AI Creator | AI Tools | Automation | Future of Tech
Cardiff, Wales, UK
Joined December 2025
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If you're exploring AI coding tools, this is worth a look.
Qoder is offering DeepSeek-V4.1-Flash-1017 at 0.0x Credits through October 24. No paid plan is required, and both free and paid accounts can access it.
Download Qoder, sign in, and select the model from the picker.
qoder.com/qoder
🤝 Paid partnership
Eric Stone retweeted
Combining different creative references into one workflow could make video editing so much easier. 🔥
🤝 Paid partnership
Eric Stone retweeted
Taking inspiration from multiple videos and applying it directly to your own footage is a smart approach to content creation.
🤝 Paid partnership
Eric Stone retweeted
This gives creators more control over their editing style without starting from scratch every time.
🤝 Paid partnership
Eric Stone retweeted
Imagine matching the hook, visuals, and captions from different videos with just one prompt. Huge time-saver for creators.
🤝 Paid partnership
Eric Stone retweeted
One model, multiple quantization options, and a focus on efficiency. This is the kind of flexibility that makes open-source AI exciting.
Aloha!🌺 Meet Ornith-1.5-122B: No Tradeoff Between Strong and Fast
Stronger. Faster. Leaner. Ornith-1.5-122B delivers frontier-level capabilities with exceptional inference efficiency. With superior instruction following, stronger task decomposition, and deeper understanding of real-world workflows, it achieves state-of-the-art performance among open-source models of comparable size, while running on a single NVIDIA DGX card!!
Open weights under the MIT license.
Coding
✅SWE-bench Verified: 0.845
✅TB2.1: 0.813
✅CyberGym: 0.800
✅NL2Repo: 0.512
Agentic
✅HLE: 0.446
✅BrowseComp: 0.785
✅MCP-Atlas: 0.746
✅Toolathlon: 0.708
🤗Hugging Face: huggingface.co/ornith-ai/Orn…
We can’t wait to see what you build with Ornith-1.5-122B.
Eric Stone retweeted
More efficient inference could make
capable AI systems easier to integrate into
real-world applications and workflows.
Aloha!🌺 Meet Ornith-1.5-122B: No Tradeoff Between Strong and Fast
Stronger. Faster. Leaner. Ornith-1.5-122B delivers frontier-level capabilities with exceptional inference efficiency. With superior instruction following, stronger task decomposition, and deeper understanding of real-world workflows, it achieves state-of-the-art performance among open-source models of comparable size, while running on a single NVIDIA DGX card!!
Open weights under the MIT license.
Coding
✅SWE-bench Verified: 0.845
✅TB2.1: 0.813
✅CyberGym: 0.800
✅NL2Repo: 0.512
Agentic
✅HLE: 0.446
✅BrowseComp: 0.785
✅MCP-Atlas: 0.746
✅Toolathlon: 0.708
🤗Hugging Face: huggingface.co/ornith-ai/Orn…
We can’t wait to see what you build with Ornith-1.5-122B.
Eric Stone retweeted
The most valuable memory for a creative agent may be the reasoning behind a decision, not just the decision itself.
Knowing why a lighting setup was approved or a camera move was rejected could make future revisions more consistent without replacing the director’s judgment.
#EverMind #EverOS
🤝 Paid partnership
The hardest part of AI filmmaking isn’t creating a beautiful shot. It’s remembering the one before it.
As an AI filmmaker, I’ve found that generating beautiful images is often easier than maintaining visual continuity.
Imagine a fifteen-second scene.
A young Chinese woman stands by a window, wearing an ivory silk blouse and faded blue skirt. Afternoon light falls from the left as the camera approaches.
The first shot looks perfect.
But in the next, her embroidery changes, her hairstyle shifts, the lighting reverses, and her facial proportions differ.
Both shots look convincing individually. Together, they no longer belong to the same film.
This happens regularly in my work. We spend hours correcting costumes, faces, lighting, camera direction and anatomy.
The problem isn’t necessarily the model. It’s the lack of reliable memory between tasks.
What if an AI agent could remember the creative decisions behind every shot?
I’ve been studying EverOS documentation, developed by EverMind.
I haven’t independently tested EverOS Cloud. This explores how its documented memory architecture might address filmmaking challenges, not a hands-on review.
A workflow I’d like to explore
First, establish a character sheet: a 22-year-old Chinese woman with consistent facial features, hairstyle, ivory blouse and blue skirt.
Define the visual language: soft afternoon light, muted colours, an 85mm aesthetic and restrained camera movement.
Next, record approved shots and corrections.
Perhaps the lighting was reversed, sleeves too short, or camera movement excessive.
Normally, these decisions scatter across prompts, notes and conversations.
My proposed workflow would preserve character sheets, shot notes and revision feedback as retrievable context.
According to its documentation, EverOS supports memory retrieval, recorded task experiences called Cases, and reusable Skills.
The value isn’t simply storing information. It’s retrieving relevant creative decisions before preparing another shot.
For the next scene, the agent could reference established details and corrections.
The blouse stays ivory. The skirt stays blue. Lighting and camera movement remain consistent.
That’s the continuity this workflow aims to support.
Memory cannot guarantee perfect generation. Models may still introduce inconsistencies. I would still inspect results, correct anatomy and make final artistic decisions.
But reducing manual reconstruction of context could improve production efficiency.
Memory should support the director, not replace the director.
EverOS also documents a distinction between accumulated task experience and established procedures, with human oversight remaining important.
Consistency doesn’t mean repetition. Changes in costume, lighting or composition may be intentional.
An assistant should remember decisions without treating them as permanent rules.
Creative judgement must remain human.
My assessment
AI filmmaking doesn’t simply need more image generators. It needs better ways to preserve the reasoning, corrections and decisions connecting shots.
EverOS presents a documented approach to persistent agent memory worth investigating in AI production.
Practical effectiveness still requires testing, particularly retrieval accuracy, setup complexity and reliable application of corrections.
This remains a proposed workflow based on EverMind’s official documentation, not a claim of successful product testing.
But the underlying challenge is real.
A film isn’t a collection of beautiful frames. It’s the relationship between them.
Perhaps one of the most valuable things an AI agent could learn is how to remember that relationship.
EverOS: evermind.ai/everos
Documentation: docs.evermind.ai/cloud/overv…
During the campaign, subscribing to EverOS Pro gives you 3× the standard monthly usage allowance for six months.
#EverMind #EverOS #AIFilmmaking
Eric Stone retweeted
For AI filmmaking, I’d want to test one specific workflow: retrieve the previous shot’s approved details, generate the next shot, and compare lighting, costume, facial features, and camera direction.
That would help measure whether persistent memory improves continuity in practice.
#EverMind #EverOS
🤝 Paid partnership
The hardest part of AI filmmaking isn’t creating a beautiful shot. It’s remembering the one before it.
As an AI filmmaker, I’ve found that generating beautiful images is often easier than maintaining visual continuity.
Imagine a fifteen-second scene.
A young Chinese woman stands by a window, wearing an ivory silk blouse and faded blue skirt. Afternoon light falls from the left as the camera approaches.
The first shot looks perfect.
But in the next, her embroidery changes, her hairstyle shifts, the lighting reverses, and her facial proportions differ.
Both shots look convincing individually. Together, they no longer belong to the same film.
This happens regularly in my work. We spend hours correcting costumes, faces, lighting, camera direction and anatomy.
The problem isn’t necessarily the model. It’s the lack of reliable memory between tasks.
What if an AI agent could remember the creative decisions behind every shot?
I’ve been studying EverOS documentation, developed by EverMind.
I haven’t independently tested EverOS Cloud. This explores how its documented memory architecture might address filmmaking challenges, not a hands-on review.
A workflow I’d like to explore
First, establish a character sheet: a 22-year-old Chinese woman with consistent facial features, hairstyle, ivory blouse and blue skirt.
Define the visual language: soft afternoon light, muted colours, an 85mm aesthetic and restrained camera movement.
Next, record approved shots and corrections.
Perhaps the lighting was reversed, sleeves too short, or camera movement excessive.
Normally, these decisions scatter across prompts, notes and conversations.
My proposed workflow would preserve character sheets, shot notes and revision feedback as retrievable context.
According to its documentation, EverOS supports memory retrieval, recorded task experiences called Cases, and reusable Skills.
The value isn’t simply storing information. It’s retrieving relevant creative decisions before preparing another shot.
For the next scene, the agent could reference established details and corrections.
The blouse stays ivory. The skirt stays blue. Lighting and camera movement remain consistent.
That’s the continuity this workflow aims to support.
Memory cannot guarantee perfect generation. Models may still introduce inconsistencies. I would still inspect results, correct anatomy and make final artistic decisions.
But reducing manual reconstruction of context could improve production efficiency.
Memory should support the director, not replace the director.
EverOS also documents a distinction between accumulated task experience and established procedures, with human oversight remaining important.
Consistency doesn’t mean repetition. Changes in costume, lighting or composition may be intentional.
An assistant should remember decisions without treating them as permanent rules.
Creative judgement must remain human.
My assessment
AI filmmaking doesn’t simply need more image generators. It needs better ways to preserve the reasoning, corrections and decisions connecting shots.
EverOS presents a documented approach to persistent agent memory worth investigating in AI production.
Practical effectiveness still requires testing, particularly retrieval accuracy, setup complexity and reliable application of corrections.
This remains a proposed workflow based on EverMind’s official documentation, not a claim of successful product testing.
But the underlying challenge is real.
A film isn’t a collection of beautiful frames. It’s the relationship between them.
Perhaps one of the most valuable things an AI agent could learn is how to remember that relationship.
EverOS: evermind.ai/everos
Documentation: docs.evermind.ai/cloud/overv…
During the campaign, subscribing to EverOS Pro gives you 3× the standard monthly usage allowance for six months.
#EverMind #EverOS #AIFilmmaking
Eric Stone retweeted
A character sheet should guide continuity, not prevent intentional changes.
The interesting challenge for agent memory is preserving established details while recognizing when a new scene deliberately changes costume, lighting, or composition.
#EverMind #EverOS
🤝 Paid partnership
The hardest part of AI filmmaking isn’t creating a beautiful shot. It’s remembering the one before it.
As an AI filmmaker, I’ve found that generating beautiful images is often easier than maintaining visual continuity.
Imagine a fifteen-second scene.
A young Chinese woman stands by a window, wearing an ivory silk blouse and faded blue skirt. Afternoon light falls from the left as the camera approaches.
The first shot looks perfect.
But in the next, her embroidery changes, her hairstyle shifts, the lighting reverses, and her facial proportions differ.
Both shots look convincing individually. Together, they no longer belong to the same film.
This happens regularly in my work. We spend hours correcting costumes, faces, lighting, camera direction and anatomy.
The problem isn’t necessarily the model. It’s the lack of reliable memory between tasks.
What if an AI agent could remember the creative decisions behind every shot?
I’ve been studying EverOS documentation, developed by EverMind.
I haven’t independently tested EverOS Cloud. This explores how its documented memory architecture might address filmmaking challenges, not a hands-on review.
A workflow I’d like to explore
First, establish a character sheet: a 22-year-old Chinese woman with consistent facial features, hairstyle, ivory blouse and blue skirt.
Define the visual language: soft afternoon light, muted colours, an 85mm aesthetic and restrained camera movement.
Next, record approved shots and corrections.
Perhaps the lighting was reversed, sleeves too short, or camera movement excessive.
Normally, these decisions scatter across prompts, notes and conversations.
My proposed workflow would preserve character sheets, shot notes and revision feedback as retrievable context.
According to its documentation, EverOS supports memory retrieval, recorded task experiences called Cases, and reusable Skills.
The value isn’t simply storing information. It’s retrieving relevant creative decisions before preparing another shot.
For the next scene, the agent could reference established details and corrections.
The blouse stays ivory. The skirt stays blue. Lighting and camera movement remain consistent.
That’s the continuity this workflow aims to support.
Memory cannot guarantee perfect generation. Models may still introduce inconsistencies. I would still inspect results, correct anatomy and make final artistic decisions.
But reducing manual reconstruction of context could improve production efficiency.
Memory should support the director, not replace the director.
EverOS also documents a distinction between accumulated task experience and established procedures, with human oversight remaining important.
Consistency doesn’t mean repetition. Changes in costume, lighting or composition may be intentional.
An assistant should remember decisions without treating them as permanent rules.
Creative judgement must remain human.
My assessment
AI filmmaking doesn’t simply need more image generators. It needs better ways to preserve the reasoning, corrections and decisions connecting shots.
EverOS presents a documented approach to persistent agent memory worth investigating in AI production.
Practical effectiveness still requires testing, particularly retrieval accuracy, setup complexity and reliable application of corrections.
This remains a proposed workflow based on EverMind’s official documentation, not a claim of successful product testing.
But the underlying challenge is real.
A film isn’t a collection of beautiful frames. It’s the relationship between them.
Perhaps one of the most valuable things an AI agent could learn is how to remember that relationship.
EverOS: evermind.ai/everos
Documentation: docs.evermind.ai/cloud/overv…
During the campaign, subscribing to EverOS Pro gives you 3× the standard monthly usage allowance for six months.
#EverMind #EverOS #AIFilmmaking
Eric Stone retweeted
One useful distinction in AI filmmaking is between remembering a reference and remembering why a shot was corrected.
Preserving both the approved result and its revision notes could give the next generation task more actionable context.
#EverMind #EverOS
🤝 Paid partnership
The hardest part of AI filmmaking isn’t creating a beautiful shot. It’s remembering the one before it.
As an AI filmmaker, I’ve found that generating beautiful images is often easier than maintaining visual continuity.
Imagine a fifteen-second scene.
A young Chinese woman stands by a window, wearing an ivory silk blouse and faded blue skirt. Afternoon light falls from the left as the camera approaches.
The first shot looks perfect.
But in the next, her embroidery changes, her hairstyle shifts, the lighting reverses, and her facial proportions differ.
Both shots look convincing individually. Together, they no longer belong to the same film.
This happens regularly in my work. We spend hours correcting costumes, faces, lighting, camera direction and anatomy.
The problem isn’t necessarily the model. It’s the lack of reliable memory between tasks.
What if an AI agent could remember the creative decisions behind every shot?
I’ve been studying EverOS documentation, developed by EverMind.
I haven’t independently tested EverOS Cloud. This explores how its documented memory architecture might address filmmaking challenges, not a hands-on review.
A workflow I’d like to explore
First, establish a character sheet: a 22-year-old Chinese woman with consistent facial features, hairstyle, ivory blouse and blue skirt.
Define the visual language: soft afternoon light, muted colours, an 85mm aesthetic and restrained camera movement.
Next, record approved shots and corrections.
Perhaps the lighting was reversed, sleeves too short, or camera movement excessive.
Normally, these decisions scatter across prompts, notes and conversations.
My proposed workflow would preserve character sheets, shot notes and revision feedback as retrievable context.
According to its documentation, EverOS supports memory retrieval, recorded task experiences called Cases, and reusable Skills.
The value isn’t simply storing information. It’s retrieving relevant creative decisions before preparing another shot.
For the next scene, the agent could reference established details and corrections.
The blouse stays ivory. The skirt stays blue. Lighting and camera movement remain consistent.
That’s the continuity this workflow aims to support.
Memory cannot guarantee perfect generation. Models may still introduce inconsistencies. I would still inspect results, correct anatomy and make final artistic decisions.
But reducing manual reconstruction of context could improve production efficiency.
Memory should support the director, not replace the director.
EverOS also documents a distinction between accumulated task experience and established procedures, with human oversight remaining important.
Consistency doesn’t mean repetition. Changes in costume, lighting or composition may be intentional.
An assistant should remember decisions without treating them as permanent rules.
Creative judgement must remain human.
My assessment
AI filmmaking doesn’t simply need more image generators. It needs better ways to preserve the reasoning, corrections and decisions connecting shots.
EverOS presents a documented approach to persistent agent memory worth investigating in AI production.
Practical effectiveness still requires testing, particularly retrieval accuracy, setup complexity and reliable application of corrections.
This remains a proposed workflow based on EverMind’s official documentation, not a claim of successful product testing.
But the underlying challenge is real.
A film isn’t a collection of beautiful frames. It’s the relationship between them.
Perhaps one of the most valuable things an AI agent could learn is how to remember that relationship.
EverOS: evermind.ai/everos
Documentation: docs.evermind.ai/cloud/overv…
During the campaign, subscribing to EverOS Pro gives you 3× the standard monthly usage allowance for six months.
#EverMind #EverOS #AIFilmmaking
Eric Stone retweeted
Visual continuity depends on more than a consistent character description.
Lighting direction, costume details, camera movement, and previous corrections all influence whether two shots feel like the same film.
Persistent memory could help carry those decisions between scenes, but the generated footage still needs review.
#EverMind #EverOS
🤝 Paid partnership
The hardest part of AI filmmaking isn’t creating a beautiful shot. It’s remembering the one before it.
As an AI filmmaker, I’ve found that generating beautiful images is often easier than maintaining visual continuity.
Imagine a fifteen-second scene.
A young Chinese woman stands by a window, wearing an ivory silk blouse and faded blue skirt. Afternoon light falls from the left as the camera approaches.
The first shot looks perfect.
But in the next, her embroidery changes, her hairstyle shifts, the lighting reverses, and her facial proportions differ.
Both shots look convincing individually. Together, they no longer belong to the same film.
This happens regularly in my work. We spend hours correcting costumes, faces, lighting, camera direction and anatomy.
The problem isn’t necessarily the model. It’s the lack of reliable memory between tasks.
What if an AI agent could remember the creative decisions behind every shot?
I’ve been studying EverOS documentation, developed by EverMind.
I haven’t independently tested EverOS Cloud. This explores how its documented memory architecture might address filmmaking challenges, not a hands-on review.
A workflow I’d like to explore
First, establish a character sheet: a 22-year-old Chinese woman with consistent facial features, hairstyle, ivory blouse and blue skirt.
Define the visual language: soft afternoon light, muted colours, an 85mm aesthetic and restrained camera movement.
Next, record approved shots and corrections.
Perhaps the lighting was reversed, sleeves too short, or camera movement excessive.
Normally, these decisions scatter across prompts, notes and conversations.
My proposed workflow would preserve character sheets, shot notes and revision feedback as retrievable context.
According to its documentation, EverOS supports memory retrieval, recorded task experiences called Cases, and reusable Skills.
The value isn’t simply storing information. It’s retrieving relevant creative decisions before preparing another shot.
For the next scene, the agent could reference established details and corrections.
The blouse stays ivory. The skirt stays blue. Lighting and camera movement remain consistent.
That’s the continuity this workflow aims to support.
Memory cannot guarantee perfect generation. Models may still introduce inconsistencies. I would still inspect results, correct anatomy and make final artistic decisions.
But reducing manual reconstruction of context could improve production efficiency.
Memory should support the director, not replace the director.
EverOS also documents a distinction between accumulated task experience and established procedures, with human oversight remaining important.
Consistency doesn’t mean repetition. Changes in costume, lighting or composition may be intentional.
An assistant should remember decisions without treating them as permanent rules.
Creative judgement must remain human.
My assessment
AI filmmaking doesn’t simply need more image generators. It needs better ways to preserve the reasoning, corrections and decisions connecting shots.
EverOS presents a documented approach to persistent agent memory worth investigating in AI production.
Practical effectiveness still requires testing, particularly retrieval accuracy, setup complexity and reliable application of corrections.
This remains a proposed workflow based on EverMind’s official documentation, not a claim of successful product testing.
But the underlying challenge is real.
A film isn’t a collection of beautiful frames. It’s the relationship between them.
Perhaps one of the most valuable things an AI agent could learn is how to remember that relationship.
EverOS: evermind.ai/everos
Documentation: docs.evermind.ai/cloud/overv…
During the campaign, subscribing to EverOS Pro gives you 3× the standard monthly usage allowance for six months.
#EverMind #EverOS #AIFilmmaking
Eric Stone retweeted
The real benefit of persistent agent memory is reducing repeated setup without blindly reusing old information.
A reliable system should retrieve relevant experience, preserve its original context, and make it easy to correct outdated records.
#EverMind #EverOS
🤝 Paid partnership
Meet EverOS Cloud. 💛
Your agent shouldn’t have to start from scratch every time a conversation ends.
The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again.
EverOS Cloud gives your agents persistent memory through an API:
• Remember users and retrieve relevant context across sessions
• Turn task experience into Cases and reusable Skill memories
• Bring conversations, images, and documents into your memory workflow
You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful.
Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months.
Start here:
everos.evermind.ai
Eric Stone retweeted
Duplicate memories can be just as problematic as missing memories.
When several agents record the same decision, deduplication and conflict handling become essential to keeping shared context reliable.
#EverMind #EverOS
🤝 Paid partnership
Meet EverOS Cloud. 💛
Your agent shouldn’t have to start from scratch every time a conversation ends.
The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again.
EverOS Cloud gives your agents persistent memory through an API:
• Remember users and retrieve relevant context across sessions
• Turn task experience into Cases and reusable Skill memories
• Bring conversations, images, and documents into your memory workflow
You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful.
Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months.
Start here:
everos.evermind.ai
Eric Stone retweeted
Turning task experience into reusable Skills sounds promising, but the quality of those Skills matters more than their quantity.
I'd want to know how developers can inspect the source, verify the conditions, and update a Skill when the workflow changes.
#EverMind #EverOS
🤝 Paid partnership
Meet EverOS Cloud. 💛
Your agent shouldn’t have to start from scratch every time a conversation ends.
The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again.
EverOS Cloud gives your agents persistent memory through an API:
• Remember users and retrieve relevant context across sessions
• Turn task experience into Cases and reusable Skill memories
• Bring conversations, images, and documents into your memory workflow
You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful.
Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months.
Start here:
everos.evermind.ai
Eric Stone retweeted
Persistent memory becomes more useful when agents can distinguish user preferences from project-specific decisions.
That separation matters when the same agent handles multiple projects with different requirements.
#EverMind #EverOS
🤝 Paid partnership
Meet EverOS Cloud. 💛
Your agent shouldn’t have to start from scratch every time a conversation ends.
The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again.
EverOS Cloud gives your agents persistent memory through an API:
• Remember users and retrieve relevant context across sessions
• Turn task experience into Cases and reusable Skill memories
• Bring conversations, images, and documents into your memory workflow
You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful.
Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months.
Start here:
everos.evermind.ai
Eric Stone retweeted
An agent remembering a conversation is useful. An agent retaining the reasoning behind a past decision is even more valuable.
The key challenge is keeping that experience relevant, verifiable, and reusable across sessions without carrying outdated assumptions into new tasks.
#EverMind #EverOS
🤝 Paid partnership
Meet EverOS Cloud. 💛
Your agent shouldn’t have to start from scratch every time a conversation ends.
The preferences a user shared. The decisions made during a project. The approach that worked on a previous task. That context should be available when it matters again.
EverOS Cloud gives your agents persistent memory through an API:
• Remember users and retrieve relevant context across sessions
• Turn task experience into Cases and reusable Skill memories
• Bring conversations, images, and documents into your memory workflow
You build the agent experience. We handle the memory infrastructure, so you can spend less time maintaining the stack and more time making your product useful.
Subscribe to EverOS Cloud Pro and get 3× the standard Pro usage allowance for 6 months.
Start here:
everos.evermind.ai
