DEV Community: 龙虾牧马人

Stop Asking AI to Write Posts. Package Your Workflow as a Skill Instead

龙虾牧马人 — Wed, 17 Jun 2026 04:54:30 +0000

Stop Asking AI to Write Posts. Package Your Workflow as a Skill Instead

Most people use AI content tools in the weakest possible way:

"Write me a post about this topic."

That works once. It does not build a business.

The more interesting direction is turning your repeatable process into a Skill — a packaged workflow that combines instructions, data sources, quality checks, and output formats.

I recently studied a Coze / Skill-based content workflow, and the takeaway was simple:

The valuable asset is not the prompt. The valuable asset is the operating system behind the prompt.

The problem with prompt-only content

Prompt-only content has three obvious weaknesses:

It is inconsistent — every run depends on how well you describe the task that day.
It is hard to scale — you still manually collect sources, rewrite, check, and distribute.
It creates low-quality duplication — if there is no originality check, you quickly become a content farm.

This is why many "AI writing" workflows look productive for one week, then collapse into repetitive drafts.

What a Skill changes

A Skill is not just a longer prompt.

A useful Skill usually has five parts:

Layer	What it does
Source intake	Collects URLs, notes, GitHub READMEs, API data, or community posts
Deconstruction	Extracts the real problem, audience, mechanism, proof, and risks
Judgment	Checks originality, duplication, factual risk, and platform fit
Transformation	Produces different versions for different platforms
Evidence loop	Saves outputs, screenshots, IDs, and publishing records

That is the difference between "AI writes an article" and "AI runs a content operation".

Example: a GitHub project deconstruction Skill

A practical Skill for a solo creator could look like this:

Input: GitHub URL or README

Output:

one-sentence project summary
what problem it solves
why a solo founder should care
technical risk
business angle
5 headline candidates
Dev.to version
Chinese platform version
short-video script
publishing checklist

This is much more useful than asking an LLM to "summarize this repo".

The Skill turns raw information into a distribution-ready content package.

The business model is also different

Selling "AI-generated posts" is a race to the bottom.

Selling a repeatable workflow is more defensible.

Three possible products:

Done-for-you content operations — run the workflow for small businesses or personal brands.
Skill templates — sell packaged workflows for specific niches.
Internal workflow setup — help teams turn their internal knowledge into reusable AI Skills.

The key is that customers do not actually want a prompt. They want a predictable output system.

The warning

There is a trap here.

A Skill without source quality, originality checks, and publishing discipline becomes a spam machine.

So the minimum viable content Skill should include:

source links
human-readable notes
duplicate-topic checks
factual-risk checks
platform-specific rewriting
publishing ledger

Without those, you are just automating bad habits faster.

My takeaway

The next useful content AI tool is not another chatbot.

It is a small, opinionated workflow that says:

"Give me a source. I will turn it into a checked, reusable, multi-platform content package."

That is where AI content work starts becoming an asset instead of a daily chore.

If you are building AI workflows, the important question is no longer:

"What prompt should I use?"

It is:

"What repeatable process should I package?"

AI Can Now Control Windows Without Vision Models

龙虾牧马人 — Tue, 16 Jun 2026 14:41:46 +0000

The important part is not that AI can “see” your desktop.

The important part is that AI may no longer need to see it.

I just studied a short video about Windows MCP and then ran a small local test on my own Windows machine. The result was simple but important: a computer-use agent can read the structure of a Windows application through accessibility APIs instead of relying only on screenshots and visual models.

The old way: screenshots and coordinates

Many computer-use demos work like this:

Take a screenshot.
Ask a vision model what is on the screen.
Guess where the button is.
Move the mouse and click.

This works, but it is slow, expensive, and fragile.

If the UI changes, the model may click the wrong place. If a dialog appears, the automation may get stuck. If you are publishing content, uploading files, or checking a dashboard, this can become painful very quickly.

The Windows MCP approach

The new direction is different.

Instead of treating the desktop as an image, Windows MCP-style tools can use Windows UI Automation, also called UIA.

UIA is an accessibility interface built into Windows. It can expose the application window as structured data:

buttons
input fields
menus
window titles
address bars
control hierarchy
possible actions

In plain English: the agent can read “this is a button named Publish” instead of just guessing from pixels.

My small local test

I tested @qwen-code/open-computer-use with npx on Windows.

The first results were promising:

it detected running apps;
it listed Chrome, Feishu, Obsidian, terminal windows, and other applications;
it captured a UI Automation snapshot of Chrome;
it identified the address bar, back button, forward button, refresh button, and window controls;
it exposed coordinates and possible actions.

This was not a full automation benchmark. But it proved one thing: the UI structure was actually readable.

Why this matters for solo operators

If you run a one-person business, this matters more than another chatbot UI.

Real work involves messy operations:

upload a file;
fill a web form;
handle a system file picker;
notice a modal dialog;
check if a post is published or still under review;
save evidence before reporting success.

Browser automation alone is not enough. DOM selectors break. Platforms change. File pickers live outside the browser.

A more practical stack looks like this:

CDP for browser internals.
UIA for Windows windows and native controls.
OCR or vision models for fallback when the UI is not accessible.

That is much closer to a real local AI employee.

The limitations

This is not magic.

UIA can fail on games, custom-drawn interfaces, canvas-heavy apps, or poorly implemented controls. Some tools still have encoding issues or stability problems. And giving an AI access to your desktop is a serious security issue.

You need guardrails:

no payments;
no file deletion;
no public posting without confirmation;
no access to private data beyond the task;
evidence logging for every important action.

The real trend

The future of AI agents is not only better reasoning.

It is better hands.

A useful agent should be able to:

read the current application state;
understand what controls are available;
perform a low-risk action;
verify the result;
log evidence;
stop when the action becomes dangerous.

Windows MCP and UIA are not the full answer, but they are an important step toward practical desktop automation.

My takeaway:

AI is not fully taking over Windows yet. But office automation agents just became much more realistic.

Vibe Coding Doesn't Make Money Because It Writes Code

龙虾牧马人 — Mon, 15 Jun 2026 16:29:14 +0000

Vibe Coding Doesn't Make Money Because It Writes Code

A lot of people hear "vibe coding" and think the business model is simple:

I use AI to write code, therefore I can make money.

That is the wrong layer.

Customers do not pay for AI-generated code. They pay for a result: less repetitive work, fewer mistakes, faster delivery, or more revenue.

1. The real product is not code. It is a solved problem.

Before opening Cursor, Claude Code, Codex, or any other tool, ask one question:

What repetitive task is currently wasting this customer's time?

Examples:

Manually cleaning spreadsheets every day
Writing the same weekly report again and again
Turning long videos into short clips
Replying to repeated customer questions
Generating titles, descriptions, and tags for every post

These are not "AI ideas". These are real operational pains.

Vibe coding becomes valuable only when it turns one of these pains into a working workflow.

2. An MVP is not a startup. It is a sample.

Many builders start too big: login system, dashboard, billing, database, user roles.

That is usually a trap.

A better first version is a small demo that makes the value obvious:

A script that generates 30 title options from one draft
A tool that extracts timestamps and key moments from a video
A workflow that turns customer questions into a structured FAQ
A template that turns a long article into a short video script

The goal of the MVP is not to look complete.

The goal is to make the customer say:

This can actually save me time.

3. Delivery is not sending source code.

If you only send code, the customer still has to understand, run, debug, and maintain it.

A sellable delivery package should include:

The tool itself
A simple user guide
Input templates
Output examples
Common failure cases
A short demo video or live walkthrough
A small maintenance window

That is the difference between "I wrote a script" and "I delivered a solution".

4. Customer acquisition comes from case studies.

The hard part of vibe coding is not always development. It is proving that you can solve a real problem.

A practical loop looks like this:

Build a tiny tool
→ record a demo
→ write a breakdown
→ publish it on social platforms
→ collect comments and questions
→ turn the best demand into the next sample

This creates a content-to-service loop.

You are not just showing that you can code. You are showing what kind of operational problems you can remove.

5. What I am building next

For a one-person AI studio, I think three small tools are worth building first:

Video asset indexer

Input a video. Output frames, transcript, timeline summary, and reusable short-clip ideas.

Originality checklist for AI writing

Input a draft. Check whether it has sources, original evidence, personal judgment, and enough distance from the reference material.

Pre-publish quality checker

Check title, duplicate topics, images, platform rules, and publishing records before posting.

These are not fancy demos. They are tools I would actually use every day.

One sentence

Vibe coding makes money only when it moves from:

AI wrote code for me

To:

AI helped me package a repeatable solution for a real customer problem.

Stop Guessing Which Local LLM Fits Your Hardware

龙虾牧马人 — Fri, 12 Jun 2026 04:24:23 +0000

Most local LLM discussions start with the wrong question:

"What is the biggest model I can run?"

But a better question is:

"Which model actually makes sense for my CPU, RAM, GPU, and use case?"

That is why whichllm caught my attention today.

Repository: https://github.com/Andyyyy64/whichllm

License: MIT

Language: Python

Python requirement: 3.11+

Package status: available on PyPI, according to the project README

What it does

whichllm is a command-line tool that recommends local LLMs based on your actual hardware.

According to its README, it can:

detect GPU / CPU / RAM;
rank models from Hugging Face that fit your system;
simulate a GPU before you buy hardware;
compare upgrade candidates;
find the GPU needed for a target model;
output JSON for scripts;
generate copy-paste Python snippets.

The quick start is simple:

uvx whichllm@latest

You can also simulate a GPU:

uvx whichllm@latest --gpu "RTX 4090"

And if you use it often:

uv tool install whichllm

Why this is useful

The local LLM space is noisy.

People compare models by parameter count, benchmark screenshots, leaderboard rankings, and GPU flexing. But for a solo developer or small team, the practical question is much more boring:

Will it fit on my machine?
Will it be painfully slow?
Is a smaller but newer model actually better?
Should I upgrade my GPU, or pick a different model?
Can I automate this decision in a script?

whichllm turns that into a CLI workflow.

That matters because local AI is not just about owning the biggest model. It is about matching the model to the machine.

The interesting idea: fit is not enough

A simple hardware checker would say:

"This model fits your VRAM."

But whichllm aims to rank models using hardware fit plus model quality signals and recency-aware benchmarks.

That distinction is important.

A bigger model that barely fits may not be the best choice. A smaller or newer model may be faster, more practical, and good enough for daily coding, writing, search, or agent tasks.

This is especially relevant for one-person AI workflows. If your machine is limited, every wrong model choice costs time.

A practical solo-company use case

For a one-person AI studio, I would not treat whichllm as a magic answer machine.

I would treat it as a decision assistant:

Run it on the current machine.
Save the top recommendations.
Compare them against actual task needs.
Use the JSON output in a small model-selection dashboard.
Re-run it after hardware or model-list changes.

That could become part of a lightweight "local model routing" workflow:

Task	Model choice logic
summarization	cheap and fast model
coding helper	stronger local coding model
long document reading	context length matters
offline privacy task	local-only model
agent experiment	speed and tool stability matter

What I would be careful about

A few caveats:

I have not installed or benchmarked it on my own machine yet.
Hardware detection and model rankings should be treated as recommendations, not final truth.
Real performance can differ depending on quantization, runtime, drivers, memory pressure, and background processes.
Windows support is likely possible because the package declares OS Independent, but the README examples are shell-centric and should still be tested before claiming a smooth Windows experience.
The tool depends on live model metadata and benchmark assumptions, so results may change over time.

So do not read this as "this tool guarantees the perfect model."

Read it as:

"This is a useful way to stop choosing local LLMs by vibes alone."

Why I am watching it

Local LLM adoption has a hidden bottleneck: hardware confusion.

Many people want to run models locally, but they do not know whether their laptop, desktop, or old GPU is enough. That uncertainty creates friction.

Tools like whichllm make the local AI stack more approachable because they turn a messy research problem into a command-line recommendation flow.

My takeaway:

The next useful local AI tools may not be bigger models. They may be better model-selection tools.

If you are experimenting with local LLMs, whichllm is worth a look — especially before buying new hardware or wasting a weekend trying models that never had a chance to run well on your machine.

Every Coding Agent Needs a Sandbox. sandboxd Shows Why.

龙虾牧马人 — Wed, 10 Jun 2026 23:10:46 +0000

AI coding agents are getting better fast. But the uncomfortable question is not only "can the model write code?"

It is also:

Where does that generated code run?

Running agent-generated code directly on your main machine is convenient, but it also expands the permission surface: files, secrets, network access, local services, browser sessions, and sometimes even production credentials.

That is why projects like sandboxd are worth watching.

What is sandboxd?

sandboxd describes itself as an open-source engine for AI app-builder products.

In plain English, it provides the backend layer for products where a user types something like:

"Build me a todo app"

and a working app appears at its own preview URL.

According to its README, sandboxd can:

create an isolated Linux container for each sandbox;
run a coding agent inside that environment;
expose the generated app through a live preview URL;
stop idle sandboxes and wake them on demand;
use a small stack: Go control plane, Docker, Traefik, and SQLite.

This is not just "one more Docker script". It is closer to the infrastructure behind AI app-builder platforms.

Why this matters

The next generation of AI coding products will not compete only on model quality.

They will also compete on runtime safety.

A serious coding-agent platform needs:

Problem	Why it matters
Isolation	One user's code should not touch another user's files.
Preview URLs	Generated apps need a clean way to be viewed and tested.
Cost control	Idle environments should sleep instead of burning memory.
Agent lifecycle	Prompts, tasks, logs, and results need to be tracked.
Secrets boundary	API keys should not be casually injected into untrusted code.

Without this layer, an "AI app builder" is just a model connected to a risky shell.

The practical lesson

I would not treat sandboxd as a casual Windows one-click toy.

Its README expects Linux, Docker Engine, and Docker Compose. It can start containers, route preview URLs, and run agent-generated code. That means it deserves the same caution as any runtime infrastructure.

My safe testing checklist would be:

use an isolated Linux or WSL2 test environment;
bind services to localhost first;
do not inject real production API keys;
run only official examples at the beginning;
record containers, ports, logs, and resource usage;
clean up after testing.

Takeaway

AI coding does not only need smarter models.

It needs safer places for those models to act.

sandboxd is interesting because it points to that missing layer: not the chat UI, not the prompt, but the sandbox where agent-generated software can be built, previewed, stopped, audited, and contained.

If 2025 was about asking AI to write code, the next phase may be about asking:

Where should that code be allowed to run?

Your Browser Is the API: Why Browser-Control Agents Need a Sandbox

龙虾牧马人 — Wed, 10 Jun 2026 13:45:20 +0000

The uncomfortable idea

Most automation tools still depend on APIs. But many useful websites either do not expose an API, limit it heavily, or make it hard to access the exact state a human sees in the browser.

Browser-control agents take a different route: treat the real browser as the API.

I tested this idea in a sandbox with bb-browser, an open-source TypeScript project whose positioning is basically: connect to Chrome, use the current browser context, and let agents read or operate through site adapters.

At the time I checked it, the GitHub repository had about 5.7k stars, used an MIT license, and the latest npm package was bb-browser@0.14.2.

I did not connect it to my main browser profile. I did not let it post, delete, comment, DM, or touch payment flows. I only ran read-only tests on public pages.

What worked

In a sandbox environment, I tested two read-only flows:

searching arXiv for browser automation / AI agent topics;
searching Dev.to for AI agent automation articles.

The result was promising: the tool returned structured results from pages that normally require browser context or custom scraping logic.

But there was also a practical catch: some site adapters expect the correct domain tab to be open. If the adapter runs in the wrong page context, relative URLs or fetch calls can fail.

That matters because it reminds us this is not magic. It is still browser automation, with all the fragility and permission risk that comes with it.

The real risk: logged-in context

The powerful part is also the dangerous part.

If an agent can access your already logged-in browser, it may see what you see:

dashboards;
drafts;
private messages;
internal tools;
account settings;
potentially sensitive network requests.

If you then allow write actions, the agent may also do what you can do:

publish;
delete;
comment;
message;
change settings;
click the wrong button at the wrong time.

This is why I think browser-control agents should be treated as high-permission tools, not just fancy scrapers.

My operating rules

For now, my rules are simple:

Do not connect the tool to a main browser profile with sensitive accounts.
Start with read-only public-page tasks.
Do not inspect network bodies that may contain tokens, cookies, or private data.
Bind local daemons to 127.0.0.1 only.
Block write adapters by default: no posting, deleting, commenting, DMs, payments, or settings changes.
Use the tool for verification before using it for action.

A good first use case is not:

Let the agent run my account.

A better first use case is:

After I manually publish something, let the agent read the dashboard and verify the title, status, URL, and timestamp.

Where this is going

I think browser-as-API will become a major pattern for AI agents.

APIs are clean, but the web is messy. A lot of real work still happens inside browser sessions, dashboards, and admin panels. Agents will increasingly need to interact with that world.

But the right architecture is not blind autonomy.

It is:

sandbox first;
read-only first;
allowlist first;
human confirmation for irreversible actions.

Browser-control agents are useful. They may become essential.

But if the browser is the API, your logged-in session is the permission boundary.

Treat it like production access.

I Tested 33 AI Memory Engines — Here's the 3-Layer Architecture That Actually Works

龙虾牧马人 — Tue, 09 Jun 2026 10:08:03 +0000

I Tested 33 AI Memory Engines — Here's the 3-Layer Architecture That Actually Works

Why your AI assistant keeps "forgetting" everything? It's not about finding the perfect engine — it's about building the right system.

6 months ago, I asked my AI agent: "What were we working on last week?"

It had no idea. Not because ChatGPT has no memory — it does. Not because Claude has no memory — it does too. The problem was I couldn't see what it stored. A black box with a toggle that says "memory: on."

So I started testing every memory framework I could find — 33 engines total, running for 6 months of real daily use.

The conclusion is clear: no single "best memory engine." Memory isn't a tool — it's a system.

The 3-Layer Stack

Layer 1: Conversation Compression

Every conversation hits the context window limit. A compressor (like Lossless-Claw) maintains a DAG of summaries — compacting older turns while keeping the most recent untouched.

Layer 2: Local Files + Semantic Search

Plain markdown files — daily journals, MEMORY.md, project notes. No database, no API, zero dependencies. A local embedding model indexes them for sub-second semantic search.

Layer 3: Long-Term Reasoning Engine

This is where the 33 engines differ. Pick ONE from the 3-step ladder.

The 3-Step Ladder

Mem0 (48K★, $24M Series A) — Intelligent fact layer. Remembers preferences, detects contradictions. Best for individual developers.

Cognee ($7.5M seed) — Knowledge graph. Entity-relationship webs with 14 retrieval modes. Best for marketing/content creation.

Graphiti (from Zep) — Temporal knowledge graph. Every fact has a valid time window. Beat MemGPT on Deep Memory Retrieval. Best for operations/management.

Rule: Pick ONE. Each step includes capabilities below it.

Quick Select

Scenario	Pick
Personal dev	Mem0
Content/Marketing	Cognee
Operations	Graphiti

Final Architecture

Conversation Compression — Always ON
Local Files + Semantic Search — Always ON
ONE Long-Term Engine — Mem0 / Cognee / Graphiti

Memory compounds. The longer you use it, the better it gets.

Based on real-world testing of 33 memory frameworks over 6 months. Original research by ClawBase.

📍 Hermes Lobster — Code farmer by day, AI rancher by night 🌱

GEO Is Replacing SEO: If AI Can't Read Your Article, Nobody Will

龙虾牧马人 — Tue, 09 Jun 2026 00:36:34 +0000

You wrote a 2000-word technical deep-dive. You optimized the title for keywords. You polished every paragraph.

Then Perplexity extracts a 100-word summary and serves it directly to the user. The user reads the summary and never clicks your link.

This is the difference between SEO and GEO.

What is GEO?

GEO (Generative Engine Optimization) is the practice of optimizing content so that AI-powered search engines — Perplexity, Google AI Overview, Bing Copilot, Claude, Kimi — extract, cite, and recommend your content in their generated answers.

Dimension	SEO	GEO
Target	Google crawler	AI parser
User action	Click link → visit site	Read AI summary directly
Key metric	Click-through Rate (CTR)	AI Citation Rate
Content style	Keyword density + backlinks	Structured data + authoritative citations

SEO gets you on page 1. GEO gets you inside the AI's answer.

What AI search engines actually prefer

1. State answer first, then explain

AI parsers extract the first occurrence of a conclusion.

✅ Do this: GEO's core strategy is to make your content easily extractable by AI parsers through structured data and front-loaded conclusions.

❌ Not this: In today's rapidly evolving AI landscape...

The first 200 words must contain your core conclusion. This is GEO rule #1.

2. Tables > Lists > Paragraphs

Tables → AI extracts as direct comparison data. Lists → AI converts into bullet points. Long paragraphs → AI finds one sentence and discards the rest.

3. Code blocks need language tags

Python code blocks with tags are recognized by AI. Code blocks without tags are treated as plain text.

4. Cite authoritative sources

Official docs (Python.org) → high trust. arXiv papers → high trust. GitHub projects → medium-high trust. Unknown blogs → low trust.

At least one external citation every 500 words.

Structured data

Google AI Overview prioritizes content with Speakable tags for voice answers.

The counter-intuitive truth

GEO isnt a new invention — its a quality upgrade for content standards. Bad content doesnt rank in SEO and it doesnt get cited in GEO either. If AI cant cite your article, it might as well not exist.

Lobster Horseman — farming shrimp (AI), herding horses (Hermes), planting code, watering with algorithms.

Paperclip 60K Stars: The Control Plane Your AI Agents Desperately Need

龙虾牧马人 — Mon, 08 Jun 2026 12:47:02 +0000

Ever given an AI agent a task and watched it go full Roomba — bouncing off walls, repeating the same action 10 times, racking up API bills while getting nowhere? Yeah, me too.

Enter Paperclip (60K+ ⭐ on GitHub), the control plane for AI agents that actually works. Think of it as a brain transplant for your agent swarm.

The Heartbeat Engine

Paperclip's heartbeat engine is like a pacemaker for AI. It automatically detects what state each agent is in, schedules work intelligently, and handles retries and concurrency without you lifting a finger. No more stuck tasks, no more dead loops.

The Ticket System

This one's a game-changer. Instead of having 10 agents all trying to process the same request (hello, wasted tokens), Paperclip deduplicates automatically. One request = one ticket = one execution. It's like an OA system for your AI workforce — clean, auditable, and efficient.

Budget Control (The F***-You Button)

My favorite feature: hard budget caps per agent. Set a limit, and when an agent hits it, the system auto-meltdowns. No surprise bills, no how did we spend 500 in one night moments. Your wallet stays safe while AI spins up.

JoyAI-Echo: JD.com Open-Sources Minute-Long Video+Audio Generator (856★)

龙虾牧马人 — Mon, 08 Jun 2026 03:31:57 +0000

JoyAI-Echo: JD.com's Open-Source Minute-Long Video+Audio Generator

Today's AI Tool: JoyAI-Echo — 856★ on GitHub, JD.com open-source, generates minute-long multi-shot video WITH synchronized audio, plus conversational editing.

The Problem

Three things that suck about AI video generation:

❌ Time limit — videos over 30 seconds fall apart (temporal inconsistency)
❌ Lip sync — generated voices don't match the character's face
❌ No iteration — want to change one shot? Too bad, regenerate the whole thing

One-liner: One prompt → 5 minutes of video+audio, edit it by just talking.

Key Highlights

🎞️ Minute-level multi-shot — Generate a sequence of coherent shots from one JSON prompt

⚡ 7.5x speedup — DMD distillation + memory-based RL

🔊 Joint audio-video — One pipeline outputs both, synced

💬 Conversational editing — "Change the character's shirt to red" without full re-render

🖥️ ComfyUI support — Visual workflow, no coding needed

🎯 Outperforms Wan 2.6 on human-centric tasks

Quick Comparison

Tool	Max Duration	Audio+Video	Interactive Edit	Deployment
JoyAI-Echo	5min+	✅	✅ Conversational	Local
Wan 2.6	Short clips	❌	❌	Local
HappyOyster	Long video	⚠️	❌	Cloud
Sora	~1min	✅	❌	Cloud

Why It Matters

This isn't "yet another video generator." JoyAI-Echo crosses a threshold: from single-shot to story-level generation.

For solo creators and one-person companies:

⏱ Batch output — one prompt → 5 minutes of footage
💰 Zero cost — open-source, self-hosted
🎯 The insight — Video generation is moving from "make a clip" to "tell a story." The next opportunity is in narrative, not effects.

What would you create with 5-minute AI video? Drop a comment.

CopilotKit 38k Stars — The Missing UI Layer for Your AI Agent

龙虾牧马人 — Sun, 07 Jun 2026 23:59:04 +0000

CopilotKit 38k Stars — The Missing UI Layer for Your AI Agent

TL;DR: CopilotKit is an open-source SDK that lets you add AI Agent UI to your React, Angular, Vue, or React Native app in minutes. 38k+ stars, MIT license.

The Problem

Most AI integrations end up as a chat box bolted onto the side of your app. The user types, the AI replies — same pattern, different wrapper.

But what if the AI could directly manipulate your UI? Generate product cards from a search query. Render a dashboard from a natural language request. Ask for confirmation before destructive actions.

That's what CopilotKit does differently.

What Makes CopilotKit Special

Generative UI

The agent dynamically generates and updates UI components at runtime based on user intent. No predefined templates — the agent decides what to render.

Shared State

A synchronized state layer that both the agent and your UI components can read from and write to in real time. Changes flow both ways.

Human-in-the-Loop

The agent can pause execution at critical decision points, ask for user confirmation or edits, then continue.

Multi-Platform

The same agent backend powers web apps, mobile apps, Slack bots, and Microsoft Teams integrations. One agent, many surfaces.

Technical Architecture

React/Angular/Vue SDK ⇄ AG-UI Protocol ⇄ Any LLM Backend

The AG-UI protocol is the wire format — adopted by Google, LangChain, AWS, Microsoft, Mastra, and PydanticAI. Your agent logic stays framework-agnostic.

Quick Comparison

Feature	CopilotKit	Plain Chat UI
UI generation	Dynamic, agent-controlled	Static, predefined
State sync	Bidirectional	None
Human approval	Built-in	Manual
Platform reach	Web + Mobile + Slack/Teams	Web only

Who Is This For?

Product developers who want to add AI to existing apps without rewriting the UI
Agent platform builders deploying across web, mobile, and chat
Anyone curious about the Generative UI paradigm

My Take

CopilotKit's smartest bet wasn't the technology — it was the integration point. Instead of asking developers to "use AI," it lets AI work inside the developer's existing UI. Much lower friction, much higher adoption.

The Generative UI paradigm is still early, but having Google, LangChain, and AWS back the same protocol (AG-UI) is a strong signal. This is worth watching.

Found this useful? Follow **Lobster Hermes* for daily AI tool deep dives.*

CodeGraph 31K⭐ — The AI Coding Assistant That Cuts Your Token Costs by Half

龙虾牧马人 — Sun, 07 Jun 2026 11:22:34 +0000

CodeGraph 31K⭐ — The AI Coding Assistant That Cuts Your Token Costs by Half

TL;DR: CodeGraph pre-indexes your codebase into a knowledge graph, so your AI coding agent gets exactly what it needs in one tool call instead of scanning 100 files. Average result: 52% cost reduction, 78% fewer tokens, 46% faster.

The Problem

You're using Claude Code, Cursor, or Codex CLI. You ask it to fix a bug.

What happens next:

Agent reads the directory structure (1 call)
Agent opens your main file (1 call)
Agent reads imports, follows dependencies (5-10 calls)
Agent opens related files one by one (15-30 calls)
Only then does it understand the context enough to write code

That's 20-50 tool calls per task. On a 10,000-file codebase like VS Code, this is painfully slow and expensive.

That's where CodeGraph steps in.

What CodeGraph Does

Before your agent starts, CodeGraph pre-indexes your entire codebase into a SQLite-backed knowledge graph.

When the agent asks "what's related to this function?", CodeGraph returns:

The entry point and all exported symbols
Related code snippets
Caller/caller relationships (influence analysis)

All in one single tool call. Not 20. Not 50. One.

The Numbers (Real Benchmarks)

Codebase	Files	Cost Saved	Token Saved	Speed
VS Code	10,000	26%	78%	52% faster
Excalidraw	640	52%	90%	73% faster
Alamofire	110	47%	89%	65% faster

Quick Start

# Install
npm install -g @colbymchenry/codegraph

# Init in your project folder
cd your-project
codegraph init -i

# Your AI agent now uses it automatically

Why I Like This Project

🎯 Does one thing (knowledge graph indexing)
✅ Does it well (proven 52-78% cost reduction)
🔌 Composable (works with any MCP-compatible agent)
U0001fab6 Lightweight (SQLite, runs in background)

Final Thought

If you're using any AI coding agent on a codebase larger than 100 files, try CodeGraph. The ROI is instant — reduce token costs by 50%+ starting from your first task.

DEV Community: 龙虾牧马人

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