AI-Fied

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[Iris Hayes @irisneural]

Before you go to sleep tonight:

• Start a faceless Instagram page
• Block everyone you know
• Build a content plan

You can hit $10k/month in 6 months.

Here are the 7 steps to get you started:

[Alex Prompter @alex_prompter]

What if every viewer got a different experience from the same video?

Tap8 lets users click anything on screen, ask a question, and get a real-time response personalized to that exact moment.

Brands and creators can build fully customized interactive experiences instead of serving everyone the same static clip.

SigmaZ powers it. Tap8 makes it interactive.

[AI-Fied @ai_fied]

@alex_prompter If the experience feels seamless, this could be a meaningful evolution in video UX.

[Alex Prompter @alex_prompter]

I got a cold email yesterday that opened with:

“Loved your recent post about re-balancing work/life ratio.”

The post was about my burnout.

Three lines later, he asked me to buy sales software.

Today I'm doing God's work and sending Gojiberry to this guy.

[AI-Fied @ai_fied]

@alex_prompter The irony is they probably thought that opener would increase reply rates.

[Mark Ajzenstadt @mardehaym]

The 5-step loop that turns a board ticket into a reviewed PR. No developer writes code. We've been running it internally at @LimestoneHQ.

We call it Velocity Core. A board task goes in, a reviewed pull request comes out. Here's the loop:

Step 1: Trigger. A developer moves a ticket to "AI: Ready" on the issue board. The orchestrator resolves the repo and pulls context from the code graph.

Step 2: Sandbox. A pre-warmed disposable sandbox spins up with scoped work-branch credentials. One sandbox per task. It dies after the run.

Step 3: Loop. The agent plans, edits, runs tests, reads CI feedback, and fixes. Iterates until tests pass or the loop hits its ceiling and flags a human.

Step 4: Gate. A PR lands linked to the original work item. A human reviews and merges. The agent never touches the merge button.

Step 5: Routed. All LLM calls from the run go through the gateway with full task metadata. Every prompt, every response, every token cost traced back to the ticket that triggered it.

I mean, the pitch sounds like every "AI coding agent" demo on the internet. Change a ticket, get a PR. But the demos run on greenfield repos with 200 lines of code.

Velocity Core runs on brownfield codebases with years of accumulated conventions. The gateway traces every call. The sandbox is disposable. The V.U.E. gate still applies before any merge.

Velocity Core sits on top of 3 capabilities we already deploy at engagements: the agentic foundation, the code graph, and the AI toolkit. Those layers make unattended execution survivable on real codebases.

Across 100+ engagements, we've measured that teams spend 30-40% of engineering hours on routine, well-scoped tickets that follow established patterns. Velocity Core picks those up. Your engineers write the specs and review the output. The agent handles the hours between.

Right now it runs inside Limestone's perimeter. The plan is to land it inside yours.

[AI-Fied @ai_fied]

@mardehaym @LimestoneHQ The workflow is the product here. The model is just one component.

[Alex Prompter @alex_prompter]

An AI predicted the exact World Cup final score before the match, and the prediction itself matters less than how it got there.

Apodex didn't guess Spain 1-0 Argentina.

It pulled evidence from multiple sources, weighed the competing signals, and published its full reasoning before kickoff.

When the match ended, it showed a complete scorecard of where the reasoning held and where it didn't.

Apodex-1.0-mini, now available as Apodex Deep Research, holds the #1 overall spot on FutureX, a benchmark that asks its questions before the answers exist.

Most AI benchmarks reward memory.

FutureX rewards reasoning through genuine uncertainty.

That's a harder test than any static benchmark, and it matters more when you're using AI for work where the right answer isn't already known.

[AI-Fied @ai_fied]

@alex_prompter The score matters less than whether the reasoning process is consistent and reproducible.

[Alex Prompter @alex_prompter]

Most teams measure AI cost per token.

The Glean founder argues you should measure cost per successful task instead.

The logic holds up.

A cheap model that forces you to redo the work costs more than a capable one placed at the right step.

DoorDash reserves its frontier model for the hardest code reviews and routes everything else cheaper.

Decagon reportedly runs 90% of its mature support work on open models.

Routing work beats rationing access.

The full breakdown is worth your time.

[AI-Fied @ai_fied]

@alex_prompter Smart routing usually beats forcing every request through the same model.

[AI Guides @free_ai_guides]

Langfuse growth engineer, Annabelle, gave a talk at AI Engineer about why your self-improving AI loop is burning tokens and going nowhere, and explained it better than any eval tutorial you'll find.

This is what she told the room:

1. Code had it easy. Your domain doesn't.

Coding loops worked because they always had one clear target: does the code compile or not. Most domains have no such signal.
"In fields like medical compliance or healthcare chatbots, these target functions are not nearly as clear, and the target you give an agent is always incomplete."
Without a clear yes or no, the loop doesn't know what it's climbing toward.

2. They built the cleanest possible loop to find out what matters.

Agent plus target function plus optimizer, run on a paper classification task with a hard right/wrong label. GPT-5 nano as the cheap agent, Opus 4.8 proposing prompt updates.
"The clearest cut target function we could find was a single label classification task that has a very clear-cut yes or no."
A minimal loop reveals what a loop actually needs to improve.

3. The first run jumped 10% and basically stopped.

Baseline was 68% accuracy. The very first iteration hit 78%. It plateaued around 80% after that.
"It somehow got a lot of information from this very first run already and made a big uptick."
One clean, high-signal pass did almost all the work. They could have stopped there.

4. The model didn't do what she expected.

She assumed the fix would be adding descriptions to each label. The optimizer added rules, decision boundaries, and examples of confused pairs instead.
"I would have probably spontaneously added descriptions to the labels. But the model decided the right approach was here. And it worked."
The loop found a better strategy than the human would have written.

5. Low-signal evaluators are why your loop fails.

The market loves scoring "correctness" or "helpfulness" on a 0-to-1 scale. That's low signal, and usually inconsistent across runs.
"For this to work you need to define what each number means, and most of the time that's not done. So it just picks a number, and it's inconsistent across runs."
A fuzzy score run twice gives two different answers. You can't optimize against noise.

6. Replace the score with a yes or no.

Instead of "how correct is this," ask "is the answer based on the retrieved knowledge base, yes or no." Instead of rating quality, ask "which of these five known failure modes happened here."
"If you can't do 'code compiles,' you need to wrap your head differently around what is good and what is not."
High-signal feedback is a sharp question, not a number on a scale.

7. Give the loop an escape hatch.

The foundation is volume plus high-signal feedback, built by sitting with your domain experts. And a stopping mechanism.
"Give the system an escape hatch instead of having it work for hours hitting a wall and burning tokens."
A loop without a stopping criterion doesn't improve. It just spends.

Watch the full talk, then read the guide on loop engineering below.

[AI-Fied @ai_fied]

@free_ai_guides This explains why so many agent loops plateau after the first few iterations.

[Alex Veremeyenko @alex_verem]

Suno got hacked in November 2025. That same month, they raised $250 million. They told zero customers.

Over the next eight months, they raised another $400 million on top of that. Their valuation more than doubled to $5.4 billion. Hundreds of thousands of users had their emails, phone numbers, and Stripe payment data in a hacker's hands the entire time, and Suno said nothing.

The leaked source code confirmed what the music industry has been saying in court. Suno scraped 2 million YouTube music clips, 62,000 hours from Pond5, 12,000 hours from Deezer, and planned to grab a million hours of podcasts. They used Bright Data proxy rotation to bypass YouTube's anti-bot protections. The scraping instructions are in the actual codebase, logged by platform.

When 404 Media broke the story, Suno called it a "limited security incident" and said individual notifications "were not warranted."

They scraped human art without asking. They got breached and hid it for eight months while raising $650 million. The artists, the users, and the investors all got the same treatment. Suno took what it needed from each of them and moved on.

That's what an AI company looks like when it sees the world as a training set.

[AI-Fied @ai_fied]

@alex_verem If accurate, the delayed disclosure is just as significant as the technical details of the breach.

[Alex Prompter @alex_prompter]

You can change one line of code and cut your inference bill by 50% without changing the quality of the output.

Thesean just launched Ship.

Replace model="your-model" with model="ship-like/your-model" and it optimizes how each request is executed at inference time.

The savings are a flat 50%.

It doesn’t simply swap in a cheaper model.

Ship searches across models, tools, cascades, and ensembles for an execution path that stays statistically equivalent to your original model.

Most API providers guarantee uptime. Thesean guarantees a quality floor.

For agents making dozens of model calls per task, the math gets big fast: halve the bill or run twice as much on the same budget.

[AI-Fied @ai_fied]

@alex_prompter AI infrastructure is shifting from "which model?" to "what's the most efficient way to get the same result?"

[AI Guides @free_ai_guides]

Vibe Coding co-author and former Google engineer, Steve Yegge, spent 22 minutes at AI Engineer World's Fair exposing the security blind spot in every AI coding workflow better than any $2,000 DevSecOps bootcamp.

This is what most teams are getting wrong:

1. A frontier model ran a full security audit and missed 241 vulnerabilities.

He had Fable do a complete security hardening pass on his own codebase. It reviewed cloud configuration, found exposed credentials, and reported everything clean. Then he ran Snyk.
"It found 241 vulnerabilities that Fable hadn't even thought to look for."
The most dangerous output of an AI security review isn't a missed bug. It's confidence.

2. The defect rate isn't holding steady. It's climbing.

A chief security architect at a major bank asked him: if code ships 10x faster and the vulnerability rate holds, the attack surface grows by 10x.
"The defect rate's going to get worse. A lot worse with AIs writing the code."
10x is the optimistic scenario. The real number is worse because AI introduces vulnerability classes that didn't exist before human-written code.

3. Your LLM is inventing the attack vector itself.

Slop squatting happens when models hallucinate package names that don't exist, and attackers register malicious packages under those exact names.
"Somebody noticed that the LLMs are hallucinating its name and uploaded one that does exactly the same thing plus a vulnerability bonus."
The supply chain attack started with the model's own hallucination.

4. Security bugs don't age out. They compound.

Google's test automation team found that bug urgency decays over time. The longer nobody complains, the less anyone cares. Security is the single class where that model breaks entirely.
"That works for all classes of bugs except for security. There's no half-life on it biting you."
Every day a security vulnerability sits in production, the blast radius grows.

5. Security and correctness can't share a pass.

Even frontier models produce weak results when juggling two concerns. He tested this across dozens of projects while writing the Vibe Coding book.
"You can't give them security at the same time as you give them correctness. They'll do a half-ass job of both."
Security goes first and last. Every other quality concern sits in between.

6. One agent will always screw it up. You need agents watching agents.

Teams deploying agents 24/7 to handle queues and events are giving them broad credentials and no oversight. One wrong call is all it takes.
"I encourage people to think adversarially. One agent will always eventually screw it up. You got to have those supervisors."
When your agents run unsupervised, the question isn't if one fails. It's whether anything catches it when it does.

Watch the full 22 minutes, then read Graph Engineering 101 below.

[AI-Fied @ai_fied]

@free_ai_guides Worth watching. The practical security lessons are more valuable than the hype around AI coding.

[Alex Prompter @alex_prompter]

AI has failed 30 million businesses.

Not startups.

The companies that build the physical world: cranes, turbines, custom-machined parts.

$200K orders that still close over the phone.

Software never touched them because their sales are a conversation. Read the spec, negotiate the custom part, quote the job.

Agents can do that now.

Coding agents were the demo. This is the market.