I'm an ex-VFX artist with 15 years in the industry, and I recently dipped back in. I saw some posts on here about Gen AI and people worried about their jobs. I just did a project—I'm not posting it yet as I don't know if it'll be crossing the rules here—but we basically had a production pipeline with about five people, and Gen AI fit in where it did.

It was very interesting to see what it can and can't do. In my opinion, it's going to be added to the pipeline. It has its place, but there were definitely certain things where we think its role is mainly as a renderer. There are certainly things that it could not do, and it needed the help of traditional visual effects tools. They had to be used in concert to get the job done.

Text-to-video absolutely cannot work. It won't work. It's just absolutely impossible. I don't even know why the Gen AI guys actually thought that medium of text-to-video would succeed. But image-to-video and video-to-video? That's definitely where it's at. text to video is so beyond frustrating it is wild. The only reason i can think of it is a thing because it causes multiple retries to get what you want which is more tokens spent for the providers. No determinisim. Which you need 100% in vfx work.

I could later, if there's interest and I get the clear, post the project. It is high quality. Five people, money was spent on it, and I would say there was about 100x cost savings in what we were able to accomplish. But we learned a lot about what the tools can and can't do. When I was working, the project we did would cost between 5-7m to get done. I didn't spend over 60K on this.

Certainly, actors are going to be okay, and of course animatiors, riggers. Voice actors are absolutely going to be okay. I don't even know what they're worrying about. The audio Gen AI stuff is so far off, it is wild. You just cant get nuance direction out of the tools. IMO, all the preproduction jobs are okay.

From my career in visual effects, Uncanny Valley was a big problem. To cross it took billions of dollars and tens of years, decades even, to get across that. Gen AI has huge Uncanny Valley problems, as I think we all realize. So we made sure out project was 'animated' stylistically. We took that animated approach because, you know, we're professionals and we've been through that circus before. We're a team of very experienced professionals.

That is essentially my thoughts on it: it's definitely going to be a renderer, another tool in the toolbox. Throughout my career, I've used many. I'd say like every couple of years I was switching renderers, doing a different renderer. I think that's the same thing here. Gen AI is going to be a renderer.

So Yeah, looking forward to your thoughts. I'd like to share my project because it was a really great learning experience of what was possible and all the different techniques that had to be used to get a shot done. Again, a lot of software had to be used, just like doing visual effects to get something done.

Budget notes:

3 mins. 5 people. VA and composer, storyboard artist, concept artist that could do previz, and gen ai technical artist.

49 500 USD of which 2660 was gen ai credits across various image and video models.

What do you guys think?

Here is some saftey research I sponsored on a threat vector in multi agent systems.

Basically, a harmful task can be transformed into a series of beneign tasks, and then results recomposed into a harmful task by an abliterated orchestrator agent driving other agents that have 'saftey' guard rails.

In short, there is no safety with this technology.

https://www.daios.tech/research/compartmentalized-harm

Here is some saftey research I sponsored on a threat vector in multi agent systems.

Basically, a harmful task can be transformed into a series of beneign tasks, and then results recomposed into a harmful task by an abliterated orchestrator agent driving other agents that have 'saftey' guard rails.

In short, there is no safety with this technology.

https://www.daios.tech/research/compartmentalized-harm

So I suspect a lot of people here are enjoying coding agents, I sure do.

I'm someone who uses a lot of mcp servers, as a lot of people here probably do. I've been talking to some other webdev and SEOs and we are wondering if the landscape is going to change abruptly because of these tools.

We have had some problems integrating agents in some legacy software like wordpress, there were some examples where the WPEngine limited us access as well as some ciritical plugins we want to have our agents access to update.

This is extremely frustrating, i liken it to dropping out of warp speed. So we started thinking, why not write closer to the metal, just have a coding agent write up a minimal go server, raw html templates and straight pg? All on docker for easy deploy. No frameworks. Optimized for speed and cost.

Of course, this would be for landing pages for business. Something very light weight to get a presence on google and very easy for the agent to manipulate. Go is chosen because errors happen at compile time and built in testing, which is ideal for coding agents. and postgress cause agents absolutey rip at writing raw sql like they do raw html. If you havn't hooked up a sql to your agent have it write raw sql it will blow your mind.

So my question is are you prioritizing dev workflows that make it easier for your agents to control? Like moving your workflows to do what agents are better at doing, not what humans are better at doing. What are you doing?

Another thing we suspect is going to happen, is Agent to Agent communications are going to take off in the near future and this is going to transform the web. So when i need something, i ask my agent, and it will discover and negotiate with other agents to get what I want, all in the background.

I also think this will put pressure to store information differently. Right now, we develop and store information and organize it so it's easy for us, humans, we like folder structures, forums, essentially lists. Agents do way better with vector search. Basically, just dump all our data into a data lake, and our agents can sort through that far more efficiently with a combination of embeddings, clustering and graphs.

Also wondering if anyone is seeing other trends with the new technology struggling to plugin to the old technology. Like, "Wow, this just isn't built for agents."

I ran a comprehensive audit of this subreddit — 25 top posts, ~280 comment branches analyzed and clustered. The dominant theme? AI anxiety, hallucination frustration, and a lot of snake oil being called out. So I want to share what's actually working.

Agent Context Engineering — Reducing Hallucinations by ~96%

A few state-of-the-art techniques that actually work:

1. Model choice matters. Pushing through Opus 4.6 or 4.8 (or equivalent frontier models) performs significantly better than cheaper models for source validation tasks. This is expensive. But you want to pick the right model for the job. Sourcing info out of GSC? Flash model. Ultra Cheap. Analysis? Brainstorm? Frontier model. And it's important to know frontier is average of the day. Nothing exemplarary.

2. Double the prompt. When you give instructions, copy and paste them twice. This is current state-of-the-art research and goes a long way toward eliminating hallucinations. Weird this works, but it does.

3. One chat = one task. Don't build up long context histories. Kick off sub-chats for specific jobs — e.g., "go get my Google Search Console data and process it like XYZ." Keep the context window tight and focused. I see people doing many things in the same chat window. That is caleld context poisoning, it blows up the probability field. Keep your chat super focused. My rule is 1 chat per decision.

4. Use a task list tool. For complex instructions, your agent should have a task list tool to stay on track. If you are doing a long horizon task, the tech isn't ready for that, its currently being figured out.

5. Clean source data. However you get it in c/p , an agent pulling it, MCP servers (which are better). Clean data in = clean output out.

6. Chain of Verification (CoVe). Have your agent kick off a sub-chat to generate questions about the data it pulled in, then answer those questions. You can double-check the answers. This catches errors that simple summarization misses.

7. OODA loop for context management. Observe, Orient, Decide, Act. Keep a decision-making framework in your context , this is the one that works for me, limit what's in the window to what matters for the current step.

Observe - get a limited range of data I need for my decision. standard seo tools, GSC, GA4, Ahrefs, Semrush ect. I usually limit these for the task at hand.

Orient - contextualize it to my goal.

Decide - execute the decision I require.

Act - What tools to write information, wordpress, other CRM.

Combine these and you can really strengthen the agent's output. The 80%+ source validation failure rates people are reporting here? That's catastrophic and most of it is fixable with proper context engineering.

This is really good for simple maintainence work like addressing content rot, updating titles and headers to current information.

Why AI Isn't Replacing SEO Work

There's a narrative dominated by the frontier labs that these systems are intelligent and will replace human intelligence. They're not. LLMs are statistical models on our information, not statistical models on our intelligence.

They look comprehensive and impressive. They're getting better every year. But the source validation errors and hallucinations you're seeing? Those are mistakes that jrs wouldn't make. That should tell you everything you need to know about these systems.

The people saying these systems are "smart" are competing for trillions of dollars of investment. That messaging isn't for us. It's for the investment community salivating at removing people of balance sheets.

What Agents Are Actually Good At

• Information retrieval — pulling data, processing it, summarizing it
• Form filling — updating WordPress, Strapi, CMS platforms at light speed
• First drafts — structure, brainstorming, source gathering
• Better search — finding and compiling information faster than any human can

They are better and faster at this than any human will ever be.

What they're not good at:

• Creativity — the strategic angle, the unique perspective
• Judgment — knowing what to publish, what to cut, what matters
• Curation — selecting the right information from the noise

This current architecture of LLM's will never beat humans at this.

I still hire English majors and linguists to drive pilots and do the writing. You need someone who knows how to write and is educated in it to give the right instructions to the LLM. If you just lazy prompt you are asking for "average," that's going to destroy your content. I think enough people have seen people do this. Clickup is a good example

The GEO / "Get Listed in LLMs" Snake Oil

This needs to be said plainly: all these LLM providers use search tools. When ChatGPT or any LLM needs current information, it doesn't use its internal training data it goes out and searches. That's tool calling to search engines.

So getting "listed in an LLM" is just standard SEO. Why would you wait two months for your content to be in a model's training data when they're going to search for it in real time anyway? That's nonsense. They search because search gives the most updated information.

An LLM searches like a human except far wider and deeper. So do your standard SEO and you're covered. The agencies selling "GEO" are exploiting people who don't understand how LLM search actually works. They are selling a service you can make in half an hour with claude code and a open router api key.

How I Made This Post

I used an agent to do a comprehensive review of this subreddit , clustering ~280 posts and comment branches by topic. The agent collected, classified, and summarized everything. I read the results, spoke my perspective, had the agent draft some ideas, then I changed things around and published.

The agent didn't come up with the ideas for this post. It suggested some things, but the communication , the strategy, the message, the judgment , that was human. The agent was the research assistant. I was the one making the calls.

I made a first draft, and then read line by line and corrected and fine tuned.

That's the model. Use the tools for what they're good at, getting a lot of information quickly. Apply your experience, creativity and judgment to what they're not. If you have career anxiety, start using the tools and applying them to what they're actually good at. The people who learn to work with agents will outpace those who don't. But the agents aren't doing the work themselves.

The fact that the frontier tech can't even make a proper reddit post should reasure everyone about their job security.

I ran a comprehensive audit of this subreddit — 25 top posts, ~280 comment branches analyzed and clustered. The dominant theme? AI anxiety, hallucination frustration, and a lot of snake oil being called out. So I want to share what's actually working.

Agent Context Engineering — Reducing Hallucinations by ~96%

A few state-of-the-art techniques that actually work:

1. Model choice matters. Pushing through Opus 4.6 or 4.8 (or equivalent frontier models) performs significantly better than cheaper models for source validation tasks. This is expensive. But you want to pick the right model for the job. Sourcing info out of GSC? Flash model. Ultra Cheap. Analysis? Brainstorm? Frontier model. And it's important to know frontier is average of the day. Nothing exemplarary.

2. Double the prompt. When you give instructions, copy and paste them twice. This is current state-of-the-art research and goes a long way toward eliminating hallucinations. Weird this works, but it does.

3. One chat = one task. Don't build up long context histories. Kick off sub-chats for specific jobs — e.g., "go get my Google Search Console data and process it like XYZ." Keep the context window tight and focused. I see people doing many things in the same chat window. That is caleld context poisoning, it blows up the probability field. Keep your chat super focused. My rule is 1 chat per decision.

4. Use a task list tool. For complex instructions, your agent should have a task list tool to stay on track. If you are doing a long horizon task, the tech isn't ready for that, its currently being figured out.

5. Clean source data. However you get it in c/p , an agent pulling it, MCP servers (which are better). Clean data in = clean output out.

6. Chain of Verification (CoVe). Have your agent kick off a sub-chat to generate questions about the data it pulled in, then answer those questions. You can double-check the answers. This catches errors that simple summarization misses.

7. OODA loop for context management. Observe, Orient, Decide, Act. Keep a decision-making framework in your context , this is the one that works for me, limit what's in the window to what matters for the current step.

Observe - get a limited range of data I need for my decision. standard seo tools, GSC, GA4, Ahrefs, Semrush ect. I usually limit these for the task at hand.

Orient - contextualize it to my goal.

Decide - execute the decision I require.

Act - What tools to write information, wordpress, other CRM.

Combine these and you can really strengthen the agent's output. The 80%+ source validation failure rates people are reporting here? That's catastrophic and most of it is fixable with proper context engineering.

This is really good for simple maintainence work like addressing content rot, updating titles and headers to current information.

Why AI Isn't Replacing SEO Work

There's a narrative dominated by the frontier labs that these systems are intelligent and will replace human intelligence. They're not. LLMs are statistical models on our information, not statistical models on our intelligence.

They look comprehensive and impressive. They're getting better every year. But the source validation errors and hallucinations you're seeing? Those are mistakes that jrs wouldn't make. That should tell you everything you need to know about these systems.

The people saying these systems are "smart" are competing for trillions of dollars of investment. That messaging isn't for us. It's for the investment community salivating at removing people of balance sheets.

What Agents Are Actually Good At

• Information retrieval — pulling data, processing it, summarizing it
• Form filling — updating WordPress, Strapi, CMS platforms at light speed
• First drafts — structure, brainstorming, source gathering
• Better search — finding and compiling information faster than any human can

They are better and faster at this than any human will ever be.

What they're not good at:

• Creativity — the strategic angle, the unique perspective
• Judgment — knowing what to publish, what to cut, what matters
• Curation — selecting the right information from the noise

This current architecture of LLM's will never beat humans at this.

I still hire English majors and linguists to drive pilots and do the writing. You need someone who knows how to write and is educated in it to give the right instructions to the LLM. If you just lazy prompt you are asking for "average," that's going to destroy your content. I think enough people have seen people do this. Clickup is a good example

The GEO / "Get Listed in LLMs" Snake Oil

This needs to be said plainly: all these LLM providers use search tools. When ChatGPT or any LLM needs current information, it doesn't use its internal training data it goes out and searches. That's tool calling to search engines.

So getting "listed in an LLM" is just standard SEO. Why would you wait two months for your content to be in a model's training data when they're going to search for it in real time anyway? That's nonsense. They search because search gives the most updated information.

An LLM searches like a human except far wider and deeper. So do your standard SEO and you're covered. The agencies selling "GEO" are exploiting people who don't understand how LLM search actually works. They are selling a service you can make in half an hour with claude code and a open router api key.

How I Made This Post

I used an agent to do a comprehensive review of this subreddit , clustering ~280 posts and comment branches by topic. The agent collected, classified, and summarized everything. I read the results, spoke my perspective, had the agent draft some ideas, then I changed things around and published.

The agent didn't come up with the ideas for this post. It suggested some things, but the communication , the strategy, the message, the judgment , that was human. The agent was the research assistant. I was the one making the calls.

I made a first draft, and then read line by line and corrected and fine tuned.

That's the model. Use the tools for what they're good at, getting a lot of information quickly. Apply your experience, creativity and judgment to what they're not. If you have career anxiety, start using the tools and applying them to what they're actually good at. The people who learn to work with agents will outpace those who don't. But the agents aren't doing the work themselves.

The fact that the frontier tech can't even make a proper reddit post should reasure everyone about their job security.

I ran a comprehensive audit of this subreddit — 25 top posts, ~280 comment branches analyzed and clustered. The dominant theme? AI anxiety, hallucination frustration, and a lot of snake oil being called out. So I want to share what's actually working.

Agent Context Engineering — Reducing Hallucinations by ~96%

A few state-of-the-art techniques that actually work:

1. Model choice matters. Pushing through Opus 4.6 or 4.8 (or equivalent frontier models) performs significantly better than cheaper models for source validation tasks. This is expensive. But you want to pick the right model for the job. Sourcing info out of GSC? Flash model. Ultra Cheap. Analysis? Brainstorm? Frontier model. And it's important to know frontier is average of the day. Nothing exemplarary.

2. Double the prompt. When you give instructions, copy and paste them twice. This is current state-of-the-art research and goes a long way toward eliminating hallucinations. Weird this works, but it does.

3. One chat = one task. Don't build up long context histories. Kick off sub-chats for specific jobs — e.g., "go get my Google Search Console data and process it like XYZ." Keep the context window tight and focused. I see people doing many things in the same chat window. That is caleld context poisoning, it blows up the probability field. Keep your chat super focused. My rule is 1 chat per decision.

4. Use a task list tool. For complex instructions, your agent should have a task list tool to stay on track. If you are doing a long horizon task, the tech isn't ready for that, its currently being figured out.

5. Clean source data. However you get it in c/p , an agent pulling it, MCP servers (which are better). Clean data in = clean output out.

6. Chain of Verification (CoVe). Have your agent kick off a sub-chat to generate questions about the data it pulled in, then answer those questions. You can double-check the answers. This catches errors that simple summarization misses.

7. OODA loop for context management. Observe, Orient, Decide, Act. Keep a decision-making framework in your context , this is the one that works for me, limit what's in the window to what matters for the current step.

Observe - get a limited range of data I need for my decision. standard seo tools, GSC, GA4, Ahrefs, Semrush ect. I usually limit these for the task at hand.

Orient - contextualize it to my goal.

Decide - execute the decision I require.

Act - What tools to write information, wordpress, other CRM.

Combine these and you can really strengthen the agent's output. The 80%+ source validation failure rates people are reporting here? That's catastrophic and most of it is fixable with proper context engineering.

This is really good for simple maintainence work like addressing content rot, updating titles and headers to current information.

Why AI Isn't Replacing SEO Work

There's a narrative dominated by the frontier labs that these systems are intelligent and will replace human intelligence. They're not. LLMs are statistical models on our information, not statistical models on our intelligence.

They look comprehensive and impressive. They're getting better every year. But the source validation errors and hallucinations you're seeing? Those are mistakes that jrs wouldn't make. That should tell you everything you need to know about these systems.

The people saying these systems are "smart" are competing for trillions of dollars of investment. That messaging isn't for us. It's for the investment community salivating at removing people of balance sheets.

What Agents Are Actually Good At

• Information retrieval — pulling data, processing it, summarizing it
• Form filling — updating WordPress, Strapi, CMS platforms at light speed
• First drafts — structure, brainstorming, source gathering
• Better search — finding and compiling information faster than any human can

They are better and faster at this than any human will ever be.

What they're not good at:

• Creativity — the strategic angle, the unique perspective
• Judgment — knowing what to publish, what to cut, what matters
• Curation — selecting the right information from the noise

This current architecture of LLM's will never beat humans at this.

I still hire English majors and linguists to drive pilots and do the writing. You need someone who knows how to write and is educated in it to give the right instructions to the LLM. If you just lazy prompt you are asking for "average," that's going to destroy your content. I think enough people have seen people do this. Clickup is a good example

The GEO / "Get Listed in LLMs" Snake Oil

This needs to be said plainly: all these LLM providers use search tools. When ChatGPT or any LLM needs current information, it doesn't use its internal training data it goes out and searches. That's tool calling to search engines.

So getting "listed in an LLM" is just standard SEO. Why would you wait two months for your content to be in a model's training data when they're going to search for it in real time anyway? That's nonsense. They search because search gives the most updated information.

An LLM searches like a human except far wider and deeper. So do your standard SEO and you're covered. The agencies selling "GEO" are exploiting people who don't understand how LLM search actually works. They are selling a service you can make in half an hour with claude code and a open router api key.

How I Made This Post

I used an agent to do a comprehensive review of this subreddit , clustering ~280 posts and comment branches by topic. The agent collected, classified, and summarized everything. I read the results, spoke my perspective, had the agent draft some ideas, then I changed things around and published.

The agent didn't come up with the ideas for this post. It suggested some things, but the communication , the strategy, the message, the judgment , that was human. The agent was the research assistant. I was the one making the calls.

I made a first draft, and then read line by line and corrected and fine tuned.

That's the model. Use the tools for what they're good at, getting a lot of information quickly. Apply your experience, creativity and judgment to what they're not. If you have career anxiety, start using the tools and applying them to what they're actually good at. The people who learn to work with agents will outpace those who don't. But the agents aren't doing the work themselves.

The fact that the frontier tech can't even make a proper reddit post should reasure everyone about their job security.

https://preview.redd.it/nfr8mizvmi3h1.png?width=4840&format=png&auto=webp&s=0d2f033edafee7b558e830f33ef1908932fb5a94

Holy shit I think I found my people, you guys are so positive and optimistic I love it. Looking forward to talking with you.

So my ideas are from synthesizing Jeremy Rifkin, Collata Perez, Noah Yuval Hirari and Samil Ishmail. I imagine this crowd is some fans of these guys.

I break down our civilization into 6 key infrastructures and major disruptions happen on convergences of tech disruptions on these key infrastructures. These enable our economy, to transform disorder to order at scale.

For example, Internet being a convergence of information and communications infrastructure, industrial revolution being a convergence of communications, energy and logistics, and the biggest and rarest, a reformation, a convergence between information and market infrastructure. Last time one happened was 1450.

I think the singularity could be described as all of these infrastructures converging. Which is what we are going through right now, multiple internet revolutions (self driving cars,), an industrial revolution and a reformation. This is the first time this has happened, they are all overlapped, which is why shit is getting so damn crazy.

It's transforming the way we organize as a species.

We are in a rare transformation, convergence of tech disruptions to information infrastructure (llms) and market infrastructure (bitcoin/blockchain). The last time this happened was in 1450 and cause our institutions to be rebuilt from the ground up. Basically, there is a very strong disruptive event as information infrastructure accelerates people's capability of using the new market infrastructure.

From this historical frame work, I'm going to make a number of predictions based on historical precedent of what happened last time. The majority of these changes are being driven by the current reformation, the convergence of information and market infrastructure. It's the biggest change we go through. Far far bigger than the industrial revolution.

1. You will see this when ai agents start using bitcoin for payments, and capital market activities like raising capital and papying dividends. Basically, agents are going to start functioning like public companies, buying and selling goods and services.
2. Agents will invert Coase's law and we will see nation states and corporations dissolve into networks of agents and people cooperating. No more heirarchies or vertically integrated institutions. Only horizontally scaling access networks.
3. You will be using different money, created by yourself or AI agents to record trades. Money will change in a very big way.
4. AI Agents will start their own religion and attract human followers.
5. Internet revolutions are expanding, We will be getting a global energy internet.

Future is going to be crazy. But historical precedent points to how this is going to go, it will be bumpy, but we are coming up on enlightment part 2.

What do you think? Hope to hear from you