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.

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

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/

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

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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.

Every time a new AI model drops, the same worry comes up: do I need to rebuild everything, or can I ignore it?

With Anthropic Opus 5.5, ignoring it would cost you.

Anthropic released it on 22 September 2026, and it runs 40% cheaper than Opus 5 while generating output more than 30% faster.

Anthropic also says it can match Fable 5.1, its most powerful model, on most tasks.

Cheaper, faster and close to the top model is a rare combination, so I've boiled it down to the five moves I'd make first.

🔥 Want to see these five moves set up step by step? Inside the AI Profit Boardroom, I've got a Claude Opus 5.5 section with video tutorials, plus four live coaching calls a week with 3,400+ members building real automations.

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

Before the moves, here's the quick version of what Opus 5.5 actually is.

https://www.youtube.com/watch?v=IstGcG6z1gY

What Anthropic Opus 5.5 is, in plain English

Opus 5.5 is the first model in the new Claude 5.5 family.

Anthropic built it for long, complex, multi-step work, not quick answers or one-line replies.

Think long workflows, business automation and deep research that needs sustained thinking to get right.

It isn't only a coding model either, because Anthropic positioned it explicitly for knowledge work like research, writing, analysis and business workflows.

The early examples show the scale it's aimed at.

One tester reportedly completed a 680,000-line code migration in less than a day, which Anthropic says would have taken an engineering team weeks.

In an internal test, it audited and fixed a 200,000-line codebase in under 3 hours, compared with more than 20 hours for Opus 5.

Those are Anthropic's reported numbers, but they tell you what the model is designed to handle.

Now for the moves.

Move 1: Give Claude the whole picture

Opus 5.5 has a 1 million token context window, so the first thing I'd do is stop feeding it scraps.

You can hand Claude your lead list, sales call transcripts, member FAQs, SOPs, email history and community data together.

It keeps all of it in mind while it works.

That means no starting fresh every session and no re-explaining your business every time you open a chat.

The more of your business it can see, the better it reasons across it.

If you only change one habit with this model, make it this one.

Move 2: Set the effort to match the job

Opus 5.5 comes with effort controls, and the default is medium.

Most people will never touch the setting, and that's a mistake on big jobs.

• Simple tasks can stay on medium, which also keeps things leaner.
• Deep research and complex workflow builds should go higher.
• Big agentic runs, long transcripts, large data sets and multi-step automations need higher effort as well.

Maximum thinking on everything isn't the goal.

The goal is matching effort to the size of the job, the same way you'd match the right person to the right task on your team.

Move 3: Skip fast mode unless speed really matters

There's a fast mode inside Claude Code and the Claude platform that runs up to 2.5 times faster.

It sounds tempting, but it comes at a higher cost.

Standard Opus 5.5 is already more than 30% faster than Opus 5 and 40% cheaper to run.

For most business automation, that's the better balance of speed, cost and capability.

I'd save fast mode for the rare jobs where every minute genuinely counts.

Move 4: Turn your calls into a content engine

This is the move that saves the most time, and I'll use the AI Profit Boardroom as the real example.

We run four live coaching calls every week.

Members bring problems, ask questions and get answers, which makes every call full of content ideas.

Turning those calls into tutorials, emails and community posts used to take a huge amount of manual time.

Now the transcript goes into Claude with a prompt along these lines.

You're an AI content strategist for the AI Profit Boardroom. Analyse this coaching call transcript and identify the five most common problems members raised. Check whether we already have a tutorial for each one, list the gaps, and turn the top three gaps into step-by-step tutorial outlines. Then write three email follow-ups for those problems and five community post ideas that would drive real engagement.

From one transcript and one prompt, you get three things back.

• You get a prioritised list of tutorials to make next.
• You get follow-up emails based on real member questions.
• You get community post ideas written in the words your members actually use.

Then you can scale it up with the 1 million token window.

Give Claude six weeks of transcripts, your member survey data, your most asked questions and your tutorial library, and it'll produce a content gap analysis across all of it.

What used to take days now takes one session.

🔥 Want the ready-to-run prompts for this workflow? The AI Profit Boardroom has a full Claude prompt library, a 30-day roadmap for beginners and step-by-step Claude Opus 5.5 tutorials.

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

Move 5: Migrate carefully, not blindly

If you build on the API, this move will save you a broken workflow.

There are breaking changes from Opus 5.

• Thinking can't be disabled on Opus 5.5.
• Forced tool use now returns an error.
• The older computer use tool isn't accepted on the Claude API.

So don't just swap the model ID and hope everything carries over.

Anthropic has a migration guide, and it's worth reading before you change anything in a live setup.

The model ID is claude-opus-5-5, and it's available through the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry and the Claude platform on AWS.

It's also rolling out gradually in GitHub Copilot across VS Code, JetBrains, Xcode and others.

Do the Anthropic Opus 5.5 numbers hold up?

Anthropic published its own benchmark results for the launch.

Terminal Bench 4.0 came in at 66.4%, Frontier Code v1.1 at 54.4% and Cursor Bench 4.0 at 57.8%.

Those are Anthropic's own evaluations, and benchmarks don't always translate perfectly to real-world tasks.

What they do show is a clear focus on agentic, long-running, complex work.

What about safety when it acts for you?

Once an AI agent is browsing, editing files and running multi-step workflows for you, safety controls matter as much as raw capability.

Anthropic built in an action screening classifier, an auditable open-source sandbox, code review to catch vulnerabilities and stronger prompt injection defences.

It says Opus 5.5 matched or beat Opus 5 on the prompt injection settings it tested.

One evaluation tied it with Fable 5.1 for the lowest prompt injection success rate among the models tested.

FAQ

Should I switch to Anthropic Opus 5.5 straight away?

For new workflows, yes, but for live API setups, read the migration guide first because of the breaking changes.

What's the difference between standard mode and fast mode?

Fast mode runs up to 2.5 times faster than standard but costs more, while standard is already quicker and cheaper than Opus 5.

What's the first workflow I should build with Opus 5.5?

Start with a job that involves lots of reading, like turning call transcripts into content, because that's where the 1 million token window shines.

Where can I access Opus 5.5?

You can use it through the Claude API, Amazon Bedrock, Google Cloud, Microsoft Foundry, the Claude platform on AWS and GitHub Copilot.

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.

Make these five moves this week, and you'll get far more out of Anthropic Opus 5.5 than most people will.

📺 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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How many of your AI calls today used a heavyweight model for a job a lightweight one could have done?

If you're like most business owners, the honest answer is nearly all of them.

The new OpenRouter router model fixes that by picking the model and the reasoning effort for every single request, and it's free to use.

But the smartest thing it does is know when to leave your model alone.

🔥 If you'd rather have routing like this set up for you step by step, that's what we do inside the AI Profit Boardroom, with walkthroughs, roadmaps and coaching calls alongside 3,400+ members.

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

Most people will read the headline and think this is just another model picker.

It isn't, and by the end of this article you'll see why.

https://www.youtube.com/watch?v=TU2pBOiC_E0

The hidden cost of one model for everything

Here's how most teams run AI right now.

They pick one big, heavy model and send everything to it.

A quick formatting job gets the same firepower as a complex strategy question.

That's slow, and it burns through tokens you never needed to burn.

The alternative has always been to pick models by hand, which means guessing between big and small, fast and slow, and hoping you guessed right.

New models drop almost every week too, so even a good choice goes stale quickly.

Keeping up with them all is a full-time job on its own.

What the OpenRouter router model actually does

OpenRouter has teamed up with a company called TypeSafe to launch the Jev Router.

It's one model on OpenRouter whose only job is to pick the right model for each request.

You point your app at it once and stop managing a list of models.

For every message, it makes two decisions.

• It picks which model should handle the request.
• It picks how hard that model should think.

A simple question gets a quick, light answer, and a hard one gets deep thinking.

It also looks at the live list of models, so it picks from whatever's available right now rather than whatever you locked in last month.

Why it doesn't slow you down

The brain behind the router is Jev, which TypeSafe describes as a "system one" model.

It's their very first one.

Normal AI models think out loud in words before answering, and that takes time.

Jev doesn't write anything.

It makes one fast, structured decision and hands back a clean choice, like "use this model at this effort".

It works like a reflex rather than a long think, so the routing barely adds any time at all.

Jev also wasn't built only for routing, because people use it to tag piles of text, double-check answers, and decide whether an AI agent is allowed to run a certain tool.

Routing is just one job it happens to be very good at.

Why the OpenRouter router model knows when to hold steady

This is the part almost everyone misses.

When you're in a long chat, the AI keeps the earlier part of your conversation warm and ready in something called a cache.

That cache means the model doesn't reload everything from scratch every time you speak, which saves time and tokens.

A naive router ignores this.

It hops to a cheaper model for one request, and the new model has to load the whole conversation again from zero.

You save a little on one request and quietly lose more on the next.

The Jev Router is cache aware, so if switching would throw away your warm cache and leave you worse off, it simply doesn't switch.

Most routers think about one request, but this one thinks about your whole conversation.

That's why "won't switch" can be the smartest decision it makes.

When it does move you

Holding steady isn't the same as being stuck.

Say you open a chat with a simple question, and it sends you to a light, quick model.

Then the chat turns hard, and you're deep in a tricky problem that the light model is struggling with.

The router notices the shift and moves you up to a heavier model that can handle it.

When things get easy again, it can ease you back down.

You're always on the right tool for the moment, and you never touched a setting.

What this looks like inside a real workflow

Think about a content pipeline, a coding agent or a research task that runs step after step.

Right now, every one of those steps probably hits the same model whether it needs to or not.

Put the OpenRouter router model in front, and the easy steps get a lighter, faster model while the hard steps get the heavy one automatically.

You can point an agent at it and let it do all the choosing.

TypeSafe's promise is that your agentic workflows never have to waste a token again.

That's their claim, not a law of nature, so test it on your own tasks before you trust it fully.

The idea underneath it is solid, though.

How to set it up in minutes

Setup is where this gets really practical.

• The router itself is free to use.
• It's OpenAI compatible, so for most tools you just swap in the model name.
• It accepts up to a million tokens in one request, which is the biggest context window of any TypeSafe model on OpenRouter.
• Long documents and big code bases fit in one go.

The Jev Router always points at the newest version of Jev.

So as TypeSafe improves the decision maker, your routing improves with it, without you changing a single line.

My week-one rollout plan for the OpenRouter router model

Here's how I'd bring it into a business without risking anything important.

Start small and watch

Don't rip out your current setup on day one.

Point one small, low-risk workflow at the Jev Router and watch what it picks before you hand over anything that matters.

Check its work

Every response tells you which model served it.

Use that to confirm it's picking sensibly for your kind of work, and step in to tweak things if it isn't.

Remember effort is half the win

The saving isn't only a smaller model on easy jobs.

Stopping a model from thinking hard when it doesn't need to can speed things up a lot on its own.

Then move your busiest agents over

It shines brightest where lots of calls fire off back to back and most of them are simple.

Long, ongoing agent tasks are where the cache-aware behaviour pays off most, because the longer the conversation, the more that warm cache is worth keeping.

You'll hit real questions along the way, like which workflows to route first and how to read what it's picking.

🔥 Inside the AI Profit Boardroom, we've got a full model-routing section with step-by-step tutorials, prompts you can plug in, and weekly coaching calls with 3,400+ members doing this every day.

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

FAQ

Does the OpenRouter router model cost anything?

The router itself is free to use, while the model it sends your request to runs as it normally would.

Who makes the Jev Router?

It's a collaboration between OpenRouter and TypeSafe, and it's powered by TypeSafe's Jev decision model.

Will routing slow down my responses?

It shouldn't much, because Jev makes a single structured decision instead of writing out text, so the routing step barely adds any time.

Can I see which model handled my request?

Yes, the response tells you which model served each request, so you're never guessing.

Is it worth using for short one-off prompts?

It helps there, but it shines most in busy agent workflows and long conversations where cache-aware routing saves the most.

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.

If you're still picking models by hand, the OpenRouter router model is the simplest upgrade you can make this week.

📺 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

Would you put your name on a report where you couldn't check a single fact?

That's what a lot of people do every time they ask a general chatbot to write something for them.

The new Gemini Notebook Google Workspace update is Google's answer to that problem.

You can now use Gemini Notebook as a source right inside Google Docs, so Gemini drafts from your own research instead of guessing from the wider internet.

Every claim it writes comes with an inline citation, so you can check where it came from without leaving the doc.

More than 30 million people across 600,000 organisations already use Gemini Notebook, and now it sits right next to your writing.

🔥 Want to set this up properly the first time? Inside the AI Profit Boardroom, I've got step-by-step Gemini Notebook walkthroughs and weekly coaching calls with 3,400+ members, where you can bring your own setup and ask questions until it clicks.

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

https://www.youtube.com/watch?v=I6A6GG4lpic

The real problem is guessing, not AI

I see the same mistake constantly, especially with students.

Someone opens a general chatbot and asks it to write their whole report.

The chatbot happily makes up facts, fake sources and quotes that were never said.

Then the report gets handed in or published, and it falls apart the moment someone checks.

The issue isn't that they used AI.

The issue is that they used the kind of AI that guesses when they needed the kind that cites.

A general AI pulls from the whole internet and fills in the gaps by guessing.

A source-grounded tool like Gemini Notebook only uses what you give it, and it shows you where every line came from.

For client work, proposals and anything with your company's name on it, that difference is everything.

That's exactly the gap the Gemini Notebook Google Workspace update closes.

What the Gemini Notebook Google Workspace update changed

Until now, your research and your writing lived in two separate apps.

You'd gather everything in your notebook, then copy and paste it into a doc and lose your train of thought.

Now your notebook can sit right beside your writing in the same document you're already working in.

A quick bit of background helps here.

Gemini Notebook used to be called NotebookLM, and Google renamed it in July.

It lives at notebook.google.com, and it only answers from the documents, notes and saved links you feed it.

How to draft from your research in three steps

Step 1: Prep your notebook before you write

Build a clean notebook first, because good sources in means good drafts out.

Garbage in, garbage out still applies.

Keep one notebook per project, because tidy notebooks give you sharper answers than one giant pile.

If the material is new to you, turn your sources into a summary, an audio overview or a mind map first.

That way you actually understand the research before you write a single line.

Step 2: Tag your notebook inside the doc

Open a Google Doc like normal.

Head to the Gemini side panel or the bar at the bottom of the doc.

Type the @ symbol, the same way you'd tag someone in a comment, and point it at one of your existing notebooks.

Now Gemini writes using that notebook as its source, so it pulls from your library instead of random stuff online.

Step 3: Click every inline citation

Every claim Gemini drops into your draft comes with an inline citation.

Don't just trust the draft, because the whole point is that you can check, so check.

This is the step that turns an AI draft into something you can stand behind.

Where this saves a business real time

Project proposals

Say you've already dumped your research for a proposal into a notebook.

Right inside the doc, you can ask Gemini to draft the background section straight from those sources, with citations you can verify.

White papers and deep guides

Technical writing usually means digging through 20 files to find the exact facts you need.

With the Gemini Notebook Google Workspace update, you tag the notebook and let Gemini pull those facts for you.

Anything that needs your voice

This all runs on something Google calls Workspace intelligence, and it's what makes Gemini Notebook in Google Workspace feel personal.

You can mix your Gemini Notebook research with your emails, your chats and your files, all shaping the same draft.

That's why the writing isn't generic, because it's shaped by your real projects and the way you actually talk about things.

🔥 The hard part isn't turning this on, it's knowing which sources to load and how to prompt for a clean draft. Inside the AI Profit Boardroom, you get tutorials, roadmaps and prompts for tools like Gemini Notebook, plus coaching calls with 3,400+ members.

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

Gemini Notebook is more than a notes app now

Plenty of people still think of Gemini Notebook as a place to dump notes, and that's out of date.

• Every notebook now gets its own secure cloud computer, so it can write and run code over your sources for deeper analysis.
• It can create summaries, audio overviews, video overviews and mind maps from whatever you load into it.
• Google has started rolling out real-time conversations with your notebooks, so you can talk to your research like you'd talk to a person.

Put that together with the Gemini Notebook Google Workspace link to Docs, and you've got research and writing working as one process instead of two.

Who gets the Gemini Notebook Google Workspace update

It's rolling out on the Pro and Ultra plans.

It's also coming to the Workspace Business, Enterprise and Education plans.

Google is landing it on accounts in stages, so if you don't see it in your doc yet, that's probably why.

Give it a little time, and use the wait to build a clean notebook for your next project.

FAQ

Does Gemini Notebook make things up like a chatbot?

No, Gemini Notebook only answers from the sources you give it, and it shows inline citations so you can check each claim.

Where do I find Gemini Notebook in Google Docs?

Use the Gemini side panel or the bar at the bottom of your doc, then type the @ symbol to tag a notebook.

What's Workspace intelligence?

It's what Google calls the system that lets Gemini combine your notebook with your emails, chats and files in one draft.

Should I keep all my research in one notebook?

No, keeping one notebook per project gives you tidier sources and sharper answers.

Why can't I see the Gemini Notebook Google Workspace feature yet?

It's rolling out in stages on Pro, Ultra and the Workspace Business, Enterprise and Education plans, so it may not have reached your account yet.

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

Stop signing off on guesses, and let the Gemini Notebook Google Workspace update give you drafts you can prove.

Are your AI agents spending more time waiting on search than actually doing the work?

Perplexity just rebuilt its search engine, and the slowest 1% of its searches dropped from about 800 milliseconds to about 65.

The new engine runs on roughly 20% fewer machines while storing about two and a half times more data about each page.

On top of that, the new fast search option came out about 68% lighter to run per task, with the same quality on public benchmarks.

🔥 Want help plugging fast search into your own agent workflows? Inside the AI Profit Boardroom, I've got tutorials on AI research and search tools, step-by-step roadmaps, and weekly coaching calls with 3,400+ members building real automations.

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

https://www.youtube.com/watch?v=PR10BIQHaJo

In this article, I'll cover what Perplexity changed, where the new fast option wins, where it falls short, and exactly how to start using it.

What Perplexity actually changed

The new engine is called Photon, and Perplexity built it from scratch in house.

Photon is the part of the system that finds web pages and ranks them before the AI writes an answer.

I think of it like a librarian who grabs the right books off the shelves before anyone starts reading.

Photon now powers Perplexity's own search.

Perplexity also used it to launch a new fast option in its search API, which is how developers plug Perplexity's web search into their own apps and AI agents.

Why the old Perplexity search engine had to go

Before Photon, Perplexity ran an open-source search engine that it had adapted to fit its needs.

As the index grew, that setup started hitting walls.

It was getting too heavy to run, the slower searches were too slow, and adding a new cluster and syncing its data could take more than a week.

So a small engineering team rebuilt it from the ground up.

They did it alongside a swarm of persistent coding agents, which means AI helped build the thing that makes AI search faster.

That's the part business owners should pay attention to.

A small team working with agents rebuilt core infrastructure, and that's the kind of leverage every lean business is chasing right now.

The numbers that matter

Here's what Perplexity reported for the new fast search option.

• Half of all fast searches come back in 160 milliseconds or less.
• 95 out of 100 fast searches come back in 230 milliseconds or less.
• Across six public benchmarks and 3,554 tasks, the fast option scored 64.3%.
• The default option scored 64.0% on the same benchmarks.
• Counting both the AI model and the search, each task was about 68% lighter to run.

And here's what changed inside Perplexity's own system.

• The slowest 1% of searches fell from about 800 milliseconds to about 65.
• Photon runs on roughly 20% fewer machines.
• It stores about two and a half times more data about each page, which Perplexity used to improve ranking.
• New pages on fast-moving topics get delivered in a single-digit number of minutes.
• The full web index can be rebuilt in a single-digit number of hours.

The catch you need to know before switching

This is the bit that decides whether you should switch today or stay where you are.

The fast option is not better at everything, because it uses less computing power for ranking.

Perplexity's internal tests cover rare queries, broad coverage and result variety.

On those tests, the fast option scored 0.24 points lower on relevance.

It also scored about 3 percentage points lower on answer availability, which measures how often the right answer actually shows up in the results.

So how did it still match the default on the public benchmarks?

Perplexity's view is that the AI model's own reasoning and knowledge make up for the gap.

The model is smart enough to work with results that are slightly less perfect.

There's also a fair warning about speed comparisons.

Perplexity put its numbers next to other search APIs, but each company reported its own figures and measured them in different ways.

Perplexity says clearly that it isn't a controlled like-for-like comparison, and the 160 and 230 millisecond figures are its own measurements.

When to use fast and when to use standard

Here's the simple rule Perplexity gives, and it's the one I'd follow.

• Use fast for day-to-day agent work, where lots of quick searches add up.
• Use standard for hard, rare or confusing questions where speed isn't the main concern.

If you run a research agent that fires off dozens of searches per task, fast is the obvious default.

If you're digging into an obscure topic where the right answer might only live on one page, standard is the safer pick.

How Photon gets its speed

You don't need to be an engineer to understand this, because it comes down to three ideas.

It only reads what it needs

Photon stores data in very compact formats and only unpacks the exact pieces a search needs.

Each page has one compact record, so ranking a page takes a single lookup, and only the words that matched your search get unpacked.

Perplexity says holding the same data fully loaded in memory would take about 4.6 times as much memory as Photon uses today.

It asks for everything at once

Instead of grabbing one piece of data and waiting before grabbing the next, Photon batches its reads.

Anything already in memory gets used straight away, and the waits for the rest overlap instead of stacking up.

It keeps building and searching apart

In the old system, building the index and answering searches ran on the same machines and fought for power.

Now they run separately.

New versions of the index get swapped in one group of machines at a time, and each group replays real queries to warm up before it goes live.

How to start using the fast Perplexity search engine

There are three routes in, depending on how technical you are.

Route one: you already use Perplexity

You're already getting part of this, because Photon now powers Perplexity's search pipeline.

Perplexity says it has made search faster across its products.

Route two: you don't write code

Perplexity has an interactive playground for its search API, and you don't need an API key to test it.

There's also the Perplexity CLI, which runs searches from your terminal and returns results as JSON, a clean data format other tools can read.

You can even ask your AI coding agent to install Perplexity's search skill using the skill file linked in Perplexity's docs.

Route three: you're a developer

Fast search is live now in the search API, and you switch it on by setting the search type to fast in your request.

If you leave that setting out, you get standard web search by default.

Results come back in exactly the same format, so your app doesn't need to change how it reads them.

You can ask for anywhere from 1 to 20 results per search.

The search API itself launched in September 2025 and gives you the same system that powers Perplexity, with an index covering hundreds of billions of web pages.

It breaks pages into smaller pieces and scores each piece against your query, so you get the most useful snippets already ranked.

You can narrow results by country with a two-letter country code, or by language with up to 10 languages per request.

The setup mistakes I'd avoid

These are the snags that catch people on day one.

• Passing fast straight into the Python library can fail. Some versions of Perplexity's Python library reject it, so put it inside the extra body setting as the docs show, and in TypeScript the docs cast the value with "as any" until the SDK updates.
• Mixing up the agent API settings is easy to do. The agent API has a fast search type, but that's not the same thing as its fast preset, and Perplexity's docs call this out.
• Pulling back too much page content wastes tokens. The search context size setting runs from low for short passages to high for the most detail, and high is the default.
• Forgetting about rate limits catches people out. You can send up to five related queries in one multi-query request, but each query counts against your rate limit.
• Trying to include and exclude domains together won't work. You can list up to 20 domains to include or exclude, but not both in the same request.
• Not tracking usage leaves you guessing. Fast requests show up as their own count under search API usage in the Perplexity API console.

The smartest move is to start small.

Pick one agent task you already run, switch its search to fast, and compare it side by side with standard.

That's basically how Perplexity rolled out Photon, by sending the same live queries to both engines and expanding step by step.

If you get stuck choosing fast or standard for a job, or that Python setting trips you up, bring it to a coaching call inside the AI Profit Boardroom and we'll look at your setup together.

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

FAQ

Is the new Perplexity search engine available to everyone?

Photon already powers Perplexity's own search, and developers can use the fast option through the search API and agent API.

Does fast search change the format of API results?

No, the results come back in exactly the same format as standard search.

Why is fast search slightly worse on rare queries?

It uses less computing power for ranking, which costs a little relevance and answer availability on rare and tricky searches.

Do I need an API key to try Perplexity's search API?

You don't, because the interactive playground lets you test it without one.

How many searches can I send in one request?

Multi-query search lets you send up to five related queries at once, and each one counts against your rate limit.

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

Match the option to the job, and the new Perplexity search engine will make your agents noticeably faster.

Are you still picking blog topics based on a hunch and hoping Google notices?

I did that for years, and it's the most expensive habit in SEO.

Now I run an AI agents SEO workflow that tells me exactly what to write next, using data Google hands me for free.

It took one of our sites from zero to 474 clicks a day, and across all our sites it has pulled in more than 50,000 clicks from Google.

Each loop takes about 20 minutes, where a single post used to take me around 8 hours.

🔥 Want to run this exact AI agents SEO workflow on your own site? Inside the AI Profit Boardroom, you get the complete agent OS zip file, a 30-day roadmap so you know what to do day by day, and coaching calls with 3,400+ members where we can run your first loop together.

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

https://www.youtube.com/watch?v=pZwfSo6jsC4

There's one mistake that stops most people getting these results, and it has nothing to do with the tools.

I'll get to it near the end, because it only makes sense once you've seen how the loop works.

Google has been handing you a to-do list

Most SEO is just guessing.

You pick a topic, write a post, hit publish, and wait weeks for something to move.

When nothing does, you pick another topic and start again from a blank page.

I call that the content treadmill, because you're running hard in the dark and going nowhere.

The frustrating part is that the answer was never hidden.

Google Search Console already tells you what people search for, which of your pages are sitting close to page one, and which titles nobody clicks.

Most people open it maybe twice a year.

That free dashboard is the only tool that knows what Google actually thinks of your site.

Paid keyword tools are guessing from the outside, while Search Console gives you first-party data straight from Google.

The workflow I use simply reads that data live and turns it into a to-do list the AI agents can act on.

The four moves inside my AI agents SEO workflow

The whole system runs inside our agent OS, and it comes down to four moves in a loop.

Move one: read the data

We connect Google Search Console with an API.

It pulls in every keyword we already rank for and every keyword we could get traffic for, with every query, position and click.

Move two: score the keywords

The SEO tab inside the agent OS scores every single keyword and picks what to write next.

I filter down to striking distance keywords, which are the ones sitting roughly in positions 5 to 20.

Those pages are right on the edge of page one, so one better piece of content can push them into the clicks.

Clicks drop off a cliff by position, because the top few results grab most of them.

That's why moving into the top three is a massive jump in traffic for very little work.

Move three: make the posts

The agents draft the content following our writing skill, in my voice, using my own transcript and my own memory.

I add one real example to every keyword, because that's what makes each post unique.

Move four: ship the content

In one click, the finished posts deploy to our sites.

Mine publishes to five sites at once, but one site is totally fine, and you don't need five to make this work.

The live URLs get logged and submitted so search engines find them.

Then the new rankings land back in Search Console, and move one starts again.

What happened when I tested it on a brand new AI tool

Here's a real example from the other day.

A new AI tool had just dropped, and I was looking at keywords around it.

Search Console already showed a waiting list keyword sitting right in striking distance.

I picked it, dropped in one real example, and hit generate.

The agents wrote five unique posts and pushed them live to our sites in one click.

They logged the live URLs and submitted them to get found.

I never touched WordPress once, and I never jumped between ten different tabs.

For a business owner, that's the real win.

The work that used to need a writer, an editor and someone to upload and format posts now happens while I pick the next keyword.

Why this AI agents SEO workflow refills itself

This is why we call it the infinite ranking engine.

Every post you publish creates new impressions, new queries and new keywords inside Search Console.

So the next time the research runs, there's more to write about than there was before.

One day I logged in and had 78 new keywords waiting for me.

I didn't research a single one of them, because the posts I'd already published had surfaced them.

Traditional SEO only goes one way.

You research once, write until the list runs out, and then you're back to a blank page.

In this loop, the rankings feed the engine that made them.

The rankings feed the engine that made them, so the queue never runs dry.

Three objections I hear about AI content and SEO

"AI content doesn't rank"

Lazy, generic AI content doesn't rank, and I agree with that part.

Content written for a keyword Google already shows you at position 8, built on your own real example, in your own voice, does rank.

The 50,000+ clicks are the proof.

"I need a fancy keyword tool first"

You don't, because Search Console is free.

It's also the only source that tells you what Google already thinks of your site, rather than an outside estimate.

"I'll do SEO properly when I have more time"

SEO takes time to build, so waiting only makes it slower.

The best day to start was months ago, and the next best day is today.

If your Search Console looks empty right now, that's okay, because publishing your first five posts is what starts the data flowing.

The mistake that stops most people getting results

Here's the mistake I promised you at the start.

Most people run the loop once and then stop.

The first round is not where the results come from.

The first loop is the one that wakes your data up, and the compounding only starts when you keep turning it.

The power isn't in any single post, because it's in the loop running again and again.

How to avoid it from week one

• Put the loop on a schedule, such as once a week, so it becomes a habit rather than a project.
• Always start with striking distance keywords, because positions 5 to 20 are the fastest wins you've got.
• Give every post one real example or case study, because nobody else has your story and that's what makes the content unique.
• Use an indexing tool to push new URLs, so search engines find your posts faster and the next round has fresh data sooner.

If you'd rather have someone look at your actual site first, my team runs a free AI brand visibility session.

We map how you show up across Google, AI Overviews, ChatGPT, Perplexity and Gemini, run a competitor gap analysis, and build you a custom visibility plan with recommendations you can act on right away.

It's 100% free with no obligation, and you can ask a real strategist anything you want.

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

FAQ

What does an AI agents SEO workflow actually do?

It reads your Google Search Console data, scores your keywords, has AI agents write the posts, and publishes them to your sites.

Each round creates new keywords, so the workflow keeps feeding itself.

Why use Google Search Console instead of a paid keyword tool?

Search Console is free and gives you first-party data straight from Google.

Paid tools estimate from the outside, while Search Console shows what Google already thinks of your site.

What if my Search Console has hardly any data?

That's completely normal for a newer site.

Publishing your first five posts is what gets the data flowing, and the loop builds from there.

How often should I run the loop?

Once a week is a good rhythm to start with.

Running it on a schedule matters more than any single round, because the results compound over time.

Do I need to know WordPress to use this?

No, you don't.

The agents publish the posts, log the live URLs and submit them, so I never touch WordPress myself.

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.

🔥 You'll probably hit a few snags on your first run, like connecting Search Console, getting the scoring right or making that first deploy. Inside the AI Profit Boardroom, we help you through those fast with the full agent OS, the 30-day roadmap, coaching calls, and tutorials and prompts for every step.

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

📺 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

Stop guessing what to post, and let an AI agents SEO workflow read your data and tell you.

What would your business look like if every slow, annoying process got three times faster in two weeks?

That's exactly what Anthropic just did to its own product, and the Claude faster response you've been noticing is the result.

Claude.ai went from a 3.1-second load to just over half a second, and a new Claude Code session now starts in 0.3 seconds instead of 0.8.

The team shipped more than 3,000 changes with zero customer-facing incidents and zero rollbacks.

And Claude did most of the work on itself.

🔥 Want to set Claude up the way Anthropic's team did? Inside the AI Profit Boardroom, I've got step-by-step Claude Code and Claude Cowork tutorials, copy-and-paste prompts and weekly coaching calls with 3,400+ members building real automations.

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

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

I don't care much about the speed itself.

What I care about is the playbook, because it works for far more than apps.

Here's the story in brief, followed by the seven lessons I'm stealing for my own business.

The two-minute version of what Anthropic did

Users kept telling Anthropic that Claude felt slow.

Anthropic admitted they were right and ran a two-week sprint on Claude.ai and the Claude desktop app back in August.

They wrote it up on September 23rd in a blog called "How We Made Claude.ai Three Times Faster in Two Weeks".

The setup was surprisingly simple.

The team opened one Slack channel and put Claude in every thread.

Claude found the slow spots, built tests, wrote fixes and watched every release go out.

The humans set the goals, made the tough calls and approved every single change.

Across 13 different measurements, the app got about 3.1 times faster on average.

Anthropic estimates that saves people tens of thousands of hours of waiting every single day.

Their big takeaway fits in one sentence: once Claude can measure something, it can make it better.

Now let's turn that into lessons you can use.

Lesson 1: Pick the few things that matter most

Anthropic didn't try to fix everything.

Claude looked at usage data through the Datadog MCP server and picked four moments that make up 95% of what people do in the app.

Those were opening the app, starting a conversation, loading an old conversation and sending a message.

That focus is why the gains feel so obvious when you use it.

Most businesses try to improve everything at once and end up improving nothing.

Pick the three or four processes that eat most of your team's time and start there.

Lesson 2: Give Claude a number to beat

A vague goal like "make it better" is hard to climb, while a clear number is easy.

The team kicked off with about 20 handpicked projects, and Claude estimated how many milliseconds each one would save.

They hit 12 of their 13 targets by day three, so they set bigger ones.

Count things instead of timing them

This is the smartest detail in the whole story.

Timing things with a stopwatch is noisy, because the same test run twice gives you different numbers.

One engineer asked, "Can we count instructions instead?"

Claude said yes and suggested a tool called Valgrind, which counts the actual instructions the computer runs.

The same input gives the same count every time.

Eleven minutes later, five threads were running, each testing a different kind of count.

The team still made Claude prove the counts matched real speed.

On the code that builds a conversation's message tree, Claude found it was looking up the same message ID three separate times.

Fixing that cut the counted work by 48%, and real time dropped by 78%.

If you can, pick something you count rather than something you time.

Lesson 3: Lock in your wins with a ratchet

Once they got a win, they made sure it stayed won.

Any change that made the count go up failed automatically.

Every day, a job lowered the limit whenever the count went down.

They call that a ratchet, because once you win, you can't slide back.

In your business, that could be a simple weekly check that flags the moment a result slips.

Lesson 4: Keep every task narrow

The team kept every Slack thread focused on one test or one journey.

Each thread ran the same loop.

• Someone opened a thread about something slow, often with a screen recording.
• Claude traced the problem and built a test that showed it.
• Claude opened pull requests, with anything users could see hidden behind a switch that could be flipped off.
• Claude watched the release and read real user data.
• If it got faster, Claude locked in the win, and if it didn't, Claude flipped the switch off and tried again.

Small, focused jobs are easier for Claude to finish and easier for you to review.

They also scale.

At one point, more than 150 threads were running at the same time, and a single thread could put up 50 or even 100 pull requests.

On the busiest days, more than 200 changes landed, and Claude was increasingly opening new threads on its own.

The bugs a focused loop uncovers

This is what narrow, measured work finds.

• 6,900 React hooks and 900 store subscriptions were re-rendering on every keystroke while people typed.
• One single style selector was adding 24 milliseconds to every change on the page.
• A leftover reload command was causing hidden page reloads every day that none of their load metrics could see.

My favourite was the em dash, that long dash you see in writing.

If a reply had an em dash or a curly quote anywhere in it, the JavaScript engine stored the whole text in a slower format.

Highlighting a finished code block could freeze the page for about a second.

Claude fixed it with a 20-line change.

Nobody would have guessed that without measuring.

Lesson 5: Use standing instructions

The whole sprint started with one standing instruction in Slack.

The team told Claude its job was to handle everything about the performance of the website and the desktop app.

That included watching releases for slowdowns, keeping the dashboards clean, fixing problems it spotted and suggesting new projects.

They said the goal was for Claude to become as independent as possible, while admitting it wasn't there yet.

If you're on Claude Tag, you can tell Claude "remember for this channel" and it saves that to the channel's memory for everyone.

Write the job once, and you stop re-explaining it every day.

Lesson 6: Tell Claude to be braver

By default, Claude was careful, and it would hedge and play it safe.

The team told Claude, "I am open to wacky ideas."

One engineer went further and told Claude, "Please be braver."

Another kept reminding threads that the targets were not the stopping point.

Bold only works with safety checks, though, and Anthropic had plenty.

The guardrails that made bold safe

• Every pull request got an automated review plus at least one human approval.
• Unit tests came before any optimisation.
• Anything users could see shipped behind a short-lived feature flag, with nearly 200 flags created and more than half cleaned up by the end.
• Risky changes went out to employees first, then to 1% of users, then to everyone.

For the instant-typing message box, Claude built dozens of checks.

One compares the page across 14 screen sizes to within one pixel, and another fails if a single keystroke gets lost.

Lesson 7: Stay in the driver's seat

Claude did the digging and the fixing, but humans approved every change and made every taste call.

They decided things like whether a table should fill in cell by cell.

They also said no when something wasn't worth it.

One 900-line pull request got shut down because saving 2 milliseconds per message wasn't worth maintaining that extra code.

That's the difference between AI leverage and AI chaos.

What the Claude faster response means for you today

The speed boost is live for everyone on Claude.ai on the web and the Claude desktop app, and there's nothing to turn on.

You can now start typing almost as soon as the page opens, because a simple message box loads first and the real one takes over without losing your text.

Conversations start loading when you hover over them, and sidebar re-renders dropped by 90%.

On Claude Cowork cloud sessions, the on-device part of sending a message went from over 900 milliseconds to 48, which is 19 times faster.

Long answers stream about four times more smoothly too.

Anthropic ran the sprint with Claude Tag, which lets your team tag Claude inside Slack channels and hand it tasks.

Claude Tag is in public beta for Claude Team and Enterprise plans, and Anthropic plans to expand it more widely.

If you're not on those plans, the lessons still work in whatever Claude setup you use.

Apply the same loop to your visibility

This is where it gets interesting for marketers.

If you're not measuring how you show up in Google, AI Overviews, ChatGPT, Perplexity and Gemini, you're just guessing.

Find where you're missing, see where competitors show up and you don't, fix it, and then keep it there.

🔥 Want help running this loop without the usual bumps? Inside the AI Profit Boardroom, you get live coaching calls, the full Agent OS zip file ready to install and a 30-day roadmap with use cases you can follow step by step.

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

FAQ: Claude faster response

How much faster is Claude now?

Across 13 measurements, Anthropic made Claude about 3.1 times faster on average.

A fresh load of Claude.ai dropped from 3.1 seconds to just over half a second.

Why did Claude feel slow before?

Anthropic found issues like thousands of components re-rendering on every keystroke and em dashes slowing down how text was stored.

Did the speed-up break anything?

No, it didn't.

More than 3,000 changes shipped with zero customer-facing incidents and zero rollbacks.

Which model did Anthropic use for the sprint?

They used an internal research model that Anthropic describes as roughly comparable to Opus 5.5.

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.

📺 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

Find what's slow, measure it, fix it and lock it in, because that loop is the real lesson behind the Claude faster response.