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Join the channel 👇
whatsapp.com/channel/0029Vb…
Partnerly connects creators with AI and tech brands and shares new paid campaign opportunities.
Join the channel 👇
whatsapp.com/channel/0029Vb…
🚀 Calling AI & tech creators!
Join our WhatsApp Channel for paid campaign updates and new collaboration opportunities 👇
whatsapp.com/channel/0029Vb…
At Partnerly, we connect creators with AI and tech brands to bring exciting products to more people.
We’re building a community wherupcoming campaigns, explore paid collaborations, and find projects that fit their content and audience.
Interested in working with AI brands? Join us and stay tuned for new opportunities! 🤝
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Caitlin Jiang retweeted
🚀 Calling AI & tech creators!
Join our WhatsApp Channel for paid campaign updates and new collaboration opportunities 👇
whatsapp.com/channel/0029Vb…
At Partnerly, we connect creators with AI and tech brands to bring exciting products to more people.
We’re building a community wherupcoming campaigns, explore paid collaborations, and find projects that fit their content and audience.
Interested in working with AI brands? Join us and stay tuned for new opportunities! 🤝
Join our WhatsApp Channel for paid campaign updates and new collaboration opportunities 👇
whatsapp.com/channel/0029Vb…
At Partnerly, we connect creators with AI and tech brands to bring exciting products to more people.
We’re building a community wherupcoming campaigns, explore paid collaborations, and find projects that fit their content and audience.
Interested in working with AI brands? Join us and stay tuned for new opportunities! 🤝
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I think the next generation of AI tutors will be defined less by how much they know and more by how well they guide.
Knowing when to explain, when to ask, and when to let the student struggle a little is a different kind of intelligence.
Knowing when to explain, when to ask, and when to let the student struggle a little is a different kind of intelligence.
A young learner speaks with Raku and can interrupt or redirect mid-thought. The same exchange turns those spoken ideas into a visual map or a story world as the talk continues. Raku carries an earlier interest or unfinished thread into the next question or mission.
This is the l in most AI education tools. The model produces the finished sentence, the child copies it, and the thinking step disappears.
Gakku keeps the learner inside that step.
The interaction is the product. It requires the child to form the idea, question it, and revise it before any polished result appears.
I have spent decades studying systems that amplify human judgment rather than substitute for it. The same principle shows up here in a form built for ages 6-16: real-time voice, persistent memory of what the learner already tried, and a refusal to complete the work.
The child remains the author. Raku only holds the thread.
Link: gakku.ai
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Caitlin Jiang retweeted
A young learner speaks with Raku and can interrupt or redirect mid-thought. The same exchange turns those spoken ideas into a visual map or a story world as the talk continues. Raku carries an earlier interest or unfinished thread into the next question or mission.
This is the l in most AI education tools. The model produces the finished sentence, the child copies it, and the thinking step disappears.
Gakku keeps the learner inside that step.
The interaction is the product. It requires the child to form the idea, question it, and revise it before any polished result appears.
I have spent decades studying systems that amplify human judgment rather than substitute for it. The same principle shows up here in a form built for ages 6-16: real-time voice, persistent memory of what the learner already tried, and a refusal to complete the work.
The child remains the author. Raku only holds the thread.
Link: gakku.ai
This is the l in most AI education tools. The model produces the finished sentence, the child copies it, and the thinking step disappears.
Gakku keeps the learner inside that step.
The interaction is the product. It requires the child to form the idea, question it, and revise it before any polished result appears.
I have spent decades studying systems that amplify human judgment rather than substitute for it. The same principle shows up here in a form built for ages 6-16: real-time voice, persistent memory of what the learner already tried, and a refusal to complete the work.
The child remains the author. Raku only holds the thread.
Link: gakku.ai
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A good AI learning companion should make children more confident in their own thinking, not more dependent on the machine. That's a much better measure of success than how quickly it can solve a problem.
The biggest mistake in AI education is automating the part where the child learns.
The questions. The wrong turns. The “wait, let me try again.”
That’s the part gakku is built around.
@gakkuai helps kids aged 6-16 practice writing, speaking and thinking through live , its AI learning companion.
What interests me from a product-design perspective is what happens when the child changes their mind. Having small kids myself, I know this happens a lot.
A learner can interrupt, revise an idea or make a choice on screen. Raku has to adapt while keeping the conversation, visuals and mission progress in sync.
The interface becomes part of the thinking. Ideas take shape on screen, and the child’s choices shape what happens next.
That’s a much more interesting design problem than generating a good reply. The system has to follow the learner’s intent, remember what came before and decide when to ask, act or wait.
For me, the design challenge is knowing how much help to give. Too little and the child gets stuck. Too much and the system takes over the decisions they need to practice making.
The child still has to explain, choose and revise.
The interesting bet here: the time spent thinking is the value of the product.
When AI can generate almost any answer, helping someone develop their own becomes a lot more interesting.
Currently trying this with my 6yo at home, will report back on how it goes.
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Caitlin Jiang retweeted
The biggest mistake in AI education is automating the part where the child learns.
The questions. The wrong turns. The “wait, let me try again.”
That’s the part gakku is built around.
@gakkuai helps kids aged 6-16 practice writing, speaking and thinking through live , its AI learning companion.
What interests me from a product-design perspective is what happens when the child changes their mind. Having small kids myself, I know this happens a lot.
A learner can interrupt, revise an idea or make a choice on screen. Raku has to adapt while keeping the conversation, visuals and mission progress in sync.
The interface becomes part of the thinking. Ideas take shape on screen, and the child’s choices shape what happens next.
That’s a much more interesting design problem than generating a good reply. The system has to follow the learner’s intent, remember what came before and decide when to ask, act or wait.
For me, the design challenge is knowing how much help to give. Too little and the child gets stuck. Too much and the system takes over the decisions they need to practice making.
The child still has to explain, choose and revise.
The interesting bet here: the time spent thinking is the value of the product.
When AI can generate almost any answer, helping someone develop their own becomes a lot more interesting.
Currently trying this with my 6yo at home, will report back on how it goes.
The questions. The wrong turns. The “wait, let me try again.”
That’s the part gakku is built around.
@gakkuai helps kids aged 6-16 practice writing, speaking and thinking through live , its AI learning companion.
What interests me from a product-design perspective is what happens when the child changes their mind. Having small kids myself, I know this happens a lot.
A learner can interrupt, revise an idea or make a choice on screen. Raku has to adapt while keeping the conversation, visuals and mission progress in sync.
The interface becomes part of the thinking. Ideas take shape on screen, and the child’s choices shape what happens next.
That’s a much more interesting design problem than generating a good reply. The system has to follow the learner’s intent, remember what came before and decide when to ask, act or wait.
For me, the design challenge is knowing how much help to give. Too little and the child gets stuck. Too much and the system takes over the decisions they need to practice making.
The child still has to explain, choose and revise.
The interesting bet here: the time spent thinking is the value of the product.
When AI can generate almost any answer, helping someone develop their own becomes a lot more interesting.
Currently trying this with my 6yo at home, will report back on how it goes.
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Caitlin Jiang retweeted
This is one of the best consumer AI apps I've seen.
Most AI apps remain static chat wrappers: text input ➔ spinner ➔ text output. But real learning is a dynamic, stateful "Interaction Flow" with intent, pacing, and rhythm.
Building an AI Agent that keeps users in a live moving from a reactive "replier" to an active "Interaction Conductor."
Orchestrating voice, canvas, and memory in real time is an entirely different engineering challenge:
1. Pacing over wall-of-text output: Instead of dumping everything at once, the system controls pacing, i.e., knowing when to speak, render UI cards, or pause for user input like a seasoned coach.
2. Interactive, live-generated workspace: The AI’s voice, canvas elements, and visual cards evolve in millisecond-aligned sync as you speak, creating a live, immersive session.
3. Full-duplex audio & interaction checkpoint recovery: Uses continuous streaming and syntactic completion so you can think without being cut off. More importantly, when you break away to ask side questions, the entire multimodal flow seamlessly resumes from your exact checkpoint in the core session.
4. Stateful memory graph: Friction points and milestones compile into persistent state across sessions to drive targeted reinforcement instead of starting from scratch.
Moving from content-driven generation to interaction-driven orchestration - building stateful, multimodal partners with agency and pacing - is where interface design is heading.
Most AI apps remain static chat wrappers: text input ➔ spinner ➔ text output. But real learning is a dynamic, stateful "Interaction Flow" with intent, pacing, and rhythm.
Building an AI Agent that keeps users in a live moving from a reactive "replier" to an active "Interaction Conductor."
Orchestrating voice, canvas, and memory in real time is an entirely different engineering challenge:
1. Pacing over wall-of-text output: Instead of dumping everything at once, the system controls pacing, i.e., knowing when to speak, render UI cards, or pause for user input like a seasoned coach.
2. Interactive, live-generated workspace: The AI’s voice, canvas elements, and visual cards evolve in millisecond-aligned sync as you speak, creating a live, immersive session.
3. Full-duplex audio & interaction checkpoint recovery: Uses continuous streaming and syntactic completion so you can think without being cut off. More importantly, when you break away to ask side questions, the entire multimodal flow seamlessly resumes from your exact checkpoint in the core session.
4. Stateful memory graph: Friction points and milestones compile into persistent state across sessions to drive targeted reinforcement instead of starting from scratch.
Moving from content-driven generation to interaction-driven orchestration - building stateful, multimodal partners with agency and pacing - is where interface design is heading.
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We talk a lot about teaching kids to use AI, but we also need to teach them how to think independently when AI is available. Learning to defend an argument, question assumptions, and revise an idea matters even more in a world where answers are cheap.
I truly believe conversational AI will drive the biggest educational leap of our generation. 1-on-1 tutoring is the gold standard for education, but it doesn't scale. AI can change that with truly active conversational learning.
Annie and team set out to solve this with @gakkuai, and they've done a great job. Gakku is an AI mentoring app for K-12 students. Rather than doing the work for them, the voice chat coach guides students through 10 min essay writing "missions" that cover brainstorming, drafting, and revising. It actively trains kids to generate their own ideas, structure logical arguments, and articulate themselves clearly.
If you have school-aged kids, I highly recommend checking it out: gakku.ai
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Caitlin Jiang retweeted
I truly believe conversational AI will drive the biggest educational leap of our generation. 1-on-1 tutoring is the gold standard for education, but it doesn't scale. AI can change that with truly active conversational learning.
Annie and team set out to solve this with @gakkuai, and they've done a great job. Gakku is an AI mentoring app for K-12 students. Rather than doing the work for them, the voice chat coach guides students through 10 min essay writing "missions" that cover brainstorming, drafting, and revising. It actively trains kids to generate their own ideas, structure logical arguments, and articulate themselves clearly.
If you have school-aged kids, I highly recommend checking it out: gakku.ai
Annie and team set out to solve this with @gakkuai, and they've done a great job. Gakku is an AI mentoring app for K-12 students. Rather than doing the work for them, the voice chat coach guides students through 10 min essay writing "missions" that cover brainstorming, drafting, and revising. It actively trains kids to generate their own ideas, structure logical arguments, and articulate themselves clearly.
If you have school-aged kids, I highly recommend checking it out: gakku.ai
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Caitlin Jiang retweeted
AI will forever change education.
I've been using @gakkuai. It's an AI-native learning app for ages 6–16 focused on writing, speaking, and thinking.
This is pretty different than every other educational app you've seen.
You get a coach that talks to you in real time and guides . You can have complete conversations, and the app learns about you and adjusts the lessons based on what you need.
That alone is pretty cool.
Check out the video I recorded.
Here is the link to download the phone app: gakku.ai
Thanks to the @gakkuai team for partnering with me on this post.
I've been using @gakkuai. It's an AI-native learning app for ages 6–16 focused on writing, speaking, and thinking.
This is pretty different than every other educational app you've seen.
You get a coach that talks to you in real time and guides . You can have complete conversations, and the app learns about you and adjusts the lessons based on what you need.
That alone is pretty cool.
Check out the video I recorded.
Here is the link to download the phone app: gakku.ai
Thanks to the @gakkuai team for partnering with me on this post.
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Caitlin Jiang retweeted
You can't solve real-world physical tasks through autoregressive sampling alone. The physical world is entirely unforgiving to hallucination or approximate pattern matching. If we want actual autonomous robotics we have to engineer causality and discrete reasoning back into the loop.
Biwei and the team at Aether AI is doing exactly the kind of foundational, non-consensus work the field actually needs right now.
Biwei and the team at Aether AI is doing exactly the kind of foundational, non-consensus work the field actually needs right now.
I have spent more than a decade studying causality. For me, causality is about understanding the rules that govern how actions change the world, and reasoning about their consequences before acting.
Scaling has made today's models remarkably capable at spotting patterns and predicting what comes next. A system that acts in the physical world needs more. It has to ask what will happen if it acts, check the outcome, and adapt when reality doesn't match the plan. In the physical world, trial and error can be costly or unsafe, and an early mistake can carry through the rest of a task.
When we started Aether AI, robotics was the obvious place to begin.
CRIS-0 is our first integrated system built on these ideas. It tracks the conditions that matter for a task, predicts how each action will change them, and replans when something unexpected happens. In our demo, that means taking a metal can off a plate before heating it, and telling an empty can from an unfinished drink while tidying a room.
Physical AI is just our first proving ground. We're building towards causal intelligence that also works for forecasting, scientific discovery, and more.
The technical write-up is linked in the comments. x.com/AetherLab_AI/s…
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CausalWM predicts motion and 3D geometry before generating future frames.
CRIS-0’s agent generates its own positioning functions (SAM3 + RGB-D + IK) from demos. When one hits an IK joint limit, it patches the function from the error, retries the stage, and keeps the patched version.
Basically the coding-agent loop, applied to robot control.
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Caitlin Jiang retweeted
CRIS-0’s agent generates its own positioning functions (SAM3 + RGB-D + IK) from demos. When one hits an IK joint limit, it patches the function from the error, retries the stage, and keeps the patched version.
Basically the coding-agent loop, applied to robot control.
Basically the coding-agent loop, applied to robot control.
I have spent more than a decade studying causality. For me, causality is about understanding the rules that govern how actions change the world, and reasoning about their consequences before acting.
Scaling has made today's models remarkably capable at spotting patterns and predicting what comes next. A system that acts in the physical world needs more. It has to ask what will happen if it acts, check the outcome, and adapt when reality doesn't match the plan. In the physical world, trial and error can be costly or unsafe, and an early mistake can carry through the rest of a task.
When we started Aether AI, robotics was the obvious place to begin.
CRIS-0 is our first integrated system built on these ideas. It tracks the conditions that matter for a task, predicts how each action will change them, and replans when something unexpected happens. In our demo, that means taking a metal can off a plate before heating it, and telling an empty can from an unfinished drink while tidying a room.
Physical AI is just our first proving ground. We're building towards causal intelligence that also works for forecasting, scientific discovery, and more.
The technical write-up is linked in the comments. x.com/AetherLab_AI/s…
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Having an opinion is easy. Explaining it in a way that makes someone else understand your point is much harder.
Good writing isn't just about putting thoughts on paper. It's about making the connection between your ideas clear to another person.
Good writing isn't just about putting thoughts on paper. It's about making the connection between your ideas clear to another person.
Most adults arguing on X never learned the difference between a strong reason and a weak one.
@gakkuai is teaching it to kids.
Look at the screenshots. The lesson is about school uniforms. The opinion is that uniforms are good, and the kid gets five reasons and has to pick the port it.
"School uniforms are cool" gets marked weak. So does "I don't like picking out clothes in the morning." Then Raku, the character inside the app, explains why that second one fails. It only matters to one person. It doesn't connect to anyone else in the school.
The same screen spells out the rule. A strong reason connects directly to the opinion and shows why it matters to other people, not just you.
What I like is the order. The kid makes the call first, sees which ones don't hold up, and only then gets the explanation. Most AI apps are built to hand over a finished answer as fast as possible. This one makes the kid do the thinking and uses the conversation with Raku to teach it, which is how writing actually gets learned.
gakku.ai
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Caitlin Jiang retweeted
Most adults arguing on X never learned the difference between a strong reason and a weak one.
@gakkuai is teaching it to kids.
Look at the screenshots. The lesson is about school uniforms. The opinion is that uniforms are good, and the kid gets five reasons and has to pick the port it.
"School uniforms are cool" gets marked weak. So does "I don't like picking out clothes in the morning." Then Raku, the character inside the app, explains why that second one fails. It only matters to one person. It doesn't connect to anyone else in the school.
The same screen spells out the rule. A strong reason connects directly to the opinion and shows why it matters to other people, not just you.
What I like is the order. The kid makes the call first, sees which ones don't hold up, and only then gets the explanation. Most AI apps are built to hand over a finished answer as fast as possible. This one makes the kid do the thinking and uses the conversation with Raku to teach it, which is how writing actually gets learned.
gakku.ai
@gakkuai is teaching it to kids.
Look at the screenshots. The lesson is about school uniforms. The opinion is that uniforms are good, and the kid gets five reasons and has to pick the port it.
"School uniforms are cool" gets marked weak. So does "I don't like picking out clothes in the morning." Then Raku, the character inside the app, explains why that second one fails. It only matters to one person. It doesn't connect to anyone else in the school.
The same screen spells out the rule. A strong reason connects directly to the opinion and shows why it matters to other people, not just you.
What I like is the order. The kid makes the call first, sees which ones don't hold up, and only then gets the explanation. Most AI apps are built to hand over a finished answer as fast as possible. This one makes the kid do the thinking and uses the conversation with Raku to teach it, which is how writing actually gets learned.
gakku.ai
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Indie developers should be watching this space closely.
You don't need to build an entire AI game on day one. A single interactive character, a branching dialogue scene, or a small story prototype could be enough to discover something compelling.
You don't need to build an entire AI game on day one. A single interactive character, a branching dialogue scene, or a small story prototype could be enough to discover something compelling.
I made a Draconia Malfoid Visual Novel.
I used Vivix's new W1 model, which is unique for it's real-time interaction.
No more static cut-outs of characters and still backgrounds.
Vivix's W1 model costs $0.003 per second and is fully interactive, shattering the cost barrier y and real-time.
Vivix W1 is also different. It's not a simple clip model, its beta API offers something unique - it lets you modify the video on the fly, a real-time interactive video model.
Most AI video is prompt in, clip out, then repeat. Every prompt a break or cut in the video. Vivix W1 is continuous, creating a seamless video experience.
That means you can have an LLM handle the conversation, and stream the new chat and emotions and scenes as signals into the API to direct the character's video in real-time, without pauses. You can take characters anywhere in the world, any setting.
That's kinda nuts.
Vivix reached out to see if I was interested, so I put together a quick AI Visual Novel system with their API.
The prototype video is impressive at this speed. The W1 model is directable in real-time. The responses, outfit changes and the dances are all live.
The W1 seamless responsiveness is something no clip generator offers. At just about 10 bucks/hour of video, gaming applications are within reach.
I do wish the audio was better, it tends to have drop-outs, and it currently doesn't do seamless scene transitions. But the model is in full scale training now, and I hope to have characters moving from location to location with the player.
The site is clip-only for now, real-time is the beta API, browser interaction is later. This is the part I am most interested in. You can check it out at: platform.vivix.ai/vivix-w1-model…
*Sponsored by @VivixLabs_HQ
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Caitlin Jiang retweeted
I made a Draconia Malfoid Visual Novel.
I used Vivix's new W1 model, which is unique for it's real-time interaction.
No more static cut-outs of characters and still backgrounds.
Vivix's W1 model costs $0.003 per second and is fully interactive, shattering the cost barrier y and real-time.
Vivix W1 is also different. It's not a simple clip model, its beta API offers something unique - it lets you modify the video on the fly, a real-time interactive video model.
Most AI video is prompt in, clip out, then repeat. Every prompt a break or cut in the video. Vivix W1 is continuous, creating a seamless video experience.
That means you can have an LLM handle the conversation, and stream the new chat and emotions and scenes as signals into the API to direct the character's video in real-time, without pauses. You can take characters anywhere in the world, any setting.
That's kinda nuts.
Vivix reached out to see if I was interested, so I put together a quick AI Visual Novel system with their API.
The prototype video is impressive at this speed. The W1 model is directable in real-time. The responses, outfit changes and the dances are all live.
The W1 seamless responsiveness is something no clip generator offers. At just about 10 bucks/hour of video, gaming applications are within reach.
I do wish the audio was better, it tends to have drop-outs, and it currently doesn't do seamless scene transitions. But the model is in full scale training now, and I hope to have characters moving from location to location with the player.
The site is clip-only for now, real-time is the beta API, browser interaction is later. This is the part I am most interested in. You can check it out at: platform.vivix.ai/vivix-w1-model…
*Sponsored by @VivixLabs_HQ
I used Vivix's new W1 model, which is unique for it's real-time interaction.
No more static cut-outs of characters and still backgrounds.
Vivix's W1 model costs $0.003 per second and is fully interactive, shattering the cost barrier y and real-time.
Vivix W1 is also different. It's not a simple clip model, its beta API offers something unique - it lets you modify the video on the fly, a real-time interactive video model.
Most AI video is prompt in, clip out, then repeat. Every prompt a break or cut in the video. Vivix W1 is continuous, creating a seamless video experience.
That means you can have an LLM handle the conversation, and stream the new chat and emotions and scenes as signals into the API to direct the character's video in real-time, without pauses. You can take characters anywhere in the world, any setting.
That's kinda nuts.
Vivix reached out to see if I was interested, so I put together a quick AI Visual Novel system with their API.
The prototype video is impressive at this speed. The W1 model is directable in real-time. The responses, outfit changes and the dances are all live.
The W1 seamless responsiveness is something no clip generator offers. At just about 10 bucks/hour of video, gaming applications are within reach.
I do wish the audio was better, it tends to have drop-outs, and it currently doesn't do seamless scene transitions. But the model is in full scale training now, and I hope to have characters moving from location to location with the player.
The site is clip-only for now, real-time is the beta API, browser interaction is later. This is the part I am most interested in. You can check it out at: platform.vivix.ai/vivix-w1-model…
*Sponsored by @VivixLabs_HQ
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The browser tools will be an important next step. A low-cost video model is interesting, but making it easy for people to test and prototype with it is what could bring more builders in.
$0.003 per second.
That's what @VivixLabs_HQ W1 costs to generate video.
W1 is a real-time interactive video model.
For the price of one Seedance 2.5 clip you get about 77 clips of the same length. An 8 second clip takes about 8 seconds to make, and in Vivix's own side by against Seedance 2.5 and Kling 3.0.
The price matters most for the real-time part. Interactive video keeps generating the whole time you're inside it, like a story that reacts to you or visuals that follow a performance. Every second costs money, so cheap is what lets it keep running.
It still has gaps in cinematic fidelity, complex motion and dense scenes, and full scale training is still underway. Real time works through the API today, and browser tools are coming.
I want to see those gaps close while the price stays this low.
platform.vivix.ai/vivix-w1-model…
0
0
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Caitlin Jiang retweeted
$0.003 per second.
That's what @VivixLabs_HQ W1 costs to generate video.
W1 is a real-time interactive video model.
For the price of one Seedance 2.5 clip you get about 77 clips of the same length. An 8 second clip takes about 8 seconds to make, and in Vivix's own side by against Seedance 2.5 and Kling 3.0.
The price matters most for the real-time part. Interactive video keeps generating the whole time you're inside it, like a story that reacts to you or visuals that follow a performance. Every second costs money, so cheap is what lets it keep running.
It still has gaps in cinematic fidelity, complex motion and dense scenes, and full scale training is still underway. Real time works through the API today, and browser tools are coming.
I want to see those gaps close while the price stays this low.
platform.vivix.ai/vivix-w1-model…
That's what @VivixLabs_HQ W1 costs to generate video.
W1 is a real-time interactive video model.
For the price of one Seedance 2.5 clip you get about 77 clips of the same length. An 8 second clip takes about 8 seconds to make, and in Vivix's own side by against Seedance 2.5 and Kling 3.0.
The price matters most for the real-time part. Interactive video keeps generating the whole time you're inside it, like a story that reacts to you or visuals that follow a performance. Every second costs money, so cheap is what lets it keep running.
It still has gaps in cinematic fidelity, complex motion and dense scenes, and full scale training is still underway. Real time works through the API today, and browser tools are coming.
I want to see those gaps close while the price stays this low.
platform.vivix.ai/vivix-w1-model…
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The interesting part of enterprise AI isn't just getting an agent to complete a task. It's making sure the next agent can start with what the last one learned. That's the kind of infrastructure worth paying attention to.
I think companies are about to realize that giving everyone an AI assistant does not automatically make the company smarter.
People are learning better prompts, building useful workflows, generating customer insights, and improving processes with tools like Claude and Codex. igence still stays within individual sessions. The next employee or agent often starts from scratch. That is why @Busabase4agent caught my attention.
What I like is that Busabase is trying to make this intelligence stick. Instead of useful context disappearing inside individual chats, people and agents can work from the same shared knowledge. Playbooks capture the methods that work, Insights makes it easier to see how the team improves over time, and AirApps can turn that knowledge into internal tools and dashboards. Underneath it all, Bases keeps the company’s information structured so different agents can use the right context without everyone constantly rebuilding it from scratch.
To me, this is where enterprise AI is heading. The real advantage won't come just from smarter agents. It will come from ensuring the work those agents and humans create becomes reusable company capability. busabase.com
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