2026-01-30 14:17:23
Clawdbot / Moltbot / OpenClaw — Part 4
After a chaotic rebrand, account hijackings, crypto scams, and serious security scrutiny, the project formerly known as Clawdbot and Moltbot has emerged as OpenClaw. This isn’t just another rename — it’s a reset. The core vision survived, security is now front and center, and the project is finally acting like the infrastructure it accidentally became.
Months ago, a weekend hack exploded into one of the fastest‑growing open‑source AI projects in GitHub history.
Days ago, it was in chaos.
If you’ve been following this series, you already know the story:
Today, that same project has a new name again — OpenClaw — and, more importantly, a chance to reset.
This is not another takedown.
This is what happened after the meltdown.
Peter Steinberger’s announcement of OpenClaw is deliberately calm — and that alone says a lot.
After Clawd (too close to “Claude”) and Moltbot (symbolic, but awkward), OpenClaw feels intentional.
This time:
The name is simple and explicit:
After watching a name change trigger real‑world damage, this boring professionalism is exactly what the project needed.
Lost in the chaos of the last chapter was an important fact:
The software itself was always compelling.
OpenClaw (formerly Clawdbot / Moltbot) is still:
That core vision hasn’t changed.
What has changed is posture.
Peter’s OpenClaw announcement makes one thing explicit:
Your assistant. Your machine. Your rules.
That line matters — especially after the security wake‑up call.
The most important part of the OpenClaw announcement isn’t the name.
It’s this:
This is an implicit acknowledgment that earlier criticism wasn’t wrong.
Self‑hosted AI agents with:
…are inherently dangerous if treated casually.
OpenClaw is now framing security as a first‑class concern, not an afterthought. That doesn’t magically solve prompt injection or misconfiguration — those remain unsolved industry problems — but it does signal maturity.
The project crossed the line from “cool hack” to “serious infrastructure.” The tone finally matches that reality.
One subtle but important shift:
OpenClaw’s announcement barely mentions Anthropic.
That’s intentional.
Earlier discourse framed the project as “Claude with hands.” That framing was viral — and legally fragile. OpenClaw is now clearly positioned as model‑agnostic infrastructure.
New model support (KIMI, Xiaomi MiMo) reinforces that:
Whether or not you agree with Anthropic’s trademark enforcement, this decoupling was inevitable if the project wanted to survive long‑term.
After everything — legal pressure, scammers, vulnerabilities, social media storms — what’s left?
Surprisingly, almost everything that mattered.
✅ The codebase
✅ The community
✅ The core vision
✅ The momentum
What didn’t survive:
That’s a good trade.
With hindsight, this saga isn’t really about names, trademarks, or even Anthropic.
It’s about what happens when:
OpenClaw is now acting like a project that understands that responsibility.
That’s the real evolution.
OpenClaw doesn’t erase what happened — but it does show learning.
The lobster metaphor still works:
not just molting to grow,
but hardening the shell afterward.
If you’re trying OpenClaw today:
The chaos chapter is over.
This one is about sustainability.
Project: https://openclaw.ai
GitHub: https://github.com/openclaw/openclaw
Discord: https://discord.com/invite/clawd
2026-01-30 14:08:22
Originally published on Medium:
https://medium.com/@supsabhi/master-method-overloading-and-overriding-in-android-kotlin-development-46a8f2da6d07
Method overloading and method overriding are very common and core techniques in OOPS programming. When you build apps, you must have come across these terms. They sound similar, but they do completely different jobs! They let you reuse and extend code in smart ways by allowing you to write clean, reusable, and flexible code.
Overloading = same name, different parameters.
Overriding = subclass replaces/extends behavior of a parent class method.
Let’s look more deeply into each one of these.
Method Overloading
Method overloading happens when multiple methods share the same name but have different parameter lists. What it means is there are multiple functions with the same name but different signatures. These differences can be in the number, type, or order of parameters. This different signature allows the compiler to pick the correct function when you call it.
So, you can say that the main goal of overloading is to make your code more readable and flexible.
A few important things need to be considered in Kotlin are:
The compiler resolves which function to call at compile time (this is called Compile-Time Polymorphism) because it knows the parameter types beforehand.
It doesn’t require inheritance. Overloading can happen entirely within one class.
Example:
class Calculator {
fun sum(a: Int, b: Int): Int {
return a + b
}
fun sum(a: Int, b: Int, c: Int): Int {
return a + b + c
}
fun sum(a: Double, b: Double): Double {
return a + b
}
}
fun main() {
val calc = Calculator()
println(calc.sum(2, 3)) // uses (Int, Int)
println(calc.sum(2, 3, 4)) // uses (Int, Int, Int)
println(calc.sum(2.5, 3.5)) // uses (Double, Double)
}
Here, all the methods have the same name sum, but they differ either in their parameter number or in parameter type.
In Kotlin, you might see overloading in functions like Toast.makeText()as below
Toast.makeText(Context context, CharSequence text, int duration)
Toast.makeText(Context context, int resId, int duration)
Overloading with extension functions or top-level functions also works — Kotlin treats overloads in that scope too.
You need to be careful with ambiguous overloads: if two overloads are too similar and the compiler cannot clearly pick one, you’ll get an “overload resolution ambiguity” error.
When & why to use overloading
When you have a method that logically does the same thing but you want to support different kinds of inputs (e.g., load(image: String), load(image: Uri), load(image: File)).
When you want to improve readability: you don’t need many method names like loadStringImage, loadUriImage; you just overload load().
Method Overriding
Overriding is about inheritance. You have a base (parent) class that defines a method (or abstract method), and a subclass (child class) provides its own implementation of that method (same signature) so that when you call it on the subclass instance, the subclass version runs. This supports runtime polymorphism. You’re essentially taking the parent’s generic instruction and giving it a specific, custom implementation in the child.
As Android development is built on inheritance, you constantly override functions that the Android system (the parent) defines.
When you create an Android Activity, you inherit the onCreate() method from a parent class like AppCompatActivity.
The parent has a default onCreate() that handles basic setup. You override it to add your specific code.
Also in Kotlin:
Classes and methods are final by default (i.e., cannot be inherited/overridden) unless you mark them with open.
In the subclass, you must use the override keyword to indicate you are overriding.
You can call the parent version via super.methodName() if needed.
For example, all lifecycle methods like onCreate(), onStart(), onPause() are open in the Activity class, so you can override them.
class MainActivity : AppCompatActivity() {
override fun onPause() {
super.onPause() // calls the original method from AppCompatActivity
// your custom logic here
Log.d("MainActivity", "App is paused")
}
}
Here:
override → tells Kotlin we’re redefining a method from the parent.
super.onPause() → calls the original version in the parent class (AppCompatActivity), to keep the Android lifecycle working properly.
Then, we can add our own logic below or above that call.
So we can say that when overriding an Android lifecycle method (like onCreate), you often call super.onCreate(...) inside your overridden function.
This is you saying, “Do the Parent’s basic setup first, and then run my special code.”
Why to use overriding
The Quick Summary: The Difference That Matters
Think of it this way:
Overloading is about variety (one name, many parameter options). It happens horizontally within one class. It lets you create multiple versions of the same method with different inputs.
Overriding is about specialization (one name, one parameter set, one new action). It happens vertically down the inheritance chain. It lets you modify how inherited methods behave.
Mastering these two concepts is key to writing clean, logical, and truly Object-Oriented Kotlin code in your Android applications!
2026-01-30 14:08:08
Ever built a feature "just in case" only to never use it? Or skipped implementing something flexible, only to refactor it weeks later?
We all face this dilemma: YAGNI (You Aren't Gonna Need It) vs. forward-looking design.
The problem? We make these decisions based on gut feeling, not data.
This post shows how to make forward-looking design measurable using ADR (Architecture Decision Records).
What we really want to know is:
How often do our predictions about future requirements actually come true?
Three dimensions to evaluate:
| Aspect | Detail |
|---|---|
| Prediction | "We'll need X feature in the future" |
| Implementation | Code/design we built in advance |
| Reality | Did that requirement actually come? |
We want to measure prediction accuracy.
YAGNI means "You Aren't Gonna Need It now", not "You'll Never Need It".
The problem is paying heavy upfront costs for low-accuracy predictions.
→ Low cost, low damage if wrong
→ Will need complete rewrite if wrong
You might think: calculate value like this.
Value = (Probability × Cost_Saved_Later) − Cost_Now
But here's the catch: we can't estimate these accurately.
If we could estimate accurately, we wouldn't need this system.
We don't need perfect estimates.
What we need:
Example:
| Area | Hit Rate | Tendency |
|---|---|---|
| Database design | 70% | Forward-looking OK |
| UI specs | 20% | Stick to YAGNI |
| External APIs | 10% | Definitely YAGNI |
Numbers are learning material, not absolute truth.
To make forward-looking design measurable, we need to record our decisions.
We use ADR (Architecture Decision Records).
Example ADR with forecast:
## Context
Customer-specific permission management might be needed in the future
## Decision
Keep simple role model for now
## Forecast
- Probability estimate: 30% (based on sales feedback)
- Cost if later: 20 person-days (rough estimate)
- Cost if now: 4 person-days (rough estimate)
- Decision: Don't build it now (negative expected value)
Estimates can be rough. What matters is recording the rationale.
In our environment, this works:
Repositories (ADR + metadata.json)
↓
Jenkins (cross-repo scanning, diff collection)
↓
S3 (aggregated JSON)
↓
Microsoft Fabric (analysis & visualization)
↓
Dashboard
We already have Jenkins scanning repos for code complexity. We can extend this for ADR metadata.
Keep ADR content free-form. Standardize only the metadata for aggregation.
docs/adr/ADR-023.meta.json:
{
"adr_id": "ADR-023",
"type": "forecast",
"probability_estimate": 0.3,
"cost_now_estimate_pd": 4,
"cost_late_estimate_pd": 20,
"status": "pending",
"decision_date": "2025-11-01",
"outcome": {
"requirement_date": null,
"actual_cost_pd": null
}
}
Minimum fields needed:
adr_id: unique identifiertype: forecastprobability_estimate: 0-1cost_now_estimate_pd: upfront cost (person-days)cost_late_estimate_pd: later cost (person-days)status: pending / hit / missoutcome: actual resultsTreat estimates as "estimates", not gospel truth.
Full scans get expensive. Collect only diffs:
# Record last commit SHA
git diff --name-only <prev>..<now> | grep '*.meta.json'
Scales as repos grow.
After 6-12 months, review:
| ID | Feature | Est. Prob | Actual | Est. Cost | Actual Cost | Result |
|---|---|---|---|---|---|---|
| F-001 | CSV bulk import | 30% | Came after 6mo | 15pd later | 1pd | Hit & overestimated |
| F-002 | i18n | 50% | Didn't come | - | - | Miss |
| F-003 | Advanced perms | 20% | Came after 3mo | 20pd later | 25pd | Hit & underestimated |
Focus on trends and deviation reasons, not absolute accuracy.
Raw facts (NDJSON, append-only):
{"adr_id":"ADR-023","type":"forecast","status":"hit",...}
{"adr_id":"ADR-024","type":"forecast","status":"miss",...}
Snapshot (daily/weekly metrics):
{
"date": "2025-01-27",
"metrics": {
"success_rate": 0.30,
"total_forecasts": 20,
"hits": 6,
"misses": 14,
"avg_cost_deviation_pd": -3.5
}
}
CTOs and executives probably care about:
Treat ADR as transaction log. Handle visualization separately.
Three evaluation moments:
"Did that requirement actually happen?"
Not "worth it" yet.
"How fast/cheap could we implement it?"
| Case | Additional work |
|---|---|
| With forward-looking design | 1 person-day |
| Without (estimated) | 10 person-days |
This is when we can say "forward-looking design paid off".
User adoption doesn't matter yet.
This evaluates business value, but involves marketing, sales, timing, competition.
For technical decisions, focus on ② implementation cost difference.
Excel management fails because:
Excel becomes "create once, forget forever".
Treat ADR as input device, visualization as separate layer.
This system's goal isn't perfect estimates or perfect predictions.
Goals are:
Wrong estimates aren't failures. Making the same wrong decision repeatedly without learning is the failure.
Treat numbers as learning material, not absolute truth.
Planning to propose:
Not sure if this will work, but worth trying to turn forward-looking design from "personal skill" into "organizational capability".
2026-01-30 14:05:19
In 2025, 71% of organizations said they already use generative AI in at least one business function, and the share keeps rising. (McKinsey) Developers are also moving fast: 84% say they use or plan to use AI tools in their development process. (stackoverflow)
That shift forces new architecture tradeoffs, because AI-native systems behave less like fixed logic and more like living products that learn, drift, and need constant feedback.
This article explains the architecture decisions that change first, what to standardize, and what to keep flexible.
AI-native engineering is not “adding a model.” It is designing for uncertainty, feedback, and measurable outcomes. When you build AI into core flows, the architecture must answer a different set of questions:
A practical phrase you will hear in upper-level planning is AI native software engineering: teams treat models, prompts, and data as first-class parts of the stack, with the same rigor as code.
Traditional architecture assumes code stays correct unless you change it. AI changes that assumption. Outputs can shift even when your application code does not.
Design choices that follow from this:
Version everything that can change: model version, prompt template, tool list, retrieval sources.
Add a “decision record” per request: inputs, policies applied, tool calls, and output.
Separate “AI decisioning” from “business commit” so you can block or rollback safely.
This is where disciplined software engineering matters. You are building a decision pipeline, not a one-time feature.
Once you accept that outputs can drift, the next question is where to draw boundaries.
A stable pattern is to keep your core system deterministic and move AI closer to the edge of the workflow.
*Keep deterministic (stable core): *
Allow probabilistic (AI-friendly edge):
This boundary reduces blast radius. It also improves integration with legacy systems because you can keep existing contracts intact while adding AI “assist” layers around them.
For AI, raw data is not enough. The system needs “evidence” it can cite, trace, and refresh.
Key decisions that change:
If you use retrieval, you must design:
AI will expose gaps you never noticed:
Treat data checks like health checks. Route failures to safe fallbacks. This is software engineering for data, not just storage.
Data creates the “truth layer,” but the system still needs to act in the real world through tools.
As soon as AI can call tools, architecture must prevent unintended actions.
Use a clear action model:
Controls to add:
This improves integration with enterprise platforms because you can map “tool permissions” to existing IAM and approval flows.
AI introduces variable latency and occasional failures. Your architecture must set budgets and fallbacks.
Design patterns that work in production:
A useful tactic is to separate “helpful” from “required.” If the AI layer fails, users should still complete critical tasks.
This is where mature software engineering meets product thinking: define what must never break, then design resilience around it.
Traditional observability tracks errors and latency. AI needs more.
Minimum AI observability checklist:
Also capture “why” data:
Without this, your AI-native systems will feel unpredictable, and teams will argue based on anecdotes instead of evidence.
Once you can measure outcomes, you can ship changes more safely.
AI does not eliminate testing. It changes what “passing” means.
This is another place where strong software engineering wins. Teams that treat prompts and evaluations as code ship faster with fewer incidents.
Enterprises need explainability, access control, and proof of intent.
Architectural controls to prioritize:
For regulated industries, the design goal is simple: you should be able to reconstruct the decision path without guessing.
This reduces friction during audits and strengthens integration with governance programs already in place.
Standardize early:
Keep flexible longer:
This approach helps startups move quickly without creating chaos, and helps enterprises scale without blocking teams.
AI changes architecture because the system must learn, adapt, and stay safe while doing it. The winners will treat quality, safety, and measurement as core parts of the product.
If you are building for production, choose an architecture that:
Keeps critical commits deterministic
Measures outcomes continuously
Makes failures survivable
Makes decisions traceable
That is how AI-native systems stay reliable over time, and how software engineering teams earn trust while they scale.
In the lower stages of vendor selection, many leaders also evaluate AI native engineering service companies based on their ability to ship these controls, not just demos, because real-world integration and audit readiness decide whether AI succeeds past pilots.
2026-01-30 14:01:39
.pem key:
chmod 400 ~/DevOps.pem
ssh -i ~/DevOps.pem ec2-user@YOUR_PUBLIC_IP
sudo yum update -y
sudo yum install nginx -y
sudo systemctl start nginx
sudo systemctl enable nginx
scp -i ~/DevOps.pem -r ~/DevOps ec2-user@YOUR_PUBLIC_IP:/home/ec2-user/
-r → recursive, copies entire folder including subfoldersNginx serves files from its root directory:
Move folder into Nginx root:
sudo mv ~/DevOps /usr/share/nginx/html/
/usr/share/nginx/html/DevOps/index.html
sudo chmod -R 755 /usr/share/nginx/html/DevOps
curl http://localhost/DevOps/index.html
EC2 instance is updated and running
Nginx is installed, started, and enabled on boot
Your folder DevOps is served by Nginx publicly
Permissions are correct — files are accessible without exposing the server unnecessarily
2026-01-30 13:51:58
This is a submission for the New Year, New You Portfolio Challenge Presented by Google AI
Hi, I'm Arkadiusz! 👋
I'm a Senior Backend Architect who has spent more time looking at terminal windows and Grafana dashboards than pretty UI layouts.
For the New Year, New You Portfolio Challenge, I wanted to break away from the standard "Hero Image + Bio" template. Instead, I asked myself: "What if my portfolio looked like the tools I use every day?"
The result is a High-Availability, resilient portfolio that visualizes my skills as "system resources" and invites visitors to interact via a CLI-style interface.
I built this project to be as lightweight and resilient as the backend systems I design.
I paired deeply with Google's Gemini throughout the process. It wasn't just about generating code; it was about rapid prototyping:
backdrop-filter and border transparency to get that perfect "Head-Up Display" look.There are three features I'm particularly happy with:
In the top right, instead of a static "Hire Me", I built a live "System Status" monitor. It simulates server load (or my caffeine levels ☕) with a randomized React state machine that transitions between Normal, Elevated, and High load, changing colors and graph shapes in real-time.
The /stack page isn't just a list. It's a filtered matrix of my 29+ mastered technologies. I added a custom "Expertise" calculation that aggregates my years of experience across active filters.
The /contact page is styled like a terminal window (visitor@portfolio:~/contact).
Hire_Me() button in the nav isn't just a link—it passes a smart query parameter to pre-fill the form with "Project Inquiry".initiate_consultation() run macros that auto-focus fields or open external scheduling tools.Deployed with ❤️ on Google Cloud Run.