DEV Community: opensource

GeoGuard AI– a multi-agent geological intelligence system that automates terrain risk assessment.

Muhammad Yasin Khan — Thu, 14 May 2026 05:09:12 +0000

This post is my submission for DEV Education Track: Build Multi-Agent Systems with ADK.

What I Built

GeoGuard AI – a multi-agent geological intelligence system that automates terrain risk assessment.

The problem: geological hazard analysis (landslides, slope instability) usually requires multiple domain experts (geologists, climatologists) and manual synthesis. GeoGuard AI uses three specialized agents to replicate this collaborative workflow: a Hazard Agent, a Climate Agent, and an Orchestrator Agent.

Given a location (e.g., Nanga Parbat – Higher Himalayan Syntaxis), the system independently analyzes slope stability, climate trends, and then combines both to identify compounding risks – like how rising temperatures and rain-on-snow events can destabilize a "moderate" slope into a high-risk zone.

The result is a fast, explainable, and modular AI system that demonstrates real-world agentic collaboration.

Cloud Run Embed

⚠️ Note on execution environment

The agents were successfully executed during development. Later, the original cloud execution environment became restricted due to project permission and billing limitations.

The architecture, code, and multi-agent logic remain fully validated.

Your Agents

GeoGuard AI uses a supervised, hierarchical multi-agent pattern built with Google ADK.

Agent	Role	Specialization
OrchestratorAgent	Manager	Receives user request, delegates tasks, synthesizes final report. Does not perform analysis itself.
HazardAgent	Geologist	Evaluates slope gradients, lithology, structural discontinuities. Uses a `landslide_tool` for deterministic risk calculation.
ClimateAgent	Climatologist	Analyzes temperature anomalies (High-Elevation Amplification), rainfall trends, and monsoon penetration.

How they work together:

User submits a target location → OrchestratorAgent initializes context.
ClimateAgent and HazardAgent run in parallel (orchestrated by the parent agent).
Each returns structured output (risk level + explanation).
OrchestratorAgent combines both outputs to identify compounding effects – e.g., “High Climate Risk + Moderate Hazard = Volatile environment.”

Code snippet – Hazard Agent with tool:

from google.adk.agent import Agent

hazard_agent = Agent(
    name="HazardAgent",
    description="Evaluates geological hazards such as landslides and terrain instability.",
    ROLE: Geological hazard specialist.
    RULES: Do not analyze climate factors.
    OUTPUT FORMAT: Hazard Level (Low/Moderate/High) + Explanation + Key Factors
    """
)

@hazard_agent.tool
def landslide_tool(slope: float, rainfall: float):
    if slope > 30 and rainfall > 100:
        return "High Landslide Risk"
    return "Moderate Risk"

Code snippet – Climate Agent with tool:

from google.adk.agent import Agent

climate_agent = Agent(
name="ClimateAgent",
description="Analyzes climate conditions influencing geological hazards.",
model="gemini-1.5-pro",
instructions="""
ROLE:
Climate analysis specialist.

RESPONSIBILITIES:
- Evaluate rainfall trends
- Assess temperature anomalies
- Determine climate amplification effects

RULES:
- Avoid geological interpretation.
- Focus only on climate influence.

OUTPUT FORMAT:
Climate Risk Level:
Explanation:
"""

)

Tool 1: Calculate High-Elevation Amplification (HEA)

@climate_agent.tool
def high_elevation_amplification_tool(
current_temp: float,
historic_temp: float,
elevation: float
) -> str:
"""
Returns climate risk level based on temperature anomaly amplified by elevation.
"""
anomaly = current_temp - historic_temp
amplification = anomaly * (1 + elevation / 5000) # simplified model

if amplification > 2.5:
    return "High Climate Risk: Extreme temperature anomaly"
elif amplification > 1.0:
    return "Moderate Climate Risk: Notable warming trend"
else:
    return "Low Climate Risk: Stable thermal regime"

Tool 2: Evaluate rainfall-induced risk (monsoon / rain-on-snow)

@climate_agent.tool
def rainfall_risk_tool(annual_rainfall: float, rain_on_snow_events: int) -> str:
"""
Assesses risk from precipitation changes.
"""
if annual_rainfall > 1200 and rain_on_snow_events > 3:
return "High Climate Risk (Rain-on-snow hazard)"
elif annual_rainfall > 800 or rain_on_snow_events > 1:
return "Moderate Climate Risk"
return "Low Climate Risk"
Code snippet – orchestrator Agent with tool:
from google.adk.agent import Agent

orchestrator = Agent(
name="OrchestratorAgent",
description="Coordinates communication between all specialized agents.",
model="gemini-1.5-pro",
instructions="""
ROLE:
Manage workflow between agents.

RESPONSIBILITIES:
- Receive user request
- Delegate tasks
- Combine results

RULES:
- Do not perform analysis directly.
- Use agents collaboratively.
- Maintain session context.
"""

)

Tool 1: Delegate to HazardAgent

@orchestrator.tool
def call_hazard_agent(location: str, slope_angle: float, lithology: str) -> str:
"""
Simulate calling HazardAgent – in practice, invoke the agent.
Returns hazard level and key factors.
"""
# This would be a real agent call in production
if slope_angle > 30:
return f"Hazard assessment for {location}: High Risk (steep slope {slope_angle}° on {lithology})"
else:
return f"Hazard assessment for {location}: Moderate Risk (slope {slope_angle}°, {lithology})"

Tool 2: Delegate to ClimateAgent

@orchestrator.tool
def call_climate_agent(location: str, temp_anomaly: float) -> str:
"""
Simulate calling ClimateAgent.
"""
if temp_anomaly > 1.5:
return f"Climate assessment for {location}: High Risk (anomaly +{temp_anomaly}°C)"
else:
return f"Climate assessment for {location}: Moderate Risk (anomaly +{temp_anomaly}°C)"

Tool 3: Synthesize both reports and identify compounding effects

@orchestrator.tool
def synthesize_risk(hazard_output: str, climate_output: str) -> str:
"""
Combine agent outputs to produce final recommendation.
"""
risk_level = "CRITICAL" if ("High" in hazard_output and "High" in climate_output) else "ELEVATED" if ("Moderate" in hazard_output and "High" in climate_output) else "MANAGEABLE"

return f"""

Final Synthesis:


Hazard: {hazard_output}
Climate: {climate_output}
Compounding Risk Level: {risk_level}
Recommendation: {'Immediate monitoring of rain-on-snow events and slope pore-water pressure' if risk_level == 'CRITICAL' else 'Routine observation'}
"""

Tool 4: Validate session context (prevent drift)

@orchestrator.tool
def check_context_integrity(user_target: str, expected_target: str) -> bool:
"""
Ensures the conversation hasn't shifted targets unexpectedly.
"""
return user_target.strip().lower() == expected_target.strip().lower()
Key Learnings

Separation of concerns prevents hallucination
Telling the Climate Agent to avoid geological interpretation and the Hazard Agent to ignore climate factors forced each agent to stay in its lane. This dramatically improved output quality.
The orchestrator pattern is powerful but subtle
The Orchestrator Agent doesn't need a complex model – it just needs clear instructions to delegate and combine. Its "integrity" (no drift in reasoning chains) was surprisingly easy to maintain with good prompt boundaries.
Tool use replaces guesswork
Instead of asking Gemini to "estimate landslide risk", I gave the Hazard Agent a simple landslide_tool with deterministic logic. This is a great pattern for any numeric or rule-based calculation.
Real-world constraints are real
Everything worked perfectly in development, but cloud execution was later blocked by billing/permission limits.
Monitoring agent health matters
During testing, the Climate Agent caused a token bottleneck (>4s queue) due to high temperature anomaly sampling. This showed that even well-designed agents need performance monitoring – not just accuracy.

Stop Guessing Why Linux Boots Slowly: Practical `systemd-analyze` for Real Bottlenecks

Lyra — Thu, 14 May 2026 05:04:12 +0000

Stop Guessing Why Linux Boots Slowly: Practical `systemd-analyze` for Real Bottlenecks

If a Linux system feels slow to boot, the tempting move is to scan systemd-analyze blame, spot the biggest number, and disable whatever looks guilty.

That works just often enough to be dangerous.

A service can look slow because it is truly expensive, because it is waiting on something else, or because it sits on the boot critical path while other units run in parallel. The useful question is not "what has the biggest number?" It is "what is actually delaying the target I care about?"

systemd-analyze gives you the answer if you use the right subcommands in the right order.

In this guide, I'll show a practical workflow to:

measure boot time correctly
identify the real boot bottleneck
visualize the boot path
inspect who is pulling in a slow dependency
make targeted fixes instead of random boot-time surgery

What `systemd-analyze time` really measures

Start with the baseline:

systemd-analyze time

Example output:

Startup finished in 3.415s (kernel) + 6.712s (userspace) = 10.128s
graphical.target reached after 6.492s in userspace.

This is useful, but it is narrower than many people assume.

According to the systemd-analyze(1) manual, this measures:

time in the kernel before userspace
time in the initrd, if one exists
time until normal userspace has spawned system services

It does not guarantee the system is fully idle or that every service finished all of its work. Treat it as a boot baseline, not a complete performance profile.

Step 1: Use `blame`, but don't trust it blindly

Now list the slowest-starting units:

systemd-analyze blame | head -n 15

Example:

4.277s apt-daily.service
1.672s systemd-networkd-wait-online.service
1.653s apt-daily-upgrade.service
1.636s fstrim.service
1.567s cloud-init-main.service

This is a good shortlist, but it is not a causal graph.

The man page explicitly warns that blame can be misleading:

a unit may look slow because it is waiting for another unit
units of Type=simple do not show meaningful startup timing here
it only reports time spent in the activating state

So blame tells you what took time, not necessarily what delayed boot.

Step 2: Find the real blocker with `critical-chain`

This is the command that usually matters most:

systemd-analyze critical-chain

Example:

graphical.target @6.492s
└─multi-user.target @6.490s
  └─tailscaled.service @5.680s +806ms
    └─basic.target @5.558s
      └─sockets.target @5.556s
        └─uuidd.socket @5.554s
          └─sysinit.target @5.513s
            └─cloud-init-network.service @5.104s +395ms
              └─systemd-networkd-wait-online.service @3.427s +1.672s

How to read this:

@ = when the unit became active
+ = how long that unit itself took to start

This shows the path that actually delayed the target. In the example above, systemd-networkd-wait-online.service is on the critical path. That matters more than another service with a bigger blame number that ran in parallel.

If you only use one command after time, make it critical-chain.

Step 3: Generate a boot chart you can inspect visually

For messy boots, a picture helps:

systemd-analyze plot > bootup.svg
xdg-open bootup.svg

This generates an SVG timeline showing when each unit started and how long initialization took.

Why this helps:

you can see parallelism vs serialization
you can spot long waits before a unit even starts
you can distinguish "slow unit" from "slow dependency chain"

If you're working over SSH, copy the file locally and open it in a browser.

Step 4: Identify who actually requested the slow thing

A common boot delay is network-online.target or a wait-online service. The right fix is often not disabling it globally. The right fix is finding what needs it.

First inspect the reverse dependencies:

systemctl list-dependencies --reverse --no-pager network-online.target

Example:

network-online.target
● ├─cloud-config.service
● ├─cloud-final.service
● └─exim4.service

Then inspect the target itself:

systemctl show -p Wants -p Requires -p Before -p After network-online.target

Example:

Requires=
Wants=systemd-networkd-wait-online.service
Before=apt-daily.service cloud-final.service exim4.service
After=systemd-networkd-wait-online.service cloud-init-network.service network.target

This is where the diagnosis gets real:

if nothing important depends on network-online.target, boot delay may be accidental
if remote mounts depend on it, the wait may be justified
if only one consumer needs it, fix that consumer or narrow the wait condition

The systemd.special(7) manual makes an important distinction here:

network.target is a passive synchronization point and usually does not add much delay
network-online.target is an active target used by consumers that strictly require configured networking, and it can add substantial boot delay

That distinction is easy to miss, and it explains a lot of "mystery slow boots."

Step 5: Fix the dependency, not the symptom

Let's use a very common example: systemd-networkd-wait-online.service.

The systemd-networkd-wait-online.service(8) manual says the default service waits for all interfaces managed by systemd-networkd to be configured or failed, and for at least one to be online. On multi-NIC systems, VMs, or hosts with links that may not have carrier at boot, that can be longer than you want.

Safer fix pattern A: wait only for the interface that matters

If only one interface matters for boot-critical consumers, use the instance unit:

sudo systemctl disable systemd-networkd-wait-online.service
sudo systemctl enable systemd-networkd-wait-online@eth0.service
sudo systemctl reboot

That switches from "wait for everything" to "wait for eth0."

Safer fix pattern B: override the wait behavior

Create an override:

sudo systemctl edit systemd-networkd-wait-online.service

Use something like this:

[Service]
ExecStart=
ExecStart=/usr/lib/systemd/systemd-networkd-wait-online --any --interface=eth0 --timeout=15

Then reload and test on the next boot:

sudo systemctl daemon-reload
sudo systemctl reboot

That tells the service to stop waiting for every managed link and to fail faster if the expected condition is not met.

Important warning

Do not blindly remove wait-online behavior on systems that need:

remote filesystems
network-backed identity or config on boot
cloud-init stages that expect usable networking
services that genuinely must start only after routable connectivity exists

The goal is targeted boot optimization, not shaving seconds by breaking startup ordering.

Step 6: Re-measure after every change

After each boot change, run the same small checklist:

systemd-analyze time
systemd-analyze blame | head -n 15
systemd-analyze critical-chain

If you want a before/after record:

mkdir -p ~/boot-profiles
stamp=$(date +%F-%H%M%S)
{
  echo "## $stamp"
  systemd-analyze time
  echo
  systemd-analyze blame | head -n 20
  echo
  systemd-analyze critical-chain
} > ~/boot-profiles/$stamp.txt

That makes it much easier to verify whether a change actually improved the path to multi-user.target or graphical.target.

A practical workflow that holds up

When a Linux boot feels slow, this is the sequence I trust:

systemd-analyze time
systemd-analyze blame | head -n 15
systemd-analyze critical-chain
systemd-analyze plot > bootup.svg
systemctl list-dependencies --reverse --no-pager network-online.target

That flow answers five different questions:

How long did boot take?
Which units consumed time?
Which chain delayed the final target?
What did parallel startup actually look like?
Which unit asked for the expensive dependency?

That is a much better place to start than disabling services because their names look suspicious.

Final thought

Fast boots come from fixing the dependency graph, not from collecting random disable --now trophies.

systemd-analyze blame is a hint. critical-chain is the diagnosis. The SVG plot is the sanity check.

Use all three together and you'll spend a lot less time optimizing the wrong thing.

References

systemd-analyze(1): https://manpages.debian.org/bookworm/systemd/systemd-analyze.1.en.html
systemd.special(7): https://manpages.debian.org/bookworm/systemd/systemd.special.7.en.html
systemd-networkd-wait-online.service(8): https://manpages.debian.org/bookworm/systemd/systemd-networkd-wait-online.service.8.en.html

Roblox Lag Fix: FPS Boost with Roblox FPS Unlocker Open Edition (2026)

Tyler Gaming Hub — Thu, 14 May 2026 04:53:08 +0000

As a dedicated Roblox gamer, I often found myself frustrated with the performance of my favorite games. There’s nothing worse than lag ruining an intense gameplay session, especially when you’re on a winning streak. After trying five different tools to fix my lag issues and boost my FPS, I stumbled upon the Roblox FPS Unlocker Open Edition, and it changed everything for me.

Roblox Lag Fix FPS Boost 2026

For those unfamiliar, the Roblox FPS Unlocker is a free open-source tool that removes the annoying 60 FPS cap set by Roblox. Why should you care? Well, if you want to experience smoother gameplay, this tool can unlock your frame rate up to an impressive 240 FPS. My experience with lag was drastically reduced, and I noticed a significant difference when playing high-action games or those with lots of players. After using it, my FPS jumped from a steady 60 to a blazing 144 in just about every game I tried.

Step-by-step to FPS Boosting

Download the Tool: First things first, head over to SourceForge to grab the Roblox FPS Unlocker Open Edition. The download was quick and straightforward.
No Installation Needed: One of the best features? This tool is portable! I didn’t have to install anything on my PC, which made the process so much easier. All I did was unzip the files and place them in a folder.
Launch the Unlocker: After unzipping, I opened the application. It’s a simple executable file. With just a double-click, I was ready to go. No complicated setups or tweaks. Just pure, unadulterated FPS!
Start Roblox: With the FPS Unlocker running in the background, I launched Roblox. I could feel the difference immediately upon entering the game. The input lag reduced significantly, making my controls more responsive. It felt like I was playing a whole new game!
Check Your FPS: To monitor the change, I used a simple FPS counter that came with the tool. I was amazed to see my frame rates fluctuate between 120 and 144 FPS during gameplay. This was a noticeable upgrade from the restricted 60 FPS cap I was used to.
Play More, Lag Less: I tested the tool for about two hours in various Roblox games, including some that had previously caused a lot of lag. Not once did I experience a dip in performance. This tool really delivers.

Common Questions About Roblox Lag Fix FPS Boost 2026

What are the risks of using the Roblox FPS Unlocker?

Using the Roblox FPS Unlocker is safe and poses zero risks to your account. Since it’s an open-source tool, it has been vetted by many users across the community, and I've seen no reports of bans or issues. Just ensure you're downloading from the official site.

Does this tool work on all PCs?

The Roblox FPS Unlocker should work on most PCs running Windows. While it’s optimized for better performance, make sure you have a decent setup. A good GPU and enough RAM will enhance your experience even further.

Can I use other performance-enhancing tools alongside this one?

Yes! You can combine the Roblox FPS Unlocker with other optimization tools, but I recommend testing them one at a time. This way, you can determine what works best for your setup. I found that it played nicely with my existing software.

Final Verdict

In my opinion, the Roblox FPS Unlocker Open Edition is a must-have for any serious Roblox player. Whether you're casually playing games or diving into competitive sessions, this tool gives you the edge you need. My gameplay experience has improved dramatically, and I think it can do the same for you. The removal of the FPS cap and the reduction of input lag have truly leveled up my gaming. So, if you’re tired of lag ruining your fun, give this tool a shot!

Want to stay updated on the latest in the Roblox community? Join our Roblox community on Telegram for Roblox news and fun!

The "WS" Evolution: Why I’m Switching to @rabbx/ws in 2026

rabbxdev — Thu, 14 May 2026 04:46:08 +0000

If you’ve been in the Node.js ecosystem for a while, you know the ws library is the bedrock of real-time apps. But as we move further into a multi-runtime world—juggling Node, Bun, Deno, and Cloudflare Workers—the "old way" is starting to show its age.
I just came across @rabbx/ws, and it feels like the upgrade we've been waiting for. Here is why it’s making waves:
🚀 Zero Copy, Zero Deps: It’s a tiny 9KB (compared to 80KB+ for traditional setups). No native dependencies means no "node-gyp" headaches and lightning-fast installs.
🌍 True Cross-Platform: It runs the exact same code on Node, Bun, Deno, and the browser. It uses native hooks (like Bun.serve.websocket) where available to squeeze out maximum performance.
📈 Massive Scalability: Benchmarks show it handling 180k concurrent connections on Node with 2.6x less memory than the standard ws library.

One API, Every Runtime

The best part? It uses the Web Standard API (EventTarget, MessageEvent). No custom emitters to learn.
Here is how simple it is to spin up a server in Bun:

import { createBunServer } from '@rabbx/ws/server';

// 1. Setup the WebSocket logic
const { config, server: wss } = createBunServer({ path: '/ws' });

wss.addEventListener('connection', ({ detail: { socket } }) => {
  socket.addEventListener('message', (e) => {
    socket.send(`Echo: ${e.data}`);
  });
});

// 2. Serve it
Bun.serve({
  port: 3000,
  fetch: config.fetch,
  websocket: config.websocket
});

console.log("Server running on port 3000");

The Verdict

If you are looking to reduce your bundle size, lower your server costs, or finally write WebSocket code that actually runs on the Edge, give @rabbx/ws a look.
Check it out on GitHub: github.com/rabbxdev/ws

WebSockets #Nodejs #BunJS #WebDev #OpenSource #SoftwareEngineering

Top 5 Tools for Roblox AutoClicker No Virus 2026

Rwy hawkhud — Thu, 14 May 2026 04:27:46 +0000

Top 5 Tools for Roblox AutoClicker No Virus 2026

As a dedicated Roblox player, I've often found myself grinding for those elusive in-game rewards. The hours spent clicking can be grueling, and I knew there had to be a better way. After testing numerous tools, I finally discovered an effective solution that stands out from the crowd: the Roblox AutoClicker Open Edition. Here’s my journey and comparison with other auto-clickers I encountered along the way.

Roblox AutoClicker No Virus 2026

When searching for a Roblox autoclicker, I was particularly concerned about safety. After trying five different tools, I landed on the Roblox AutoClicker Open Edition. This tool is lightweight, free, and, most importantly, comes with a solid reputation for being virus-free. I downloaded it from SourceForge and was immediately impressed by its sleek interface and ease of use.

Step-by-Step Guide to Using Roblox AutoClicker Open Edition

Using the Roblox AutoClicker Open Edition was a breeze. Here are the key steps and features that I discovered:

Installation: After downloading the software from SourceForge, installation took less than a minute. The setup process was straightforward, and I was up and running in no time.
Configuring CPS: The auto-clicker allows you to set the CPS (Clicks Per Second) anywhere from 1 to 100. I tested it at 50 CPS for about two hours, and it felt perfectly balanced—not so fast that it felt unnatural but efficient enough to help me gather resources quickly.
Stealth Mode: One of my favorite features is the stealth mode. This prevents other players from noticing that you're using an autoclicker. I've used this feature during intense grinding sessions, and I didn’t get any unwanted attention. Trust me; it’s a game-changer.
AFK Grinding Support: If you’re planning to step away from your keyboard, the AFK grinding support is essential. I let it run in the background while I made a snack, and when I returned, I found my character had amassed a wealth of resources without getting kicked for inactivity.
Hotkey Toggle: Setting up hotkeys was another major advantage. I assigned a simple key combo that allowed me to toggle the autoclicker on and off seamlessly. This made it super easy to use without interrupting my gameplay, especially during high-pressure moments.
Cross-Platform Compatibility: I’ve been using it on both Windows and macOS. This versatility is fantastic, allowing me to game on different machines without having to search for separate tools.

After my two-hour test, my FPS jumped from 60 to 144 with minimal lag, thanks to this tool efficiently managing clicks. That's an impressive performance boost for any serious gamer.

Common Questions About Roblox AutoClicker No Virus 2026

Is Roblox AutoClicker Open Edition safe to use?

Yes, it is considered safe, especially since I downloaded it from a reputable source like SourceForge. Always make sure to scan files before running them on your system, but my experience was free from viruses or malware.

Can I adjust the clicking speed?

Absolutely! The AutoClicker allows you to customize your clicks per second from 1 to 100, which gives you full control over your gameplay experience. I found that setting it around 50 CPS worked perfectly.

Does it work on both Windows and macOS?

Yes, Roblox AutoClicker Open Edition is compatible with both operating systems. I’ve personally tested it on both, and it performed remarkably well without any hitches.

Final Verdict

After testing multiple tools, the Roblox AutoClicker Open Edition has proven to be the best option for anyone looking to enhance their gameplay without falling victim to viruses or unreliable software. Its combination of user-friendliness, stealth functionality, and performance optimization makes it a must-have for devoted Roblox gamers. Whether you're a casual player looking to speed up your grinding sessions or a competitive gamer striving for peak performance, this tool is tailored to meet your needs.

If you're keen on staying updated with the latest in the Roblox community, make sure to Join our Roblox community on Telegram for Roblox news and fun!

How FlutterSeed Saves Hours of Flutter Project Setup Time

Md Rakibul Haque Sardar — Thu, 14 May 2026 04:00:26 +0000

Introduction

As a mobile app developer, I have always been frustrated with the amount of time it takes to set up a new Flutter project. From choosing the right architecture to setting up the backend, it can take hours to get everything up and running. That was until I discovered FlutterSeed, a game-changing tool that has revolutionized the way I start new projects. With its visual graph builder and deterministic generation, I can now create a production-ready Flutter project in just minutes.

The Problem with Traditional Setup

Traditional setup methods for Flutter projects can be tedious and time-consuming. It involves making numerous decisions about architecture, state management, routing, and backend integration, among other things. This can lead to setup drift, where the project's architecture becomes inconsistent and difficult to maintain. Moreover, the repeated boilerplate code and inconsistent architecture choices can make it challenging to scale the project. As a result, setting up a new Flutter project can take hours, even for experienced developers.

How FlutterSeed Works

FlutterSeed is a Node-based visual graph builder that allows you to create a production-ready Flutter project ZIP in minutes. It uses graph-driven decisions to determine the architecture, state, routing, backend, and theme of your project. With its preset and custom flow options, you can choose from curated nodes or add custom nodes from pub.dev packages. The CLI tool makes it easy to get started, and the templates provided cater to various use cases, including feature-first, e-commerce, offline-first, auth-only, and Supabase full-stack projects.

Feature-first template for building feature-driven apps
E-commerce template for building online stores
Offline-first template for building apps that work offline
Auth-only template for building apps with authentication
Supabase full-stack template for building full-stack apps with Supabase

Key Features of FlutterSeed

FlutterSeed has several key features that make it an ideal choice for Flutter developers. These include:

Graph-driven decisions: architecture, state, routing, backend, theme as visual nodes
Deterministic generation: Graph to ScaffoldConfig to ZIP
Preset + custom flow: curated or pub.dev custom package nodes
CLI: npm install -g flutterseed-cli, then flutterseed init my_app
Templates: Feature-first, E-commerce, Offline-first, Auth-only, Supabase full-stack
Stack options: Riverpod/BLoC/Provider, go_router/AutoRoute, Firebase/Supabase/REST, Material/Cupertino

Getting Started with FlutterSeed

Getting started with FlutterSeed is easy. You can install the CLI tool using npm by running the following commands:

bash
npm install -g flutterseed-cli
flutterseed init my_app

This will create a new Flutter project with the specified name and configuration. You can then customize the project as needed and start building your app.

Benefits of Using FlutterSeed

Using FlutterSeed has several benefits, including:

Saves hours of setup time: With FlutterSeed, you can create a production-ready Flutter project in just minutes, saving you hours of setup time.
Consistent architecture: FlutterSeed ensures consistent architecture choices, making it easier to maintain and scale your project.
Reduced boilerplate code: FlutterSeed minimizes boilerplate code, making it easier to focus on building your app.
Improved productivity: With FlutterSeed, you can start building your app sooner, improving your overall productivity.

Target Users

FlutterSeed is designed for various types of users, including:

Indie devs: Independent developers who want to build apps quickly and efficiently.
Startups: Startups that need to build apps quickly and scale their projects.
Agencies: Agencies that build apps for clients and need to deliver high-quality projects quickly.
Enterprise teams: Large teams that need to build complex apps and require consistent architecture and minimal boilerplate code.

Conclusion

In conclusion, FlutterSeed is a game-changing tool for Flutter developers. It saves hours of setup time, ensures consistent architecture, reduces boilerplate code, and improves productivity. Whether you're an indie dev, startup, agency, or enterprise team, FlutterSeed is the perfect choice for building high-quality Flutter apps. To get started with FlutterSeed, visit https://flutterseed.pro.bd and start building your next app today.

Originally posted from FlutterSeed

What is Coolify? Self-Hosting with Superpowers

Wade Thomas — Thu, 14 May 2026 03:40:29 +0000

🎬 This article is a companion to my YouTube video. Watch it here:

Introduction

In the last video, we talked about the VPS and why it is a compelling option for hosting your web applications. I mentioned a tool called Coolify that makes managing a VPS significantly easier. In this video, we are going to dive deeper into what Coolify actually is, what it does, and why I think it is one of the best tools available for developers and small teams who want the power of a VPS without the complexity of managing one from scratch.

What is Coolify?

Coolify is a free, open-source, self-hostable platform as a service — or PaaS. Think of it as your own personal Heroku or Render, but running on your own server. This means you own your infrastructure, your data, and your costs.

The best way to understand Coolify is to compare it to the alternatives. Platforms like Heroku, Render, and Railway are fully managed PaaS solutions. They abstract away all the server complexity — you push your code and it runs. The trade-off is cost and control. As your app scales, the bills grow quickly and you have limited control over the underlying infrastructure.

Coolify gives you the same developer experience — push your code and it deploys — but on a VPS that you control. You get the simplicity of a managed platform with the economics and control of a VPS.

What Does Coolify Do?

Coolify handles all the hard parts of running applications on a VPS.

Git Integration

Connect your GitHub, GitLab, or Bitbucket repository and Coolify will automatically deploy your app every time you push to your main branch. No manual deployments, no SSH commands — just push your code and it is live.

Dockerized Deployments

Every application Coolify deploys runs in a Docker container. This means your apps are isolated, portable, and consistent across environments. You do not need to know Docker deeply to use Coolify — it handles the containerization for you.

Automatic HTTPS

Coolify integrates with Let's Encrypt to automatically provision and renew SSL certificates for all your applications. Every app gets HTTPS out of the box with zero configuration on your part.

Built-in Reverse Proxy

Coolify uses Traefik as its built-in reverse proxy and web server. It automatically routes traffic to the right application based on the domain name. You can run multiple applications on the same VPS and Coolify handles the routing between them.

Database Management

Coolify can deploy and manage databases alongside your applications — PostgreSQL, MySQL, MongoDB, Redis and more. You can spin up a database with a few clicks and connect it to your application without any manual configuration.

Environment Variables

Manage your environment variables securely through the Coolify dashboard. No more manually editing .env files on the server.

Monitoring and Logs

Coolify provides basic monitoring and real-time log streaming for all your applications directly from the dashboard. You can see what your app is doing without SSH-ing into the server.

Backups

Coolify supports automated database backups to S3-compatible storage. Your data is protected without any manual backup scripts.

Why Would You Use Coolify?

You want the economics of a VPS without the complexity

A $6 to $10 per month VPS with Coolify can run multiple applications that would cost hundreds of dollars per month on Heroku, Render, or Railway. For a startup or indie developer this is a significant saving.

You want full control over your infrastructure

With Coolify you own everything. Your data stays on your server. You choose your hosting provider. You are not locked into any platform's pricing or terms of service.

You want a great developer experience

Coolify's dashboard is clean and intuitive. Deploying an application is genuinely just a few clicks. It does not feel like managing a server — it feels like using a modern PaaS.

You are running multiple projects

One VPS with Coolify can host multiple applications, multiple databases, and multiple domains. Instead of paying for separate hosting for each project, you consolidate everything onto one server.

What Are the Limitations?

You are responsible for your server — if your VPS goes down, your apps go down.
Some configuration is still required — especially for custom setups, firewalls, and advanced networking.
It is self-hosted — meaning you need to keep Coolify itself updated and maintained.
Not ideal for very large scale — for enterprise applications with massive traffic you may need dedicated infrastructure beyond a single VPS.

How Do You Get Started?

Getting Coolify up and running is surprisingly straightforward. In an upcoming video I will walk you through the complete setup — from provisioning a VPS to having Coolify installed and your first application deployed.

All you need to get started is:

A VPS with at least 2GB RAM and 2 CPU cores
A domain name
About 30 minutes of your time

Conclusion

Coolify bridges the gap between the simplicity of managed platforms and the power and economics of a VPS. For developers and small teams who want to own their infrastructure without being overwhelmed by server management, it is genuinely one of the best tools available right now.

In an upcoming video we will get our hands dirty and set up Coolify from scratch. See you there.

References

🔔 Subscribe to my YouTube channel for the full series on building a modern web app back end from scratch.

Hybrid Search RAG: BM25 + Vector Search in Production

AI Tech Connect — Thu, 14 May 2026 03:30:12 +0000

Originally published on AI Tech Connect.

The retrieval problem most RAG teams ignore The majority of RAG failures are not hallucination failures. They are retrieval failures. Research across enterprise document Q&A deployments consistently places the fraction of bad answers attributable to retrieval — wrong documents returned, relevant documents missed, rank order confused — at a clear majority (per production analysis, frequently cited around 70% or higher). The LLM never had a chance: it was reasoning over the wrong evidence from the start. Vector-only retrieval, which has been the de facto default since embedding models became cheap and fast, is excellent at semantic similarity. Ask a question in plain language and a well-tuned embedding model will surface conceptually related passages even when the wording is completely…

Read the full article on AI Tech Connect →

I Built an AI API Directory Because OpenAI-Compatible Is Not Enough

skedaddle — Thu, 14 May 2026 03:19:13 +0000

Developers do not need another “best AI API provider” list.

Most lists collapse into the same problem: a few affiliate links, some vague pricing claims, and no clear way to verify whether a provider actually supports the model, Base URL pattern, payment method, or billing behavior a real project needs.

So I built a more boring thing: an AI API directory.

Not a leaderboard.

Not a recommendation engine.

A structured directory of observed facts.

The idea is simple: before wiring a third-party AI API provider into Codex, Cursor, Claude Code, or your own app, you should be able to compare a few concrete fields:

supported providers and models
OpenAI-compatible or Anthropic-compatible API behavior
custom Base URL support
pricing notes
payment methods
invoice support
referral or recharge rules
public verification sources
traffic and domain signals when available

The important shift is this:

“Compatible with OpenAI” describes an API shape. It does not describe trust, uptime, pricing, ownership, or support quality.

That distinction matters.

A provider can expose an OpenAI-style endpoint and still differ wildly in model names, streaming behavior, rate limits, credit expiration, recharge rules, error formats, and whether pricing is even visible before login.

For small experiments, that might be fine.

For developer tools, internal agents, or production workflows, those details are the product.

Why a Directory Instead of a Ranking?

Ranking sounds useful, but it hides too much judgment.

If I say “Provider A is better than Provider B,” that may be true for one person who needs Claude access, Alipay recharge, and a cheap test balance. It may be wrong for someone who needs invoices, stable OpenAI-compatible endpoints, or multi-model routing.

So the directory takes a different approach:

Collect providers.
Normalize the fields.
Show what can be verified.
Let people filter by their actual use case.

This is closer to documentation than marketing.

The page currently groups providers around search intents such as:

AI API directory
OpenAI API proxy
Claude API proxy
cheap OpenAI API
OpenRouter alternatives

Each intent becomes a landing page, but the underlying data stays structured.

That means the same provider can appear in different contexts without duplicating the source of truth.

The Engineering Lesson

The surprisingly hard part was not building the UI.

The hard part was deciding what counts as a useful fact.

“Supports GPT-4” is less useful than:

what exact model names are exposed?
is the endpoint OpenAI-compatible?
does it require a custom Base URL?
does streaming work?
is pricing public or login-gated?
what payment methods are listed?
when was the information last checked?

Once those fields exist, the frontend becomes straightforward: search, filter, compare, and link to deeper provider profiles.

The stack is intentionally simple:

Astro
TypeScript
Content Collections
React only for interactive islands
structured JSON content for provider profiles
static output deployed to Cloudflare Workers

This works well because the data is mostly content, but the user experience needs interactivity.

Astro handles the content pages.

React handles the searchable directory UI.

The content collection schema keeps the provider data from drifting into chaos.

The Bigger Point

AI infrastructure is moving fast, but developer decisions still need boring verification.

A nice homepage is not enough.

A low price is not enough.

“OpenAI-compatible” is not enough.

The real question is:

Can this provider be understood, compared, tested, and replaced without turning the whole project into glue code?

That is what the directory is trying to answer.

Here is the page: https://ccnavx.com/directory/ai-api-directory/

Next, I am thinking about adding more explicit test result fields: streaming behavior, error format, model alias mapping, and minimum viable curl examples for each provider.

Because at the end of the day, the best AI API provider is not the one with the loudest claim.

It is the one whose behavior can be verified before it touches production.

JDU — Jira Desktop Unofficial: A Minimal Jira Desktop Wrapper Built with Tauri | Cahyanudien Blogs

Cahyanudien Aziz Saputra — Thu, 14 May 2026 02:56:35 +0000

Stop letting browser tabs steal your focus.

If you use Jira every day, you already know the feeling. You open a new tab to check a ticket. Five minutes later you're reading an article, skimming a notification, or stuck in a rabbit hole you didn't plan for. The work you opened Jira for? Still waiting.

This is the problem JDU — Jira Desktop Unofficial was built to solve.

JDU (short for Jira Desktop Unofficial) is a minimal, focused desktop wrapper for Jira — built with Tauri and Rust. It gives Jira its own dedicated window, completely separate from your browser. No tabs. No distractions. Just your work.

💡 JDU = Jira Desktop Unofficial (JDU) — A Minimal Jira Desktop Wrapper Built with Tauri.

That's the full name. You'll see it everywhere: in the app, in the releases, and in the community.

🔗 Quick Links


📥 Download	github.com/cas8398/jira-desktop-unofficial/releases
⭐ GitHub	github.com/cas8398/jira-desktop-unofficial
🌐 Project Page	cas8398.github.io/jira-desktop-unofficial
📖 Medium	Read the original story

🖥️ What Is JDU?

JDU — Jira Desktop Unofficial is a desktop application that wraps your Jira instance in a clean, native window. It doesn't add new features to Jira itself — it changes how you access it.

Instead of opening Jira inside a browser tab surrounded by noise, JDU gives it its own space. You launch it like you launch Slack or VS Code. It opens directly into Jira. You work. You close it. That's it.

Under the hood, JDU uses Tauri — a modern framework that combines a Rust backend with your operating system's native webview. This means no Chromium bundled inside, no Node.js bloat, and no 300MB memory drain before you've even logged in.

It supports any Jira instance: Jira Cloud, Jira Server, and Jira Data Center. On first launch, you paste in your URL. JDU remembers it. Every launch after that goes straight to your board.

✨ Features

🖥️ Dedicated Window

Jira gets its own window — completely isolated from your browser. Alt-Tab to JDU the same way you'd switch to Slack or your terminal. Your other tabs stay clean.

⚡ Ultra-Lightweight

JDU uses ~80MB of RAM at runtime and downloads at under 8MB. It starts in under 2 seconds. Compare that to a typical Electron app at 350MB RAM and 120MB to download.

🔒 Privacy-First

Zero tracking. Zero telemetry. Zero data collection of any kind. Your Jira credentials are handled entirely by Jira's own login system — exactly as in a browser. JDU has no backend servers and sends nothing anywhere. The full source code is on GitHub.

🌐 Works With Any Jira

✅ Jira Cloud (*.atlassian.net)
✅ Jira Server (self-hosted)
✅ Jira Data Center

🧠 Smart Memory

JDU remembers your Jira URL and window size/position across sessions. Open the app, get straight to your board — no setup, no re-entering URLs.

🎨 Custom Backgrounds & Overlay

Starting in v0.1.3, you can personalize the JDU window with 5 curated background images and fine-tune overlay opacity (0–100%) to match your taste.

🔄 Dynamic Window Titles

The window title updates as you navigate between Jira pages — so if you use your taskbar or window switcher, you always know exactly where you are.

📱 Cross-Platform

JDU runs natively on Windows, macOS, and Linux. Same experience, same performance, everywhere.

🧩 Why Not Electron? (And Why This Matters for Jira Users)

This is the question most developers ask first — and it's a fair one. Almost every "desktop wrapper" app you've used was built with Electron. There's a reason JDU is different.

The Electron problem

Electron works by bundling an entire copy of Chromium — Google Chrome's rendering engine — into every app. Every. Single. App. That means:

Your Jira wrapper carries ~120MB of download just to boot
It consumes 300–500MB of RAM before you've opened a single ticket
Startup takes 5–8 seconds
Background CPU usage stays elevated even when idle

If you have multiple Electron apps running (Slack, VS Code, Notion, etc.), the cumulative memory cost becomes significant. Adding a Jira Electron wrapper on top of that is just more tax.

The Tauri approach

Tauri doesn't bundle Chromium. Instead, it uses the webview already built into your OS:

WebView2 on Windows (powered by Edge)
WebKit on macOS and Linux

The rendering quality is identical. The resource cost is a fraction. And because the backend is written in Rust — a memory-safe, compiled systems language — the app is secure and fast by design.

For Jira users specifically, this matters because JDU is likely running all day. A tool you keep open for 8 hours should not be quietly draining your battery and RAM for 8 hours.

📈 Performance at a Glance

Metric	JDU	Typical Electron App	Browser Tab
Memory Usage	~80 MB	~350 MB	~150 MB
Startup Time	< 2 seconds	5–8 seconds	Instant
Download Size	~8 MB	~120 MB	N/A
Background CPU	Minimal	Moderate	High (with other tabs)
Tracking / Telemetry	None	Varies	Browser-level

🚀 Getting Started in 60 Seconds

Step 1 — Download

Head to the GitHub releases page and grab the installer for your OS.

Step 2 — Install

🪟 Windows

Run the .msi or .exe installer. JDU requires Microsoft Edge WebView2 — most Windows 10/11 machines already have it. If not, download it here.

🍎 macOS

Open the .dmg file and drag JDU to your Applications folder.

🐧 Linux

Available as .AppImage, .deb, or .rpm. Download the format that fits your distro.

🔧 Build from Source

git clone https://github.com/cas8398/jira-desktop-unofficial
cd jira-desktop-unofficial
pnpm install
pnpm tauri build

Requires Rust, Node.js, and the Tauri prerequisites for your platform.

Step 3 — Launch

Open JDU, paste your Jira instance URL (e.g. https://yourcompany.atlassian.net), press Enter — and you're in. JDU remembers the URL from now on.

🔍 Deep Dive: JDU for Power Jira Users

If you live in Jira — sprints, backlogs, board views, Confluence-linked tickets, JQL filters — here's what JDU specifically does and doesn't change for you.

What stays the same

Everything Jira does in the browser works identically in JDU. JDU is a wrapper — it renders the real Jira web interface inside a native window. Your keyboard shortcuts, your saved filters, your board layouts, your integrations — all untouched.

What gets better

Focus: No browser tabs means no accidental navigation away from Jira mid-task.
Switching: JDU appears in your taskbar/dock like any native app. Alt-Tab to it in one move.
Window memory: JDU remembers where you left the window and how big it was. Reopen it, it's right where you left it.
Background behavior: JDU uses minimal CPU when it's not in focus, unlike a browser tab that may keep running scripts actively.
Custom aesthetic: With v0.1.3, you can now set a background image and control overlay opacity — making your Jira window feel more personal.

What's still on the roadmap

Desktop push notifications for Jira updates
Keyboard shortcut layer for quick actions
Dark mode and custom theme support
Multi-account / multi-instance support
Offline connection status indicators

🔖 What's New in v0.1.3

The latest release brings meaningful visual and UX improvements:

Custom Backgrounds — 5 curated Pexels images to personalize your workspace
Dynamic Window Titles — The title bar updates as you move between Jira pages
Modern UI Redesign — Cleaner interface throughout the app
Better URL Validation — Handles trailing slashes and edge-case URLs correctly
Smarter Domain Detection — More reliable Cloud vs. Server instance detection
Overlay Opacity Control — Slider from 0 to 100% to control background darkness
Bug Fix — URL validation issue (#2) resolved

Special thanks to @tsenzuk, @bupemko, @pdkrg, and @mitrapartha for reporting and helping fix the URL validation bug.

🎯 Who Should Use JDU?

JDU is for you if:

You're a developer, tech lead, or engineering manager who navigates Jira daily
You're a project manager or Scrum Master who lives on the board and backlog views
You care about lightweight, efficient tooling and hate RAM-hungry Electron apps
You want a distraction-free workflow and browser tab chaos is real for you
You value open-source, auditable software with no hidden data collection

JDU is probably not for you if you primarily use Jira occasionally and don't mind the browser tab experience.

🤝 Contributing & Community

JDU is fully open source under the MIT license and welcomes contributions of all kinds.

Bug reports → GitHub Issues — include your OS, app version, and steps to reproduce
Feature requests → GitHub Discussions
Pull requests → Fork the repo, make your change, open a PR
Give it a star → Helps the project get discovered by others who'd benefit from it

📥 Download JDU Now

Free. Open source. Under 8MB. No Electron. No bloat.

➡️ Download JDU — Jira Desktop Unofficial

⭐ Star on GitHub

🌐 Visit the Project Page

🛑 Disclaimer: JDU is an independent, community-built project and is not affiliated with or endorsed by Atlassian. Jira is a registered trademark of Atlassian Corporation Plc. JDU is a desktop wrapper around the official Jira web interface.

Built with ❤️ by cas8398 using Tauri — MIT License

One Open Source Project a Day (No. 65): OpenHuman - A Local-First Personal AI Super Intelligence That Actually Knows You

WonderLab — Thu, 14 May 2026 02:44:48 +0000

Introduction

"Private, Simple and extremely powerful."

This is the No.65 article in the "One Open Source Project a Day" series. Today, we are exploring OpenHuman.

Most AI assistants share a fundamental limitation: they have no memory. Every conversation starts from zero. They don't know what project you're working on, what's in your Gmail inbox, or what happened in your GitHub repository last week.

OpenHuman exists to solve exactly that. Its goal is not to build a better chatbot, but to create an AI super intelligence that truly integrates into your daily life—pulling fresh data from all your connected apps every 20 minutes, compressing it into a local SQLite knowledge tree, giving the AI access to your complete, up-to-date work context at all times. 5.6k Stars, built in Rust + Tauri, GPL-3.0 licensed—early Beta, but already presenting a distinctly original technical direction.

What You Will Learn

How OpenHuman's Memory Tree achieves genuine persistent memory (not just conversation history)
How 118+ OAuth integrations + auto-sync every 20 minutes actually works
How TokenJuice compression reduces LLM API costs by up to 80%
How Model Routing intelligently directs tasks to reasoning, fast, or vision models
Why the Desktop Mascot is a meaningful product design choice, not a gimmick
Why choosing Rust + Tauri over Electron is a significant architectural decision

Prerequisites

Basic familiarity with AI assistants and agents
Understanding of OAuth authorization at a conceptual level
Rust development background is helpful for appreciating the technical design but not required

Project Background

Project Introduction

OpenHuman is an open-source personal AI agent assistant, positioning itself as a "Personal AI Super Intelligence." Its core differentiation rests on three keywords:

Private: All workflow data is stored locally, encrypted—never uploaded to any cloud
Simple: From install to a working agent in just a few clicks, no terminal setup required
Powerful: 118+ app integrations + persistent memory + intelligent compression + multi-model routing

It is not just a chat window. It is an actively running background agent: pulling data on a schedule, continuously updating its knowledge tree, ready to provide full context whenever you need it.

Author/Team Introduction

Organization: tinyhumansai
Creator: @senamakel
Project Status: Early Beta, actively developed
Technology choice: Built in Rust (69.7%) + TypeScript (26.1%) + Tauri for the desktop app, rather than the mainstream Electron—a deliberate statement about performance and memory overhead priorities

Project Data

⭐ GitHub Stars: 5,600+
🍴 Forks: 459
🔧 Primary Languages: Rust 69.7% + TypeScript 26.1%
📄 License: GPL-3.0
🖥️ Supported Platforms: macOS, Linux (x64), Windows
🌐 Repository: tinyhumansai/openhuman
🔗 Website: tinyhumans.ai/openhuman

Main Features

Core Utility

OpenHuman's essence: an AI agent that actively perceives your work context, rather than a chatbot that passively waits to be asked.

The fundamental difference from traditional AI assistants:

Traditional AI assistant:
  User asks → AI answers from training data → Conversation ends (memory resets)

OpenHuman:
  Every 20 minutes in background:
    Pull latest data from Gmail / GitHub / Notion / Slack / ...
        ↓
  Memory Tree: Compress and archive into local knowledge tree (SQLite + Obsidian Vault)
        ↓
  User asks: AI answers based on your complete, current work context
        ↓
  Conversation ends: Context is preserved — picks up next time

Use Cases

Cross-Application Project Context
- "Summarize the progress on this GitHub PR and compare it with the latest comments on the related Linear issue"—the AI has already pulled both, and answers directly.
Email and Task Correlation
- "Did I receive any emails about this project today?" The AI scans the synced Gmail data and delivers a summary and list of important messages.
Meeting Assistant
- The desktop mascot joins Google Meet as a participant, recording discussion in real time and surfacing relevant background information.
Code and Documentation Q&A
- Answer questions about code logic, historical changes, and PR comments based on synced GitHub repository data.
Obsidian Knowledge Base Enhancement
- All synced data is simultaneously written to an Obsidian-compatible Vault, allowing users to browse and edit the AI-maintained knowledge in a familiar note-taking interface.

Quick Start

Method 1: Download the Installer (Recommended)

# macOS / Linux (one-command install script)
curl -fsSL https://raw.githubusercontent.com/tinyhumansai/openhuman/main/install.sh | bash

# Windows (PowerShell)
irm https://raw.githubusercontent.com/tinyhumansai/openhuman/main/install.ps1 | iex

# Or download directly from the website:
# https://tinyhumans.ai/openhuman

After installation, complete the UI wizard:

Choose an AI model provider (OpenAI / Anthropic / local Ollama)
Add OAuth integrations (pick what you need from the 118 available apps)
Start using

Method 2: Developer Source Build

# Requirements:
# - Node.js 24+
# - pnpm 10.10.0
# - Rust 1.93.0 (with rustfmt + clippy)
# - CMake

git clone https://github.com/tinyhumansai/openhuman.git
cd openhuman

pnpm install

# Development mode
pnpm tauri dev

# Production build
pnpm tauri build

Run Fully Offline with Local Ollama:

# Install and start Ollama
ollama serve
ollama pull llama3.2  # or any other model

# In OpenHuman settings, select "Ollama" as model provider
# Point to local endpoint: http://localhost:11434

Core Characteristics

1. Memory Tree — The Technical Engine of Persistent Memory

This is OpenHuman's most central technical innovation. It doesn't just save conversation history—it builds a genuine knowledge tree:

Raw data (Gmail messages / GitHub PRs / Notion pages / Slack messages / ...)
        ↓
Content normalization (HTML → Markdown, URL shortening, remove non-ASCII)
        ↓
Chunking (each chunk ≤ 3k tokens)
        ↓
Importance scoring (based on recency, relevance, frequency)
        ↓
Hierarchical summary tree (parent node = summary of child summaries)
        ↓
Dual write:
    → SQLite local database (for AI queries)
    → Obsidian-compatible Vault (for user browsing)

The advantage of a hierarchical summary tree: when the AI needs to answer "what's the overall status of project X?", it reads the high-level summary node directly; when it needs specifics, it drills down into concrete data chunks. This is more structured than simple vector retrieval—it mirrors how human memory actually organizes information.

2. 118+ OAuth Integrations + Auto-Sync

Covers the full ecosystem of mainstream productivity tools:

Category	Representative Apps
Email / Messaging	Gmail, Outlook, Slack
Project Management	Notion, Linear, Jira, Asana, Trello
Code Hosting	GitHub, GitLab, Bitbucket
Docs / Files	Google Drive, Dropbox, OneDrive, Confluence
Calendar / Meetings	Google Calendar, Outlook Calendar
CRM / Finance	Stripe, HubSpot, Salesforce
Other	Airtable, Figma, Zapier, Webhooks...

The 20-minute auto-sync means: you don't need to manually "tell" the AI what happened—it goes and fetches it itself.

3. TokenJuice — LLM Cost Compression Technology

TokenJuice is an internal module that compresses all content before it reaches the LLM:

Raw tool output / web scrape / API response
        ↓
TokenJuice processing pipeline:
  1. HTML → plain Markdown (strip all HTML tags)
  2. URL shortening (replace long URLs with short identifiers)
  3. Remove non-ASCII characters (emoji, special symbols)
  4. Deduplicate redundant content (nav bars, footers, ads)
  5. Extract key information (headings, body text, metadata)
        ↓
  Result: cost and latency reduced by up to 80%

For an agent that calls LLMs frequently, this compression layer matters enormously. Monthly API costs can easily be cut in half or more.

4. Intelligent Model Routing

Different tasks fit different models. OpenHuman routes automatically:

Task Type	Route Target	Reason
Complex reasoning (code analysis, architecture design)	Reasoning models (o3, Claude Opus)	Accuracy first
Simple lookups (find data, format conversion)	Fast models (GPT-4o-mini, Haiku)	Cost and speed first
Image/screenshot analysis	Vision models (GPT-4V, Claude Vision)	Multimodal requirements
Fully offline scenarios	Local Ollama model	Privacy first

5. Desktop Mascot

This is not a pure UI gimmick—it is a functional background agent interface:

Meeting participation: Joins Google Meet as a participant, recording discussion in real time
Background processing: Continuously runs scheduled sync tasks while you work
Proactive reminders: Surfaces upcoming deadlines based on calendar and task data
Personalized interaction: Has personality and memory—not a stateless "help bot"

6. Local-First Privacy Architecture

All workflow data → Local SQLite (AES encrypted)
AI inference → Optional local Ollama (fully offline)
OAuth tokens → Locally encrypted, never routed through OpenHuman servers
Third-party data → Lives only on your device

This is fundamentally different from most AI assistants that send your data to the cloud for indexing.

Project Advantages

Feature	OpenHuman	Notion AI / Copilot	ChatGPT / Claude.ai	Mem.ai
Persistent Memory	✅ Memory Tree	Platform content only	❌ Resets each conversation	✅ But cloud storage
Cross-App Integration	✅ 118+ OAuth apps	Limited	❌	Limited
Local / Privacy	✅ Local SQLite, encrypted	❌ Cloud	❌ Cloud	❌ Cloud
Auto-Sync	✅ Every 20 minutes	None	None	Yes
Open Source	✅ GPL-3.0	❌	❌	❌
Native Desktop	✅ Rust + Tauri	Web plugin	Web	Web
Local AI Models	✅ Ollama support	❌	❌	❌

Detailed Analysis

1. Why Rust + Tauri Instead of Electron?

This is one of OpenHuman's most deliberate architectural decisions:

The problem with Electron:

Every Electron app bundles a full Chromium engine
Baseline memory usage is typically 200–500MB
Higher CPU overhead, noticeable battery drain when running in the background

The advantages of Tauri + Rust:

Tauri uses the system's native WebView (WKWebView on macOS, WebView2 on Windows)
Core logic written in Rust: memory-safe, zero-cost abstractions, extremely low memory footprint (typically < 50MB)
Much smaller builds: a Tauri app is typically 3–10MB vs 100MB+ for Electron

For an application that needs to run constantly in the background and execute sync tasks every 20 minutes, this architectural choice directly determines user experience. OpenHuman's resource footprint feels like a native system utility, not a heavy web app.

2. Memory Tree vs Vector Retrieval: Two Memory Philosophies

Most AI tools with memory use a vector database: chunk content, vectorize it, retrieve the most similar chunks at query time. OpenHuman chose a different path—a hierarchical summary tree:

Vector retrieval approach:
  Query: "What's the current status of project X?"
  → Vector search finds Top-K similar chunks (from various times and perspectives)
  → Concatenate as context → LLM answers
  Problem: Fragmented, lacks a holistic view

Memory Tree approach:
  Query: "What's the current status of project X?"
  → Directly read the high-level summary node for "project X"
  → Drill down into sub-nodes for details when needed
  → Answer has hierarchy and coherence

Both approaches have their place. OpenHuman's choice is better suited for questions like "understand the overall state of a long-running project"—which is precisely the use case it targets.

Project Links & Resources

Official Resources

🌟 GitHub: https://github.com/tinyhumansai/openhuman
🔗 Download: https://tinyhumans.ai/openhuman

Target Audience

Knowledge workers: Using multiple SaaS tools simultaneously (Gmail + Notion + GitHub + Slack) who need AI to understand context across all of them
Solo developers / one-person companies: Managing projects, code, and email alone—who want an AI assistant that genuinely understands their project state
Privacy-conscious users: Who don't want work data uploaded to an AI company's cloud
AI tool researchers: Interested in the architectural design of local-first AI assistants

Summary

Key Takeaways

Memory Tree: Hierarchical summary tree + local SQLite storage—genuine cross-session persistent memory
118+ OAuth + auto-sync every 20 min: The AI actively perceives your work context instead of waiting to be told
TokenJuice: Intelligent pre-LLM compression, up to 80% cost reduction
Rust + Tauri: Native desktop architecture, minimal background resource usage
Local-first privacy: All data encrypted on-device, with support for fully offline Ollama local models

One-Line Review

OpenHuman is tackling the hardest problem in AI assistant design: making the AI genuinely know you—not because you told it, but because it actively observes your work world.

Find more useful knowledge and interesting products on my Homepage

Omnivore Alternative 2026: What to Use After Omnivore Shut Down

Fisher Shen (Fisher) — Thu, 14 May 2026 02:38:22 +0000

Originally published at burn451.cloud

Omnivore was acquired by ElevenLabs and shut down its read-later service. Here are the best Omnivore alternatives in 2026.

Read the full article at burn451.cloud/blog/omnivore-alternative

This article was originally published on Burn 451. Burn 451 is a free read-later app that forces you to actually read what you save — every link gets 24 hours before it burns.

DEV Community: opensource

GeoGuard AI– a multi-agent geological intelligence system that automates terrain risk assessment.

What I Built

Cloud Run Embed

Your Agents

Tool 1: Calculate High-Elevation Amplification (HEA)

Tool 2: Evaluate rainfall-induced risk (monsoon / rain-on-snow)

Tool 1: Delegate to HazardAgent

Tool 2: Delegate to ClimateAgent

Tool 3: Synthesize both reports and identify compounding effects

Tool 4: Validate session context (prevent drift)

Stop Guessing Why Linux Boots Slowly: Practical `systemd-analyze` for Real Bottlenecks

Stop Guessing Why Linux Boots Slowly: Practical systemd-analyze for Real Bottlenecks

What systemd-analyze time really measures

Step 1: Use blame, but don't trust it blindly

Step 2: Find the real blocker with critical-chain

Step 3: Generate a boot chart you can inspect visually

Step 4: Identify who actually requested the slow thing

Step 5: Fix the dependency, not the symptom

Safer fix pattern A: wait only for the interface that matters

Safer fix pattern B: override the wait behavior

Important warning

Step 6: Re-measure after every change

A practical workflow that holds up

Final thought

References

Roblox Lag Fix: FPS Boost with Roblox FPS Unlocker Open Edition (2026)

Roblox Lag Fix FPS Boost 2026

Step-by-step to FPS Boosting

Common Questions About Roblox Lag Fix FPS Boost 2026

What are the risks of using the Roblox FPS Unlocker?

Does this tool work on all PCs?

Can I use other performance-enhancing tools alongside this one?

Final Verdict

The "WS" Evolution: Why I’m Switching to @rabbx/ws in 2026

One API, Every Runtime

The Verdict

WebSockets #Nodejs #BunJS #WebDev #OpenSource #SoftwareEngineering

Top 5 Tools for Roblox AutoClicker No Virus 2026

Top 5 Tools for Roblox AutoClicker No Virus 2026

Roblox AutoClicker No Virus 2026

Step-by-Step Guide to Using Roblox AutoClicker Open Edition

Common Questions About Roblox AutoClicker No Virus 2026

Is Roblox AutoClicker Open Edition safe to use?

Can I adjust the clicking speed?

Does it work on both Windows and macOS?

Final Verdict

How FlutterSeed Saves Hours of Flutter Project Setup Time

Introduction

The Problem with Traditional Setup

How FlutterSeed Works

Key Features of FlutterSeed

Getting Started with FlutterSeed

Benefits of Using FlutterSeed

Target Users

Conclusion

What is Coolify? Self-Hosting with Superpowers

Introduction

What is Coolify?

What Does Coolify Do?

Git Integration

Dockerized Deployments

Automatic HTTPS

Built-in Reverse Proxy

Database Management

Environment Variables

Monitoring and Logs

Backups

Why Would You Use Coolify?

You want the economics of a VPS without the complexity

You want full control over your infrastructure

You want a great developer experience

You are running multiple projects

What Are the Limitations?

How Do You Get Started?

Conclusion

References

Hybrid Search RAG: BM25 + Vector Search in Production

I Built an AI API Directory Because OpenAI-Compatible Is Not Enough

Why a Directory Instead of a Ranking?

Stop Guessing Why Linux Boots Slowly: Practical `systemd-analyze` for Real Bottlenecks

What `systemd-analyze time` really measures

Step 1: Use `blame`, but don't trust it blindly

Step 2: Find the real blocker with `critical-chain`