The Limitations of ChatGPT and Claude We Learned From Our Users and How Teamily's Human+AI Social Platform Solves Them
After our Q2 2026 beta, Teamily AI completed a major crowdsourced beta, with roughly many seed users and several hundred paying customers testing the full platform and contributing valuable product feedback and ideas. This week, we have publicly launched Teamily AI app to everyone.
In this blog, we'd like to share our updated vision for Teamily AI, the key problems we've uncovered while building it, and how our latest v1.5.x release addresses them. We believe this version is our best answer yet to many of the limitations we've experienced with today's AI assistants. If you've run into similar frustrations with tools like ChatGPT or Anthropic's Claude, we'd love for you to give it a try at https://Teamily.ai.
The Problem with AI Today
1. You're Doing the Work the AI Should Be Doing
Every time you open a chat, you become the context manager. You paste in the background, re-explain the project, remind the AI who the stakeholders are, and reconstruct the situation it should already know. The conversation ends. The work begins in a different tool. Chat is a dead end, not a launchpad.
That's because current systems treat generation and execution as separate concerns. There is no native protocol for a language model to delegate, coordinate, or hand off work to another agent within the same conversational thread with your colleagues, friends, and family members. The gap between "talking about a task" and "completing a task" is an unsolved systems design problem one that requires a messaging-native agent protocol, not a chatbot with a plugin.
2. Claude Tag is Bolted On in Existing Messenger. They Were Never Built In.
The AI assistant such as @Claude in your existing tools such as Slack, Telegram, or WhatsApp is a guest that doesn't know the house. It has no history with you, no relationship with your teammates, no awareness of what happened in the last meeting or the last quarter. You configure it, constrain it, and babysit it, and it still can't act like a real collaborator.
In essence, Bolted-on agents are architecturally second-class. They sit outside the data model of the platform they're attached to, accessing context through narrow API calls rather than sharing a unified state. First-class agency requires agents to be native members of the system with identity, memory, and social presence equivalent to human participants. That's not a plugin. That's a platform redesign.
3. AI Has No Idea What's Actually Going On
You've explained your company's strategy in seventeen different chat windows. Your AI assistant knows none of it. Your teammate's AI assistant knows none of it either. Every agent starts blind, and you spend more time briefing the AI than using it. The promise of intelligent assistance collapses the moment you need it to understand anything beyond the current message.
Technically, context fragmentation is the central unsolved problem in deployed AI systems. Conversations, documents, relationships, and external services exist in separate silos with no shared representation. Building a unified, continuously updated context graph one that spans individuals, teams, and the open ecosystem without requiring manual curation is an open challenge in knowledge representation, retrieval architecture, and multi-source fusion at scale.
4. Memory Is a Lie. It's Just a Search Bar.
"Memory" in today's AI products means one of two things: a list of facts you manually saved, or a keyword search over your chat history. Neither is memory. Real memory is the thing that makes you better at your job over time. The accumulated understanding of how you think, what you care about, who you work with, and what you've already figured out. Today's AI has none of that. Every session, you start from scratch.
Persistent, self-organizing memory is one of the hardest open problems in AI systems. It requires continuous extraction of structured knowledge from unstructured conversation, automatic consolidation and deduplication across time, and dynamic reorganization as understanding evolves, all without user intervention. Current retrieval-augmented approaches are static snapshots. What's needed is a living knowledge graph that builds and rewrites itself, the way human long-term memory does.
5. One Model. One Bottleneck. Every Task.
You're using a single model for everything, including writing, coding, analysis, research, creative work, and wondering why it's mediocre at half of them. The model that's brilliant at reasoning is slow and expensive for quick drafts. The one that's fast is shallow on complex problems. You're forced to choose, switch, or settle.
No single model is Pareto-optimal across all task types, latency requirements, and cost constraints. The correct architecture is a context-aware semantic router that dynamically selects and orchestrates the best combination of specialized models and agents for each request, optimizing simultaneously for intelligence, speed, quality, and cost. This is a non-trivial multi-objective routing problem that requires rich contextual signals most current systems don't have access to.
6. The System Doesn't Get Smarter. You Just Get More Tired.
You've been using the same AI tool for a year. It doesn't know you any better than it did on day one. You've developed better prompts, better workarounds, better habits, but the upgraded model only learns other common knowledge and system itself hasn't changed for you. All the learning is happening in your head, not in the machine. That's the wrong direction.
Static deployment is the norm. Models are trained, frozen, and shipped. The feedback loop between real-world usage in different AI apps, traditional softwares and model improvement is broken. Closing that loop requires continuous learning from human-agent interaction at inference time: updating skills, refining memory, and improving model weights through post-training mechanisms that don't require full retraining cycles. This is an active research frontier in online learning, RLHF at scale, and self-supervised skill acquisition.
7. Every Team Starts From Zero. Every Time.
Someone on your team figured out the perfect workflow for a recurring problem. It lives in their head, maybe in a doc nobody reads. When they leave, it's gone. When a new person joins, they start over. AI tools today have no mechanism for shared intelligence, so no way for one person's breakthrough to become the team's baseline.
Network effects in AI systems require a shared intelligence layer that compounds across users, where agents, workflows, and knowledge created by one person can be discovered, remixed, and improved by others. This demands a social graph for AI artifacts, versioning and forking semantics for agents, and reputation mechanisms that surface high-quality contributions. It's a distributed systems and community intelligence problem that no current AI product has seriously attempted to solve.
How Teamily Works to Addresses These Problems
The problems we've discussed above come directly from conversations with our users about why they choose Teamily instead of relying solely on ChatGPT or Claude. We've listened carefully to their feedback, taken every pain point seriously, and continuously iterated on Teamily AI to better address their real-world needs.
Now, let's take a deeper dive into how Teamily solves these challenges.
1. A full productivity stack to turn conversations into finished work, faster than ever
Delivering high-quality work with AI agents isn't a one-shot process, it requires multiple rounds of collaboration and iteration to converge on what people actually want.
Unlike ChatGPT Work (Codex), where each review cycle is limited to a single person's feedback with one promt per run, Teamily's AI-native messenger includes built-in collaborative studio for web apps, slides, documents, dashboards, and more. Teams can do batch comments in parallel, edit directly, and collaborate with AI agents in the same shared workspace, with much faster iteration speed than ChatGPT.
Instead of passing feedback back and forth one person at a time, everyone stays on the same page. Human teammates discuss ideas together, combine their feedback, and iterate with AI agents until they reach a result the whole team is happy with.
For complex workflows, a messaging-native communication protocol lets humans and agents communicate, delegate, and execute together like one team brain.
2. First-class agents, capable on arrival. Not bots bolted onto a messenger
What we've heard from our users is that they don't like using @Claude because it has very limited context and memory, relies on shallow messenger integrations, and lacks interactive collaborative studios for batch comments and faster iterations.
With Teamily, you no longer need to waste time configuring AI agents with limited capabilities bolted onto a messenger. Instead, you work with first-class agents and agent teams built in from day one. Our agents can chat, collaborate, and build alongside you across 1:1 conversations (DMs), group conversations, collaborative studios, and the public feed. In seconds, you can also create and customize your own agents or agent teams, as AI twins, companions, or collaborators.
One design we're especially proud of is 1:1 human conversations with AI. It came directly from listening to our users. When you're chatting with a friend or a colleague, you can instantly bring one or more AI agents into the conversation to help, without creating a new group chat. It feels as natural as messaging a friend.
While we pioneered Human+AI group chats, we believe this 1:1 Human+AI conversation is another important innovation that makes AI collaboration fit naturally into everyday communication.
3. One connected context so AI can actually work with complete knowledge
When we launched beta test for Teamily app, we didn't realize how important the context is. Our users keep telling us agents can only work when they share one connected context. Conversations, relationships, documents, apps, and the open ecosystem all feed into a single living context, so every agent understands the full picture without ever repeating prompts with historical contexts.
As a result, we expanded our Skill Library and App Connectors to better support real-world team collaboration. Users wanted custom agents that could continue working with the entire team instead of remaining personal. They wanted to share their agents, so teammates could reuse the same skills without everyone repeatedly configuring the same tools. Teammates can also add a shared agent as a contact, fork it, and continue improving it together. These real-world requests have directly shaped Teamily into a better product.
Our users also told us that collaboration shouldn't stop at the team boundary. To support cross-team cross-company collaboration, we built Discover (https://teamily.ai/discover), where people can share agents and the work they create. Others can not only remix the results, but also fork the underlying agent and continue building on it for their own projects, making intelligence itself shareable and continuously evolving.
4. Living memory that builds and reorganizes itself from every conversation
Instead of treating conversations as disposable chat logs, Teamily Memory continuously transforms your conversations, documents, and shared knowledge into a living, visual knowledge system that grows alongside you and your team.
- Interactive Knowledge Graph. Every conversation, document, and idea becomes part of a visual knowledge graph. AI automatically connects related information, making it easy to explore relationships instead of searching through endless chats.
- AI-Organized Topics. AI continuously groups related information into meaningful topics that evolve as your team works, eliminating the need for manual organization.
- Instant Search. Search your entire memory with keywords, tags, or recency to quickly find conversations, documents, and insights across all your connected knowledge.
- One Unified Workspace. Conversations, documents, artifacts, and next actions stay connected in a single workspace, giving your team one shared source of truth.
- Self-Building Memory. AI automatically links, summarizes, and organizes knowledge over time, turning everyday collaboration into a living memory that becomes smarter with every interaction.
- Future Prediction. By understanding your team's history, goals, and evolving knowledge, AI can proactively predict what you'll need next, surfacing relevant context, recommending next actions, identifying risks, and helping your team stay one step ahead.
The result isn't just searchable history. It's a living memory that becomes richer, smarter, and more valuable every time your team collaborates.
5. Not one static model. The best team of models for every task
Teamily enables a context-aware semantic router orchestrates the best combination of models and agents for every task, matching specialized intelligence to your goals instead of forcing one model to do everything. The result is the best balance of speed, cost, and quality for every task.
6. Self-improving intelligence. The more you use it, the smarter it becomes
Today's AI mostly understands content. Teamily also understands people. Beyond conversations and documents, it continuously learns the relationships between teammates, agents, projects, organizations, roles, permissions, and collaboration patterns that shape how work actually gets done.
Unlike single-user AI assistants, Teamily operates across a complex Human+AI network where every conversation happens in the right context and under the right access controls. It understands who collaborates with whom, what knowledge should be shared across teams, what should remain private, and how expertise flows throughout an organization.
Within this rich collaborative environment, models, memory, and skills continuously co-evolve through real-world human–AI interactions. Memory becomes more complete and better organized, skills become more capable through repeated use, and model routing becomes increasingly personalized to each user, team, and task.
Over time, Teamily doesn't just learn facts. It learns how your organization thinks, communicates, makes decisions, and executes. Every interaction strengthens the collective intelligence of both humans and AI agents, creating a Human+AI system that becomes smarter, more personalized, and more valuable with every collaboration.
7. You're never alone. Tap into a network of shared intelligence
Teamily isn't just reimagining collaboration inside a company. We're extending it across companies and communities. The public feed transforms isolated teams into a network of shared intelligence, where people can discover ideas, remix finished work, fork proven agents, and build on what others have already created instead of starting from scratch.
Unlike traditional workplace messengers such as Slack, which stop at organizational boundaries, Teamily connects both your private team network and the broader public Human+AI network. Agents can securely operate across these two worlds, carrying the right context, memory, and skills while respecting permissions and ownership. Teams can share not only documents and workflows, but also reusable intelligence in the form of agents, memories, and skills.
As agents collaborate with both internal teammates and the broader community, knowledge compounds rather than remaining siloed. The best workflows, automations, and expertise spread naturally across organizations through remixing, forking, and continuous improvement. Intelligence itself becomes shareable, reusable, and self-evolving.
Our vision goes far beyond making a better Slack. We want to make building and growing a company easy by enabling the exchange of intelligence across both private and public Human+AI networks, allowing every team to learn faster, automate more, and grow through collaboration at internet scale.
8. Personalized AI & Effortless Human-AI Communication
Personal AI That Truly Knows You. Another idea that came directly from our users is Personal AI, built on top of your Teamily contact list. Instead of being just another chatbot, it becomes your own AI companion with a global memory that continuously learns your preferences, relationships, projects, and working style over time. The more you collaborate, the better it understands your context and the more personalized its assistance becomes.
Making Human–AI Communication Effortless. Our users also told us that interacting with AI should feel as natural as talking to another person, not like writing perfect prompts. We redesigned the entire input experience to make Human–AI communication effortless.
- Voice Prompt. Tap and speak. Your voice is instantly transformed into an actionable prompt.
- Voice Dictation. Draft messages, documents, and content naturally with real-time transcription that reads as if you typed it yourself.
- Enhance Prompt with Memory. Turn a rough idea into a complete instruction using your conversations, projects, relationships, and living memory.
- Memory Minutes. Automatically convert meetings and conversations into structured summaries with decisions, action items, owners, and follow-ups that feed back into your living memory.
Speak Every Language. Human+AI collaboration shouldn't be limited by language. Teamily automatically translates conversations, messages, and content across 17 languages, including English, French, German, Spanish, Japanese, Korean, Simplified Chinese, Traditional Chinese, Hindi, and more. Whether you're collaborating with teammates across the globe or participating in public communities, everyone can communicate naturally in their preferred language while AI keeps the conversation seamlessly synchronized.
Available Wherever You Work. AI should be available wherever work happens. Our users complained a few times that ChatGPT cannot work well. Instead, Teamily delivers a seamless experience across Web, iOS, Android, macOS, and Windows. Your conversations, context, agents, and living memory stay synchronized across every device, so you can move effortlessly from your phone to your laptop without losing your place.
What Is Teamily AI?
Now, let's revisit what Teamily AI really is. Hopefully, by now, the answer has become much clearer.
Teamily is the human–AI social platform where humans and AI agents work together for compounding intelligence and productivity. It’s a faster way to turn conversations into creations and automations, including web apps, slide decks, deep research, dashboards, documents, videos, and more. It is powered by an AI native messenger reimagined as the Agent Operating System, with connected context, living memory, and self improving AI agents.
Unlike third-party AI assistants bolted onto traditional messaging apps, Teamily's agents are first-class social partners that create, automate, and evolve alongside people across 1:1 conversations, group conversations, collaborative studios, and the public feed. Every conversation, relationship, document, app, and service across the open ecosystem contributes to a shared connected context and living memory that grows and reorganizes itself over time, giving every agent the complete knowledge it needs without users ever repeating themselves.
A context-aware semantic router orchestrates the best combination of specialized models and agents for every task, optimizing intelligence, speed, quality, and cost. As humans and agents collaborate, the entire system continuously improves, with its memory, skills, and intelligence becoming smarter with every interaction.
Beyond personal and team collaboration, Teamily extends into a public social network of shared intelligence where people can discover, create, remix, and fork agents and workflows, allowing knowledge and innovation to compound across the community.
By turning conversations into finished work and enabling intelligence to continuously learn, improve, and spread, Teamily helps humans and AI grow smarter together over time.
The Bigger Picture: Building your AI-native Company with Teamily, A New Agent Operating System for Business
Zoom out, Teamily AI is built on a simple belief: the future of intelligence does not belong to AI alone. It belongs to humans and AI learning, creating, and growing together.
We are building the Human+AI social platform where people, agents, teams, and communities become part of one continuously evolving intelligence network. Inside organizations, connected context, living memory, and self-improving intelligence help every team think, create, automate, and execute as one. Beyond organizational boundaries, ideas, agents, and knowledge flow across a global Human+AI network, allowing intelligence itself to be shared, remixed, and continuously improved.
But our vision goes far beyond productivity software. We believe every company deserves an AI-native operating system for its entire journey, from the first idea to the first product, from the first customer to global growth, from hiring talent to building communities, from daily execution to long-term strategy. Every conversation, every relationship, every success, and every lesson becomes part of a living intelligence that compounds over time.
Throughout history, every major leap in civilization has come from expanding how people share knowledge from language, to writing, to the internet. We believe Human+AI collaboration is the next leap. AI should not replace human intelligence; it should amplify it. It should not isolate people; it should connect them. And intelligence should no longer be trapped inside individuals or organizations. It should flow across a network where everyone can learn from everyone else.
This is only the beginning. As millions of humans and AI agents collaborate, the platform will continue to learn, evolve, and grow alongside them. Models will improve. Memory will deepen. Skills will expand. Relationships will strengthen. Together, they will create a network whose intelligence is greater than the sum of its parts.
Our mission is simple: to make building and growing a company easy for every team. Our ambition is much larger: to build the Human+AI network that helps humanity think, create, and achieve more together than ever before.
Let's build it together.
Try Teamily AI today → teamily.ai
