• Concept over Tokens: LCMs move away from traditional token-by-token processing and instead focus on understanding and generating language at the conceptual level.
• Better Long-Context Understanding: By reasoning with broader ideas, LCMs handle longer text inputs more effectively than current models.
• Improved Reasoning Abilities: Their structure supports more advanced, layered thinking—helpful for tasks requiring logical steps and deep understanding.
• Closer to Human-Like AI: Meta’s research brings us closer to AI systems that think more like humans, making interactions smoother, smarter, and more intuitive.

The Role of Tokenization in LLMs

The Problem

• Splits meaningful expressions into fragmented parts
• Operates on arbitrary units, ignoring semantic cohesion
• Struggles with long-context reasoning and hierarchical planning

The Solution: Concepts

• Better handling of long contexts
• More abstract reasoning
• Language and modality independence

What Are Large Concept Models?

• Concept Encoder (SONAR): Input (text or speech) is broken into sentences. Each sentence is turned into a “concept embedding” using a tool called SONAR (which works in 200 languages and supports text + speech).
  
• Large Concept Model (LCM): A Transformer operating entirely in concept space. It reasons over the concept sequence to generate output concepts. The concept embeddings are processed by the LCM to perform a task, like summarizing or translating. The result is a new set of concept embeddings.
  
• Concept Decoder (SONAR): Translates concept embeddings back into natural language (or speech) output.
  

Limitation

Why Diffusion?

Two Architectures

One-Tower Diffusion LCM

Two-Tower Diffusion LCM

• Tower 1: Contextualizer: This is a Transformer that reads all the previous sentence embeddings (x<n) and encodes them using causal attention—meaning it reads them one by one in order, without peeking ahead. This creates a meaningful summary of the past context.
  
• Tower 2: Denoiser: This second Transformer takes in a noisy version of the next sentence embedding and tries to recover the clean version (x₀ₙ). It does this gradually through multiple steps of denoising. To help guide this process, it uses cross-attention to look at the context produced by the first tower.
  
• Adaptive LayerNorm (AdaLN): The denoiser adapts its internal computations based on how noisy the input is at each step. It does this using a small network that learns how much to scale, shift, and blend each layer’s output, depending on the timestep of the diffusion process. This helps the model adjust its behavior at different noise levels.
  
• Training setup: To help the model learn, during training:
  
  • The context is slightly shifted so that each prediction only sees past sentences.
  • A zero-vector is used as a placeholder at the start of the sequence to predict the first sentence.
  • Some context rows are randomly dropped (with a probability pcfg) so the model also learns how to denoise without context—this supports classifier-free guidance during inference.

• Base-LCM – the naive autoregressive version predicting the next concept deterministically.
• Diffusion-Based LCMs – including both One-Tower and Two-Tower architectures that generate concepts stochastically.
• Quant LCMs – a variant not covered in detail in the paper, but included for comparison.
• smaLLaMA – an instruction-tuned LLM used as an additional performance baseline.

• T5-3B – an encoder-decoder Transformer fine-tuned for summarization.
• Gemma-7B, LLaMA 3.1-8B, and Mistral-7B-v0.3 – decoder-only, instruction-finetuned LLMs.

Metrics Used for Evaluation

• ROUGE-L: Measures the longest common subsequence between generated and reference summaries.
• Coherence: A classifier-based metric evaluating logical flow and topic consistency.
• OVL-3 (Overlap Score): Quantifies how extractive or abstractive a summary is.
• Repetition Rate: Measures the frequency of repeated words/phrases in the generated output.
• CoLA (Corpus of Linguistic Acceptability): Assesses grammatical acceptability based on linguistic norms.
• SH-4 (Source Attribution): Evaluates how well the generated summary attributes content to the original source.
• SH-5 (Semantic Coverage): Measures the completeness of semantic content transfer from source to summary.

Option 1: Using uv (Recommended)

# Set up environment and install CPU dependencies
uv sync --extra cpu --extra eval --extra data

# For GPU support (example: Torch 2.5.1 + CUDA 12.1)
uv pip install torch==2.5.1 --extra-index-url https://download.pytorch.org/whl/cu121 --upgrade
uv pip install fairseq2==v0.3.0rc1 --pre --extra-index-url https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cu121 --upgrade

Option 2: Using pip

# Install pip dependencies
pip install --upgrade pip
pip install fairseq2==v0.3.0rc1 --pre --extra-index-url https://fair.pkg.atmeta.com/fairseq2/whl/rc/pt2.5.1/cpu
pip install -e ".[data,eval]"

Preparing the Data

# Prepare Wikipedia data with SONAR and SaT
uv run --extra data scripts/prepare_wikipedia.py /output/dir/for/the/data

Fitting a Normalizer

python scripts/fit_embedding_normalizer.py \
  --ds dataset1:4 dataset2:1 dataset3:10 \
  --save_path "path/to/new/normalizer.pt" \
  --max_nb_samples 1000000

Pre-training LCMs

python -m lcm.train +pretrain=mse \
  ++trainer.output_dir="checkpoints/mse_lcm" \
  ++trainer.experiment_name=training_mse_lcm

CUDA_VISIBLE_DEVICES=0,1 torchrun --standalone --nnodes=1 --nproc-per-node=2 \
  -m lcm.train launcher=standalone \
  +pretrain=mse \
  ++trainer.data_loading_config.max_tokens=1000 \
  ++trainer.output_dir="checkpoints/mse_lcm" \
  +trainer.use_submitit=false

Finetuning the Two-Tower Diffusion LCM

CUDA_VISIBLE_DEVICES=0,1 torchrun --standalone --nnodes=1 --nproc-per-node=2 \
  -m lcm.train launcher=standalone \
  +finetune=two_tower \
  ++trainer.output_dir="checkpoints/finetune_two_tower_lcm" \
  ++trainer.data_loading_config.max_tokens=1000 \
  +trainer.use_submitit=false \
  ++trainer.model_config_or_name=my_pretrained_two_tower

Evaluating LCMs

python -m nltk.downloader punkt_tab

torchrun --standalone --nnodes=1 --nproc-per-node=1 -m lcm.evaluation \
  --predictor two_tower_diffusion_lcm \
  --model_card ./checkpoints/finetune_two_tower_lcm/checkpoints/step_1000/model_card.yaml \
  --data_loading.max_samples 100 \
  --data_loading.batch_size 4 \
  --generator_batch_size 4 \
  --dump_dir evaluation_outputs/two_tower \
  --inference_timesteps 40 \
  --initial_noise_scale 0.6 \
  --guidance_scale 3 \
  --guidance_rescale 0.7 \
  --tasks finetuning_data_lcm.validation \
  --task_args '{"max_gen_len": 10, "eos_config": {"text": "End of text."}}'

Key Findings

Conclusion

• Large Concept Models (LCMs) by Meta: The Era of AI After LLMs?
• Official GitHub repo
• Original paper
• Meta Large Concept Models (LCM): End of LLMs?

1. mail
2. mailx
3. mutt
4. mpack
5. sendmail

sudo apt install mailutils -y

yum install mailx

Testing Mail command

mail –s "Test Email" email_address

mail –s "Test Email" james@example.com

Output

echo "sample message" | mail -s "sample mail subject" email_address

echo "Hello world" | mail -s "Test" james@example.com

Output

mail -s "subject" -A message.txt email_address

mail -s "Important Notice" -A message.txt james@example.com

Output

mail –s "test header" email_address email_address2

sudo apt install mailx

yum install mailx

Testing Mailx command

echo "message body" | mail -s "subject" email_address

echo "Make the most out of Linux!" | mail -s "Welcome to Linux" james@example.com

sudo apt install mutt

sudo yum install mutt

Testing Mutt command

mutt -s "Test Email" email_address < /dev/null 

mutt -s "Greetings" james@jaykiarie.com < /dev/null 

Output

echo "Message body" | mutt -a "/path/to/file.to.attach" -s "subject of message" -- email_address

echo "Hey guys! How's it going ?" | mutt -a report.doc -s "Notice !" -- james@jaykiarie.com

Output

sudo apt install mpack 

sudo yum install mpack

Testing mpack command

mpack -s "Subject here" -a file email_address

mpack -s "Sales Report 2019" -a report.doc james@jaykiarie.com

Output

sudo apt install sendmail

sudo yum install sendmail

Testing sendmail command

sendmail email_address < file

Hello there !

sendmail james@example.com < report.doc 

Output

Subject: Sendmail test email
Hello there!

• Install msmtp using one of the following commands:
  
  • Debian/Ubuntu: sudo apt install msmtp msmtp-mta
  • RedHat/CentOS: sudo yum install msmtp
• Next, create or edit the ~/.msmtprc file with your Gmail credentials:
  

  account gmail
  host smtp.gmail.com
  port 587
  from your_gmail_address@gmail.com
  auth on
  user your_gmail_address@gmail.com
  password your_gmail_password
  tls on
  tls_starttls on
  tls_trust_file /etc/ssl/certs/ca-certificates.crt  # Path may vary
  logfile ~/.msmtp.log
  
  account default : gmail
  

  Replace your_gmail_address@gmail.com with your Gmail account and your_gmail_password with your App password. You can obtain this by going into the Security page of your Google Account.
  Also, ensure the tls_trust_file path is correct for your system.
  
• Set strict file permissions so only you can read and write to it. This is vital as it contains your password.
  

  chmod 600 ~/.msmtprc
  
  

• You can now use msmtp as a drop-in replacement for sendmail or pipe to it:
  

  echo "This is a test email sent via msmtp." | mail -s "msmtp Test" -a attachment.txt recipient@example.com -r your_gmail_address@gmail.com
  
  

  Or:
  

  msmtp recipient@example.com <<EOF
  From: your_gmail_address@gmail.com
  Subject: Test Email with msmtp
  
  This is the body of the email.
  EOF
  
  

1. You can write a simple Bash script to send an email notification based on certain condition. This script sends an email whenever the disk usage is over 90%:
   

   #!/bin/bash
   
   # Check disk space
   disk_usage=$(df -h / | awk 'NR==2 {print $5}')
   
   # Send email if disk usage is above 90%
   if [[ ${disk_usage%\%} -gt 90 ]]; then
     echo "Warning: Disk usage on / is above 90% ($disk_usage)" | mail -s "Disk Space Alert" admin@example.com
   fi
   

   This script can be scheduled to run at regular intervals to check whether the disk usage exceeds the limit.
   
2. You can also use a Bash script to send a file as an attachment:
   

   #!/bin/bash
   
   #  Create a log file
   echo "This is a sample log message." > mylog.txt
   
   #  Send the log file as an attachment
   mail -s "Log File" -a mylog.txt admin@example.com < /dev/null
   

   The script generates a file named mylog.txt and attaches it to the email. When using mail with attachments in scripts, consider using mutt for more reliable attachment handling, especially for complex file types.
   

1. What is the easiest way to send an email from the Linux terminal?

echo "This is the body of the email." | mail -s "Email Subject" recipient@example.com

2. Can I send emails from a script?

#!/bin/bash

# Define email variables
RECIPIENT="admin@example.com"
SUBJECT="System Backup Report"
BODY="The system backup completed successfully on $(date)."

# Send the email
echo "$BODY" | mail -s "$SUBJECT" "$RECIPIENT"

echo "Report email sent to $RECIPIENT."

3. How do I send an email with an attachment in Linux?

sudo apt-get install mutt

echo "Please find the report attached." | mutt -s "Report Attached" -a /path/to/file.zip -- recipient@example.com

• How To Install and Configure Postfix on Ubuntu 22.04
• JavaMail Example - Send Mail in Java using SMTP

• SSL verification is essential for secure, trusted connections between clients and servers. It involves a multi-step process, including domain validation, certificate authority validation, and certificate chain verification.
• Let’s Encrypt and Certbot provide free, automated SSL certificates. These tools simplify the SSL setup and renewal process.
• Always configure strong SSL/TLS settings and security headers to enhance security.
• Test your SSL setup with browsers, command-line tools, and online services to ensure it’s working as expected.
• Automate certificate renewal and monitor expiration to prevent service disruptions.
• Troubleshoot common issues like chain errors, mismatches, and firewall blocks to maintain a secure environment.
• Follow best practices for ongoing security and compliance to protect your users and data.

• A server running Ubuntu or any other Linux distribution.
• Root or sudo privileges on your server.
• Basic familiarity with the command line.
• A domain name pointed to your server (for testing SSL configurations).
• Basic understanding of SSL/TLS concepts

1. Server Presents Certificate: When a client initiates a secure (HTTPS) connection, the server responds by sending its SSL certificate to the client.
   
2. Certificate Authority (CA) Validation: The client checks whether the certificate was issued by a trusted Certificate Authority (CA) included in its trust store. If the CA is not trusted, the verification fails.
   
3. Domain Name Matching: The client verifies that the certificate’s Common Name (CN) or Subject Alternative Name (SAN) matches the domain it is trying to reach. This ensures the certificate is intended for the requested website.
   
4. Certificate Chain Verification: The client examines the entire certificate chain, making sure each certificate in the chain (from the server’s certificate up to the root CA) is valid and properly signed. The chain must be complete and unbroken.
   
5. Expiration and Revocation Checks: The client checks that the certificate is within its valid date range (not expired or not yet valid) and, if possible, confirms that it has not been revoked by the CA.
   

Certificate Authority (CA) Validation

Domain Validation

Certificate Chain Validation

Expiration Check

Revocation Status

• SSL Encryption ensures that the data transmitted between a client (like a browser) and a server is encrypted, making it unreadable to anyone who might intercept the traffic. This protects sensitive information such as login credentials, payment details, and personal data from eavesdroppers.
• SSL Verification is the process by which the client checks the authenticity and validity of the server’s SSL certificate. This step ensures that the client is communicating with the legitimate server and not an imposter (preventing “man-in-the-middle” attacks).

Key Differences: SSL Verification vs. SSL Encryption

What Is a Certificate Chain?

• The server (leaf) certificate: This is the certificate presented by the server you are connecting to. It is issued specifically for the domain you’re visiting (e.g., www.example.com).
• Intermediate certificate(s): These are certificates issued by trusted Certificate Authorities (CAs) that act as intermediaries between the server certificate and the root certificate. Intermediates help distribute trust and security, allowing root CAs to remain offline and protected. There may be one or more intermediate certificates in a chain.
• The root certificate: This is the anchor of the chain, issued by a root CA. Root certificates are self-signed and are included in the trust stores of operating systems, browsers, and devices. Trust in the root CA is what ultimately allows the client to trust the entire chain.

1. DNS Lookup: Resolving the Domain Name

2. Establishing a TCP Connection and Initiating the TLS Handshake

3. Certificate Chain and Trust Validation

• If the chain is complete and valid: The browser proceeds to the next step.
• If the chain is incomplete, broken, or contains an untrusted certificate: The browser will halt the process and display a security warning.

4. Hostname Verification

• If the hostname matches: The process continues.
• If there is a mismatch: The browser warns the user that the certificate does not match the site, which could indicate a security risk.

5. Certificate Revocation Status Check (OCSP/CRL)

• OCSP (Online Certificate Status Protocol): The browser queries the certificate authority’s OCSP server to check the certificate’s status in real time.
• CRL (Certificate Revocation List): The browser downloads a list of revoked certificates from the CA and checks if the server’s certificate is on the list.

6. Secure Page Load or Security Warning

• If all checks pass: The browser completes the TLS handshake, establishes an encrypted connection, and loads the web page securely. You’ll typically see a padlock icon in the address bar.
• If any check fails: The browser interrupts the connection and displays a prominent warning, such as “Your connection is not private” or “This site’s security certificate is not trusted.” Users are strongly discouraged from proceeding.

7. Inspecting Certificate Details

• openssl s_client: This tool can be used to connect to a server and verify its SSL/TLS certificate.
• curl --verbose: This tool can be used to download a website and verify its SSL/TLS certificate.
• openssl verify: This tool can be used to verify the server certificate against the provided certificate chain.

1. openssl s_client

• -connect example.com:443: This parameter specifies the server to connect to, including its domain name and port number (443 is the default port for HTTPS). In this case, we’re connecting to example.com on port 443.
• -servername example.com: This parameter specifies the server name indication (SNI) extension to use during the TLS handshake. SNI allows a server to present multiple certificates on the same IP address and port number, based on the domain name requested by the client. In this case, we’re indicating that we want to connect to example.com.
• -showcerts: This parameter tells openssl to display the entire certificate chain sent by the server, including the server certificate and any intermediate certificates.

Output
CONNECTED(00000003)
depth=2 C = US, O = DigiCert Inc, OU = www.digicert.com, CN = DigiCert SHA2 Secure Server CA
verify return:1
depth=1 C = US, O = DigiCert Inc, OU = www.digicert.com, CN = RapidSSL TLS DV RSA CA G1
verify return:1
depth=0 C = US, ST = California, L = San Francisco, O = "Facebook, Inc.", CN = example.com
verify return:1
---
Certificate chain
 0 s:/C=US/ST=California/L=San Francisco/O=Facebook, Inc./CN=example.com
   i:/C=US/O=DigiCert Inc/OU=www.digicert.com/CN=RapidSSL TLS DV RSA CA G1
 1 s:/C=US/O=DigiCert Inc/OU=www.digicert.com/CN=RapidSSL TLS DV RSA CA G1
   i:/C=US/O=DigiCert Inc/OU=www.digicert.com/CN=DigiCert SHA2 Secure Server CA
 2 s:/C=US/O=DigiCert Inc/OU=www.digicert.com/CN=DigiCert SHA2 Secure Server CA
   i:/C=US/O=DigiCert Inc/OU=www.digicert.com/CN=DigiCert Global Root CA
---
Server certificate
-----BEGIN CERTIFICATE-----
MIIF...
-----END CERTIFICATE-----
subject=/C=US/ST=California/L=San Francisco/O=Facebook, Inc./CN=example.com
issuer=/C=US/O=DigiCert Inc/OU=www.digicert.com/CN=RapidSSL TLS DV RSA CA G1
---
No client certificate CA names sent
Peer signing digest: SHA256
Server Temp Key: ECDH, P-256, 256 bits
---
SSL handshake has read 3053 bytes and written 305 bytes
---
New, TLSv1/SSLv3, Cipher is ECDHE-RSA-AES256-GCM-SHA384
Server public key is 2048 bit
Secure Renegotiation IS supported
Compression: NONE
Expansion: NONE
No ALPN, server accepted to use http/1.1
---

2. curl --verbose

• https://example.com: This is the URL of the server we’re connecting to.
• -v: This parameter increases the output verbosity, allowing us to see more details about the connection process, including SSL/TLS verification.

* About to connect() to example.com port 443 (#0)
*   Trying 192.0.2.1...
* Connected to example.com (192.0.2.1) port 443 (#0)
* Initializing NPN, negotiated protocol: h2
* ALPN, server accepted to use h2
* Using HTTP2, server supports multi-plexing
> GET / HTTP/2
> Host: example.com
> User-Agent: curl/7.64.0
> Accept: */*
>
< HTTP/2 200
< content-type: text/html; charset=UTF-8
<
<!DOCTYPE html>
...
* Connection #0 to host example.com left intact

3. openssl verify

• -CAfile chain.pem: This parameter specifies the file containing the trusted certificate chain. You can obtain the trusted certificate chain from the Certificate Authority (CA) that issued your server certificate. This file should contain the root certificate and any intermediate certificates in PEM format.
• server.pem: This is the file containing the server certificate to be verified. You can obtain the server certificate from your Certificate Authority or from your server’s SSL/TLS configuration. This file should contain the server certificate in PEM format.

1. Check your server’s SSL/TLS configuration files or directories for the server certificate and intermediate certificates. These files are usually named server.pem, server.crt, or server.key.
2. If you have access to your Certificate Authority’s documentation or support, you can request the trusted certificate chain from them. They may provide you with a bundle of certificates in PEM format.
3. If you’re using a service like Let’s Encrypt, you can find the trusted certificate chain in the fullchain.pem file generated during the certificate issuance process.
4. Ensure that both the chain.pem and server.pem files are in PEM format and contain the correct certificates for verification.

Output
server.pem: OK

1. SSL Labs: Visit https://www.ssllabs.com/ssltest/ and enter your domain to receive a detailed report on your SSL configuration, including the grade of your SSL implementation, supported protocols, and cipher suites.
2. SSL Checker: Use online SSL certificate checker from DigiCert is useful to verify the installation and configuration of your SSL certificates. These tools can detect issues such as certificate expiration, incorrect installation, or misconfigured intermediate certificates.

Enabling SSL Verification in Python

import requests

# Make a GET request to the API endpoint with a timeout of 10 seconds
resp = requests.get('https://api.example.com', timeout=10)

# Print the status code of the response
print(resp.status_code)

Enabling SSL Verification in Node.js

const axios = require('axios');

// Create a new instance of the https.Agent with SSL verification enabled
const agent = new (require('https').Agent)({ rejectUnauthorized: true });

// Define a function to handle the request
function makeSecureRequest() {
  axios.get('https://api.example.com', { httpsAgent: agent })
    .then(response => {
      console.log(`Request successful. Status: ${response.status}`);
      // Handle successful response
    })
    .catch(error => {
      console.error(`Request failed. Error: ${error.message}`);
      // Handle failed request
    });
}

// Call the function to make the request
makeSecureRequest();

const axios = require('axios');

// Create a new instance of the https.Agent with SSL verification enabled
const agent = new (require('https').Agent)({ rejectUnauthorized: true });

// Define a function to handle the request
function makeSecureRequest() {
  axios.get('https://api.example.com', { httpsAgent: agent })
    .then(response => {
      console.log(`Request successful. Status: ${response.status}`);
      // Handle successful response
    })
    .catch(error => {
      if (error.code === 'ECONNRESET' || error.code === 'ENOTFOUND') {
        console.error(`SSL verification failed. Error: ${error.message}`);
        // Handle SSL verification failure
      } else {
        console.error(`Request failed. Error: ${error.message}`);
        // Handle other types of errors
      }
    });
}

// Call the function to make the request
makeSecureRequest();

• Use them only for internal or testing purposes: Self-signed certificates should not be used for public-facing websites or applications that require high security.
• Implement a secure certificate management process: Ensure that self-signed certificates are properly managed, including secure storage, access control, and revocation procedures.
• Use a secure protocol: Ensure that the connection to the server or application using a self-signed certificate is encrypted using a secure protocol like TLS 1.2 or TLS 1.3.
• Inform users about the self-signed certificate: If a self-signed certificate is used for a public-facing application, inform users about the potential security risks and provide them with instructions on how to proceed safely.
• Use a trusted CA for production environments: For production environments, use certificates issued by a trusted Certificate Authority to ensure the highest level of security and trust.
• Regularly review and update self-signed certificates: Regularly review and update self-signed certificates to ensure they are not expired or compromised.
• Use a secure method for certificate distribution: Use a secure method to distribute self-signed certificates to clients or users, such as through a secure network or encrypted channel.
• Monitor for certificate validation errors: Monitor for certificate validation errors and take prompt action to address any issues that arise.
• Document self-signed certificate usage: Document the use of self-signed certificates, including their purpose, scope, and management procedures.

• Always use certificates from trusted Certificate Authorities (CAs): Ensure that the certificates used are issued by a trusted Certificate Authority (CA) to establish trust with clients and prevent man-in-the-middle attacks.
• Automate certificate renewal and monitor expiration: Implement a process to automatically renew certificates before they expire to prevent service disruptions and ensure continuous encryption.
• Use strong protocols (TLS 1.2/1.3) and ciphers: Utilize the latest Transport Layer Security (TLS) protocols (1.2 and 1.3) and strong ciphers to ensure secure data transmission and protect against known vulnerabilities.
• Implement security headers (HSTS, CSP, etc.): Configure security headers such as HTTP Strict Transport Security (HSTS), Content Security Policy (CSP), and others to enhance the security of your application and protect against common web attacks.
• Regularly test your SSL configuration with tools like SSL Labs: Periodically test your SSL configuration using tools like SSL Labs to identify vulnerabilities, ensure compliance with best practices, and optimize your SSL setup.
• Keep your server and libraries up to date: Regularly update your server and libraries to ensure you have the latest security patches and features, which helps prevent known vulnerabilities and ensures ongoing security.
• Document and inventory all certificates in use: Maintain a record of all certificates used, including their purpose, expiration dates, and revocation status, to ensure easy management and timely renewal or revocation.

How to fix SSL: CERTIFICATE_VERIFY_FAILED error?

• The server’s SSL certificate is expired or not yet valid.
• The server’s SSL certificate is not trusted by the client.
• The server’s SSL certificate does not match the domain name of the server.
• The server’s SSL certificate is missing intermediate certificates.
• The server’s SSL certificate has been revoked.

• Check the server’s SSL certificate expiration:
  
  • Use tools like OpenSSL to check the certificate’s expiration date: openssl x509 -in server.crt -noout -dates
  • If the certificate is expired, renew it with a trusted Certificate Authority (CA) by following their renewal process.
• Verify the server’s SSL certificate trust:
  
  • Check the certificate’s issuer to ensure it is a trusted CA: openssl x509 -in server.crt -noout -issuer
  • Verify the certificate is properly configured on the server by checking the server’s SSL configuration files.
  • If the certificate is not trusted, consider obtaining a new certificate from a trusted CA by following their issuance process.
• Check the server’s SSL certificate domain name:
  
  • Verify the certificate’s subject to ensure it matches the domain name of the server: openssl x509 -in server.crt -noout -subject
  • If the domain name does not match, obtain a new certificate with the correct domain name by following the CA’s issuance process.
• Check for missing intermediate certificates:
  
  • Use tools like OpenSSL to verify the certificate chain: openssl s_client -connect server:443 -servername server
  • If intermediate certificates are missing, obtain them from the CA and configure them on the server by updating the SSL configuration files.
• Check the server’s SSL certificate revocation status:
  
  • Use tools like OpenSSL to check the certificate’s revocation status: openssl ocsp -issuer server.crt -cert server.crt -url http://ocsp.example.com
  • If the certificate is revoked, obtain a new certificate by following the CA’s issuance process.

What are some Common SSL Verification Errors & Fixes?

1. What Is SSL Verification?

openssl s_client -connect example.com:443 -servername example.com | openssl x509 -noout -dates

2. Why do I get SSL verification failed errors?

• Check the certificate’s expiration date using openssl x509 -in server.crt -noout -dates and renew it if it’s expired.
• Verify the certificate’s domain name matches the server’s domain using openssl x509 -in server.crt -noout -subject and obtain a new certificate if there’s a mismatch.
• Ensure the certificate is trusted by the client by checking if the Certificate Authority (CA) is included in the client’s trust store. You can use openssl x509 -in server.crt -noout -issuer to check the certificate’s issuer.
• If the certificate is not trusted, consider obtaining a new certificate from a trusted CA by following their issuance process.
• Use tools like OpenSSL to verify the certificate chain and identify any missing intermediate certificates: openssl s_client -connect server:443 -servername server. If intermediate certificates are missing, obtain them from the CA and configure them on the server by updating the SSL configuration files.
• Check the certificate’s revocation status using openssl ocsp -issuer server.crt -cert server.crt -url http://ocsp.example.com and obtain a new certificate if it’s revoked.

3. Can I disable SSL verification in curl or Python?

curl -k https://example.com

import requests

requests.get('https://example.com', verify=False)

4. How do I verify an SSL certificate manually?

1. Check the certificate’s expiration date to ensure it is not expired.
2. Verify the certificate’s issuer to ensure it is a trusted Certificate Authority (CA).
3. Check the certificate’s subject to ensure it matches the domain name of the website.
4. Use tools like OpenSSL or SSL Labs to scan the website’s SSL configuration and identify any potential issues.

openssl s_client -connect example.com:443

5. Is SSL verification required for secure APIs?

• How to Troubleshoot SSL Protocol Errors: This tutorial provides a comprehensive guide on identifying and resolving SSL protocol errors, ensuring secure connections between clients and servers.
• OpenSSL Essentials: Working with SSL Certificates, Private Keys, and CSRs: This tutorial covers the fundamental concepts and practical applications of OpenSSL, including certificate management, key generation, and certificate signing requests.
• Steps to Configure SSL on Tomcat and Setup Auto-Redirect from HTTP to HTTPS: This tutorial outlines the process of configuring SSL/TLS on Apache Tomcat, including setting up automatic redirects from HTTP to HTTPS, ensuring secure communication for web applications.
• How to Create a Self-Signed SSL Certificate for Nginx in Ubuntu: This guide demonstrates the creation and configuration of self-signed SSL certificates for Nginx on Ubuntu, providing a basic level of encryption and authentication for web servers.

1. Source Code (.java): You write your Java program.
2. Compilation (javac): The Java compiler (javac) translates the source code into Java bytecode (.class files).
3. Execution (JVM): The JVM reads and executes these bytecode files across platforms.

javac HelloWorld.java

// Decompiled version (simplified)
public class HelloWorld {
    public static void main(String[] args) {
        System.out.println("Hello, World!");
    }
}

• Loads compiled .class files from the file system into memory for execution and processing by the JVM runtime environment
• Verifies bytecode integrity through comprehensive security checks to ensure the code hasn’t been tampered with or corrupted during transmission
• Executes the program via interpretation or JIT compilation depending on performance requirements and execution frequency patterns
• Provides runtime services including automatic memory management through garbage collection and multi-threading capabilities for concurrent execution

• Debugging obfuscated classes: When dealing with intentionally obfuscated Java bytecode, decompilers help reverse-engineer the original logic by converting complex, unreadable bytecode back into understandable Java source code, making it possible to identify bugs, security vulnerabilities, or understand the program’s behavior despite deliberate obfuscation techniques.
  
• Recovering lost source code: In situations where original source code has been accidentally deleted, corrupted, or is no longer available, decompilers can reconstruct a functional approximation of the original Java code from compiled .class files, allowing developers to restore or recreate their applications when the source code repository is inaccessible or incomplete.
  
• Analyzing malicious code: Security researchers and malware analysts use decompilers to examine potentially harmful Java applications by converting their compiled bytecode into readable source code, enabling detailed analysis of the code’s behavior, identification of security threats, and understanding of how malicious software operates to develop appropriate countermeasures and security patches.
  
• Learning how libraries work internally: Developers can use decompilers to explore the internal implementation of third-party libraries and frameworks by examining their compiled bytecode, gaining insights into design patterns, algorithms, and coding techniques used by experienced developers, which helps improve their own programming skills and understanding of complex software architectures.
  

Process Overview

1. Parse .class file structure (constant pool, methods, fields)
2. Map bytecode instructions to Java equivalents
3. Generate syntactically correct Java code
4. Output source code for viewing or editing

1. http://javadecompilers.com/ — Supports multiple decompilers and batch uploads.
2. https://bytecodeviewer.com/ — Offers interactive UI and supports both .class and .jar files.
3. https://www.decompiler.com/ — Lightweight and fast, suitable for quick decompilation.

javac MyProgram.java

javac *.java

javac -d out/ MyProgram.java

javac -g MyProgram.java

java MyProgram

steps:
  - name: Compile Java Code
    run: javac -d build/ src/**/*.java

• javac for compilation
• JUnit for testing
• Maven or Gradle for packaging

1. Ignoring Compilation Flags: javac has flags for warnings, debugging, and optimizations. Use them. Many developers overlook powerful flags like -Xlint:all for comprehensive warnings, -g for debugging information, and -O for optimizations that can significantly improve code quality and performance during development cycles.
   
2. Forgetting JVM’s Role: Compilation isn’t enough. Understanding JVM memory and threading models is vital. Developers often focus solely on compilation without considering how the JVM will execute their bytecode, leading to performance issues, memory leaks, and unexpected behavior in production environments where JVM tuning becomes critical.
   
3. Not Using Versioning: Compiling with mismatched JDK and runtime versions can break things. This common oversight occurs when developers use different Java versions for compilation versus runtime, causing compatibility issues, missing features, or runtime errors that are difficult to diagnose and resolve in complex deployment scenarios.
   
4. Skipping Decompilation in Debugging: Decompiled .class files can reveal a lot about third-party bugs or integrations. For example, if you’re working with a closed-source JAR file that’s throwing unexpected NullPointerExceptions, you can use a decompiler like CFR to inspect the class structure and method implementations. This may help uncover missing null checks, improperly initialized variables, or logic errors in third-party code that aren’t documented publicly. Decompilation can turn opaque bytecode into actionable insights.
   

• How to Write Your First Program in Java
• Difference Between JDK, JRE, and JVM
• Compile and Run Java Program from Another Java Program

• Neural networks mimic the human brain to process and learn from data, forming the backbone of modern AI systems.
• Core components include:
• Input layer – receives raw data features.
• Hidden layers – process inputs through weighted connections and activation functions.
• Output layer – produces final predictions or classifications.
• Weights and biases – adjust during training to minimize error.
• Activation functions – introduce non-linearity (e.g., ReLU, sigmoid).
• Loss function – measures how far predictions are from actual values.
• Optimizer – updates weights to reduce loss (e.g., SGD, Adam).
• Training involves forward and backward propagation, where the model iteratively adjusts weights to minimize prediction error.
• Hyperparameter tuning and data preprocessing are critical for optimizing performance and avoiding overfitting.
• Neural networks power real-world applications like image recognition, natural language processing, and recommendation systems.
• Types of neural networks serve different purposes:
• Feedforward Neural Networks (FFNNs) – simplest form, where data flows one way.
• Convolutional Neural Networks (CNNs) – excel in image and spatial data analysis.
• Recurrent Neural Networks (RNNs) – designed for sequential data like time series or text.
• Understanding the building blocks—layers, activation functions, loss functions— lays the foundation for exploring advanced AI models.

• Understand basic mathematical structures such as vectors, matrices, and matrix multiplication.
• Understand the chain rule for backpropagation, gradients, and partial derivatives.
• Familiarity with probability distributions and statistical concepts such as variance, and common loss(e.g., cross-entropy, MSE).
• Proficiency in Python plus experience with numerical and ML libraries like NumPy, TensorFlow, or PyTorch.
• Knowledge of supervised learning, training/validation/test splits, overfitting vs. underfitting, and basic model evaluation metrics.

Key Components of a Neural Network

• Sigmoid and tanh transform outputs into ranges between 0 to 1 or -1 to 1.
• ReLU provides 0 for negative inputs and linear values for positive inputs.
• Softmax serves output layers by generating class probabilities.

Python Demo: Learn y = 2x with a Single Neuron

import numpy as np

# Simple backprop for one neuron learning y = 2*x

# 1. Data
x = np.array([1.0, 2.0, 3.0, 4.0])
y = 2 * x  # true outputs

# 2. Initialize parameters
w = 0.0  # weight
b = 0.0  # bias
lr = 0.1  # learning rate

print(f"{'Epoch':>5} {'Loss':>8} {'w':>8} {'b':>8}")
print("-" * 33)

# 3. Training loop
for epoch in range(1, 6):
    # Forward pass: compute predictions
    y_pred = w * x + b

    # Compute loss (mean squared error)
    loss = np.mean((y_pred - y) ** 2)

    # Backward pass: compute gradients
    dw = np.mean(2 * (y_pred - y) * x)  # ∂Loss/∂w
    db = np.mean(2 * (y_pred - y))      # ∂Loss/∂b

    # Update parameters
    w -= lr * dw
    b -= lr * db

    # Print progress
    print(f"{epoch:5d} {loss:8.4f} {w:8.4f} {b:8.4f}")

Epoch     Loss        w        b
---------------------------------
    1  30.0000   3.0000   1.0000
    2  13.5000   1.0000   0.3000
    3   6.0900   2.3500   0.7400
    4   2.7614   1.4550   0.4170
    5   1.2653   2.0640   0.6061

• Data: We want our neuron to learn y=2x.
• Parameters: w and b are both set to zero.
• Forward pass: Compute your guesses y^=w x+b.
• Loss: We measure the error using mean squared error.
• Backward pass: we compute how much to change w and b by computing their gradients.
• Update: Apply a small adjustment to w and b by multiplying the gradient with the learning rate.
• Repeat: The loss decreases and w gets closer to 2 while b moves toward 0.

import tensorflow as tf
from tensorflow.keras import layers

# Load MNIST dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

# Preprocess: flatten 28x28 images to 1D, normalize pixel values
x_train = x_train.reshape(-1, 784).astype("float32") / 255.0
x_test  = x_test.reshape(-1, 784).astype("float32") / 255.0

# Define a simple feed-forward neural network
model = tf.keras.Sequential([
    layers.Dense(128, activation='relu', input_shape=(784,)),  # hidden layer
    layers.Dense(10, activation='softmax')                     # output layer for 10 classes
])

model.compile(optimizer='adam',
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Train the model for 5 epochs
model.fit(x_train, y_train, epochs=5, batch_size=32, validation_split=0.1)

# Evaluate on test data
test_loss, test_acc = model.evaluate(x_test, y_test)
print(f"Test accuracy: {test_acc:.4f}")

import numpy as np
from tensorflow import keras
from tensorflow.keras import layers

# XOR input and outputs
X = np.array([[0,0],[0,1],[1,0],[1,1]], dtype="float32")
y = np.array([0, 1, 1, 0], dtype="float32")

# Define a simple 2-2-1 neural network
model = keras.Sequential([
    layers.Dense(2, activation='relu', input_shape=(2,)),   # hidden layer with 2 neurons
    layers.Dense(1, activation='sigmoid')                   # output layer with 1 neuron
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])

# Train the model
model.fit(X, y, epochs=1000, verbose=0)  # train for 1000 epochs

# Test the model
preds = model.predict(X).round()
print("Predictions:", preds.flatten())

• TensorFlow (with Keras): It is an open-source library from Google. Keras is integrated with TensorFlow to offer beginner-friendly tools for defining and training machine learning models.
• PyTorch: Meta (Facebook) developed PyTorch, which provides dynamic computation graphs and a Pythonic style. It is widely used across research fields and industry applications.
• Scikit-learn: A general machine learning library in Python. It features basic neural network models such as MLPClassifier, which work well for small-scale problems or as a starting point.
• Others: We can include frameworks like MXNet, Caffe, Microsoft CNTK, and high-level tools like FastAI or Hugging Face Transformers.

• Understanding Feedforward and Feedback Networks (or recurrent) neural networks
• Training Neural Networks for Beginners
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Steps to Create and Develop Your Own Neural Network
• How neural networks are trained

• Unified Omni-Modality Perception: Ming-lite-omni is built on Ling’s smart MoE system and uses special routers to handle different types of input—like text, images, and audio—without mixing them up. Everything works smoothly, no matter the task.
• Unified Perception and Generation: It can take in a mix of things like text, images, or sounds, understand them together, and respond in a clear and connected way. This makes it easier for users to interact with and improves how well it performs.
• Innovative Cross-Modal Generation: Ming-lite-omni can speak in real time and create high-quality images. It does a great job at understanding pictures, following instructions, and even having conversations that combine sound and visuals.

• Ming-Lite-Omni Hugging Face
• Ming-Omni: Official Github
• Ming-Omni: A Unified Multimodal Model for Perception and Generation Research Paper