Jēkabs Porietis

Just a human being , live, without regret, and find positive all around your self

[Tech with Mak @techNmak]

Most engineers have seen this formula.

P(A|B) = P(B|A) × P(A) / P(B)

Almost none can explain what it actually does.

Here's Bayes' Theorem in plain English, and where it's hiding inside systems you use every day.

The core idea in one sentence:

Bayes' Theorem updates your belief about something after seeing new evidence.

That's it. Four terms:

Prior → what you believed before the evidence

Likelihood → how probable the evidence is, given your hypothesis

Evidence → how common the evidence is overall

Posterior → your updated belief after seeing the evidence

A concrete example:

Say 40% of all emails are spam (your prior).

You see a new email containing the word "lottery."

10% of spam emails contain "lottery." Only 1% of legitimate emails do.

Plug into Bayes:

P(spam | "lottery") = (0.10 × 0.40) / P("lottery") ≈ 87%

The word "lottery" updated your belief from 40% → 87%.

That's Bayes in action. Prior belief + new evidence = updated belief.

Where it lives in AI:

1/ Spam filters
The Naive Bayes classifier, the algorithm behind most spam filters - applies this exact calculation word by word across an entire email. Each word shifts the probability up or down. It's called "naive" because it assumes each word is independent of the others, which isn't realistic, but works remarkably well in practice.

2/ Medical diagnosis AI
A patient has symptom X. What's the probability of disease Y? Bayes updates the base rate (how common the disease is) with the likelihood of seeing that symptom in patients who have it. Same formula, different domain.

3/ Your LLM's uncertainty
Modern language models don't just predict the next token, they assign a probability to every possible token. The sampling process (temperature, top-p) is directly working with those probability distributions. Bayesian reasoning is embedded in every response your model generates.

The insight most engineers miss:

Bayes doesn't give you certainty. It gives you a rational way to update uncertainty.

That's exactly why it's foundational to AI - real-world systems are never certain. They're always working with incomplete, noisy, probabilistic information.

Every model that learns from data is, at its core, doing some version of this:

Start with a belief. See evidence. Update the belief.

That's Bayes. That's machine learning.

[Grand Slam Racing @GrandSlamRacing]

NSE6-R8BA-G8QG-L5VU-L2A9

[Hacking Articles @hackinarticles]

Pic of the Day

#infosec #cybersecurity #cybersecuritytips #pentesting #cybersecurityawareness

[Red Hat @RedHat]

Future-ready your security. Red Hat Enterprise Linux 10.2 and 9.8 bring post-quantum cryptography and AI-driven migration tools to help you scale across the hybrid cloud. Learn more about the upcoming GA: https://t.co/7RYusVUxs7 #RHSummit #RHEL

[Cyber Security News @The_Cyber_News]

⚠️Hackers Use Microsoft Teams to Steal Credentials and Manipulate MFA

Source: https://t.co/vP8XcWXkkW

The intrusion began with unsolicited external chat requests sent to employees via Microsoft Teams. Once contact was established, the threat actor initiated interactive screen-sharing sessions, leveraging direct visibility into user desktops to execute discovery commands including ipconfig /all, whoami, and net start.

Victims were then explicitly instructed to type their credentials into locally created text files named credentials.txt and cred.txt and to add attacker-controlled devices to their MFA configurations.

#cybersecuritynews

[Jēkabs Porietis @Jecis_]

@systemdesignone Check out my recent work 😊 https://t.co/DKxm6bUByU

[Jēkabs Porietis @Jecis_]

@simonhamp https://t.co/DKxm6bUByU

[Jēkabs Porietis @Jecis_]

understand Laravel-specific patterns so check it out https://t.co/bhtqkpq2GR
@taylorotwell @DailyLaravel

[Jēkabs Porietis @Jecis_]

Been working on a RAG package for Laravel that takes a different approach than the existing options on Packagist.
The problem: Most RAG packages chunk your files by character count, embed the chunks, and do a vector similarity search. That works for documents, but for code it

[Jēkabs Porietis @Jecis_]

misses the relationships that actually matter — which model belongs to which controller, what events fire from where, which services bind to what. What this does differently: Instead of treating PHP files as flat text, it uses nikic/php-parser to build 12 AST extractors that

[Defense of Ukraine @DefenceU]

We’ve launched the first UNITE — BRAVE NATO competition, connecting Ukrainian and NATO companies to build joint battlefield solutions.
Priority areas:
FPV defense, anti-Shahed, SIGINT, autonomous guidance, high-altitude platforms.
€10M total fund.
Visit https://t.co/UQ65rMrzEE

[Hacking Articles @hackinarticles]

Mimikatz Cheat Sheet for Pentesters

🔥 Telegram: https://t.co/upuP8k8ckB
✴ Twitter: https://t.co/Za7rYILz6E

Mimikatz is a well-known post-exploitation tool used to extract plaintext passwords, NTLM hashes, Kerberos tickets, and other credentials from Windows systems. It is widely used in Active Directory attacks, credential dumping, and privilege escalation. ()

⚡ Useful Mimikatz Commands

🔐 privilege::debug
🔑 token::elevate
🧠 sekurlsa::logonpasswords
📦 sekurlsa::wdigest
📜 lsadump::sam
👤 lsadump::lsa
🔄 lsadump::dcsync
🎟️ kerberos::list
👑 kerberos::golden
📂 dpapi::cred

🧠 Mindmap:
https://t.co/h4GQXJm9BK

#CyberSecurity #ActiveDirectory #Mimikatz #Pentesting #RedTeam #CredentialDumping #InfoSec

[Nikki Siapno @NikkiSiapno]

How CI/CD pipelines work
(explained in 2 mins or less):

A CI/CD pipeline is an automated workflow that facilitates continuous integration (CI) and continuous delivery or deployment (CD) by managing code building, testing, and release processes.

It integrates the various stages of the software development lifecycle (SDLC) into a seamless, repeatable process.

These stages include source code management, automated testing, artifact creation, and deployment orchestration.

Continuous ‘delivery’ and ‘deployment’ are sometimes used synonymously.

But there is a clear and important distinction between the two.

Delivery is about ensuring the software can be released at any time.

It requires manual intervention to deploy to production.

Deployment, on the other hand, does the release through automated workflows.

Learn more here: https://t.co/pPPVI1DEfC

What else would you add?

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[sysxplore @sysxplore]

Forward Proxy v.s Reverse Proxy

[Jeremy KD8TUO 🇺🇸⏦⎓⎎⎍⏚ @ElectronicsbyJH]

This looks ridiculous, but I love it!

[OccupytheWeb @three_cube]

An IMSI catcher can be used to track the location of specific people. It can be used for surveillance and cyberwar.

[Charly Wargnier @DataChaz]

Wow, such a great open-source drop!

Put Claude on steroids with 120+ scientific skills spanning maths, biology, chemistry, medicine, engineering, & more 🤯

If you’ve ever wanted Claude to act like a research assistant, this gets very close.

Free and open source.

Repo in 🧵↓

[OccupytheWeb @three_cube]

The President of the US is a pedophile! What are we going to do?

[Nikki Siapno @NikkiSiapno]

API Gateway vs Load Balancer.