2026-11-15 05:00:39
A step-by-step guide designed to introduce fundamental concepts and practical skills to newcomers, making complex topics accessible.
Adding a new script tag and directly appending it to the <head> element of the page is the easiest way to add <script> tags in the React app.
Learn BFS and DFS, powerful algorithms to traverse and search data structures. Examples and step-by-step JavaScript code included.
A beginner-friendly tutorial on how to build hardcore blockchain infrastructure in Substrate, an open source framework.
The Parameters type is a utility type in TypeScript that lets us take the arguments of a function, and turn it into a new type. Let's look at how it works.
Microsoft Surface is a laptop product owned by Microsoft and in this story, I will guide you on How to Chromecast Microsoft Surface.
The Omit utility Type lets us take types, remove elements, and make entirely new types. Let's look at how it works.
It’s not like there aren’t enough text editors. But consider this: with about 60 lines of code (that’s close to nothing), you can make your own secure and stable text editor. It will be pretty simple and won’t have many features, but it will be of your making, even if you barely know how to code.
You've probably heard of Python, an interactive programming language that makes it easy to work with data. Today it's used for everything from websites and apps to statistics, machine learning, and AI.
A while ago I was trying to perform an analysis of a Medium publication for a personal project. But getting the data was a problem – scraping only the publication’s home page does not guarantee that you get all the data you want.
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Manipulating data is a core skill for any developer. In an API-driven environment, so much of the data you receive is formatted in a way that doesn't directly match the way that your application or UI needs it. Each web service and third-party API is different. This is where the ability to sort, normalize, filter, and manipulate the shape of data comes in.
Webpack is a module bundler for JavaScript as well as HTML applications. It grabs modular pieces of code compartmentalized in folders. Followed by reconstituting them into a compiled form. Then it's read as a whole by the application. You must be thinking, "Why break up a working code?" As your code scales, so does its complexity. Finding a correlation between each block becomes a challenge.
In the last post Arrays in JS, we learned about what arrays are, how we can store data in them and some methods which can be used on the array to get certain results.
Introduction
Liskov’s Substitution Principle | SOLID as a Rock
If you’re willing to learn Python programming language that is highly in-demand in the software industry, then here is a list of Best and Free Python Courses, Classes, Tutorials, Training, and Certification programs available online for 2019. This list includes both paid and free courses to help you learn Python. Also, it is ideal for beginners, intermediates, as well as experts.
What does a potential Java junior need to know to get their first job or even qualify for a trainee position in a good company? What tools will help a Java programmer reach the next level? Which technologies should you study, and which ones are better to hold off on?
Why you should be using AWS Lambda alias for your versioning strategy
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In this article, I will show you a beginner-friendly approach to find whether a number is Automorphic or not in Java.
Sometimes you just want to dip your toe into a potentially deep and complex subject. Maybe you only have 10 minutes to spare and you want to get something up and running quickly.
In the first quarter of 2020, I had the experience of being in a short term advisory role for a company that had built a type of Linkedin product for a specific vertical market. They had done a valiant job, but it was clear that scalability was a problem and they had created a problem for themselves by having used MongoDB as the datastore.
Github is not only a place for storing our code and sharing libraries with other developers. Thanks to powerful Markdown syntax it’s possible to create sort of wiki pages. There are thousands of great resources in the Github - some of them are so big that recommending them is pointless.
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URL shortener Java and Spring boot tutorial. A step-by-step guide that is easy to follow. This is a common interview task that should be understood by devs.
Need to transfer in the data from RDBMS to HDFS or vice versa? Check this out!
This article was first published on our open-source platform, SimpleAsWater.com. If you are interested in IPFS, Libp2p, Ethereum, Zero-knowledge Proofs, DeFi, CryptoEconomics, IPLD, Multiformats, and other Web 3.0 projects, concepts and interactive tutorials, then be sure to check out SimpleAsWater.
To get the most out of this article, please make sure that you know basics about Synchronous and Asynchronous Programming and, if possible, about javascript callbacks.However, I will try to keep most of the stuff away from these topics so that you can understand at least 85% of it.
Our random story generator will use a few lists of phrases and every time we run our program, a sentence will be randomly formed by picking phrases from them.
Recommended Flutter courses to take to start your journey of becoming a Flutter developer, based on my own experience by taking the courses myself.
Here are 10 Fantastic JavaScript Projects for Beginners.
Learn the basics of Git and GitHub.
Python is in the midst of a resurgence. It never went away, but usage now grows like never before. With machine learning developers and data scientists relying on Python, much of the web development ecosystem around the language continues to grow.
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Further advancements in blockchain technology drive the rise of decentralized financial technology (DFT).
How React Testing Library Can Improve Your Mental Health Part 2
From breaking down complex jargon to exploring real-world applications and ethical considerations, this article makes AI approachable for everyone.
I will show you how to download and install Python in less than 2 minutes! We will be up and running super fast and get to working on some cool stuff. So what are you waiting for, let's get started!
Arrays are objects that enable storing a collection of items and data under a single variable name and have the capability to perform a certain operation.
Learn how to automate your desktop but with a fun example.
This Javascript Cheatsheet for Spread Operators explains the use case scenarios and the basic concepts behind Spread Operators when coding in Javascript.
As a student who travels a lot (pub - campus - cafe - home), I tend to carry around my macbook air because its light (and I hate heavy things). However, macbook air isn't really a powerful machine and it often drives me mad when the code I'm trying to run is too heavy to run
This blog will help you navigate through Linux.
Ruby on Rails framework (RoR) is the dream of every developer. It does pretty much everything for you without much effort, and once you learn it and deeply understand it, you can build an entire website in a couple of hours (Of course, styling it is a different story). The hard part of rails is to understand the magic behind it.
A popular challenge that beginner programmers participate in is called 100 Days of Code. Although I never participated in it myself, I see countless tweets with screenshots and progress reports of people sharing bits of apps they made. 100 Days of Code is a great way to keep people in the programmer mindset. It gets them familiar with what it is like to code every day. Still, I wonder if it is the best approach for everyone. For them, One Game a Month could be a great alternative.
There is a mystical aura around password creation. Some kind of occult knowledge reached only by the distant cryptographic hermits who ascended to nirvana after decades of meditation on the Patagonian glaciers and liters of instant coffee. In this article, we will try to translate a few drops of this ancient wisdom so that we, mere mortals, have secure accounts to store pictures of cats and e-books that we will never read.
Understand how to reverse a string using recursion in C++ and Java
Let's discuss the strings primitive data type and it means. This will answer the question of what's the difference between variables and strings.
In this post, we will see how an action can be dispatched using Redux on login, and set the app state accordingly.
This tutorial explains how to use Postgres in your Express application.
Remembering passwords has become a headache these days.
Imagine you have six images on your home page. You might proceed with loading the images separately. After all, they are six different images and to be used in six different places. And you might be right ... from your perspective. You also know that loading six different images will force the browser to make six different HTTP requests to the server. And each time it makes a request, a few milliseconds gets added to your page loading time. That's bad news if you have fifty images.
Let's cover the awesomeness of primitive data types. What are integers, floats, or strings!? I don't know... Let's find out!
To begin with, the Java programming language is easy enough to learn and has the greatest impact on the industry. So the question is: “which course to choose?"
Algorithms for beginners: fundamentals of recursion.
Programming languages have different type systems that provide developers with different levels of type safety, flexibility and usability.
Arrays are one of the most used Data Structures in JavaScript and pretty much any language.
Let's talk about 'for' loops and why they are so important! Anything that requires repetition. 'For' loops are your friends!
We cover the most basic 'hello world' exercise! This will be fun and now you are on your way to an exciting journey!
HyperV, Virtualization, VirtualBox, VM, Virtual Machine, Containers
Throughout this article, I covered the basic syntax of foreach loops in C#, common mistakes to avoid, advanced techniques such as LINQ queries and lambdas
Learn how to write a function that makes a circle of squares!
In this part of the series, I'll demonstrate generating HTTP responses from a simple Node.js Express server.
I am learning Linux Command Line, and for this learning-in-public journey, I have chosen to share my notes.
Why Java?
In this part of the Learn Python for Beginners series... We go over how to use the turtle module and learn computer programming in a fun visual way. Things like functions, variables, and more will start to make sense instantly.
What I would say to myself if I could travel to my past.
Before I became a programmer I loved to play games. I played games for many years before I even knew the most basic concepts about coding. However these days I see that people are trying to introduce their kids to programming and looking for ways to make programming concepts more approachable. I think that using existing games people love is a great way to do just that. That is why I wanted to start this new coding for gamers blog series. In this tutorial I will show you how to do that by walking you through the process of extracting a feature from a video game called The Long Dark and recreating it on your own.
Let's cover logical AND, OR & NOT..
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When most people think about evolution, what comes to mind is “the big bang”, usually describing it as way different types of living organisms developed from earlier forms during the history of the earth. Technology has also evolved over time, there are many technologies, major ones being web development, computer systems, software engineering, artificial intelligence and many more. The main focus of this article is on the evolution of Web Design. A deeper dive into HTML which is the backbone of every web page document that you see on the internet today. Yes, other supplements exist e.g. CSS, thats adds design for websites, but without HTML, displaying a webpage document is not possible.
In this tutorial, you'll learn the most common and useful commands that you can run in almost any UNIX-like environment, including Linux and Mac OS X.
What are functions? We discuss how functions work and allow you to use clean, reusable code. We do this while visualizing our code through the Turtle library in python.
Are you struggling while starting learning CSS and HTML? Do you feel overwhelmed with sooooo many properties? Don’t they look like the same? Haven’t you tried with a couple of tutorials (that you feel they are easy to do at the moment) but feel you don’t have the knowledge to start from the scratch?
Blockchain Gaming is One of the Best Methods of Self Employment on Blockchain for People who Don't want A Skill Focused Job AND want to Have Fun while Earning.
I'll show you how to set up a firewall and lock down the whole server for anyone to access with iptables and knockd.
A complete lists of resources to help you get started in the Web3 and crypto world.
Software development is a challenging and lucrative career option. Our daily utility items — light bulbs, televisions, cars, banking, shopping — everything is driven by intelligent pieces of codes.
Let's discuss string slicing and you'll know how string slicing is an amazing aspect of python programming.
The Git Bisect command uses a binary search algorithm to find which commit in your project’s history introduced a bug.
In applications that are i/o heavy, it can get clunky to synchronously execute high-latency functions one after the other.
Learn to use JavaScript to style your project with CSS! A tutorial for beginners.
Let's set up your computer so it's ready to code.
Learning is a process of creating knowledge in your brain. You have to allow your brain to make connections.
In this part of the series, I'll demonstrate generating HTTP requests from a simple React app using XHR, Fetch, and Axios.
The Basics
8 CSS Properties to know if you are a beginner.
A guide to where and how to start a career in quantum computing without all the physics.
Let's go over the solution of how to create a circle out of squares, the exercise which I gave you in the last tutorial.
JavaScript (JS) is a programming language used to make websites dynamic.
It is good to remind yourself of the basics sometimes.
Let's cover conditionals and control flow! This includes your Boolean logic combined with Boolean operators combined with if then type of statements!
We will discuss the difference between an interactive python terminal vs. A python script.
There is a big disconnect between what we learn while getting a CS degree and writing software that makes money.
Building an AI system is a complex process of reverse engineering human traits and capabilities in a machine and then leveraging its computing strength...
Let's talk about the most important thing in programming... BOOOOOOOLEAN ALGEBRA. Hmm... Is that a True statement OR False? Watch the video AND find out... Or NOT... Up to you.
In this one, we explore the concept of looping constructs, visually, in computer programming.
A guide on how to protect yourself and your projects while on Github.
We will discover what are variables in this video. How is data stored into variables and then how you can retrieve it later for easier access. We will look at all this through a visualization using the Turtle library from Python.
Explore UTF-8 character encoding, its history, and why it's essential for representing diverse languages and symbols in computer systems efficiently
Let's see how functions accept multiple arguments, or parameters, or inputs. Those are interchangeable terms.
How do you get the whole page to reload to show the updated data every time a user makes a change?
Let's talk about yet another fascinating data structure known as dictionaries. Ever wondered how your iPhone stores phone numbers and then allows for simple and efficient lookups? Well, no need to wonder anymore after you watch this video!
Let's talk about the list data structure of python and discuss why it's so wonderful.
Let's talk about the append method of a list and how it works
Visit the /Learn Repo to find the most read blog posts about any technology.
2026-11-15 03:00:02
A small NumPy operation can modify data you never intended to touch. Understanding views, copies, and memory sharing can prevent silent bugs in machine learning pipelines.
Imagine preparing a dataset for a machine learning model. You load the original data, select a few columns, normalize their values, and pass the result to your training algorithm.
Everything appears to work correctly.
Later, when you inspect the original dataset, something looks wrong. Some values have changed, even though you never directly modified the original array.
This is one of those Python behaviors that can make debugging surprisingly difficult. The problem usually comes down to how NumPy handles memory. When you create a new array from an existing one, NumPy does not always allocate new memory. Sometimes, it creates a view that points to the same underlying data.
This matters most during preprocessing, where slicing, normalization, reshaping, and feature selection are common. Let’s look at why it happens, how to detect it, and how to avoid it.
A view creates a new array object that shares memory with another array.
A copy creates a separate array with its own data.
import numpy as np
original = np.array([10, 20, 30, 40, 50])
view = original[1:4]
view[0] = 999
print("Original:", original)
print("View:", view)
Output:
Original: [ 10 999 30 40 50]
View: [999 30 40]
The original array changed even though we modified only the view variable. Basic slicing creates a view, so both arrays reference the same memory for the selected elements.
Now, the same operation with .copy():
original = np.array([10, 20, 30, 40, 50])
copied = original[1:4].copy()
copied[0] = 999
print("Original:", original)
print("Copy:", copied)
Output:
Original: [10 20 30 40 50]
Copy: [999 30 40]
The original array is unchanged. The difference isn’t in the variable names or values. It’s in how NumPy manages memory. If you’ve seen the same idea with plain Python lists, see Why .copy() in Python Still Mutates Your Original List.
Machine learning preprocessing often involves selecting features and transforming numerical values.
import numpy as np
data = np.array([
[100, 20, 5],
[200, 30, 10],
[300, 40, 15],
[400, 50, 20]
], dtype=float)
Suppose the first two columns are features you want to center around zero. A developer might write:
features = data[:, :2]
features -= features.mean(axis=0)
print(features)
print(data)
The features look correct:
[[ -150. -15.]
[ -50. -5.]
[ 50. 5.]
[ 150. 15.]]
But the original dataset has also changed:
[[-150. -15. 5.]
[ -50. -5. 10.]
[ 50. 5. 15.]
[ 150. 15. 20.]]
The cause is this line:
features = data[:, :2]
Slicing created a view of data. Then features -= ... modified the values in place, and since features and data share memory, the change appeared in both.
This is especially dangerous when the original dataset is needed for another preprocessing step, a model comparison, or evaluation.
Instead of:
features = data[:, :2]
use:
features = data[:, :2].copy()
features -= features.mean(axis=0)
print("Processed features:")
print(features)
print("Original dataset:")
print(data)
The processed features are transformed, and the original dataset stays intact.
This .copy() call makes the intent explicit: the preprocessing should work on independent data. Copying everything is not always necessary, though, because it increases memory usage, especially with large datasets. The better habit is knowing when NumPy shares memory and when it doesn’t.
NumPy provides np.shares_memory() for this:
import numpy as np
original = np.arange(10)
view = original[2:7]
copied = original[2:7].copy()
print(np.shares_memory(original, view))
print(np.shares_memory(original, copied))
Output:
True
False
You can also inspect the .base attribute:
print(view.base is original)
Output:
True
The .base attribute shows which array owns the memory behind a view, but it isn’t a complete test for memory sharing. For reliable checks, use np.shares_memory().
A common mistake is assuming that any operation returning a new array creates independent memory. Different operations behave differently:
| NumPy operation | Typical behavior |
|---|---|
arr[1:5] |
View |
arr.copy() |
Copy |
arr.reshape() |
View when possible, otherwise copy |
arr.flatten() |
Copy |
arr.ravel() |
View when possible, otherwise copy |
arr[arr > 10] |
Copy |
arr[[0, 2, 4]] |
Copy |
The key distinction is basic indexing versus advanced indexing.
arr = np.array([10, 20, 30, 40])
selected = arr[1:3] # basic slicing
selected[0] = 999
print(arr) # [ 10 999 30 40]
arr = np.array([10, 20, 30, 40])
selected = arr[[1, 2]] # advanced indexing
selected[0] = 999
print(arr) # [10 20 30 40]
Basic slicing returns a view, and advanced indexing returns a copy. Two expressions that look like they do the same selection can behave differently in memory, so don’t assume that selecting elements creates independent data.
The difference between in-place and regular arithmetic matters just as much:
arr = np.array([10, 20, 30])
view = arr[:]
view += 5 # in-place: modifies arr
print(arr) # [15 25 35]
arr = np.array([10, 20, 30])
view = arr[:]
view = view + 5 # creates a new array; view is reassigned
print(arr) # [10 20 30]
In the second version, view + 5 produces a new result, and the name view is rebound to it. The original array is untouched.
Preprocessing code often uses in-place operations to save memory:
features -= mean
features /= standard_deviation
If features is a view, the original data changes too.
A typical pipeline includes selecting features, handling missing values, scaling numerical columns, encoding categories, splitting data, and preparing arrays for training. Shared memory can cause trouble at several of these steps.
Problem 1: Accidentally modifying the original dataset. If preprocessing changes the original array, later experiments may use altered values instead of the initial data. Results become hard to reproduce.
Problem 2: Modifying shared training data. If your train and test arrays are views of the same source, an in-place transformation on one can silently change the other. This is a memory-sharing bug, not data leakage, but it can produce results that look valid while being wrong.
Data leakage is a separate issue. It happens when information from the test set influences training, for example by fitting a scaler on the full dataset before splitting. The correct approach is to split first, fit preprocessing only on the training data, and then apply the learned transformation to the test data.
Many scikit-learn transformers, such as StandardScaler, return new arrays by default, so this bug is less common there. Some of them accept a copy=False argument, though, and hand-written in-place NumPy code doesn’t protect you at all.
Problem 3: Difficult debugging. The code runs without errors. The model trains, predictions look reasonable, and metrics seem plausible. The real problem may be hidden in a preprocessing step earlier in the pipeline.
Before modifying an array, ask:
+=, -=, or /=?np.shares_memory()?If the original must stay unchanged, make an explicit copy:
X_processed = X_original.copy()
Then transform the independent array. For large datasets, weigh the memory cost before copying everything.
NumPy views and copies look similar at first. Both are new variables, both can hold the same values, and both can be passed into machine learning functions. Their memory behavior is different.
A view shares memory with its source, while a copy owns its data. That difference becomes critical when preprocessing uses in-place operations.
The main lesson: don’t assume a new variable means new memory. When a dataset changes unexpectedly, check how the array was created before blaming the preprocessing formula, the model, or the data itself.
Sometimes, the fix is just one missing .copy().
2026-11-15 02:00:02
this is my second progress update for the Decentralize AI Hackathon by HackerNoon and Nosana.
In my first article, I Built a Local AI Linux Assistant That Doesn't Rely on the Cloud, I introduced zkzkAgent: an AI agent that manages a Linux machine through plain language and runs entirely on my own hardware with Ollama.
That is still how it works by default. But running everything locally has a ceiling, and I kept hitting it.
qwen3.5:2b). It fits in memory and answers quickly. A 27B model does not fit. Until now, anyone on modest hardware had two options: accept a small model, or send everything to a centralized AI provider.yes.
remove_file. Only my machine can run it, and only after I type yes.models/LLM.py, and it is controlled by one setting in `.env`:MODEL_TYPE="NOSANA"
NOSANA_API_KEY="nos_..."
NOSANA_BASE_URL="https://inference.nosana.com/v1"
NOSANA_MODEL="qwen/qwen3.8-27b"
if MODEL_TYPE == "NOSANA":
API_KEY = _env("NOSANA_API_KEY")
if not API_KEY.startswith("nos_"):
raise RuntimeError("NOSANA_API_KEY missing or invalid (must start with 'nos_').")
BASE_URL = _env("NOSANA_BASE_URL", NOSANA_URL).rstrip("/")
MODEL_NAME = _env("NOSANA_MODEL") or _pick_nosana_model(BASE_URL, API_KEY)
llm = ChatOpenAI(
model=MODEL_NAME,
base_url=BASE_URL,
api_key=API_KEY,
timeout=120,
max_retries=2,
stream_usage=True, # final chunk carries token counts
)
elif MODEL_TYPE == "OLLAMA":
pass #the other implement of the code
Three details are worth explaining:
GET /models while a GPU is actually serving it. If `NOSANA_MODEL` is empty, the agent fetches that list and uses the first available model, so it never starts against a model that nobody is serving.stream_usage=True, the last streamed chunk includes token usage. That is what usage is billed on, and it is the basis for the cost numbers I plan to publish next.nosana-ollama.json, a Nosana job definition that starts the official Ollama container on a GPU node and pulls qwen3:14b. It is for people who want their own model server on Nosana instead of the shared API.To test the integration in real work, I switched to the 27B model on Nosana and gave zkzkAgent a task: create a new project called zkzk_ai, a calculator web app with a FastAPI backend and an HTML, CSS, and JavaScript frontend, and open it in VS Code.
Step 1: Route and Plan
The classifier sent the request to the planner (`NEEDS_PLANNING`). The planner wrote a 9-step plan, including the full content of every file, and then stopped and waited for me:
Reply 'ok' (or 'yes') to run this plan, or tell me what to change.
Enter your request: ok
INFO:core.agent:[ENTRY ROUTER] Pending plan approved → routing directly to execute
Step 2: Execute
Once I approved the plan, the executor got to work. It made 10 tool calls, one at a time, across 10 graph iterations (with a maximum of 15).
Here's what it actually did:
|
# |
Tool |
Action |
|---|---|---|
|
1 |
|
Created |
|
2 |
|
Wrote |
|
3 |
|
Created the |
|
4 |
|
Wrote |
|
5 |
|
Wrote |
|
6 |
|
Wrote |
|
7 |
|
Wrote |
|
8 |
|
Wrote |
|
9 |
|
Wrote |
|
10 |
|
Opened the project in VS Code |
Nothing complicated happens behind the scenes. The model running on Nosana picks a tool; that tool executes on my machine, and the result is sent back to the model.
To make this clearer, here's an actual example from the execution log (trimmed for readability):
Each call follows the same loop. The model on Nosana picks the next tool, the tool runs on my machine, and the result goes back into the conversation. In the log, one turn of that loop looks like this (trimmed):
INFO:httpx:HTTP Request: POST https://inference.nosana.com/v1/chat/completions "HTTP/1.1 200 OK"
Tools in last message: [{'name': 'create_project_folder', 'args': {'working_directory': '/home/aloha-zkaria', 'project_name': 'zkzk_ai'}}]
iteration: 0/15
INFO: tools_module.files_tools.createProjectFolder.
TOOL: Successfully created project folder /home/aloha-zkaria/zkzk_ai
Step 3: A Follow-Up Edit
Next, I asked: "Can you change the zkzk_ai Calculator to be zkzk?" The classifier sent this to the planner as well, which wrote a 7-step plan. After I approved it, the agent read static/index.html and then changed the name in three places: the page title and heading, the FastAPI app title, and the README. It did not touch the calculator logic.
Step 4: Does It Actually Run?
I started the app with `uvicorn app:app`. The page loaded, and the calculator API answered:
INFO: 127.0.0.1:52166 - "GET / HTTP/1.1" 200 OK
INFO: 127.0.0.1:52166 - "GET /static/style.css HTTP/1.1" 200 OK
INFO: 127.0.0.1:52172 - "GET /static/script.js HTTP/1.1" 200 OK
INFO: 127.0.0.1:58438 - "POST /api/calculate HTTP/1.1" 200 OK
Across both requests, every model call in the saved log, 18 in total, went to `inference.nosana.com` and returned `200 OK`. Every file write, command, and app launch happened locally.
I reviewed the code the agent wrote, and tested the backend directly:
- No eval. Expressions are parsed with Python's ast module, and only numbers, parentheses, and `+ - * / // % **` are allowed. When I sent __import__("os"), it was rejected with "Unsupported expression element: Call".
- Clear errors. Division by zero and complex results (such as (-1)**0.5) return a readable message instead of crashing.
- Input limits. Expressions are capped at 500 characters in both the browser and the API.
- Keyboard support. Enter evaluates, Backspace deletes, Esc clears and `^` types a power.
The code is on GitHub at [zkzkGamal/calculator], and the full run log is in the [zkzkAgent repo]
My first article was about an agent that doesn't rely on the cloud. This update doesn't change that. It adds a choice. Private or sensitive work stays on local Ollama, where nothing leaves the machine. Heavy planning can use a much larger model on Nosana's network instead of a centralized provider. In both modes, the tools, the files, and the final yes stay with me.
1. A real benchmark. Run the same five tasks on the local 2B model and on the 27B model on Nosana, and measure success rate, tool calls, time, tokens, and cost. Numbers instead of impressions.
2. Hybrid routing. Because the graph has separate nodes, each node can use its own model. The classifier and chat replies are short and could stay on a small local model, while the planner and executor use Nosana. Plain conversations would then never leave the machine.
3. Automatic fallback. If Nosana can't be reached, switch to local Ollama automatically, the same way the agent already turns Wi-Fi back on by itself.
4. Close the safety gaps. Enforce plan-level confirmations. Also put `run_command` behind a confirmation or an allow-list: today it can run any shell command, and with a remote model choosing the arguments, that matters more than before.
5. Fix the calculator bug with the agent doing the fix.
git clone https://github.com/zkzkGamal/zkzkAgent.git
cd zkzkAgent
pip install -r requirements.txt
cp .env.example .env # set MODEL_TYPE="NOSANA" and your NOSANA_API_KEY
./cli.sh
Full links:
zkzkAgent: https://github.com/zkzkGamal/zkzkAgent The calculator it built: https://github.com/zkzkGamal/calculator
Full Nosana run log: https://github.com/zkzkGamal/zkzkAgent/blob/main/docs/nosana-run-2026-10-25.log
First article: https://hackernoon.com/i-built-a-local-ai-linux-assistant-that-doesnt-rely-on-the-cloud
2026-11-15 00:02:32
How are you, hacker?
🪐 What’s happening in tech today, November 14, 2026?
The HackerNoon Newsletter brings the HackerNoon homepage straight to your inbox. On this day, we present you with these top quality stories.
By @adityareddy_dev [ 7 Min read ] Desk Studio runs a design job like a studio inside your coding agent: roughs, a pick, polish, pixel proof, sign-off. One real job, 8.9, $1.34. Read More.
🧑💻 What happened in your world this week?
It's been said that writing can help consolidate technical knowledge, establish credibility, and contribute to emerging community standards. Feeling stuck? We got you covered ⬇️⬇️⬇️
ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME
We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.See you on Planet Internet! With love, The HackerNoon Team ✌️
2026-11-14 23:01:17
How are you, hacker?
🪐Want to know what's trending right now?:
The Techbeat by HackerNoon has got you covered with fresh content from our trending stories of the day! Set email preference here.
## Meet TwIL-8B, webAI's AI Model for Math, Coding, and Reasoning 
By @aimodels44 [ 3 Min read ]
TwIL-8B is an AI model designed for math, coding, and reasoning. Explore its balanced and verbose variants, benchmark results, and limitations. Read More.
By @proofofusefulness [ 5 Min read ] The Proof of Usefulness Hackathon announces its final winners, awarding $118,000 in cash prizes and software credits across five categories. Read More.
By @packagemanager [ 8 Min read ] Cuttlefish.MD packages 2,806 license-checked SKILL.md files into nine plugins for Claude Code, Codex, Cursor and Gemini CLI. Here's how to install it. Read More.
By @hackernooncontests [ 4 Min read ] Meet the 10 finalists and three winners of HackerNoon's $2,500 Shipaton 2026 Writing Contest, celebrating the stories behind new apps. Read More.
By @thomascherickal [ 20 Min read ] I asked Gemini 4 Argon for 10 Billion-Dollar Products that I could build with it. It Blew My Mind, the products are insane and solve huge problems! Read More.
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By @zbruceli [ 23 Min read ] SpaceX put thousands of routers in orbit is now coming down to earth: steel towers, rooftop leases, zoning boards and all for 5G land network. Read More.
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By @adityavithaldas [ 3 Min read ] AI is making creation easier. What happens to professional identity when expertise, creative judgment, focus, and audience trust become the differentiators? Read More.
By @aimodels44 [ 10 Min read ] Learn how Qwen3.8-27B Defiance works, including its uncensored weights, custom AI personas, GPU requirements, performance, and limitations. Read More.
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By @anomalydetection [ 8 Min read ] An Anchorage witness filmed a tentacled object near JBER. We fact-check the claims, the solar geometry, and why AARO's last jellyfish was balloons. Read More.
By @zbruceli [ 23 Min read ] Guido van Rossum wrote Python over a 1989 Christmas break. By 2018 it ran AI and the web, and a fight over the walrus operator (PEP 572) drove him to resign. Read More.
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By @nfrankel [ 10 Min read ] Why does Playwright get blocked while Chrome works? Read More.
By @hockeystick [ 31 Min read ] Anthropic's S-1: $16.3B in half-year revenue, ~900% NRR, an $80.7B mostly-paper loss, a blank compute bill and governance that keeps control with founders. Read More.
By @editingprotocol [ 6 Min read ] Learn how to use AI in writing without sacrificing originality, personality, or the human perspective behind your byline. Read More.
By @mcsee [ 10 Min read ] AI removes the manager's cognitive limit. You should use it to sharpen your decisions, not just to summarize emails. Read More.
By @axsaucedo [ 24 Min read ] Part 1 of a 2-part series on agent identity and security Read More.
By @exfiltration [ 240 Min read ]
Interchangeable encrypted reasoning blocks let attackers decode hidden chain-of-thought and leak PII and credentials from major LLM APIs. Read More.
🧑💻 What happened in your world this week? It's been said that writing can help consolidate technical knowledge, establish credibility, and contribute to emerging community standards. Feeling stuck? We got you covered ⬇️⬇️⬇️
ANSWER THESE GREATEST INTERVIEW QUESTIONS OF ALL TIME
We hope you enjoy this worth of free reading material. Feel free to forward this email to a nerdy friend who'll love you for it.
See you on Planet Internet! With love,
The HackerNoon Team ✌️
.gif)
2026-11-14 23:00:39
Trends are general directions in which something is developing or changing, especially in fashion, technology, or culture. Understanding trends is crucial for businesses and individuals to anticipate future developments, adapt strategies, and remain relevant in a rapidly evolving world.
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First of all, Nostradamus has nothing to do with Tarot.
Humanity may be a few steps away from the Internet of Anything and Everything (or whatever the all-encompassing global digital interweb is going to be called in the years to come), but don't kid yourselves: it's coming. Right now the brightest minds in computer science, software development, entrepreneurship, policymaking, and digital arts are hard at work designing the future tenets of the Web 3.0 and the process shows no signs of slowing down.
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The gaming industry is rapidly developing, and new trends are constantly emerging. I discuss 5 significant trends in the gaming industry for the rest of 2022.
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This past year has disrupted the tech and social media landscape. What does 2021 have in store? Here are 5 predictions including OnlyFans' new model and more.
The Big Picture: Macro-Trends That Will Shape the Next 10 Years... And how to take advantage of them
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Disruption, disruption, disruption. That has been the name of the game when it comes to new technologies for the better part of the last decade. Virtually every industry has seen this buzzword used in some capacity over the last few years, and rightfully so. New advancements in technology have allowed companies in various industries to attract new customers, provide more value to these customers, and operate more efficiently overall. This has led to a change in the traditional way of doing things as new trends and methods emerge. One industry that is also noticing this trend of disruptive change is the outdoor industry.
Why Google Chrome is the most popular browser nowadays and what web development trends can shape that?
Working in Sales for the past 5 years means the browser tab I most consistently have open is my email. It's also one of the very few active app notifications I have on my phone.
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What are the qualities of a good application for Gen Z and Millennials?" 2023 trends for app design and development requires user-friendly interface, security
The mid-term results for the cryptographic industry in 2019 still remain uncertain. On one hand, market capitalization increased by 125 per cent in the second quarter alone. On the other, users have already lost more than $4 billion this year due to the actions of hackers and scammers, which is more than the entire year of 2018. It is unlikely that 2019 will meet the original expectations. Yet, experts predict a lot of interesting happenings for crypto exchanges in 2020.
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What are the leading web design trends of 2021 are they worth investing in? From retro fonts to Augmented Reality to 3D modeling — there’s a lot to enjoy.
In today’s digital world, all industries are making a massive shift towards technology to improve their products and services. Customers expect their problems to be addressed as fast as possible. And, those companies that can achieve that are going to put themselves in the driver’s seat.
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2022 saw the data space grow by leaps and bounds. Here are the top 9 things our team of data experts expects to see in 2023.
Automation and artificial intelligence (AI) are changing the nature of jobs and many believe that robots will completely put humans out of work in the coming years.
In order for your business to succeed in a market driven by digital devices, you absolutely need to take advantage of the latest developments being made. By staying on top of current trends, you can gain remarkable insight on what consumers want, how to focus your brand identity, and the best methods for increasing sales throughout the fiscal year. Take a look at these ideas that are being utilized by businesses of all sizes and learn how you can prepare your own company for the future.
as we look towards 2024, the U.S. real estate market is at a crossroads of evolving challenges and emerging opportunities.
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At a group job interview for a San Francisco startup, a woman hiring manager shot a Nerf arrow at my head to demonstrate the cliché “play hard” mentality in an interview setting. At first, I was embarrassed to be singled out in this way. Then, I was appalled. I wondered why any professional would do this, let alone a woman to another woman. The answer: company culture.
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When the travel industry will recover after the pandemic, what will the travel experience look like and how modern tech will influence it?
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With millions of developers preparing to move to a more decentralized environment, the time has come to pick up the pace and reward early adopters.
Contributing significantly to eCommerce and its spiking sales levels, consumer habits, behaviors, and shopping trends in the USA are changing. And this change is not about to stop anytime soon, with post-Covid trends in eCommerce continuing to place further benefits on this sector and those that take advantage of these trends.
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Let's discuss three reasons behind the immense popularity of the Wordle game.
Transitioning into the new year, 2023, feels like a much-needed fresh start for cry on many accords. 2022 delivered some of the toughest conditions to remember.
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A picture is worth a thousand words. Throughout history, art has gone through many phases like Romanticism, Classicism and many other types of -isms. These are regarded as styles of painting of those periods. However if you think about it, from a modern perspective, these phases can be considered fads, although the more precise term would be trends, due to their longevity.
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We analyzed Google Ads data for the Holiday shopping season to look at what the trends were for ads, advertised, and sales/discounts in 2021.
Visit the /Learn Repo to find the most read blog posts about any technology.
