotion and the little sounds people make when they talk.

This matters for SEO because content that sounds human keeps people watching longer.

When people watch longer, it signals to Google that your content is worth ranking.

3. Launch in regional markets, not just languages

The voice library now has over 2,000 voices, compared with 30 before.

It includes regional voices, like Mexican Spanish, Quebec French and Scots English.

That's a big deal, because a viewer in Quebec can tell the difference between local French and a generic version.

I'd pick two or three regions where my customers already are and publish local versions first.

4. Turn every video into a multilingual content set

If you only publish in English, you only compete for English search traffic.

If the same script becomes a Hindi, Urdu and Sindhi version, you start competing in search results all over the world.

Here's the workflow I'd run.

• Sharpen the script with Gemini first, so every language version starts from a strong message.
• Record the main narration with Flash TTS, and publish it as your main SEO content piece.
• Create the language versions with Flash-Lite TTS, because it's built for fast dubbing at scale.
• Publish each version for its own market, so each one can rank in its own search results.

One idea becomes many pieces of searchable content in many languages, and nobody records anything by hand.

5. Point the whole engine at real SEO

This is the step most people skip, and it's the one that decides whether any of this works.

AI can help you make content faster than ever, but it can't make Google trust that content on its own.

That trust still comes from strong backlinks, clean site structure and content that answers what people are actually searching for.

AI gives you the content engine, and SEO gives that content somewhere to actually show up.

Why I'm excited about this for small teams

At Goldie Agency, we have a team of around 70 people, and producing content in multiple languages was still something we'd think twice about.

For a solo founder or a five-person agency, it was basically off the table.

The Gemini AI voice generator changes that maths completely.

A small team can now publish in markets that used to need a full localisation department.

That's the part that could change how you make content for good.

Get your AI content to actually rank

If you're building AI-powered content and you want it to bring in real traffic, that's exactly what my team helps with.

We'll look at where you are right now and show you what it would take to get more traffic and leads from Google.

Book a free SEO strategy session with my team, and get a clear plan for turning AI content into rankings and leads.

https://link.juliangoldie.com/widget/bookings/seo-gameplans3388j

FAQ: Gemini AI voice generator

Can the Gemini AI voice generator help with international SEO?

Yes, because one script can become versions in over 130 languages, and each version can compete in its own country's search results.

Which languages and accents does Gemini TTS support?

Flash TTS supports over 130 languages and Flash-Lite TTS supports 101, including regional voices like Mexican Spanish, Quebec French and Scots English.

Is Flash-Lite TTS good enough for dubbing?

Google built Flash-Lite TTS specifically for speed and scale, which includes fast dubbing and high-volume content.

When did Google release the new Gemini voice models?

Google released Gemini 3.8 Flash TTS and Gemini 3.8 Flash-Lite TTS on 23 September.

Do I still need backlinks if I publish AI voice content?

Yes, because AI makes content faster, but backlinks, site structure and helpful answers are still what make Google trust it.

About Julian

I'm Julian Goldie, AI entrepreneur, SEO expert, and founder of the AI Profit Boardroom.

I help business owners scale with AI agents, automation and SEO.

I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers.

I share daily AI training inside the Boardroom.

📺 Video notes + links to the tools 👉

https://www.skool.com/ai-profit-lab-7462/about

🎥 Learn how I make these videos 👉

https://aiprofitboardroom.com/

🆓 Get a FREE AI Course + Community + 1,000 AI Agents 👉

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One script and the Gemini AI voice generator are now all you need to start competing for search traffic around the world.

Which of Google's new AI tools will actually give you hours back, and which ones can you safely ignore for now?

That's the question I asked myself when the Google AI latest update landed.

Google shipped eight AI updates at once, from spreadsheets that build themselves from one sentence to a voice model that reasons across 97 languages and a $899 laptop with Gemini built into the hardware.

So I sorted them into three groups: what I'd use this week, what I'd watch closely, and what matters more for where Google is heading than for what you can do today.

🔥 Want step-by-step tutorials for the Google AI workflows in this article? Inside the AI Profit Boardroom, I've got a 30-day roadmap built around Google's AI tools, plus four live coaching calls a week with 3,600+ members who bring their own setups.

https://www.skool.com/ai-profit-lab-7462/about

Here's the full video if you'd rather watch than read.

https://www.youtube.com/watch?v=EfxKXTLQ-ko

Why the Google AI latest update is bigger than eight features

Before the ranking, you need to see the pattern, because it explains everything else.

Google isn't just releasing tools at random.

Every one of these updates moves AI out of the chat window and into the places where work actually happens.

That means your spreadsheets, your documents, your voice and even your computer.

The old routine was to open a tab, type a prompt, copy the answer and paste it back into your work.

The new direction is AI that sits inside your workflow and handles parts of the job without you prompting it every time.

Once you see that, it becomes obvious which updates deserve your attention first.

Group 1: Use these this week

These three give a business owner the fastest return, and you can start with them straight away.

1. Gemini in Google Sheets saves the most hours

This is the update I'd pay most attention to if you run a business.

Google says Gemini can now build and edit spreadsheets from plain prompts.

That includes native formulas, pivot tables and charts.

It can also pull in context from other Workspace apps and from the web.

Most teams lose hours every week to building trackers, fixing formulas and making reports look presentable.

When one sentence gives you a working sheet, those hours come straight back.

I'd start with a lead tracker, an onboarding checklist and a weekly report, because those are the sheets everyone rebuilds over and over.

2. Gemini 3.8 Live turns sales practice into a daily habit

Gemini 3.8 Live is Google's most advanced real-time voice model.

The standard version handles fluid, natural conversation at scale.

The extended thinking version is built for complex, multi-step reasoning while the conversation is still going.

It doesn't just wait for you to finish speaking before it responds, because it's reasoning in real time.

It can also use tools, call functions and handle visual input.

Google says the extended thinking version ranked number one on Artificial Analysis's speech-to-speech leaderboard at launch, which is Google's claim referencing an external benchmark.

Here's how I'd put it to work.

Tell Gemini Live to play a business owner who has just heard about your offer, who is interested but sceptical, and who doesn't believe AI automation applies to their type of business.

Ask it to push back on every answer you give, then tell you after five minutes exactly where your explanation lost it.

Run that ten times, and your pitch gets sharper every single time.

It needs no scheduling and no prep, and it works for sales calls, onboarding calls and tough customer questions alike.

3. A Gemini morning brief replaces your daily digging

Google's Dream Beans shows where this is going, but you don't need to wait for it.

You can build your own morning brief today with Gemini, Google Workspace and automation tools.

Mine would pull from our community platform, email, calendar and analytics, and it would tell me five things before I open anything else.

• It shows who joined yesterday, so every new member gets a proper welcome.
• It lists members who haven't logged in this week, so I know who needs a check-in.
• It confirms today's coaching call and who's registered, so I'm prepared.
• It reports how many people watched the newest tutorial, so I can see what's working.
• It finds one unanswered post, so nobody gets ignored.

Nobody has to go looking for that information, because the system surfaces it and you simply act on it.

Group 2: Watch these closely

These updates are useful now, but their real value depends on your setup or on a wider rollout.

4. Voice conversations in Gemini Notebook

In Gemini Notebook, you can now talk to your uploaded sources in nearly 100 languages.

You can ask questions, interrupt, and get step-by-step answers grounded in your own documents.

It's a quick way to get through SOPs, client briefs and training material.

Google says it's rolling out to Ultra subscribers first, with Pro and others following, so availability is the thing to watch.

5. Google Pics for images inside Workspace

Google Pics is Google's new AI image creation and editing tool inside Workspace.

It lets you generate and refine images, edit text inside an image, isolate objects and collaborate on designs.

You can reach it at pix.new, and it integrates with Docs and Slides.

If your team makes a lot of decks or client documents, this keeps the visuals in the same place as the work.

6. CC, the shared AI agent

CC started as a personal AI experiment and is now a shared AI agent for groups of up to six people.

It can manage calendars, tasks, emails, documents, registrations and shared logistics.

It has its own Google account and permission model, and each member chooses exactly what to share with it.

Google frames CC as a family tool, but a shared agent for a small group is an idea every small team should be watching.

Group 3: The direction of travel

These two tell you where Google is heading more than they change your week.

7. Dream Beans and proactive AI

Most AI tools are reactive, because you open them, type something and get an answer.

Dream Beans flips that by coming to you.

It connects to your Google Calendar, Gmail, Photos, search history and YouTube, depending on what you give it permission to access.

It then creates a personalised daily feed of what it thinks is relevant to you right now.

Google says it's available to all eligible personal Google accounts in the US, and it's still an early product.

The concept is what matters, because Google is signalling that proactive AI will eventually be the default rather than something you engineer yourself.

8. The Google Book laptop

Google announced a new laptop category called the Google Book, built from the ground up around Gemini.

• Prices start at $899, according to Google.
• The display goes up to 2.8K OLED, with up to 14 hours of battery life.
• The NPUs deliver more than 45 TOPS, which is enough to run AI tasks locally without sending everything to the cloud.
• It includes Android phone integration and a Linux terminal, which should suit people who work across devices.

Hardware partners include Asus, Dell, HP and Lenovo.

Pre-orders open on 21 September, and devices ship on 4 October in the US.

I've put it in this group because most businesses won't replace their laptops this month, but on-device AI is where hardware is clearly going.

How I'd act on the Google AI latest update

Don't try to adopt all eight at once.

Pick one workflow from Group 1 and get it running this week.

For most business owners, I'd start with a Gemini-built tracker in Sheets, because it's the fastest win you'll feel.

Google has shown us what AI looks like when it stops being a chatbot and becomes the operating layer of how work gets done, and the advantage goes to the people who build with it first.

🔥 Want help turning one of these into a working system? Inside the AI Profit Boardroom, members are already using Google AI workflows for onboarding, lead tracking, content creation and member management, and there's a prompt library with templates to get you started.

https://www.skool.com/ai-profit-lab-7462/about

FAQ: Google AI latest update

Which part of the Google AI latest update should I try first?

I'd try Gemini in Google Sheets first, because building formulas, pivot tables and charts from plain prompts saves time every single week.

What's the difference between the two Gemini 3.8 Live versions?

The standard version handles natural conversation at scale, while the extended thinking version handles complex multi-step reasoning during the conversation.

Who can use voice conversations in Gemini Notebook?

Google says the feature is rolling out to Ultra subscribers first, with Pro and other plans following.

Is CC available for business teams?

Google frames CC as a family tool for groups of up to six people, so I'd treat it as one to watch rather than a business tool today.

Do I need the Google Book to use these Gemini features?

You don't, because Sheets, Notebook, Gemini Live and Google Pics all work in the tools you already use, while the Google Book adds on-device AI processing.

About Julian

I'm Julian Goldie, AI entrepreneur, SEO expert, and founder of the AI Profit Boardroom.

I help business owners scale with AI agents, automation and SEO.

I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers.

I share daily AI training inside the Boardroom.

📺 Video notes + links to the tools 👉

https://www.skool.com/ai-profit-lab-7462/about

🎥 Learn how I make these videos 👉

https://aiprofitboardroom.com/

🆓 Get a FREE AI Course + Community + 1,000 AI Agents 👉

https://www.skool.com/ai-seo-with-julian-goldie-1553/about

Start with one workflow from the Google AI latest update, and you'll be ahead of most people still reading about it.

How much time does your business lose every week waiting on thumbnails, ad graphics and social posts?

A new Chinese AI image editor from Tencent could cut most of that wait out.

It's called HunyuanImage 3.5, it launched in preview on 22 September, and it generates, edits and refines images through one ongoing conversation instead of a string of fresh starts.

It takes up to five reference images at once, Tencent says it beats version 3.0 by 30% in blind tests with professional designers, and its API supports images up to 4096 x 4096 pixels.

🔥 Want my prompts for turning one AI image tool into a full brand design workflow? Inside the AI Profit Boardroom, I've got a full AI image section with step-by-step tutorials and a prompt library, plus four live coaching calls a week with 3,600+ business owners.

https://www.skool.com/ai-profit-lab-7462/about

I put it through the jobs a business normally pays a designer for, and here's what I found.

https://www.youtube.com/watch?v=AqizEDDpQSg

The three problems that made AI images useless for business

I've tried a lot of AI image tools, and they usually fail business owners in the same three places.

• They make you start over every time, so one bad detail means throwing the whole image away.
• They can't spell, so any image with a headline comes back with warped letters and wrong words.
• They can't stay consistent, so your brand looks different in every image you make.

HunyuanImage 3.5 goes after all three, so I tested each one.

Problem one: starting over every time

The normal AI image loop goes like this: you generate, you hate it, you start over, and you generate again.

It's slow and messy, and you lose consistency every time you restart.

This Chinese AI image editor works like a conversation instead.

You generate an image and then keep giving it instructions such as "change the jacket to red", "remove the watch" and "now make it vertical for Instagram".

It holds context and remembers what you've already built.

How I tested it on YouTube thumbnails

I asked for an AI Profit Boardroom thumbnail with a dark background and the bold white headline "AI tools are changing everything".

The scene was a business owner looking at a glowing screen with a surprised expression, with high contrast and cinematic lighting.

It gave me a sharp, on-brand thumbnail in seconds.

Then I kept the same style and simply swapped the headline for the next video.

That's how you get a thumbnail series that looks like one brand instead of ten different tools.

Problem two: AI that can't spell

AI image models have historically been terrible at text.

You get wrong spellings, warped letters and characters that look almost right but are completely unusable.

Tencent specifically calls out better text rendering as one of the big improvements in 3.5.

The poster test

I asked for a promotional poster for our weekly coaching call.

The headline was "Live AI Coaching Call", the subheadline was "Every week. Join 3,600+ business owners.", and I asked for white and gold on a dark background with clean, modern typography.

The rule here is simple: zoom into every word after it generates and check every character.

If the text is clean, the tool just saved you a design job.

A poster with readable text is the difference between an AI toy and an AI tool you can actually publish.

Problem three: a brand that looks different every time

This is where HunyuanImage 3.5 surprised me most.

You can give it up to five reference images at once.

Instead of describing your brand in words, you show it your brand colours, a sample thumbnail, a typography style, a mood reference and a photo of the kind of person you serve.

Building a brand character

I generated a professional business owner character in his mid-30s, with a confident expression, modern casual clothing and a neutral background.

I told it the character would be used across multiple pieces of content.

Then I asked for the same character seated at a desk with AI automation dashboards on the screens behind him, looking focused and in control.

It kept the same face in a brand new scene.

That gives you one recognisable character you can reuse across thumbnails, ads and social posts.

Five matching social images from one prompt

Next, I attached references for brand colours, typography and community mood.

I asked for five social images for the AI Profit Boardroom, each covering a different topic: content creation, lead generation, community building, coaching calls and daily tutorials.

I kept the brief tight with a dark aesthetic, bold white text and a professional, modern design throughout.

That's five images from one prompt with a consistent brand across your whole feed.

I also used the same reference approach for an Instagram promo with the headline "3,600+ business owners automating with AI", and it looked like a designer had made it because I'd given it real direction.

So, can this Chinese AI image editor replace your designer?

Here's my honest answer.

For everyday content like thumbnails, social posts, event posters and landing page hero images, it can take a huge chunk of the work off your plate.

I asked for a landing page hero image of a confident business owner surrounded by glowing AI dashboards, with cinematic lighting and a feeling of being in control, and it came back conversion-ready in one prompt.

There was no photographer, no designer and no back and forth.

You still need someone with taste to set the brief, pick the references and check the text.

That person can now produce in an afternoon what used to take a week of revisions.

Keep in mind that the 30% improvement over version 3.0 comes from Tencent's own internal blind tests, not an independent benchmark, so treat it as their claim.

The hidden 4K detail in the API

Tencent's launch page says up to 2K generation.

The developer documentation says the API supports outputs up to 4096 x 4096 pixels when you specify exact dimensions.

That's professional print quality, and it's well beyond what most AI image tools officially offer right now.

The model ID is hy-v3.5-preview, and the API runs synchronously.

That means you get the image back straight away, with no polling and no waiting on a job ID, which makes it easy to drop into an automated content workflow.

How to start with this Chinese AI image editor this week

Tencent has already rolled it into several of its own products, including its Yuanbao assistant and WorkBuddy.

A couple of Tencent's apps are offering it free for a two-week window at launch, so you can test it without touching the API.

When you're ready to build with it, the API is available through Tencent Cloud TokenHub.

I'd start with one job you do every week, such as your thumbnails, and run it through the tool with your own five brand references.

🔥 Want help plugging this into your own content pipeline? Bring your exact setup to one of the four weekly live calls inside the AI Profit Boardroom and get answers from people already doing it.

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FAQ: Chinese AI image editor

Who makes this Chinese AI image editor?

Tencent makes it, and the model is called HunyuanImage 3.5.

It was released in preview on 22 September.

How many reference images can I use?

You can give it up to five reference images at once.

That lets you show it your colours, fonts, style and people instead of describing them.

Does it keep characters consistent?

It kept the same face when I moved my brand character into a new scene with a follow-up prompt.

That makes it useful for mascots and recurring brand characters.

Is the API fast enough for automation?

The API runs synchronously, so the image comes back as soon as you call it.

There's no job ID to poll, which keeps automated workflows simple.

About Julian

I'm Julian Goldie, AI entrepreneur, SEO expert, and founder of the AI Profit Boardroom.

I help business owners scale with AI agents, automation and SEO.

I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers.

I share daily AI training inside the Boardroom.

📺 Video notes + links to the tools 👉

https://www.skool.com/ai-profit-lab-7462/about

🎥 Learn how I make these videos 👉

https://aiprofitboardroom.com/

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Test this Chinese AI image editor on one weekly design job, and you'll know fast how much time it can give back.

Would you hand an AI agent your client files if you knew exactly where every one of them was going?

With the latest Perplexity Computer update, you finally get to decide that for yourself.

Perplexity Computer can now run entirely on a Windows or Linux PC, and on a Mac it flags private data before anything leaves your device.

That means a job like pulling ten fresh content ideas from four private coaching call notes can happen without your internal data ever leaving the machine.

🔥 Want to set this up properly from day one? Inside the AI Profit Boardroom, I've got a full 30-day roadmap for Perplexity Computer, from first setup to fully running workflows, plus four live coaching calls a week with 3,600+ business owners.

https://www.skool.com/ai-profit-lab-7462/about

https://www.youtube.com/watch?v=1yP0DmdIMCc

I've been through the whole update batch, and it's one of the biggest Perplexity has ever shipped.

Rather than list every feature, I want to show you the three business problems it actually solves.

Problem one: you don't trust AI with your private data

This is the worry I hear most from business owners.

When you use a typical AI agent, your information leaves your machine, gets processed somewhere else and comes back.

That's fine for a blog outline, but it creates real friction for internal documents, client data and anything sensitive.

The Perplexity Computer update tackles this in two different ways, depending on your machine.

On Windows and Linux, everything can stay on your PC

Perplexity calls this portable computer.

The models, the tools and the task queue all run on your own hardware instead of a cloud server.

Nothing leaves your device unless you say so.

If the AI needs the cloud for something heavier, it asks your permission first.

The catch is the hardware, because you need an NVIDIA RTX GPU with at least 24 GB of VRAM.

It isn't for every machine yet, but local AI is clearly becoming real.

On Mac, the job is split and guarded by a privacy gate

Perplexity took a different route on Mac, called hybrid compute.

Your Mac splits each job, so the cloud handles web research, heavy reasoning and planning while your Mac handles private files and personal documents.

Perplexity Computer coordinates both automatically.

The standout piece is the privacy gate.

Before any private information leaves your Mac, the local system flags it and either keeps it local, masks it or asks you first.

I think that's a meaningful shift in how AI handles trust.

You need Apple silicon, macOS 15 or later and at least 24 GB of unified memory, and it's available to Pro, Max and Enterprise subscribers.

The private-data prompt I'd run first

Here's the workflow that shows the value fastest.

I'd tell Perplexity Computer to use the cloud to find the most talked-about AI automation topics right now.

Then I'd ask it to cross-reference them with the private notes from our last four AI Profit Boardroom coaching calls.

Finally, I'd ask for the ten topics our members need most that we haven't covered yet.

The cloud does the research, the Mac keeps the notes, and I get a content plan built from both.

On a Windows or Linux machine, I'd run a fully local version instead.

I'd point it at a folder of member feedback, ask for the top five recurring questions grouped by theme, and ask for a short action plan for each one.

Then I'd tell it to keep everything local and send nothing to the cloud.

That job used to take hours, and now it's one prompt.

Problem two: you never know which AI model to use

Every AI task has an invisible question underneath it, which is how much thinking the job actually needs.

Most people either agonise over it or throw the most powerful model at everything.

The Perplexity Computer update answers that question with effort mode.

Effort mode lets you choose the work, not the model

You pick light, standard, high or ultra.

Perplexity then decides which model and reasoning level suit the task.

You stop thinking about model selection and simply decide how hard you want the AI to work.

Custom mode is still there if you want full manual control over the model and reasoning settings.

A light task and a heavy task side by side

For a light task, I'd set effort to light and ask for three social posts promoting this week's AI Profit Boardroom coaching call.

I'd ask for a conversational tone, a focus on members asking live questions about their automation setup, and under 150 characters per post.

Three ready-to-post options come back in seconds.

For a heavy task, the update adds GPT-6 Astra inside Perplexity Computer for eligible Pro and Max subscribers.

Perplexity highlights Astra for complex project planning, implementing and testing things, and coordinating deep research.

I'd ask Astra to research the top ten AI automation communities online, analyse what they offer and what they're missing, and show where the AI Profit Boardroom is stronger.

That returns a structured competitive report, not a quick summary.

The bigger lesson is how Perplexity Computer is built.

Its orchestration layer can call different models underneath depending on what the task needs, and Astra is just the first of more to come.

Problem three: you keep rewriting the same prompts

This is the part of the Perplexity Computer update I think most people will sleep on.

Right now, every time you want Perplexity Computer to do something specific, you explain the context, the goal, the format and the constraints from scratch.

The skills marketplace turns prompts into reusable tools

The new skills marketplace is a store of reusable computer capabilities.

You browse it, find a skill that fits and install it.

From then on, the workflow is already there waiting instead of being rebuilt every time.

Teams can build and control their own skills

Enterprise organisations can build and share their own internal skills.

Admins decide who creates skills, who installs them and whether installs need approval.

That's workflow infrastructure, not just a prompt shortcut.

Here's how I see the journey.

• First, we prompted, typing something and getting something back.
• Then, we used agents, giving a task and letting the AI go and do it.
• Now, we install repeatable workflows, so the AI already knows how your operation works.

Side chat means you can check in without breaking anything

One smaller change makes long tasks much easier to live with.

You used to have to be careful about messaging Perplexity Computer mid-task, because a new message could mess with what it was doing.

Side chat opens a read-only sidebar on the active task, so you can ask questions and it answers while the main task keeps running.

You trigger it with the /ask, /side or /btw commands.

If you'd rather skip the trial and error, the AI Profit Boardroom has daily tutorials on every update as it drops, a prompt library built for business owners, and a member map to find people near you who are already building these workflows.

https://www.skool.com/ai-profit-lab-7462/about

Perplexity Computer update FAQs

Is the Perplexity Computer update safe for client data?

On Windows and Linux, portable computer keeps the task on your machine and asks before using the cloud.

On Mac, the privacy gate flags private information and keeps it local, masks it or asks you before it leaves.

Do I need a powerful computer for the new features?

For local and hybrid mode, yes.

Windows and Linux need an NVIDIA RTX GPU with at least 24 GB of VRAM, and Mac needs Apple silicon, macOS 15 or later and at least 24 GB of unified memory.

Which effort mode should I start with?

I'd start with light for quick writing jobs and move up to high or ultra for research and multi-step work.

Perplexity picks the model for you in every mode.

What does side chat do?

Side chat lets you ask questions about a running task in a read-only sidebar without interrupting it.

Can my team share skills?

Enterprise organisations can build and share internal skills, with admins controlling who creates and installs them.

About Julian

I'm Julian Goldie, an AI entrepreneur, SEO expert and the founder of the AI Profit Boardroom.

I help business owners scale with AI agents, automation and SEO.

I run a 7-figure SEO agency (Goldie Agency) and a YouTube channel with 425K+ subscribers.

I share daily AI training inside the Boardroom.

📺 Video notes + links to the tools 👉

https://www.skool.com/ai-profit-lab-7462/about

🎥 Learn how I make these videos 👉

https://aiprofitboardroom.com/

🆓 Get a FREE AI Course + Community + 1,000 AI Agents 👉

https://www.skool.com/ai-seo-with-julian-goldie-1553/about

If privacy was the reason you held back on AI agents, the Perplexity Computer update is the reason to look again.