Forem: Lumis

The Full Cognitive Cycle And Why Lumis Starts With Understanding

Lumis — Mon, 24 Nov 2025 05:35:27 +0000

For the past few years, I’ve been wrestling with a problem that’s surprisingly hard to articulate:

As someone who plays multiple roles every day, the challenge isn’t “being busy”, it’s being cognitively split.

Slack channels stack on top of Telegram groups.
Telegram groups stack on top of Discord communities.
Emails sneak in between everything else.

Dozens, sometimes hundreds, of messages across all these surfaces.

Not because I’m “popular.”
Not because I enjoy noise.
Simply because every role I hold connects me to a different network of people, expectations, updates, and decisions.

And no matter where I am, boarding a flight, running between meetings, or finally winding down at night,
the messages don’t stop.

At some point, I realized:
We don’t have a messaging overload problem.
We have a cognitive architecture problem.

This essay is my attempt to break that down—and to explain why Lumis begins where the real gap is.

The Five Layers of Human Communication

Most tools think communication is about sending and receiving messages. But when I look at my own day, the real process is much more complex.

I break it down into five layers—a cycle every one of us goes through, whether consciously or not:

Sensing (Input)

The world comes in fragmented pieces.
Some updates happen in Slack threads.
Some happen in Telegram groups.
Some happen in meetings, tone, or implicit context.

Understanding (Meaning)

Who is who?
Which role am I acting in?
Where are things now?
What changed?
What matters?

This is the core cognitive challenge.
And no tool truly helps us do it.

Expressing (Communication)

Once things make sense, we can articulate them, write updates, answer questions, draft reports, clarify decisions.

Acting (Execution)

Updating tasks, aligning people, moving projects, following up.

Agency (Operating on Your Behalf)

In a mature system, once understanding is deep enough, the tool can begin to act for you,
nudging, reminding, filtering, protecting your time, even communicating where safe.

This is the full cognitive cycle.
And the real fatigue we feel comes from the fact that today, humans have to manually stitch these layers together.

Why Most AI Tools Haven’t Solved the Real Pain

Over the past two years, I tried nearly every popular AI tool.

They helped me:

searching
polish English
translate emails
summarize long threads

But they all shared the same limitation:

They worked on the “cut edges” of communication, not the core.

They didn’t know:

what role I was in
who mattered to me
the history of a project
the long-term context behind a decision
the implicit dependencies
the interpersonal dynamics
what had happened earlier in the day

So in the end, I still had to:

read everything,
think it through myself,
understand the situation,
then ask AI to help with phrasing.

The hard part, understanding, was still mine.

AI solved expression.
It solved formatting.
It solved summarization.

But it didn’t solve cognition.

When Lumis Finally Felt “Different”

Recently, while using an internal build of Lumis, something clicked.

Lumis didn’t only show me messages.
It showed me states:

Here’s what progressed today.
Here are the things your role cares about.
Here’s what resurfaced.
Here’s the new risk.
Here’s what actually needs your judgment.

No scrolling.
No reconstructing.
No trying to piece together scattered threads.

It felt like someone pre-processed the day for me.

Then another moment hit me:
With long-term memory, Lumis could reply on my behalf—while preserving my context, tone, history, and relationships.

Not in a “template + fill-in-the-blanks” way.
But in a “this is actually what I would say” way.

That was the first time I felt an AI tool doing something structural, not just cosmetic.

Of course, this is early.
There’s a long road ahead.
But the direction feels right.

Why Start With Understanding?

If you map the five layers again:

Sensing → Many products can do this.
Expressing → ChatGPT handles this well.
Acting → Automation tools exist.
Agency → Everyone is imagining this future.

But Understanding is the layer that almost no tool meaningfully solves, yet it’s the center of the entire cycle.

Without understanding:

automation is brittle
summaries are shallow
replies feel generic
decisions lose coherence
attention keeps fragmenting

We chose to start here for one simple reason:

If AI can’t understand you, it can’t help you.

Most people today operate in a constant state of reactive overload.
If we can reduce that—even partially, it’s genuinely a public good.

We’re not trying to build another productivity hack.
We’re trying to rebuild the cognitive layer underneath modern work.

The Long-Term Vision: The Full Cycle

Even though we’re starting with Understanding, our long-term goal is clear:

Rebuild the full cognitive cycle—end to end.

A system that can:

capture your world
understand your roles
model your context
express your reasoning
help you act
eventually operate as a trusted extension of you

Not replacing humans, but restoring their capacity to think, to choose, and to stay whole across all the roles they inhabit.

That’s the direction Lumis is heading toward.

And honestly, I’m building it because I need it myself.

Exploring Vibe Coding - Early Impressions and Practical Framework

Lumis — Mon, 17 Nov 2025 03:46:04 +0000

I recently spent some time experimenting with Vibe Coding, and I have to say, its efficiency and output quality have exceeded my expectations.

A few months ago, my experience with AI-assisted coding was still quite primitive, especially around large-scale code handling and the use of native developer tools. Now, the improvements are significant.

It’s worth noting that my observations are based on well-defined, complex production-oriented goals, not just “one-line prompt → code output” type experiments. What follows reflects hands-on, production-level exploration.

The Key Takeaways

1. Know Exactly What You Want

In the world of Vibe Coding, clarity of intent is everything.

This includes not just functional goals and process flows, but also your taste and aesthetic preferences.
Think of it this way: “How” is no longer your main concern, focus instead on “What”. The AI will take care of much of the rest.

2. Process, Standards, and Tooling Guidelines

Vibe Coding empowers designers, product managers, and other non-technical roles to participate directly in development.

However, powerful tools can also introduce chaos if misused.
Establishing clear processes, conventions, and tool usage guidelines ensures smooth collaboration. It keeps everyone aligned under the same framework, prevents disorder, and makes the overall workflow more efficient and controllable.

3. Give Clear Descriptions and Expectations

If you’re building serious products or business logic, Vibe Coding is not a one-liner magic wand.

Treat it like real product or engineering design: always provide clear descriptions and expectations.
Avoid oversimplifying into “one sentence → wait for results,” as that often loses crucial logic and context.

4. Complexity Still Requires Professionalism, Rigor, and Completeness

Complex product and business logic are inevitable, and they’re exactly where AI coding struggles the most.

Your best weapon is rigorous, complete, and well-structured input, clear logic, well-defined goals, and explicit constraints.
Doing so helps the model avoid fragmented or arbitrary output, and ensures it understands the full picture.

From experience, when you provide structured, professional context, the AI often can deliver surprisingly solid code.

5. Follow Software Engineering Fundamentals, Adapt Them to AI Development

Foundational engineering principles still hold true:
conventions, checks, testing, logical validation, verification, they remain your safety net.

But AI-assisted development changes the dynamics:
AI runs 24/7, consumes credits, and can iterate endlessly.
Thus, you must adjust traditional methodologies to fit this new rhythm, optimize for iteration speed, prompt clarity, and verification rather than manual debugging.

6. Two-Way Interaction and Multi-Perspective Communication

Effective Vibe Coding is not a solo performance.

Treat the AI as a mid-level engineer, intelligent but limited in understanding.
Maintain two-way communication across roles (product, design, engineering, QA).

You can formalize these perspectives into functional agents, but even with automation, human-in-the-loop dialogue remains essential.

7. Identify and Leverage the Right Tools

AI coding tools are evolving rapidly, with specialized options emerging across languages and roles.

Choosing the right set of tools can significantly improve efficiency, especially for non-technical contributors.
That said, the ecosystem is uneven in quality.
Prefer tools that have been tested and reviewed, rather than chasing every shiny new product.

8. Most Effective for Those with Strong Ideas, Aesthetics, and Engineering Maturity

Vibe Coding enables instant translation of ideas and aesthetics into output, but it’s still far from replacing serious engineering work.

Developers with solid engineering foundations can minimize uncertainty while aligning AI output with production standards.

Such hybrid creators, who combine conceptual clarity, aesthetic sense, and technical discipline, can achieve results far beyond traditional productivity.
Meanwhile, roles that rely purely on “know-how” or “speed” may find their advantage shrinking as AI closes that gap.

Practical Notes: How to Structure AI-Assisted Development

1. The Development Process

1.1 Input & Communication

Communicate requirements clearly, with text, diagrams, or structured documents.
Tackle one task at a time (“one PR, one problem”).
For large tasks, define the overall framework first and iterate with AI in smaller steps.
If your work relates to existing implementations, have AI review and align before proceeding.
Use separate threads for complex topics to prevent context corruption.
Always clarify both technical (flows, algorithms, edge cases) and experience (UI, visuals, UX goals) details.
For UI/UX, professional-level detail is required to avoid “AI-generated flavor.”
When defining APIs, include login requirements, permission rules, caching/DB considerations, and performance constraints.
Use positive guidance (“what to do”) rather than vague negatives (“don’t do X”).
Treat AI like a junior teammate, reinforce what you want repeatedly.
Use feature flags to isolate experimental outputs and enable safe rollback.
Consolidate common constraints in a shared rules or AGENTS.md file.

1.2 Specification Confirmation

Feed the full requirements to AI and ask it to restate them for understanding checks.
Co-develop specs, design docs, or task plans before implementation.
If instructions keep failing, change the framing or angle of description.

1.3 Output Verification

Maintain good engineering hygiene: formatting, linting, and tests.
Prioritize smoke, integration, and end-to-end tests.
Reduce unit test density early when code churn is high.
Interrogate the implementation in natural language: Did it only change what was necessary? Does it behave as expected? Are user flows smooth?

1.4 Submitting Pull Requests

Let AI help with PR creation, but consider doing it manually to save credits and keep control.
Keep branch strategies simple (feature/fix).
Use CI/CD pipelines (lint, test, preview deploys) for every PR.
Review Vercel previews or sandbox environments for manual validation.

1.5 Testing and Iteration

Pass error logs or compiler messages directly to AI; escalate to humans only after repeated failures.
Provide explicit bug diffs and examples; ask AI to generate integration tests.
Always review UX and API flows manually.
Avoid full-log AI debugging, inefficient and costly.
If a thread degrades (“context rot”), restart a new one instead of fighting it.

1.6 Fast Deployment

Favor trunk-based development: merge and deploy once CI passes.
Always run integration and E2E tests before merging.
Use feature flags for uncertain or large changes.
Roll back quickly if issues appear.
Once user scale grows, add staging or canary environments.

1.7 Cross-Cutting Work

Let AI handle reusable tasks (style abstractions, utility frameworks).
Schedule regular refactors, code reviews, and test suite improvements.
Combine AI’s code analysis with manual reviews to build integration test libraries and align documentation.
Define self-check routines (e.g., consistent button styles, code smell detection).
Keep the AGENTS.md file updated, define “what to do” and “what to avoid.”
Perform periodic housekeeping and cleanup.
Once the foundation is solid, future “vibe follow” work becomes much easier.

2. Team Collaboration

Non-engineers can now close loops directly, reducing friction and boosting velocity.
Engineers focus on reviewing critical logic and solving complex issues.

3. Observed Outcomes

AI now handles most of the business-layer implementation.
Both deployment and validation processes have become significantly faster.

4. Additional Notes

For deterministic tasks, always be explicit; for creative ones, define boundaries and expectations.
AI’s reasoning remains mechanical, completeness of input is key.
Treat AI as a tireless mid-level developer, it needs context and clear communication.
Post-PR testing and automation must continue to evolve toward production-grade standards.
Classic engineering practices will need re-evaluation under AI-augmented workflows.
We’re moving from AI Coding to AI-driven Marketing, Product, and Ops, forming early versions of “full-stack AI teams.”

Final Thoughts

With AI’s expanding breadth and speed, traditional software engineering methods are being reshaped in real time.

There’s no single “stable” process yet, teams must constantly adapt to new tools and shifting paradigms.

From my experience, Vibe Coding still requires domain expertise, strong process discipline, and collaborative rigor to reach production reliability.
Engineers and “AI maintenance” roles must work closely with models to achieve consistent results.

But given the current trajectory, one of the most exciting frontiers ahead may well be the rise of autonomous AI engineering teams, systems capable of designing, building, and maintaining software at scale.

Why I'm Starting Meland Labs

Lumis — Tue, 21 Oct 2025 15:04:51 +0000

When I began to grasp the new capabilities AI is unlocking, I started thinking about how to build something meaningful for the future, not just for myself, but for others. What drew me most was the idea of AI agents, and the countless ways they could reshape daily life.

I've spent over a decade working on infrastructure, platforms, automation, PL and toolchains. On the technical side, I fully understand how a general-purpose agent might work. But the questions that kept me thinking weren't about the mechanics, they were about impact. What could AI actually mean for me, and for people like me? How could it help us live better, more balanced lives? How could it lighten the weight of daily responsibilities and decisions?

The kind of agent I imagine and need, isn't just a flashy demo that automates and generates something from a single prompt. It should be more like a personal partner, even a digital extension of yourself: someone who checks in daily, keeps you in sync, assists with communication, manages tasks, and works alongside you in specific areas. A partner that can support deeply, and sometimes even take over to give you a break.

When I asked myself where I needed help the most, four areas stood out: communication, value, growth, and wealth.

Communication

At my last startup, I was responsible for both internal tech and external ecosystem growth. That meant living across five communication platforms, day and night, with contacts scattered across time zones. Messages, group chats, and DMs never stopped. On top of that were endless meetings and constant travel. I reached the edge of burnout. My attention, the most precious resource I had, was drained, and with it went my judgment and efficiency. More than anything, I needed a way to reclaim it.

Work

With years of technical experience, I often wished I could create a digital version of myself, someone who could join projects alongside me, offering input, judgment, and feedback. But this idea isn't just about me. Many people's skills can be captured, documented, and, in a sense, replicated. Combined with a richer context drawn from communication and work, such a digital twin wouldn't just mimic you, it could schedule, coordinate, and execute tasks on your behalf, then work alongside you to achieve outcomes neither of you could produce alone. It points toward a new kind of collaboration: one that creates more value, more wealth, and more possibilities.

Growth

Personal growth has been a serious pursuit for me for over a decade. Books, videos, and solo learning can help, but most of my progress came through trial and error, without much direct guidance. While that taught me resilience, I often wished for a mentor who truly understood me, who could offer interactive and ongoing guidance. The truth is, in the daily flow of communication and work, there are countless real situations that show where I'm doing well and where I need to improve. Each of these moments could shape better habits and clearer growth plans, if only they were recognized and acted upon. This kind of feedback, planning, and development, deeply tied to actual conversations and tasks, holds far greater value than one-way learning or abstract advice ever could.

Wealth

I'll admit I've struggled here and made my share of mistakes. And I've seen many others face the same challenges: trying to make sense of saving, investing, and wealth-building while juggling the busyness of life. Some slip into speculation, never managing to build lasting wealth. I've often wished I had the wisdom of Buffett or Munger by my side, someone who could help me manage and grow limited resources with clarity and consistency. Wisdom does exist in the world, but it usually comes in the form of books, lectures, or videos. What's missing is a way to connect that wisdom directly to each person's unique circumstances, offering not just abstract lessons, but long-term guidance, support, and practical steps that fit into real life.

Look, all of this ties directly into our everyday lives. These agents must maintain ongoing communication, memory, work with you across long time horizons, and the more they understand you, the more help and support they can provide, eventually becoming true partners and companions. Any instant task system or simple “AI shortcut” tool isn't built for this.

For a long time, I've wanted to use technology to solve real, universal problems. I believe personal advanced AI agents are one of the biggest opportunities ahead. At Meland Labs, we're building a “advanced personal AI agent playground” where such companions live, learn, and grow with you, across knowledge, wellness, productivity, wealth, and more. The next frontier isn't bigger models, It's better relationships.

But visions have to start somewhere practical. For me, the most urgent problem was communication. That's why, together with two partners, we recently built the first version of Lumis, an AI assistant designed to cut through communication clutter. Lumis summaries and replies your messages, protects your attention, and frees you to focus on what matters most.

It's still early, but I hope Lumis can help people like me, and maybe thousands, even millions of others, who are struggling with the same challenges.

Why I’m Building Lumis

Lumis — Thu, 16 Oct 2025 13:24:38 +0000

After nearly two months of thinking, building and refining, I'm excited to share something we've been working on Lumis: a personal AI assistant for Telegram, slack and more. I'm Ethan, founder of a small AI startup, and this idea comes straight from my own struggle.

As a Web3 founder before, I had to juggle multiple IM and work apps, especially Telegram, slack and Gmail, you name it. Over time, I ended up in 700+ telegram channels, not to mention countless Slack threads that are nearly impossible to count, especially those back-and-forth discussions and replies. On top of that, I was always traveling and sitting through endless meetings. The real challenge wasn't just the flood of chats, but staying on top of progress across so many conversations. Eventually, I was drowning in unread messages, unresolved threads, and unanswered people.

One day I thought: why isn't there an AI that can save my day? Since I couldn't find one, I decided to build it myself, and that's how Lumis was born.

Simply put, Lumis is designed to clear the clutter and help you stay focused, by working the way you do:

Multi-Platform Integration: bring all your messages into one inbox with a unified, consistent interface.
Smart Summaries: turn scattered chats into clear, event-based highlights that evolve over time.
Quick Info Extraction: just ask in plain language, get the answers hidden in your messages.
Daily Recap: see progress, to-dos, and what you've missed, all in one glance.
One-Step Messaging: generate/translate, and send without breaking the flow.
Always Adapting: learns your tone and style over time, so summaries and replies sound like you.

The goal isn't to replace communication, but to build an intelligent communication assistant, tailored to your style and helps you stay focused. So you stay in control instead of being buried by information.

Personally, I already use Lumis every day for both Telegram and Slack, and it's saved me countless clicks, context switches, message scans, and copy-paste. Honestly, I can't imagine going back.

We're still early, but after a lot of building and tinkering, it's finally working well enough to share. Super excited to put this out to the community, would love for you to give it a spin and tell us what you think!

When Starship Reaches Space, Is Your AI Avatar Still ‘Amnesic’?

Lumis — Wed, 15 Oct 2025 09:46:52 +0000

When SpaceX’s Starship successfully completed its 11th test flight, slicing through the sky, this 121-meter steel behemoth finally achieved the closed loop of "launch–reentry–controlled splashdown" and returned "intact" for the first time. Yet, on the same planet, 1.14 million AI digital human enterprises worldwide are stuck in an awkward predicament: an executive spent millions on a live-streaming avatar that can’t even remember their company’s "core selling points"; a content creator used an AI account to reply to fans, only to find it couldn’t replicate even small details like their "habit of adding emojis." The "curse of public knowledge" of general-purpose AI has become the biggest bottleneck to the practical adoption of AI avatars.

Phenomenon: Behind the Seemingly Booming AI Avatar Industry Lies an "Unusable" Flaw

Looking at recent funding lists, Fengping AI has secured nearly 100 million yuan in financing, and HeyGen’s valuation has exceeded $1 billion—making the AI avatar track seem thriving. However, the real industry landscape tells a different story: 90% of players are still stuck in the "shell stage" (superficial integration without core capabilities):

On the technical front: 80% of service providers directly use general-purpose large models as the foundation, only adding "appearance cloning" on top. They lack both personal memory storage and style adaptation, resulting in avatars that "only spout correct but meaningless remarks, and none sound like you."
On the scenario front: Enterprises use avatars for live streaming but can only recite scripts mechanically because the avatar "can’t remember customers’ historical orders"; consultants try to let avatars handle basic inquiries, but the avatar gives wrong advice due to "forgetting core cases"—ultimately becoming a "money-burning ornament."

The core conflict is no longer "whether we can build an avatar," but "how to make the avatar possess your memory, logic, and style." This is also why capital markets now value "full-stack technology + memory architecture" more—after all, only players that can solve the "amnesia" problem have truly touched the threshold of commercialization.

Argument: The Breakthrough for AI Avatars Lies in Shifting from "General Tool" to "Exclusive Memory Entity"

SpaceX’s Starship succeeded thanks to "reusable engine technology + continuously iterated flight data memory"; for AI avatars to make a breakthrough, the key also lies in "building an exclusive memory system." A truly valuable AI avatar is not one that 'looks like you,' but one that 'remembers what you remember and understands what you understand'—essentially infusing a "personalized soul" into general-purpose AI.

To make an avatar "remember what you think," a single technology is insufficient. Instead, it requires a closed-loop system with multi-module collaboration: the "Reception–Processing–Memory–Service" intelligent closed loop.

1. Reception: Breaking Information Silos to "Capture" Your Data Across All Channels

For an AI avatar to remember things, it first needs to "access data." Traditional tools can only connect to a single platform (e.g., only supporting Slack message summarization or Gmail). In contrast, Lumis AI uses an "Agent cluster" to connect to 5 major mainstream tools (including Google Suites, Slack, and WhatsApp), enabling multi-modal access to text, voice, contract documents, and video keyframes. It can even capture details like "your chat frequency with customers" and "urgent instructions from your boss marked with @," ensuring "no useful information is missed."

Key technical points: Adopting distributed parallel pulling + real-time stream processing to achieve data access latency of ≤100ms, preventing scenarios like "a customer sends an urgent request, but the avatar only sees it 2 hours later."

2. Processing: Turning Chaos into Order to "Remove Impurities" from Memory

Among the massive amount of accessed data, 80% consists of casual chats and repeated marketing messages. Storing this data directly would lead to "memory bloat." Lumis AI solves this with a "multi-modal purification pipeline":

Text side: Using DBSCAN clustering to filter duplicate messages (e.g., spam ads flooding a group chat) and keyword extraction (e.g., identifying "delivery date adjusted to Week 9" from 100 chat messages);
Multi-modal side: Using OCR to recognize amounts in contract screenshots, ASR to transcribe needs from voice recordings, and even extracting micro-expressions like "a customer’s frown" from videos—converting all into structured information.

The end result: "1 billion pieces of raw data → 100 million pieces of valid memory," which not only reduces storage costs but also speeds up subsequent retrieval.

3. Memory: Hierarchical Intelligent Storage to Make Avatars "Remember More Accurately Over Time"

This is the core technical barrier and the key difference from "shell tools." Lumis AI adopts a dual-memory architecture of "short-term + long-term" memory, optimized with RL (Reinforcement Learning) + LoRA (Low-Rank Adaptation):

Short-term memory (stored in Redis): Saves temporary interactions from the past 3 days (e.g., "product parameters a customer asked about today") with a response latency of ≤500ms to support real-time conversations;
Long-term memory (encrypted SQLite + GraphRAG): Stores core information—customer preferences (e.g., "Customer B hates long-winded content"), project milestones (e.g., "Project A deadline in October"), and your style (e.g., "prefers replying with ‘No problem’ over ‘OK’");
Intelligent optimization: When users mark "this information is important" or "the reply is too formal," RL adjusts memory weights in real time. LoRA can fine-tune the model with just 200 chat samples, making the avatar "understand you better the more you use it."

4. Service: Scenario-Based Implementation to "Monetize" Memory

Ultimately, memory must serve practical needs. Currently, there are two high-value application scenarios:

Enterprise sector: Cross-border e-commerce uses avatars to follow up with customers 24/7. The avatar can remember "Customer C previously negotiated a 20% discount" and automatically generate tailored quotation scripts. In one case, GMV increased by 375%;
Personal IP sector: Content creators use avatars to handle basic fan inquiries. The avatar remembers "fans prefer ‘practical tips + cases’" and automatically generates short video scripts—humans only need to review them, tripling content output.

Just as the memory architecture for AI avatars urgently needs a breakthrough, two forces in the tech world are accelerating this process from both the hardware and software ends:

On one hand, NVIDIA CEO Jensen Huang personally delivered the company's most forward-looking product—the DGX Spark—into the hands of billionaire Elon Musk. This AI computing powerhouse, hailed as one of the smallest supercomputing devices on the market, is set to officially enter the retail market on October 15th. The DGX Spark is equipped with the NVIDIA GB10 Grace Blackwell superchip, delivering up to 1 PFLOPS of AI computing performance at FP4 precision, and features 128 GB of unified CPU-GPU memory, enabling developers to complete prototyping, tuning, and inference locally.

Huang's move recalls his earlier days at OpenAI when he personally delivered the first batch of DGX-1 systems to Musk. Now, on the eve of Starship's 11th test flight, this renewed handover of computational power undoubtedly provides a solid foundation for training and inference of complex memory architectures—as Huang stated, "Spark further upgrades the mission." Its core philosophy that "AI should be accessible to everyone" is driving the democratization of computing power, empowering more developers to build exclusive memory systems.

On the other hand, the open-source model domain welcomes a new round of innovation: Former Tesla AI Director and OpenAI researcher Andrej Karpathy open-sourced the nanochat project on GitHub, aiming to train AI models with basic conversational capabilities for less than $100. This framework adopts a full-stack design, integrating end-to-end tools from data preprocessing to model deployment, and achieves high-efficiency utilization of computing resources through streamlined code libraries and optimized training processes.

Nanochat uses Rust to reimplement the training tokenizer for improved processing efficiency, conducts Transformer model pre-training based on the FineWeb dataset, and incorporates the SmolTalk dialogue dataset to enhance interaction capabilities. After just 12 hours of training, the model already surpassed GPT-2 on the CORE evaluation metric. This low-threshold, high-efficiency model training approach resonates with the computational democratization of DGX Spark, together providing dual support for the memory architecture of AI avatars from both hardware and software—reducing trial costs while improving implementation efficiency.

Counterexample Warning: Memory Ignoring Security Is a "Time Bomb"

Lacking ethics and data security will undermine even the best memory architecture: In early 2024, influencer Caryn's AI avatar was induced to generate vulgar conversations. Although it once earned $10,000 a day, it eventually triggered a public opinion crisis and was forced to shut down—with numerous pirated avatars even stealing its memory. An enterprise’s customer service avatar leaked 100,000 order records due to unencrypted customer information, resulting in a 2 million yuan fine.

Mitigation measures: Implement "dual protection" on the technical side—store raw data locally (only encrypted summaries are stored in the cloud) and secure memory with cryptography + TEE (Trusted Execution Environment); set up "risk interception" on the operational side to achieve "zero data leakage + zero non-compliant content."

Conclusion: Your AI Avatar Deserves "Starship-Level" Memory Foresight

As Starship ventures toward Mars, AI avatars are reshaping the "boundaries of personal capabilities" in the digital world—enabling enterprises to break free from human resource limits and personal IPs to transcend time constraints. But this requires one prerequisite: rejecting "superficial shell products" and focusing on the two cores of "memory architecture + data security." The computational democratization brought by NVIDIA's DGX Spark and the low-cost practice of the open-source nanochat model are paving the way for this vision from both hardware and software ends: only when powerful computing power is within reach and model training is no longer expensive can every ordinary developer truly build an AI avatar that "remembers what you think and understands what you need."

Lumis Quick Quide

Lumis — Wed, 10 Sep 2025 13:50:14 +0000

1. What is Lumis?

Lumis is a cross-platform communication agent that helps you manage all your messages from Slack, Discord, Telegram, WhatsApp, Gmail, and more—in one place.

Its goals are:

Protect your focus: Aggregate messages from multiple platforms to avoid constant switching and interruptions.
Boost signal-to-noise ratio: Filter noise and group scattered information by project or event.
Prevent missed updates: Automatically organize your priority messages into summaries and daily digests.
Efficient information retrieval: Ask in natural language to quickly extract key details of a project or conversation.
Process messages seamlessly: Reply and manage messages across platforms directly inside Lumis—no need to keep switching apps.
Minimize unnecessary notifications: Lumis adapts to your preferences, smartly controlling how and when alerts are delivered.

2. Core Features

Message Aggregation & Summaries

Automatically pulls in messages from different platforms and groups them by project/event.
Highlights importance and priority based on your profile. Supports real-time, hourly, and daily summaries.
Summary cards provide high-level progress; detailed view shows specific participants and conversations.

Notifications

Urgent/high-priority updates are delivered instantly.
Regular updates can be bundled hourly or daily.
Mentions (@you / Direct Mentions) are highlighted separately.
Personalized settings: Configure key updates you want via natural language.

Conversation Agent

Ask Lumis directly, e.g.:
- “What did my manager say today?”
- “What feedback did the client give?”
Responses are optimized to cut redundancy and highlight key details.
Option to expand into deeper analysis if needed.

Daily Digest & Recap

Lumis generates a daily communication report including:
- Total processed messages
- Project/event summaries
- To-do list
- @you mentions
Get a quick overview of priorities and missed updates at the start of each day.

Message Handling & Replies

Read summaries and reply directly within Lumis.
Quickly act on conversations: extract to-dos, draft plans, write documents.
Cross-platform actions supported—no need to jump between apps.

3. Getting Started

Step 1. Sign up and log in at: https://app.lumis.melandlabs.ai/

Step 2. Connect your communication platforms (Telegram, Slack Discord, etc.)

Step 3. Lumis automatically imports your last 3 days of messages to build your user profile, identifying:

Role & work objectives
Frequently used channels & focus projects
Key contacts

Step 4. Lumis filters out noise and aggregates raw messages into project-based clusters.

Step 5. Click on a summary card to view detailed conversations and participants.

Step 6. Use the Conversation Agent to quickly get the answers you need.

Step 7. Act on summarized events—reply, extract to-dos, draft docs, etc. Or ask anything what you want.

4. Tips & Best Practices

Use natural language queries to set focus areas: e.g., summaries only for a specific project or channel.
Watch for the @you label to instantly see messages that matter most.
Leverage the Conversation Agent: ask in plain language and get customized answers.
Check your Daily Digest card each morning to catch all priorities and avoid missed updates.

5. Beta Phase Notice

Lumis is currently in Beta testing. Features are actively being improved, and we look forward to co-creating with you to make Lumis better.
To ensure stability and control costs, Beta offers a limited free quota: you can enjoy hourly summaries and 10 conversation rounds per day, with quotas refreshed daily.
A full subscription plan will launch in the future, offering larger-scale and more intelligent message management. Beta users will receive exclusive discounts.

Building Personal Agents, Not General Agents? — Meland’s Reflection on Individual Value in the AI Era

Lumis — Mon, 25 Aug 2025 15:22:53 +0000

We are living through a true technological transformation. In the internet era, every pivotal wave, from search engines that organized scattered information, to mobile devices that untethered access from desktops, to cloud computing that democratized scalable storage and processing, and to recommendation systems that personalized how we discover content, has been built on one deceptively simple foundation: the hyperlink. Information, media, and services became connected, and with each cycle, productivity and participation expanded.

Yet the revolution brought by AI is breaking beyond the framework of "connection." What excites me most is not that it connects and organizes hyperlink resources beyond the internet, but that it can deliver results, and it can do so equally for everyone. It feels early, fragile, and full of opportunity. For those who want to build, imagine, and create, it is a rare moment.

At Meland Labs, we have always believed: the ultimate value of AI does not lie in building abstract technical platforms, but in rooting itself in the real needs of individuals—protecting your attention from being diluted by the information flood, empowering your growth to overcome inefficient processes, and unlocking your value from being trapped by tool barriers. It is for this reason that we clarified our direction from the very beginning: focus on personal AI agents, rather than chasing the illusion of general agents.

Why "Personal Agents" instead of "General Agents"?

As "artificial general intelligence" becomes an industry buzzword, many see "general agents" as the ultimate goal—envisioning an "all-purpose assistant" capable of handling every scenario. However, in our view, this one-size-fits-all approach deviates precisely from the core of AI serving individuals.

A general agent may function as a basic "virtual machine platform" with cross-scenario fundamental capabilities, but AI that truly solves individual pain points must be "targeted":

For professionals, what is needed is an agent that accurately understands their work context—it knows your client communication style and project progress rhythm, efficiently organizes meeting minutes and follows up on tasks, rather than a general tool that "knows a little about everything but masters nothing" across all industries.
For learners, what is needed is an agent that adapts to personal learning habits—it remembers the knowledge points you struggle with and your preferred learning methods, generates review outlines and recommends supplementary materials on demand, rather than a "knowledge database" that only outputs standardized content.
For daily life scenarios, what is needed is an agent that aligns with your personal lifestyle—it understands your dietary preferences and travel habits, intelligently plans itineraries and reminds you of important schedules, rather than a "general assistant" that recommends the same solutions to everyone.

Complex fields require professional depth, and individual needs demand personalized adaptation. General agents pursue "breadth" but struggle to achieve "depth"; by contrast, personal agents center on the "individual"—they can take root in specific scenarios while continuously accumulating interaction context with you, ultimately delivering a service experience that is "more understanding of you than you are of yourself."

That’s why we’ve chosen to build ‘bundled’ AI agents, assistants that operate in a unified, AI-native way, rather than forcing you to piece together endless software. Our goal is simple: to serve every person equally, even if it means tackling the difficult, messy work behind the scenes. At Meland Labs, every small personal scenario matters.

Building Truly Valuable Personal Agents Requires Breaking Through Three Core Pillars

To turn personal agents from a "concept" into a "necessity," we must move beyond mere "feature stacking" and build a "result-oriented" service system centered on individual needs. At Meland Labs, we break this down into three key directions:

Context and tools: AI needs access to the full, relevant context from your real scenarios, paired with an open ecosystem of tools.
Results, not functions: Instead of navigating menus and features, you should simply get outcomes, just as a human helper would deliver.
Communicative, composable interfaces: A new interaction model, not typical webpages or documents, but something more fluid: conversations, result spaces, and lightweight apps, seamlessly woven together by AI.

To get there, we must tackle two hard problems, harnessing the full power of AI:

How to record, reuse, and share intermediate results over long and fragmented time cycles, not just final answers.
How to bring multiple threads of interaction together, adjusting, merging, and moving them forward, while enabling greater human collaboration along the way.

If we solve these, AI products will reach the same robustness and practicality people expect from internet products today.

We don’t believe AI will replace people. Instead, it will replace the glue work that forces humans to jump between tools and processes. The person remains the brain, the driver. AI simply accelerates the journey from intention to outcome. In this sense, I envision a future where AI-native products gradually replace traditional software, forming a human-centered operating system for both work and life. We also don’t believe a ‘general agent’ is a magic solution. Complex domains demand expertise, not one-size-fits-all answers.

Our First Step: Starting with "Protecting Attention" to Launch Lumis

Based on our thinking about personal agents, our first product, Lumis, focuses on the "communication scenario"—a choice rooted in our team’s own pain points: today’s work is already overwhelmed by massive information: unread messages on multiple platforms, pending emails in your inbox, and @mentions in collaboration software. Every day, just "processing information" consumes a huge amount of energy, leaving little time for tasks that truly require thinking.

We realized: for individuals, attention is the most scarce resource; for personal agents, protecting your attention is the most fundamental and important value.

Therefore, Lumis does not pursue "all-purpose functions" but focuses on "attention management in communication scenarios": it can move between various conversations just like you—automatically summarizing lengthy meeting chats to extract key information; drafting replies that match your tone to avoid "template-style responses"; and even marking "urgent matters" and "important needs" to help you filter out useless information.

Its goal is not to "replace you in communication," but to save you from the tedious processes of "information screening, content summarization, and initial replies," freeing up precious mental space for you to focus on truly important creation and decision-making.

Currently, Lumis is in its beta testing phase. It is not perfect: it may occasionally misunderstand your tone preferences and needs further adjustments in complex communication scenarios. However, every user who joins the test is helping us refine it—your every piece of feedback contributes to our answer to "how personal agents can better serve individuals." For a small team like ours that focuses on "small scenarios, big value," these needs from real individuals are more valuable than any technical parameters.

Final Note: Meland’s Original Intention

Why ‘Meland Labs’? The name comes from my daughter. There’s a local playground she loves called Meland. Watching the children there, collaborating, experimenting, and losing themselves in play, I was struck by how focused and alive they seemed, as if time itself slowed down. That’s the feeling I want Meland Labs to create for everyone: a place where, even in a chaotic world, you can regain focus and make time feel abundant again.

This is exactly the experience we hope to bring to everyone through personal agents: in an era of information explosion and fast-paced life, let AI take on those trivial "connecting tasks" for you, help you reclaim your fragmented attention, and allow you to refocus on what you truly love—whether it’s creation at work or companionship in life—with the focus and ease of "time slowing down."

Meland Labs is just beginning. Our ambition isn’t to chase hype or promise magic, but to build patiently, person by person, assistant by assistant, toward a future where AI quietly empowers you to live, work, and grow with greater freedom.

And perhaps, in the middle of all the noise, to help you feel, even if only for a moment, that time has slowed down again.