My Perspectives | Md. Nazmussakib

Welcome to Sakib AI

Tue, 27 Oct 2026 00:00:00 +0000

Artificial intelligence is moving quickly, and the most interesting questions are not always about the latest model or product. They are also about how these systems work, where they are useful, what their limitations are, and how they affect people.

I created Sakib AI as a space to explore those questions. Here, I plan to share my perspectives on AI research and technology, discuss noteworthy news with links to original reporting, and document ideas that emerge from my academic work.

This is an introduction to the publication, not a claim of completed research or an endorsement of any particular AI product. I hope to make complex topics more approachable while remaining careful about evidence, uncertainty, and real-world impact.

Thanks for reading.

Can We Benefit From AI Without Sacrificing Privacy?

Mon, 12 Oct 2026 00:00:00 +0000

Why AI systems want data

Data can make AI services more useful. A personalized assistant may remember your preferences. A medical model may identify patterns in health records. A fraud-detection system may learn from transaction behavior.

But the same information can reveal sensitive details about individuals. When data is collected, stored, combined, or shared, privacy risks increase. Even information that seems harmless in isolation can become revealing when linked with other records.

Privacy policies are often long and difficult to understand. People may agree to data collection without fully appreciating how their information could be used later. In workplaces or essential services, refusing consent may not be realistic.

That is why privacy cannot rely only on a checkbox. Organizations should collect only what they need, limit retention, restrict access, and explain how data is used.

Technical approaches

Several methods can reduce privacy risks. On-device processing can keep some information on a person’s device. Federated learning can allow models to learn from distributed data without centrally collecting every raw record, although it does not eliminate privacy risks by itself.

Differential privacy can provide mathematical protections in certain statistical settings. Encryption and access controls help protect stored and transmitted information. Each approach has limitations and must be matched to the actual threat.

The surveillance concern

AI can make large-scale monitoring easier by analyzing images, speech, location data, and behavior. Such capabilities can support legitimate security applications, but they can also enable intrusive tracking and discriminatory enforcement.

The question is not only whether the technology works. It is who controls it, what oversight exists, and whether affected people can challenge misuse.

My perspective

I believe privacy should be treated as a design requirement, not an optional feature added after deployment.

People should be able to benefit from useful AI without surrendering unnecessary personal information. Organizations that cannot clearly explain why they need a dataset should reconsider collecting it. Trustworthy AI requires technical protections, transparent policies, and meaningful accountability.

Question for readers: Would you use a more personalized AI assistant if it required storing much more of your personal information?

Is AI's Environmental Cost Worth Its Benefits?

Thu, 03 Sep 2026 00:00:00 +0000

AI has a physical footprint

AI may feel like software that exists somewhere in the cloud, but it depends on physical infrastructure: data centers, electricity, cooling systems, networking equipment, and specialized chips. Training and running models consume energy, and some cooling systems also use water.

As demand grows, communities and policymakers are asking how AI infrastructure affects power grids, local water resources, and emissions.

The impact is not uniform. It depends on the efficiency of hardware and software, how often models are used, the energy mix powering facilities, and the location and design of data centers.

Training versus everyday use

Training a large model can require substantial computing resources, but the total cost of serving that model to millions of users can also be significant. A single prompt does not tell us much about the overall footprint without considering model size, hardware, workload, and scale.

Comparisons such as “one AI question uses exactly this much energy” should therefore be treated cautiously unless their assumptions and measurements are clear.

AI can also support sustainability

AI may help optimize electricity demand, improve building efficiency, identify equipment failures, and accelerate certain kinds of scientific research. Those benefits are promising, but they should be measured rather than assumed.

There is also a rebound effect to consider: when computing becomes cheaper and more efficient, people may use more of it. Efficiency improvements do not always reduce total resource consumption.

My perspective

I do not think the right question is simply whether AI uses energy. Every major technology has costs. The better question is whether its benefits justify those costs, and how we can reduce the burden.

Companies should publish meaningful environmental information, invest in efficient systems, and consider local impacts when expanding infrastructure. Researchers should explore smaller models and more efficient methods when they meet the task’s needs.

Question for readers: Should AI companies disclose the energy and water impacts of their largest systems?

Will AI Agents Change How We Use Software?

Tue, 14 Jul 2026 00:00:00 +0000

From answering questions to taking action

Most people first encountered modern AI through chatbots: type a question, receive a response. AI agents aim to go further. They may use tools, browse information, write code, interact with applications, and complete sequences of steps toward a goal.

Imagine asking a system to compare travel options, prepare a budget, and organize the results into a document. An agent could potentially coordinate several tools instead of requiring you to switch between applications manually.

This does not mean every agent can reliably complete every task. Complex workflows involve unexpected errors, permissions, ambiguous instructions, and information that changes over time.

Why reliability matters more than a demo

An agent may successfully complete a short demonstration but fail when a website changes or an instruction is unclear. Mistakes become more serious when systems can send messages, spend money, change files, or interact with customer accounts.

A useful agent needs well-defined permissions, checkpoints for important actions, logs that explain what happened, and a way to recover from failure. In many cases, asking a human for approval is a feature, not a weakness.

What this could mean for businesses

Small businesses may benefit from agents that help manage support requests, draft follow-up messages, organize appointments, or summarize operational data. But automation should be introduced carefully. Businesses must protect customer information, verify outcomes, and understand the costs of errors.

Traditional software is unlikely to disappear overnight. Databases, accounting systems, security controls, and specialized applications still provide essential structure. Agents may become a new interface that coordinates those systems.

My perspective

I think AI agents could change the way we interact with software, but the most important innovation will not be a chatbot that clicks buttons. It will be reliable systems that can complete useful work while remaining understandable and controllable.

The winning products may combine the flexibility of language models with the predictability of conventional software.

Question for readers: What repetitive digital task would you most like to delegate to an AI agent?

Should Powerful AI Models Be Open Source?

Tue, 02 Jun 2026 00:00:00 +0000

The case for openness

Open AI models can help students, researchers, startups, and smaller organizations experiment without relying entirely on a few large companies. Researchers can inspect model behavior, reproduce findings, and adapt systems to local languages or specialized needs.

Openness can also encourage competition. If only a handful of companies control the most capable systems, they may influence pricing, access, and the direction of innovation.

However, the phrase open source AI can mean different things. Some releases include model weights but not training data or complete training code. It is important to examine what is actually available and what the license permits.

The risks of releasing powerful capabilities

A broadly accessible model can be used for beneficial research, but it may also lower barriers for spam, fraud, disinformation, or cyber misuse. Once model weights are widely distributed, restricting their use becomes difficult.

The severity of these risks depends on what the model can actually do. A small language model and a highly capable autonomous system should not automatically be treated as equivalent.

Is there a middle ground?

Possible approaches include staged releases, independent safety evaluations, responsible-use documentation, and different access arrangements for capabilities with higher misuse potential. None is perfect: restrictions can concentrate power, while unrestricted releases can be difficult to reverse.

A healthy AI ecosystem needs both accountability and meaningful opportunities for independent research.

My perspective

I favor openness where its benefits are strong and its risks are manageable, rather than assuming every model should be completely closed or completely public.

Transparency is important, but transparency can take several forms: public evaluations, documentation, third-party audits, and access for qualified researchers. Decisions about releasing the most capable systems should be based on evidence of their capabilities and likely misuse, not branding alone.

Question for readers: Should governments require independent safety testing before especially powerful AI models are released?

Can Artificial Intelligence Ever Be Truly Fair?

Thu, 16 Apr 2026 00:00:00 +0000

Why fairness is difficult

AI is sometimes described as objective because it uses mathematical models rather than personal feelings. But mathematics does not automatically remove human bias. Models learn from data collected in the real world, where opportunities and outcomes are not always equal.

Imagine a hiring system trained on past recruitment decisions. If historical hiring favored certain groups, the system may learn patterns that reproduce those inequalities. Even if developers remove obvious demographic fields, other variables can act as proxies.

Different meanings of fairness

Fairness is not a single technical setting. One definition might require similar error rates across groups. Another might require equal access to positive outcomes. A third might focus on whether individual decisions are justified.

These goals can conflict. For example, if groups have different measured outcome rates in historical data, it may be mathematically difficult to satisfy several fairness metrics simultaneously. That means choosing a fairness measure is partly a social and ethical decision, not just an engineering problem.

What developers can do

Developers should examine how training data was collected, identify who may be missing, and evaluate performance across relevant groups. They should document limitations and monitor systems after deployment. Organizations also need meaningful ways for affected people to challenge decisions.

Fairness testing should reflect the actual use case. A model recommending movies has different consequences from one influencing access to healthcare, credit, education, or employment.

My perspective

I do not think we should demand that AI solve every injustice in society. But we should not allow automation to make existing injustices harder to see or challenge.

A fairer AI system begins with clear goals, representative evidence, independent evaluation, and accountability. Sometimes the right decision is not to automate a high-stakes process at all.

Question for readers: If two fairness goals conflict, who should decide which one matters most?

Who Owns AI-Generated Art, Writing, and Ideas?

Thu, 05 Mar 2026 00:00:00 +0000

A new kind of creative tool

Generative AI can produce illustrations, music, video, code, and writing from short instructions. For creators, this opens exciting possibilities. Someone without advanced design skills can explore visual concepts. A small business can draft marketing materials. A researcher can brainstorm alternative explanations.

But the technology also raises difficult questions: Who owns the output? What rights do creators have when their work is used for training? How much human contribution makes an AI-assisted work original?

Three different questions

These issues are often mixed together, but they are distinct.

Training: Was copyrighted material used to develop the system, and was that use legally permitted? Rules and litigation differ by jurisdiction and circumstance.

Output: Does the generated result qualify for copyright protection? In some legal systems, human authorship is central to copyright eligibility. The degree of human creative control can matter.

Similarity: Could a particular output infringe someone else’s protected work, even if it was generated by AI? The answer depends on the actual material and applicable law.

A platform’s terms of service may also govern how customers can use outputs. That does not resolve every copyright question.

The creator’s dilemma

Artists and writers may benefit from faster workflows while also worrying that their work will be copied, imitated, or devalued. Audiences may struggle to distinguish human-created work from synthetic media. Businesses may publish content without checking whether it resembles existing material.

Better attribution tools, clearer licensing arrangements, and practical disclosure standards could help. But a single rule is unlikely to fit every use, from brainstorming a title to generating a commercial illustration.

My perspective

I see AI as a powerful creative assistant, not a reason to disregard human creators. Innovation and fair treatment of artists should not be opposing goals.

When using AI, I think people should be transparent about meaningful assistance where it matters, review outputs carefully, and avoid presenting another person’s work as their own. Policymakers should pursue clearer rules that protect creative labor without blocking legitimate experimentation.

Question for readers: Should an AI-generated image receive copyright protection if a person spent hours directing and editing it?

Should AI Make Medical Decisions?

Wed, 21 Jan 2026 00:00:00 +0000

The promise of AI in healthcare

Healthcare generates enormous amounts of information: medical images, laboratory results, clinical notes, and data from wearable devices. AI can help identify patterns in these records and may assist clinicians in detecting disease, prioritizing cases, or monitoring patients.

For example, a medical-image model might flag suspicious regions in a scan for closer review. A predictive model might identify patients who need additional follow-up. These tools could be especially valuable in settings where specialists are scarce or workloads are high.

But promising results in a research dataset do not automatically mean a system is safe in a real hospital.

The challenge of generalization

A model trained using images from one institution may encounter different scanners, patient populations, image quality, or clinical procedures elsewhere. Performance can decline when the data changes.

Accuracy also tells only part of the story. In disease detection, false negatives may delay treatment, while false positives can lead to anxiety and unnecessary testing. Researchers should evaluate sensitivity, specificity, calibration, subgroup performance, and clinical usefulness—not just a single headline score.

Patient privacy, informed consent, cybersecurity, and bias are equally important. A system that performs well overall may still work less reliably for underrepresented groups.

Who should make the final decision?

AI can support clinical judgment, but responsibility cannot simply be delegated to a prediction. Clinicians need to understand the tool’s intended use, limitations, and evidence. Hospitals need monitoring procedures. Regulators and developers need clear standards for validation and updates.

Explainable AI can sometimes help users inspect what influenced a model’s prediction, but a visually appealing explanation is not proof that the model’s reasoning is medically sound.

My perspective

I am particularly interested in the potential of AI for medical imaging and cancer detection. My view is that the strongest near-term role for AI is decision support rather than unaccountable decision replacement.

The goal should not be to remove clinicians from care. It should be to help them detect important signals earlier, reduce avoidable workload, and make better-informed decisions. A useful healthcare AI system must be accurate, equitable, understandable enough for its setting, and rigorously tested with real clinical workflows.

Question for readers: Would you feel comfortable if an AI system reviewed your medical scan before a doctor did?

Will AI Become More Intelligent Than Humans?

Tue, 18 Nov 2025 00:00:00 +0000

First, what does “more intelligent” mean?

AI already outperforms people at some narrow tasks. Computers can process enormous amounts of data, search complex spaces, and recognize patterns at scales that would be impossible for an individual. But intelligence is not one simple number.

Human intelligence includes learning, reasoning, creativity, social understanding, physical interaction, and the ability to pursue goals in changing environments. A system can excel at one benchmark while failing at a task a child finds easy. That makes broad comparisons difficult.

Researchers sometimes use the term artificial general intelligence (AGI) for systems with broad capabilities across many domains. There is no universally accepted test or definition of AGI, and experts disagree about how close current systems are.

Capability is not the same as understanding

A model that writes excellent code may still make elementary errors when the requirements change. A model that produces a persuasive explanation may not have verified the facts behind it. Strong performance is real and valuable, but it should not be confused with unlimited competence.

Future AI systems may become more capable through better training, tools, memory, reasoning methods, and interaction with the world. Whether those advances lead to intelligence beyond humans across most domains remains uncertain.

Why the question matters now

Even without superintelligence, powerful AI can reshape research, employment, cybersecurity, and public information. We do not need to settle the AGI debate before thinking seriously about oversight.

If systems become more autonomous, the key questions will include: Can humans understand what they are doing? Can their actions be constrained? Who is accountable when they cause harm? Can independent researchers evaluate their capabilities and risks?

My perspective

I believe we should be curious about advanced AI without treating bold timelines as established facts. Claims that AGI is inevitable next year—or impossible forever—often go beyond what current evidence can demonstrate.

The more useful approach is to prepare for a range of futures. Invest in research and education, build evaluation methods, and create safeguards that scale with capability. We should neither underestimate AI’s potential nor surrender critical judgment to predictions about it.

Question for readers: What would convince you that an AI system has genuinely general intelligence?

Can We Trust AI Answers When They Sound So Confident?

Thu, 24 Jul 2025 00:00:00 +0000

The problem with convincing language

One of the most impressive features of modern AI is how naturally it communicates. A chatbot can explain a scientific idea, write a professional email, or answer a complicated question in seconds. That fluency is useful—but it can also be misleading.

An AI system can produce an answer that is clear, detailed, and wrong. It may invent a citation, misunderstand a question, or combine true facts with an unsupported conclusion. These mistakes are often called hallucinations. The important point is that confident wording does not guarantee accurate reasoning or reliable evidence.

Why errors happen

Many language models learn statistical patterns from large collections of text. Their objective during generation is not identical to a human researcher checking every claim against a trusted source. A model may produce plausible text even when its underlying information is incomplete or outdated.

The risk depends on the task. If you ask AI to suggest a headline, a mistake may be easy to fix. If you ask it to interpret a legal obligation, diagnose a medical problem, or calculate a financial decision, an error can have serious consequences.

What responsible use looks like

The solution is not to abandon AI. It is to match the level of verification to the stakes.

For everyday writing, read the output carefully and correct anything that does not reflect your intent. For factual research, check primary sources, dates, quotations, and links. For calculations or technical work, reproduce the result independently where possible. In healthcare, law, and other high-stakes settings, qualified professionals must remain responsible for decisions.

Developers also have work to do. AI systems should communicate uncertainty, make sources easier to inspect, and be evaluated on accuracy rather than impressive demonstrations alone.

My perspective

Trust should be earned through evidence, not presentation. I think the most useful AI assistant is not the one that always has an answer. It is the one that can recognize uncertainty, explain its limitations, and help the user verify important claims.

AI literacy should include a simple habit: ask, “How would I know if this answer were wrong?” That question changes AI from an authority into a tool for better thinking.

Question for readers: Have you ever discovered that a convincing AI answer was incorrect?

Will AI Replace Human Jobs—or Change What Work Means?

Wed, 12 Mar 2025 00:00:00 +0000

Why this question matters

When people ask whether artificial intelligence will replace jobs, they are often asking a more personal question: Will the skills I spent years developing still matter? The concern is understandable. AI can already draft documents, summarize reports, analyze code, and respond to customer questions. Yet a task and a job are not the same thing.

Most jobs involve several kinds of work: gathering information, making judgments, communicating with people, checking quality, and taking responsibility when something goes wrong. AI may perform some of those tasks well while struggling with others. That is why the effect on employment is likely to vary by occupation, organization, and country.

Automation is not the whole story

Consider a small business that uses AI to answer routine customer questions. The system might reduce the time employees spend on repetitive requests. But the same business may still need people to handle unusual cases, build customer relationships, supervise the system, and improve the service.

There are at least three possible outcomes. A company might reduce hiring for some tasks. It might use the savings to expand and hire in other areas. Or it might keep its workforce while expecting employees to produce more. Which outcome occurs depends on business decisions, competition, regulation, and demand—not just the technology itself.

The risk of an unequal transition

The benefits of AI will not necessarily be distributed fairly. Workers with access to training and good tools may become more productive. Workers in highly routine roles may face pressure before they have time to retrain. New graduates may also find that some traditional entry-level tasks are increasingly automated.

A useful response requires more than telling everyone to “learn AI.” Schools, employers, and policymakers should identify which skills remain valuable, provide practical training, and make career transitions possible. Critical thinking, domain knowledge, communication, and the ability to verify AI outputs are likely to remain important.

My perspective

I do not see the future as a simple contest between humans and machines. The more important question is who gets to benefit from higher productivity, and who carries the cost of change.

We should measure AI success not only by how many tasks it can automate, but also by whether it improves the quality of work, creates opportunities, and gives people more control over their time. A future with powerful AI and widespread insecurity would be a poor outcome, even if the technology itself were impressive.

Question for readers: Which part of your own work could AI improve—and which part still needs a human?

⚡️ Turn Jupyter Notebooks into Blog Posts

Mon, 15 Jul 2024 00:00:00 +0000

As a researcher or data scientist, your work often lives in Jupyter Notebooks. But sharing those insights effectively usually means taking screenshots, messy copy-pasting, or exporting to PDF.

Hugo Blox changes that. With the {{< notebook >}} shortcode, you can render your actual .ipynb files directly as beautiful, interactive blog posts or project pages. Keep your code, outputs, and narrative in one place.

Table of Contents

Why publish notebooks?

Tip

Reproducible Research: By publishing the actual notebook, you allow others to download and run your code, verifying your results and building upon your work.

No more screenshots – Render crisp code and vector plots directly from your source.
Theme consistent – Notebooks automatically adapt to your site’s theme (including dark mode).
Flexible sourcing – Display notebooks from your assets/ folder, page bundles, or even directly from a remote GitHub URL.
Interactive – Users can copy code blocks or download the full notebook to run locally.

Example: Data Science Workflow

Below is a live example of a notebook rendered right here in this post. Notice how the markdown, code, and outputs (text, HTML, and JSON) are all preserved and styled.

Launch Readiness Analysis

Python · Kernel: Python 3 · nbformat 4.5 · 6 cells

Download notebook

Language: Python
Version: 3.11
Kernel: Python 3 python3
nbformat: 4.5
Authors: Ownable CMS

Markdown

Ship Notebook Stories in Minutes

Hugo Blox Notebook renderer turns your .ipynb experiments into beautiful long-form posts. Use this sample to see how markdown, code, and outputs flow together.

Markdown

Drop notebooks inside assets/notebooks/ (or import them as page resources).
Reference them with {{</* notebook src="your.ipynb" */>}}.
Control code, outputs, metadata badges, and download links via shortcode params.

In [1]

demo quickstart

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import math
accuracy = 0.982
print("Collecting data...")
print("Training notebook-ready block...")
print("Done!")
accuracy

Collecting data...
Training notebook-ready block...
Done!

0.982

In [2]

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from IPython.display import HTML
HTML("<div style='font-family:Inter,ui-sans-serif;'><strong>Launch Readiness:</strong> <span style='color:#22c55e;'>98.2% confidence</span><br><em>Notebook blocks are theme-aware and dark-mode friendly.</em></div>")

Launch Readiness: 98.2% confidence
Notebook blocks are theme-aware and dark-mode friendly.

In [3]

metrics

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metrics = {
 'metrics': {
 'engagement_rate': 0.73,
 'read_time_minutes': 4.6,
 'subscribers': 1280
 }
}
metrics

{
"metrics": {
"engagement_rate": 0.73,
"read_time_minutes": 4.6,
"subscribers": 1280
}
}

Markdown

Tip: Pair this block with Call-to-Action cards or the Embed shortcode to link to GitHub repos, datasets, or ARXIV preprints.

How to add a notebook

Save your notebook. Place your .ipynb file in assets/notebooks/ (for global access) or inside a page bundle (like content/blog/my-post/analysis.ipynb).
Add the shortcode. In any Markdown page, simply use: {{< notebook src="analysis.ipynb" >}}
Customize. You can hide code cells for non-technical audiences (show_code=false) or just show the output (show_outputs=true).

Important

Hugo Blox respects your privacy. Notebook rendering happens statically at build time—no third-party services required.

Next steps

Try it out: Drop one of your existing notebooks into this site and see how it looks.
Link your papers: Use the Embed shortcode to link your notebook to your latest arXiv preprint or GitHub repository.
Get help: Join the community on or check the .

Happy researching! 🚀

🎉 Easily create your own simple yet highly customizable blog

Fri, 27 Oct 2023 00:00:00 +0000

Welcome 👋

Table of Contents

Overview

The Hugo Blox website builder for Hugo, along with its starter templates, is designed for professional creators, educators, and teams/organizations - although it can be used to create any kind of site
The template can be modified and customised to suit your needs. It’s a good platform for anyone looking to take control of their data and online identity whilst having the convenience to start off with a no-code solution (write in Markdown and customize with YAML parameters) and having flexibility to later add even deeper personalization with HTML and CSS
You can work with all your favourite tools and apps with hundreds of plugins and integrations to speed up your workflows, interact with your readers, and much more

Get Started

Quick Start Guide

New to Hugo Blox? Follow these steps to get your site up and running in minutes!

👉
📚
💬 or
🐦 Twitter: #MadeWithHugoBlox
💡
⬆️ Updating Hugo Blox? View the and

Important

Remember to backup your site before making major updates!

Crowd-funded open-source software

To help us develop this template and software sustainably under the MIT license, we ask all individuals and businesses that use it to help support its ongoing maintenance and development via sponsorship.

As a token of appreciation for sponsoring, you can unlock awesome rewards and extra features 🦄✨

Ecosystem

: Automatically import publications from BibTeX

Inspiration

are building with this template.

Features

Enhanced Markdown Support

Hugo Blox now supports GitHub and Obsidian-style callouts! Use standard Markdown alert syntax like > [!NOTE] for better portability.

Page builder - Create anything with no-code and
Edit any type of content - Blog posts, publications, talks, slides, projects, and more!
Create content in , , or
Plugin System - Fully customizable
Display Code and Math - Code syntax highlighting and LaTeX math supported
Integrations - , , Maps, Contact Forms, and more!
Beautiful Site - Simple and refreshing one-page design
Industry-Leading SEO - Help get your website found on search engines and social media
Media Galleries - Display your images and videos with captions in a customizable gallery
Mobile Friendly - Look amazing on every screen with a mobile friendly version of your site
Multi-language - 35+ language packs including English, 中文, and Português
Multi-user - Each author gets their own profile page
Privacy Pack - Assists with GDPR
Stand Out - Bring your site to life with animation, parallax backgrounds, and scroll effects
One-Click Deployment - No servers. No databases. Only files.

Version Requirements

The new Markdown alert syntax requires Hugo v0.132.0 or later. Make sure you’re using a compatible version!

Themes

Hugo Blox and its templates come with automatic day (light) and night (dark) mode built-in. Visitors can choose their preferred mode by clicking the sun/moon icon in the header.

for your site. Themes are fully customizable.

License

Released under the license.

🧠 Sharpen your thinking with a second brain

Thu, 26 Oct 2023 00:00:00 +0000

Create a personal knowledge base and share your knowledge with your peers.

Hugo Blox web framework empowers you with one of the most flexible note-taking capabilities out there.

Create a powerful knowledge base that works on top of a local folder of plain text Markdown files.

Use it as your second brain, either publicly sharing your knowledge with your peers via your website, or via a private GitHub repository and password-protected site just for yourself.

Mindmaps

Hugo Blox supports a Markdown extension for mindmaps.

With this open format, can even edit your mindmaps in other popular tools such as Obsidian.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- netlify
- netlify-cms
- slides
```

renders as

- Hugo Modules
- Hugo Blox
- netlify
- netlify-cms
- slides

Anh here’s a more advanced mindmap with formatting, code blocks, and math:


```markmap
- Mindmaps
- Links
- [Hugo Blox Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/kit)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$
```

renders as

- Mindmaps
- Links
- [Hugo Blox Docs](https://docs.hugoblox.com/)
- [Discord Community](https://discord.gg/z8wNYzb)
- [GitHub](https://github.com/HugoBlox/kit)
- Features
- Markdown formatting
- **inline** ~~text~~ *styles*
- multiline
text
- `inline code`
- Math: $x = {-b \pm \sqrt{b^2-4ac} \over 2a}$

Highlighting

Highlight important text with mark:

<mark>Highlighted text</mark>

Callouts

Use (aka asides, hints, or alerts) to draw attention to notes, tips, and warnings.

Use the > [!NOTE] syntax to create a callout.

> [!NOTE]
> A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

renders as

Note

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

Or use the warning callout type so your readers don’t miss critical details:

Warning

A Markdown aside is useful for displaying notices, hints, or definitions to your readers.

📈 Communicate your results effectively with the best data visualizations

Wed, 25 Oct 2023 00:00:00 +0000

Hugo Blox is designed to give technical content creators a seamless experience. You can focus on the content and Hugo Blox handles the rest.

Use popular tools such as Plotly, Mermaid, and data frames.

Embed Rich Content

HuggingFace Model

google/embeddinggemma-300m

sentence-similarity

2.1178M

2.064k

HuggingFace Dataset

fka/prompts.chat

dataset

a.k.a. Awesome ChatGPT Prompts This is a Dataset Repository mirror of prompts.chat — a social platform for AI prompts. 📢 Notice This Hugging Face dataset is a mirror. For the …

27.363k

9.913k

GitHub Repository

kit

by HugoBlox • HTML

🧱 Describe your site, AI builds it, you own it as Markdown. Snap together Tailwind blocks like Lego — landing pages, blogs, portfolios, docs & more. No AI slop. Free to deploy anywhere 👇

2.951k

9.78k

academic blog documentation-tool +17

Custom embed with manual data

My Custom Resource

example.com

A great resource for learning

Custom Images

Embed beautiful images from any source with Hugo image processing (Unsplash, custom URLs, etc.):

Data Visualization Guide

example.com

Beautiful data visualization workspace

You can also add images to any platform embed:

plotly.py

by plotly • Python

The interactive graphing library for Python ✨

2.882k

743

18.855k

d3 dashboard declarative +11

Charts

Hugo Blox supports the popular format for interactive data visualizations. With Plotly, you can design almost any kind of visualization you can imagine!

Save your Plotly JSON in your page folder, for example line-chart.json, and then add the {{< chart data="line-chart" >}} shortcode where you would like the chart to appear.

Demo:

You might also find the useful.

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example flowchart:

```mermaid
graph TD
A[Hard] -->|Text| B(Round)
B --> C{Decision}
C -->|One| D[Result 1]
C -->|Two| E[Result 2]
```

renders as

graph TD A[Hard] -->|Text| B(Round) B --> C{Decision} C -->|One| D[Result 1] C -->|Two| E[Result 2]

An example sequence diagram:

```mermaid
sequenceDiagram
Alice->>John: Hello John, how are you?
loop Healthcheck
John->>John: Fight against hypochondria
end
Note right of John: Rational thoughts!
John-->>Alice: Great!
John->>Bob: How about you?
Bob-->>John: Jolly good!
```

renders as

sequenceDiagram Alice->>John: Hello John, how are you? loop Healthcheck John->>John: Fight against hypochondria end Note right of John: Rational thoughts! John-->>Alice: Great! John->>Bob: How about you? Bob-->>John: Jolly good!

An example class diagram:

```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```

renders as

classDiagram Class01 <|-- AveryLongClass : Cool Class03 *-- Class04 Class05 o-- Class06 Class07 .. Class08 Class09 --> C2 : Where am i? Class09 --* C3 Class09 --|> Class07 Class07 : equals() Class07 : Object[] elementData Class01 : size() Class01 : int chimp Class01 : int gorilla Class08 <--> C2: Cool label

An example state diagram:

```mermaid
stateDiagram
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```

renders as

stateDiagram [*] --> Still Still --> [*] Still --> Moving Moving --> Still Moving --> Crash Crash --> [*]

Data Frames

Save your spreadsheet as a CSV file in your page’s folder and then render it by adding the Table shortcode to your page:

{{< table path="results.csv" header="true" caption="Table 1: My results" >}}

renders as

Table 1: My results
customer_id	score
1	0
2	0.5
3	1

Interactive Buttons

Add engaging call-to-action buttons to your data visualization posts:

Basic Buttons

Learn Plotly

{{< button url="/" >}}Contact Us{{< /button >}}

{{< button url="https://plotly.com/python/" new_tab="true" style="secondary" >}}Learn Plotly{{< /button >}}

Styled Buttons for Data Actions

View Dashboard

Download Data

View Source Code

{{< button url="#" style="primary" size="lg" align="center" icon="chart-bar" >}}View Dashboard{{< /button >}}

{{< button url="/data/results.csv" style="outline" icon="document-arrow-down" >}}Download Data{{< /button >}}

{{< button url="https://github.com/HugoBlox" new_tab="true" style="ghost" icon="arrow-top-right-on-square" icon_position="right" >}}View Source Code{{< /button >}}

Multiple Aligned Buttons

Try Jupyter

Open in Colab

{{< button url="https://jupyter.org/" new_tab="true" style="secondary" rounded="full" align="center" >}}Try Jupyter{{< /button >}}

{{< button url="https://colab.research.google.com/" new_tab="true" style="primary" rounded="full" align="center" icon="rocket-launch" >}}Open in Colab{{< /button >}}

✅ Manage your projects

Mon, 23 Oct 2023 00:00:00 +0000

Easily manage your projects - create ideation mind maps, Gantt charts, todo lists, and more!

Ideation

Hugo Blox supports a Markdown extension for mindmaps.

Simply insert a Markdown code block labelled as markmap and optionally set the height of the mindmap as shown in the example below.

Mindmaps can be created by simply writing the items as a Markdown list within the markmap code block, indenting each item to create as many sub-levels as you need:


```markmap {height="200px"}
- Hugo Modules
- Hugo Blox
- netlify
- netlify-cms
- slides
```

renders as

- Hugo Modules
- Hugo Blox
- netlify
- netlify-cms
- slides

Diagrams

Hugo Blox supports the Mermaid Markdown extension for diagrams.

An example Gantt diagram:

```mermaid
gantt
section Section
Completed :done, des1, 2014-01-06,2014-01-08
Active :active, des2, 2014-01-07, 3d
Parallel 1 : des3, after des1, 1d
Parallel 2 : des4, after des1, 1d
Parallel 3 : des5, after des3, 1d
Parallel 4 : des6, after des4, 1d
```

renders as

gantt section Section Completed :done, des1, 2014-01-06,2014-01-08 Active :active, des2, 2014-01-07, 3d Parallel 1 : des3, after des1, 1d Parallel 2 : des4, after des1, 1d Parallel 3 : des5, after des3, 1d Parallel 4 : des6, after des4, 1d

Todo lists

You can even write your todo lists in Markdown too:

- [x] Write math example
 - [x] Write diagram example
- [ ] Do something else

renders as

Write math example
- Write diagram example
Do something else

My Perspectives | Md. Nazmussakib

Welcome to Sakib AI

Can We Benefit From AI Without Sacrificing Privacy?

Why AI systems want data

Consent is not always enough

Technical approaches

The surveillance concern

My perspective

Is AI's Environmental Cost Worth Its Benefits?

AI has a physical footprint

Training versus everyday use

AI can also support sustainability

My perspective

Will AI Agents Change How We Use Software?

From answering questions to taking action

Why reliability matters more than a demo

What this could mean for businesses

My perspective

Should Powerful AI Models Be Open Source?

The case for openness

The risks of releasing powerful capabilities

Is there a middle ground?

My perspective

Can Artificial Intelligence Ever Be Truly Fair?

Why fairness is difficult

Different meanings of fairness

What developers can do

My perspective

Who Owns AI-Generated Art, Writing, and Ideas?

A new kind of creative tool

Three different questions

The creator’s dilemma

My perspective

Should AI Make Medical Decisions?

The promise of AI in healthcare

The challenge of generalization

Who should make the final decision?

My perspective

Will AI Become More Intelligent Than Humans?

First, what does “more intelligent” mean?

Capability is not the same as understanding

Why the question matters now

My perspective

Can We Trust AI Answers When They Sound So Confident?

The problem with convincing language

Why errors happen

What responsible use looks like

My perspective

Will AI Replace Human Jobs—or Change What Work Means?

Why this question matters

Automation is not the whole story

The risk of an unequal transition

My perspective

⚡️ Turn Jupyter Notebooks into Blog Posts

Why publish notebooks?

Example: Data Science Workflow

Ship Notebook Stories in Minutes

How to add a notebook

Next steps

🎉 Easily create your own simple yet highly customizable blog

Overview

Get Started

Crowd-funded open-source software

Ecosystem

Inspiration

Features

Themes

License

🧠 Sharpen your thinking with a second brain

Mindmaps

Highlighting

Callouts

Did you find this page helpful? Consider sharing it 🙌

📈 Communicate your results effectively with the best data visualizations

Embed Rich Content

google/embeddinggemma-300m

fka/prompts.chat

kit

My Custom Resource

Custom Images