***Audio note: Bob’s audio quality is rough in this episode. We apologize for the recording issue and appreciate you bearing with us.***

This episode is about one of the algorithm’s most effective tricks: it does not always tell you what to believe. Sometimes it just asks the question in a way that makes the conclusion feel obvious.

Isn’t that suspicious?

Why would they allow that?

How could that be legal?

What are they hiding?

The feed does not need to finish the argument. It gives you a few fragments, frames them with suspicion, and lets your brain do the rest. By the time you reach the conclusion, it feels like something you discovered yourself.

Robert, Jeff, and Billy use California voting as the main case study: mail-in ballots, ballot harvesting, witness signatures, X marks, late-count movement, Skid Row narratives, and the broader fear that the system has been designed to make fraud effectively invisible.

But the bigger question is not just whether one election claim is true or false.

The bigger question is how a media bubble turns procedural complexity into suspicion, suspicion into certainty, and certainty into political identity.

The guys compare how the same stories are being served to each of them in completely different ways: Gavin Newsom investigations, Trump corruption claims, California election fraud, CBS and 60 Minutes, Elon’s trillion-dollar narrative, Iran reconstruction rumors, data centers, and the upcoming Kill Your Algo feed-swap experiment.

The episode keeps coming back to one core idea:

The algorithm does not always give you the conspiracy.

Sometimes it gives you the question — and lets you build the conspiracy yourself.

Loaded questions as algorithmic design
How feeds use questions, insinuation, and selective fragments to push users toward conclusions stronger than the evidence supports.

California voting as the case study
Mail-in ballots, late counting, signature rules, witness concerns, ballot harvesting, and why complicated systems are easy to turn into conspiracy fuel.

From “could happen” to “is happening”
How the feed collapses the distance between a theoretical vulnerability and proof of widespread misconduct.

The illusion of independent discovery
Why conclusions feel more powerful when users believe they reached them on their own.

Corruption through partisan filters
Why each side’s feed highlights the other side’s corruption while softening, ignoring, or rationalizing its own.

Election trust and political identity
What happens when people are taught to believe elections are legitimate only when their side wins.

CBS, 60 Minutes, and media capture
The hosts discuss whether journalism can still function as referee when corporate ownership, political pressure, and audience incentives all point in different directions.

Elon, SpaceX, and the trillionaire split-screen
One feed sees rockets, genius, and vision. Another sees subsidy, inequality, and systemic risk.

Iran and the $300 billion question
How vague deal terms and reconstruction rumors become feed-specific proof of betrayal, genius, weakness, or corruption.

Data centers as the next wedge issue
Public land, water, noise, labor, AI infrastructure, and the local backlash forming around the physical cost of the digital economy.

Feed Swap preview
The guys prepare to trade algorithmic realities and predict what they expect to find inside each other’s feeds.

What if the algorithm’s most dangerous move is not giving you the answer?

What if it asks the question in a way that makes your worst conclusion feel like common sense?

Look at the next political story your feed serves you and ask:

Am I being shown evidence — or am I being invited to complete the conclusion?

Kill your algo.

Topics CoveredCore QuestionListener Prompt

This week on Kill Your Algo, the guys try to figure out what the internet wanted them to believe: Trump assassination-attempt coverage, political violence narratives, gerrymandering, Iran war messaging, UFO apathy, and the increasingly weird problem of deciding whether anything online is real anymore.

Jeff starts by fighting the machines at work, Bill brings fact-mode energy to viral political claims, and Bob investigates the missing/dead nuclear scientist narrative only to find that the algorithm may be better at connecting dots than proving patterns. Plus: Iran Lego propaganda, AI videos, cyanide bombs in the American West, billionaire discourse, puppy yoga, and a coyote mistaken for a lost dog. Show Notes

In this episode:

• Jeff opens with life inside the AI/code machine: vibe coding, AI slop, and whether more machine output actually means better work.
• The hosts compare how their feeds framed the Trump assassination-attempt story: serious threat, staged narrative, Secret Service failure, media apathy, or conspiracy object.
• Tweet of the Week includes Spencer Pratt as an unlikely LA political character, Iran Lego propaganda, and a viral charity-statistics fight.
• Bill reviews his fact-mode homework and how political stories get distorted by timeline jumps, implication, and partisan framing.
• The guys dig into corruption, insider trading, Congress, and how the algorithm points outrage at the other team instead of the system.
• Gerrymandering becomes a case study in how both parties frame power-grabbing as democracy protection.
• Bob breaks down the missing nuclear scientist narrative and asks whether it is really a pattern — or just modern amplification doing what it does.
• Conspiracy Corner covers AI realism, UFO apathy, whisper campaigns, and cyanide bombs in the American West.
• Doggie Day Care brings the needed reset: puppy yoga, husky howling, and one almost-rescued “dog” that turned out to be a coyote.
• This week’s homework: figure out how to trade feeds or compare feeds with someone else for a week.

S1 Ep8: When the Algo Lets You Spike the Football

What happens when your feed doesn’t just inform you—but hands you something to celebrate?

This week on Kill Your Algo, we run a live experiment: what happens when you deliberately retrain your feed with 20+ new follows? The result isn’t just new content—it’s a shift in tone, confidence, and what feels true.

We break down how algorithms don’t just show you information… they reinforce it.

From “spiking the football” moments to strange, unverified stories that feel important but lack confirmation, we test where signal ends and bias begins.

• A real-time experiment manipulating the algorithm
• How quickly your feed adapts—and what it prioritizes
• Why “spiking the football” content dominates engagement
• The feedback loop: liking → more of the same → stronger belief
• Stories that show up in your feed but nowhere else
• How different feeds create completely different realities

If your feed keeps making you feel right… how would you know if you’re wrong?

We decide the next experiment live on the show.

Want to participate?
Try this:

• Follow 15–25 accounts outside your normal viewpoint
• Engage (like, comment, watch) for 2–3 days
• Track what changes in your feed
• Notice what starts to feel “true”

Your feed isn’t neutral.
It’s trained—by you.

If this episode made you think differently about your feed, share it with someone who lives in a completely different one.

Kill your algo.

What happens when you start nudging the system—and it nudges back?

In this episode of Kill Your Algo, we talk about a live experiment: can you push an AI or a feed to move in a direction you choose? Not by hacking it—but by subtly changing your inputs.

Same prompts. Slightly different framing. Watch how the responses shift.

Then we take it a step further.

Because this isn’t just about AI—it’s about your feed.

Every like, follow, pause, and comment is a signal. Over time, those signals shape what you see, what gets amplified, and eventually, what feels true.

The question isn’t just what is the algorithm showing you?
It’s what are you training it to show you?

This week’s experiment:
Step outside your normal feed. Follow voices from a different perspective. Engage—lightly, intentionally—and observe what changes.

Your recommendations. Your “For You.” Your sense of what’s trending.

We’ll do the same—and compare results next episode.

Kill Your Algo.