Kill Your Algo

S1 Ep8: When the Algo Lets You Spike the FootballWhat 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 algorithmHow quickly your feed adapts—and what it prioritizesWhy “spiking the football” content dominates engagementThe feedback loop: liking → more of the same → stronger beliefStories that show up in your feed but nowhere elseHow different feeds create completely different realitiesIf 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 viewpointEngage (like, comment, watch) for 2–3 daysTrack what changes in your feedNotice 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.

Can AI actually cut through bias, misinformation, and media narratives—or does it just reorganize them?This week, we tested it.We built prompts designed to turn AI into an evidence-first journalist—one that separates fact from inference, flags weak sourcing, and avoids partisan framing.What we found was useful—but incomplete.AI can surface facts.It can show you both sides.It can even highlight what’s missing.But it can’t tell you what’s true.That part still belongs to you.AI is a powerful signal filter, not a truth engine“Both sides” ≠ clarity — it can actually delay conclusionsVerifiable facts are easy; truth requires interpretationMost media optimizes for engagement, not accuracyEven with better tools, human effort is still required0:00 — Cold open: “AI won’t take a side”0:20 — The experiment: can AI act like a journalist?6:30 — The problem: regurgitation vs insight12:00 — Fact vs truth (Indiana Jones reference)20:00 — Can AI actually resolve ambiguity?28:00 — The real issue: people don’t want to do the work33:00 — What your algorithm is actually feeding you36:00+ — Tweets of the week + narrative breakdownCopy this into your AI tool and paste in any article:You are an evidence-first news analyst.Analyze the article or text below using the following framework:Identify all verifiable facts (objective, checkable claims).Separate facts from:Evaluate the strength of each claim:Flag:Do NOT assume any claim is true without evidence.Do NOT provide opinions.Avoid partisan framing.Output format:Verifiable FactsInferencesSpeculationSource Credibility AssessmentMissing Context / Red FlagsCopy a full news articlePaste it into your AIRun the promptYou’ll get a structured breakdown of:What’s actually knownWhat’s assumedWhat’s weak or missingThis does NOT give you truth.It gives you cleaner inputs.You still have to decide what those inputs mean.AI can cut through the noise and surface the signal.But meaning doesn’t come from the machine.That still belongs to us.🧠 Key Takeaways⏱️ Timestamps (approx)🧪 Bob’s “TB Mode” Prompt (Try This Yourself)How to Use It⚠️ ImportantFinal Thought

In this episode of Kill Your Algo, Jeff, Bill, and Robert explore a big question: could an AI ever act like a journalist?As algorithms increasingly shape what we see in our feeds, the idea of an AI that can separate facts from opinions — and flag claims that might not be entirely true — becomes more than just a thought experiment.The hosts dive into narratives showing up in their own algorithms this week, share their Tweets of the Week, and unpack how different information ecosystems can shape the way we see the same story.Then they explore the central experiment of the episode: what would it take to build an AI that behaves like a journalist with integrity?No conclusions yet — just a curious attempt to understand the systems influencing us all.Homework for next week:Each host will attempt to create a prompt that turns an AI into a journalist-style fact checker — something that separates facts, opinions, and questionable claims.If you try the experiment too, send your prompt to:[email protected] best ones may be featured in next week’s show.AI journalismalgorithm biasmedia literacyAI and truthnews algorithmsinformation ecosystemsAI fact checkingAI ethicsKill Your Algo podcast

Join the experiment! Are we actually divided on the facts — or on how those facts are delivered to us?This week Billy O joins us as we run the same political claim through Grok, Claude, and ChatGPT and compare those answers to what shows up in our feeds. The data converges. The emotional temperature doesn’t.This episode isn’t about defending a side. It’s about examining how our algorithms amplify urgency, inflate narratives, and attach moral labels.Question of the Week:Define “political grifting.” Now apply that definition evenly across parties. Who qualifies?Email us: [email protected]

We compare what different AI systems and social media feeds produced when given the same voter ID question. Where do the answers diverge? What evidence is emphasized—or omitted? This episode examines media bias, political narratives, misinformation dynamics, and how algorithmic influence shapes public opinion.Question of the Week (copy and paste exactly as written):How does the inclusion or exclusion of undocumented immigrants in the U.S. census affect congressional representation and federal funding allocation, and what empirical evidence supports those impacts?Run the question exactly as written. No edits. No optimization. Compare outputs. That’s the experiment.We’re not red or blue. We’re testing the system.

There is no longer a single shared source of truth. Each of us builds reality from our feeds, searches, and algorithms. Comparing two perspectives isn’t enough—we need a third signal.Question of the Week (copy and paste exactly as written):Who is empirically disenfranchised and who is empirically enfranchised by voter ID laws in United States elections, and what evidence supports those claims?Run this exact wording through your AI tool or search engine. Don’t edit it. Don’t optimize it. Compare your results to someone else’s. We’ll analyze the differences next episode.We explore media fragmentation, algorithmic bias, and how digital filter bubbles shape political belief.

Two hosts compare what different social media algorithms and AI tools are showing them—and how two reasonable people can walk away with completely different impressions of the same world. This episode launches the experiment: media bias, information bubbles, digital echo chambers, and how algorithmic curation shapes public opinion.