Category: Computing & AI

The Future is Now: How Mojo🔥 is Outpacing Python at 90000X Speed

The Future is Now: How Mojo🔥 is Outpacing Python at 90000X Speed

Calling all AI wizards and machine learning mavericks! Get ready to be blown away by Mojo, a revolutionary new programming language designed specifically to conquer the ever-evolving realm of artificial intelligence.

Just last year, Modular Inc. unveiled Mojo, and it’s already making waves. But here’s the real kicker: Mojo isn’t just another language; it’s a “hypersonic” language on a mission to leave the competition in the dust. We’re talking about a staggering 90,000 times faster than the ever-popular Python! I wanted to share a minor disclaimer there, this is not the “Official” benchmark by The Computer Language Benchmark Game or anything institutional, it is all Modular’s internal benchmarking!

That’s right, say goodbye to hours of agonizing wait times while your AI models train. With Mojo, you’ll be churning out cutting-edge algorithms at lightning speed. Imagine the possibilities! Faster development cycles, quicker iterations, and the ability to tackle even more complex AI projects – the future is wide open.

Mind-Blowing Speed and an Engaged Community

But speed isn’t the only thing Mojo boasts about. Launched in August 2023, this open-source language (open-sourced just last month, on March 29th, 2024!) has already amassed a loyal following, surpassing a whopping 17,000 stars on its GitHub repository. That’s a serious testament to the developer community’s excitement about Mojo’s potential.

The momentum continues to build. As of today, there are over 2,500 active projects on GitHub utilizing Mojo, showcasing its rapid adoption within the AI development space.

Unveiling the Magic Behind Mojo

So, what’s the secret sauce behind Mojo’s mind-blowing performance? The folks at Modular Inc. are keeping some of the details close to their chest, but we do know that Mojo is built from the ground up for AI applications. This means it leverages advancements in compiler technology and hardware acceleration, specifically targeting the types of tasks that AI developers face every day (SIMD, vectorisation, and parallelisation)

Here’s a sneak peek at some of the advantages:

  • Multi-Paradigm Muscle: Mojo is a multi-paradigm language, offering the flexibility of imperative, functional, and generic programming styles. This allows developers to choose the most efficient approach for each specific task within their AI project.
  • Seamless Python Integration: Don’t worry about throwing away your existing Python code. Mojo plays nicely with the vast Python ecosystem, allowing you to leverage existing libraries and seamlessly integrate them into your Mojo projects.
  • Expressive Syntax: If you’re familiar with Python, you’ll feel right at home with Mojo’s syntax. It builds upon the familiar Python base, making the learning curve much smoother for experienced developers.

The Future of AI Development is Here

If you’re looking to push the boundaries of AI and machine learning, then Mojo is a game-changer you can’t afford to miss. With several versions already released, including the most recent update in March 2024 (version 0.7.2), the language is constantly evolving and incorporating valuable community feedback.

Dive into the open-source community, explore the comprehensive documentation, and unleash the power of Mojo on your next groundbreaking project. The future of AI is here, and it’s moving at breakneck speed with Mojo leading the charge! Go ahead and get it here

One Trick Pony

Just be warned that Mojo is not general purpose in nature and Python will win hands down on generic computational tasks due to,

  • Libraries –
    • Python boasts an extensive ecosystem of libraries and frameworks, such as TensorFlow, NumPy, Pandas, and PyTorch, with over 137,000 libraries.
    • Mojo has a developing library ecosystem but significantly lags behind Python in this regard.
  • Compatibility and Integration –
    • Python is known for its compatibility and integration with various programming languages and third-party packages, making it flexible for projects with complex dependencies.
    • Mojo, while generally interoperable with Python, falls short in terms of integration and compatibility with other tools and languages.
  • Popularity (Availability of devs)
    • Python is a highly popular programming language with a large community of developers and data scientists.
    • Mojo, being introduced in 2023, has a much smaller community and popularity compared to Python.
    • It is just now open sourced, has limited documentation, and is targeted at developers with system programming experience.
    • According to the TIOBE Programming Community Index, a programming language popularity index, Python consistently holds the top position.
    • In contrast, Mojo is currently ranked 174th and has a long way to go.
Mastering Cyber Defense: The Impact Of AI & ML On Security Strategies

Mastering Cyber Defense: The Impact Of AI & ML On Security Strategies

The cybersecurity landscape is a relentless battlefield. Attackers are constantly innovating, churning out new threats at an alarming rate. Traditional security solutions are struggling to keep pace. But fear not, weary defenders! Artificial Intelligence (AI) and Machine Learning (ML) are emerging as powerful weapons in our arsenal, offering the potential to revolutionize cybersecurity.

The Numbers Don’t Lie: Why AI/ML Matters

  • Security Incidents on the Rise: According to the IBM Security X-Force Threat Intelligence Index 2023 https://www.ibm.com/reports/threat-intelligence, the average organization experienced 270 data breaches in 2022, a staggering 13% increase from the previous year.
  • Alert Fatigue is Real: Security analysts are bombarded with a constant stream of alerts, often leading to “alert fatigue” and missed critical threats. A study by the Ponemon Institute found that it takes an average of 280 days to identify and contain a security breach https://www.ponemon.org/.

AI/ML to the Rescue: Current Applications

AI and ML are already making a significant impact on cybersecurity:

  • Reverse Engineering Malware with Speed: AI can disassemble and analyze malicious code at lightning speed, uncovering its functionalities and vulnerabilities much faster than traditional methods. This allows defenders to understand attacker tactics and develop effective countermeasures before widespread damage occurs.
  • Prioritizing the Vulnerability Avalanche: Legacy vulnerability scanners often generate overwhelming lists of potential weaknesses. AI can prioritize these vulnerabilities based on exploitability and potential impact, allowing security teams to focus their efforts on the most critical issues first. A study by McAfee found that organizations can reduce the time to patch critical vulnerabilities by up to 70% using AI https://www.mcafee.com/blogs/internet-security/the-what-why-and-how-of-ai-and-threat-detection/.
  • Security SIEMs Get Smarter: Security Information and Event Management (SIEM) systems ingest vast amounts of security data. AI can analyze this data in real-time, correlating events and identifying potential threats with an accuracy far exceeding human capabilities. This significantly improves threat detection accuracy and reduces the time attackers have to operate undetected within a network.

The Future of AI/ML in Cybersecurity: A Glimpse Beyond

As AI and ML technologies mature, we can expect even more transformative applications:

  • Context is King: AI can be trained to understand the context of security events, considering user behaviour, network activity, and system configurations. This will enable highly sophisticated threat detection and prevention capabilities, automatically adapting to new situations and attacker tactics.
  • Automating Security Tasks: Imagine a future where AI automates not just vulnerability scanning, but also incident response, patch management, and even threat hunting. This would free up security teams to focus on more strategic initiatives and significantly improve overall security posture.

Challenges and Considerations: No Silver Bullet

While AI/ML offers immense potential, it’s important to acknowledge the challenges:

  • Explainability and Transparency: AI models can sometimes make decisions that are difficult for humans to understand. This lack of explainability can make it challenging to trust and audit AI-powered security systems. Security teams need to ensure they understand how AI systems reach conclusions and that these conclusions are aligned with overall security goals.
  • Data Quality and Bias: The effectiveness of AI/ML models heavily relies on the quality of the data they are trained on. Biased data can lead to biased models that might miss certain threats or flag legitimate activity as malicious. Security teams need to ensure their training data is diverse and unbiased to avoid perpetuating security blind spots.

The Takeaway: Embrace the Future

Security practitioners and engineers are at the forefront of adopting and shaping AI/ML solutions. By understanding the current applications, future potential, and the associated challenges, you can ensure that AI becomes a powerful ally in your cybersecurity arsenal. Embrace AI/ML, and together we can build a more secure future!

#AI #MachineLearning #Cybersecurity #ThreatDetection #SecurityAutomation

P.S. Check out these resources to learn more:

NIST Artificial Intelligence Risk Management Framework (AI RMF 1.0): https://www.nist.gov/itl/ai-risk-management-framework) by National Institute of Standards and Technology (NIST)

Understanding The Implications Of The Data Breaches At Microsoft.

Understanding The Implications Of The Data Breaches At Microsoft.

Note: I started this article last weekend to try and explain the attack path  “Midnight Blizzard” used and what Azure admins should do to protect themselves from a similar attack. Unfortunately, I couldn't complete/publish it in time and now there is another breach at Microsoft. (🤦🏿) Now, I had to completely redraft it and change the focus to a summary of data breaches at Microsoft and a walkthrough on the current breach. I will publish the Midnight Blizzard defence later this week.
Microsoft Data Breach

The Timeline of the Breaches

  • 20th-25th September 2023: 60k State Department Emails Stolen in Microsoft Breach
  • 12th-25th January 2024: Microsoft breached by “Nation-State Actors”
  • 11th-14th February 2024: State-backed APTs are weaponising OpenAI models 
  • 16th-19th February 2024: Microsoft admits to security issues with Azure and Exchange servers.
Date/MonthBreach TypeAffected Service/AreaSource
February 2024Zero-day vulnerabilities in Exchange serversExchange serversMicrosoft Security Response Center blog
January 2024Nation State-sponsored attack (Russia)Email accountsMicrosoft Security Response Center blog
February 2024State-backed APTs are weaponising OpenAI modelsNot directly impacting MS services
July 2023Chinese Hackers Breach U.S. Agencies Via Microsoft CloudAzureThe New York Times, Microsoft Security Response Center blog
October 2022BlueBleed Data Leak, 0.5 Million user data leakedUser Data
December 2021Lapsus$ intrusionSource code (Bing, Cortana)The Guardian, Reuters
August 2021Hafnium attacks Exchange serversExchange serversMicrosoft Security Response Center blog
March 2021SolarWinds supply chain attackVarious Microsoft products (indirectly affected)The New York Times, Reuters
January 2020Misconfigured customer support databaseCustomer data (names, email addresses)ZDNet
This is a high-level summary of breaches and successful hacks that got reported in the public domain and picked up by tier 1 publications. There are at least a dozen more in the period, some are of negligible impact, and others are less probable

Introduction:

Today, The digital landscape is a battlefield, and even tech giants like Microsoft aren’t immune to cyberattacks. Understanding recent breaches/incidents and their root causes, and effective defence strategies is crucial for Infosec/IT and DevSecOp teams navigating this ever-evolving threat landscape. This blog post dives into the security incidents affecting Microsoft, analyzes potential attack paths, and equips you with actionable defence plans to fortify your infrastructure/network.

Selected Breaches:

  • January 2024: State actors, purported to be affiliated with Russia leveraged password spraying and compromised email accounts, including those of senior leadership. This highlights the vulnerability of weak passwords and the critical need for multi-factor authentication (MFA).
  • January 2024: Zero-day vulnerabilities in Exchange servers allowed attackers to escalate privileges. This emphasizes the importance of regular patching and prompt updates to address vulnerabilities before they’re exploited.
  • December 2021: Lapsus$ group gained access to source code due to misconfigured access controls. This underscores the importance of least-privilege access and regularly reviewed security configurations.
  • Other incidents: Supply chain attacks (SolarWinds, March 2021) and data leaks (customer database, January 2020) demonstrate the diverse threats organizations face.

Attack Paths:

Understanding attacker motivations and methods is key to building effective defences. Here are common attack paths:

  • Social Engineering: Phishing emails and deceptive tactics trick users into revealing sensitive information or clicking malicious links.
  • Software Vulnerabilities: Unpatched software with known vulnerabilities offers attackers an easy entry point.
  • Weak Passwords: Simple passwords are easily cracked, granting access to accounts and systems.
  • Misconfigured Access Controls: Overly permissive access rules give attackers more power than necessary to escalate privileges and cause damage.
  • Supply Chain Attacks: Compromising a vendor or partner can grant attackers access to multiple organizations within the supply chain.

Defence Plans:

Building a robust defense requires a multi-layered approach:

  • Patch Management: Prioritize timely patching of vulnerabilities across all systems and software.
  • Strong Passwords & MFA: Implement strong password policies and enforce MFA for all accounts.
  • Access Control Management: Implement least privilege access and regularly review configurations.
  • Security Awareness Training: Educate employees on phishing, social engineering, and secure password practices.
  • Threat Detection & Response: Deploy security tools to monitor systems for suspicious activity and respond promptly to incidents.
  • Incident Response Planning: Develop and test a plan to mitigate damage, contain breaches, and recover quickly.
  • Penetration Testing: Regularly test your defenses by simulating real-world attacks to identify and fix vulnerabilities before attackers do.
  • Network Segmentation: Segment your network to limit the potential impact of a breach by restricting access to critical systems.
  • Data Backups & Disaster Recovery: Regularly back up data and have a plan to restore it in case of an attack or outage.
  • Stay Informed: Keep up-to-date on the latest security threats and vulnerabilities by subscribing to security advisories and attending industry conferences.

Conclusion:

Cybersecurity is an ongoing battle, but by understanding the tactics employed by attackers and implementing these defence strategies, IT/DevOps admins can significantly reduce the risk of breaches and protect their networks and data. Remember, vigilance and continuous improvement are key to staying ahead of the curve in the ever-evolving cybersecurity landscape.

Disclaimer: This blog post is for informational purposes only and should not be considered professional security advice. Please consult with a qualified security professional for guidance specific to your organization or mail me for an obligation free consultation call.

References and Further Reading:

Achieve Peak Performance: AI Tools for Developers to Unlock Their Potential

Achieve Peak Performance: AI Tools for Developers to Unlock Their Potential

You were scrolling through Twitter or your favourite SubReditt on the latest tech trend and a sudden feeling of FOMO creeps in. You’re not alone.

While the notion of a “10x developer” has traditionally been considered aspirational, the emergence of AI-powered tools is levelling the playing field, empowering developers to achieve remarkable productivity gains. While there might be 1000s of possible “AI tools”, I’ll restrict to tools which could yield a direct productivity boost to a developer’s day-to-day work as well as the outcome.

1. AI Pair-Developer / Code Assistants

Sourcegraph Cody & Github Copilot — Read, write and understand code

If you have used GitHub Copilot. Think of a Cody as a Turbocharger for Copilot. If you have not used Copilot, you should first try it. Either of these can understand your entire codebase, code graphs, and documentation and help you write efficient code, write unit tests, and document the codebase for you.

While the claim of a 10x speed increase is not substantiated, it shows clear intent to improve productivity drastically. However, it’s in beta, and the tool acknowledges that it’s not always correct, though they’re making rapid improvements. Yes, GitHub Copilot X is there — but then, your organisation needs to be on the Enterprise plan or you might have to add an additional $10-20 per user per month, and Cody is already here.

2. AI Code reviews – Offload the often mundane task of code reviews

While CodeRabbit and DeepCode (now acquired by Snyk) are some of the trailblazers in this space, I have not had the opportunity to work with either of them for any stretch of time. If you know about their relative strengths or benefits, please add a comment, and I will incorporate it.
The tool I use most regularly is called Robin-AI-Reviewer, from the good folks at Integral Healthcare (funded by Haystack). My reasoning is two-fold, It is open-source and if it is good enough for HIPPA-compliant app development and certification assessment, it’s a good starting point.

3. AI Test writing – Delegate the task of writing tests to AI- CodiumAI

CodiumAI serves as an AI test-writing assistant. It analyses your code, docstrings, and comments to suggest tests intelligently. CodiumAI addresses a critical aspect of software development that often consumes valuable time: testing. While numerous tools prioritize code writing and optimization, ensuring code functionality is equally vital. CodiumAI seamlessly fills this gap, and its intelligent test generation capability can substantially enhance development efficiency and maintain superior code quality.

4. AI Documentation Assistant — Get AI to write docs for you

This is a no-brainer, who loves writing code walkthroughs and docs? No? Didn’t think so! Mintlify serves as your team’s technical writer. It reads and interprets your code, turning it into a clear, readable document. By all accounts, it is a definite must.

Disclaimer: I have not personally used this and have been mostly able to get this done with Cody, itself. And then, I am no longer doing the primary documentation as my main responsibility.

5. AI Comment Assistant – Readable AI — Never write comments again

Readable AI automates the process of generating comments for your source code. It’s compatible with several popular IDEs, like VSCode, Visual Studio, IntelliJ, and PyCharm, and it can read most languages.

6. AI Tech Debt Assistant – Grit.io

Grit.io is an automated technical debt management tool. Its prime function is auto-generating pull requests that manage code migrations and dependency upgrades. Grit is in beta and available for free till beta moves to RC1. But it actually has about 50+ pattern libraries and it is growing.

I absolutely love it and Grit alleviates a significant portion of the manual work involved in managing migrations and dependency upgrades. They say it 10x’s the refactoring and migration process. I’d say at 33% of what they say, It will still be 300% of what productivity increases. And it is a considerable gain. If you’re an Engineering Leader and you have a “Budget” for 1 tool only, It should be this!

7. AI Pull Request Assistant – An “AI” powered DIff tool

What The Diff AI is an AI-powered code review tool. It writes pull request descriptions, scrutinises pull requests, identifies potential risks, and more. What The Diff claims to be able to significantly speed up development timelines and improve code quality in the long run. It could take a great deal of pain out of the process.

Disclaimer: I have not personally used this

8. AI-driven residential Wizard – Adrenaline AI — Explain it to me

Adrenaline AI helps you understand your codebase. The tool leverages static analysis, vector search, and advanced language models to clarify how features function and explain anything about it to you. The thing I like about this tool very much is, it can be leveraged to automate the “How tos” for your software engineering teams!

9. AI collaboration companion for software projects

Stepsize AI by Stepsize is an AI companion for software projects. It seamlessly integrates with tools like Slack, Jira, and GitHub, providing insightful overviews of your activities and offering strategic suggestions.

The tool uses a complex AI agent architecture, providing long-term “memory” and a deep understanding of the context of your projects.

10. AI-Driven Dev Metrics Collection – Hivel.ai

While strictly speaking, not an AI-driven “assistant” to an average developer, I feel it is nevertheless a good tool for the Engineering org and Engineering leaders to keep track and make course corrections. It provides a Cockpit/Dashboard of all the metrics that matter.

Hivel is built by an awesome team of devs and led by Sudheer

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