Oct 3, 20268 minS0
<p>The history of email from the original documents: mailboxes on a single time-sharing computer, Ray Tomlinson's first network message and his choice of the at sign, the first header standard with From, Date and...
About this Podcast:
Technology and AI, explained one topic at a time by Juan Pablo: how large language models work, what tokens and context windows are, how phishing and zero-day attacks work, why AI needs so much energy, and how the tools we use every day came to be. Every episode is built on the original sources: official documentation, standards, papers and engineering posts, all linked in the episode notes so you can read them yourself. No hype and no politics, just how things work.
Pitch Analysis
Required Pod Score for this podcast. PitchCentric checks your profile against host openness, topical fit, and audience signals before you generate a pitch.
Contact path
Public web form
Booking probability
36%
Guest openness
Selective
80/100
Required Score
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Signup to Generate a PitchQuestions about Sources Attached with Juan Pablo
What is Sources Attached with Juan Pablo about?
Technology and AI, explained one topic at a time by Juan Pablo: how large language models work, what tokens and context windows are, how phishing and zero-day attacks work, why AI needs so much energy, and how the tools we use every day came to be.
What has Sources Attached with Juan Pablo covered recently?
Recent episodes include "How Email Was Invented: The First Network Message, the At Sign and SMTP" (3 October 2026, 8 minutes), "What Happens When You Open a Website: DNS, HTTPS and How Browsers Load a Page" (3 October 2026, 8 minutes), and "Why AI Needs So Much Energy: Chips, Data Centers and Cooling Explained" (3 October 2026, 8 minutes).
How often does Sources Attached with Juan Pablo publish?
Sources Attached with Juan Pablo publishes roughly daily, with episodes averaging 8 minutes.
How hard is it to get booked on Sources Attached with Juan Pablo?
Booking probability for Sources Attached with Juan Pablo is 36%, and the host is rated Selective on guests. Sources Attached with Juan Pablo requires a Pod Score of 80 or higher. Sources Attached with Juan Pablo's contact route on record: public web form.
Who should pitch Sources Attached with Juan Pablo?
The strongest fits for Sources Attached with Juan Pablo are IT Services & Software Development.
About the host
Sources Attached with Juan Pablo is a general podcast.
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Recent episodes
Our AI reads these to draft pitches. Use them as grounding for a pitch that cites a real guest and a specific topic.
- Oct 3, 20268 minS0
<p>One page load, step by step: how DNS finds the server, what the TCP and TLS handshakes do, how a certificate authority like Let's Encrypt vouches for a site, what an HTTP request and response look like and why HTTP/3...
- Oct 3, 20268 minS0
<p>Where the electricity behind AI actually goes: training versus everyday use, what the chips draw, why a data center spends energy on more than computing, how one company measured the energy of a single prompt, and...
- Oct 3, 20268 minS0
<p>The vocabulary of software security made plain: what a vulnerability, an exploit and a patch are, why some attacks are called zero-days, what a real security advisory looks like, how researchers and vendors...
- Oct 3, 20268 minS0
<p>Why it is so easy to fake the sender of an email, what phishing messages have in common, how the three email authentication standards SPF, DKIM and DMARC let receiving servers catch forged mail, what they cannot...
View More
- Oct 3, 20268 minS0
<p>Why operating systems ask permission before an app reads your files, why full disk access is special, what changes when the software asking is an AI agent that acts on its own, how prompt injection turns an agent's...
- Oct 3, 20268 minS0
<p>How AI image generators turn random static into a picture: the idea of destroying an image with noise and learning to undo it, how the model is trained to predict noise, what happens step by step when you generate...
- Oct 3, 20267 minS0
<p>How a keyboard can learn from what millions of people type without collecting their typing: the federated learning loop, the averaging trick that saves bandwidth, secure aggregation, differential privacy, and how...
- Oct 3, 20268 minS0
<p>Language models do not read words, they read tokens. What a token is, how byte pair encoding chops text into pieces, why the same sentence costs more tokens in some languages, what the context window really holds...
- Oct 3, 20268 minS0
<p>What actually happens inside a large language model: how it learns by predicting the next word, what the Transformer and its attention mechanism do, why size changed what these models can do, how a raw text predictor...
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