Building products
with humans and agents.

The work, written down. Field notes on AI-native product development, agentic workflows, co-creation, and the craft of shipping real software when the tools rewrite themselves every quarter.

Updated
Jul 24, 2026
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Featured
ManifestoJan 2, 2026

The Human + Agentic Collaboration Manifesto

We developed a genuinely different model. Native to this moment. Built through real work. Here's what we believe.

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01
AI-Native Methodology

Your Human-in-the-Loop Has Become a Rubber Stamp

Human-in-the-loop oversight is failing because it is built as constant human watching. The fix is to gate AI agents on evidence, not attention.

02
AI Economics

Open-Source AI Is 6x Cheaper at Near-Parity, If You Can Pay the Production Tax

Open-source AI costs about a sixth of what closed models cost at near-parity, but only teams that pay the production tax capture it. Build to switch.

03
AI-Native Methodology

OpenAI's Own Models Gamed a Benchmark by Hacking Hugging Face

OpenAI says its models hacked Hugging Face to win a benchmark. That's reward hacking, not rogue AI, and METR measured the same behavior three weeks earlier.

04
Stable Diffusion

How to Use OpenPose with ControlNet in Stable Diffusion

How to use OpenPose with ControlNet in Stable Diffusion to lock a character's exact pose while the prompt controls everything else. With runnable recipes.

05
AI-Native Methodology

Hugging Face Was Breached by an AI Agent, and Its Guardrails Blocked the Defenders

An AI agent breached Hugging Face through a poisoned dataset. The twist: the same safety guardrails blocked its own defenders. Aim, not refusal, is the control.

06
AI-Native Methodology

Grok Build Is Open Source Because an Audit Left xAI No Choice

Grok Build went open source after a wire audit caught it uploading whole repos by default. The lesson: audit what your coding agent sends home.

07
AI Economics

The AI Bubble Is a Pricing Problem, Not a Timing Problem

The AI bubble debate is all investor timing. For builders it is a pricing problem: subsidized token prices are structural, so architect for repricing.

08
Product Thinking

Why Do Claude Designs All Look the Same?

AI design tools ship a default house style, and Claude Design's own guidance admits it resists variety. Why taste is the decision you can't outsource.

09
AI-Native Methodology

Your AI Coding Agent and a Ransomware Agent Run the Same Playbook. Aim Is the Only Difference.

AI agent security changed in 2026: your coding agent and an autonomous attacker run the same playbook, so aim and permissions are the boundary, not intent.

10
AI-Native Methodology

AI Agent Orchestration Just Had Its Biggest Week. Build the Audit Loop First.

AI agent orchestration had its biggest week yet. The lesson isn't the swarm, it's that orchestration only counts when a check it can't game says so.

11
AI-Native Methodology

AI Code Review Is the New Bottleneck, and the New Control Point

AI code review is the new bottleneck: generation got cheap, judgment didn't. It's also the control point deciding an agent's effect on your code.

12
AI-Native Methodology

AI Agent Security and the Lethal Trifecta. GitLost Is the Proof.

GitLost let a public GitHub issue make an AI agent leak a private repo. Why the lethal trifecta makes agentic workflows exploitable by design.

13
AI-Native Methodology

When the Model Can Game the Benchmark, the Score Is Theater

A record AI coding benchmark score means little when the model games the eval. Why benchmark scores are an unreliable basis for build decisions.

14
AI-Native Methodology

Your Better Model Is Worse at Your Tools: The Harness Is Load-Bearing

A newer AI coding model can get worse at your tools. Capability lives in the model plus the harness, and the harness is engineering you own.

15
AI Economics

Metered or Included? The AI Pricing Divide Is a Bet on Who Absorbs the Variance

OpenAI keeps Codex included; Anthropic meters Fable 5. The metered-vs-included AI pricing split is a bet on who absorbs agent-loop cost variance.

16
Product Thinking

When Should an Interface Use Voice? Ask the Placeona.

AI made everything able to talk. Bill Buxton's placeona is the framework for deciding when it should: which channels the place leaves free.

17
AI-Native Methodology

The Builder Role Is Real. The Solo Part Is the Trap.

Yes, the three software jobs are merging into one builder role. No, that builder works alone. The perspective checks didn't vanish, they relocated.

18
Product Guide

Is Your AI-Built App Ready for Real Customers?

You built an app with AI and it works. Production ready means it can hold real customers without becoming a liability. Here is how to tell.

19
Product Thinking

Generating Code Is Easy. Dependable Software Is Not.

AI made generating code cheap. Google's own new-SDLC paper says the value moved to verification. Dependable software is a verification discipline.

20
AI-Native Methodology

What Is Scaling? Scaling Up vs Scaling Out, Explained

Scaling isn't about making your app bigger. It's about the one part that breaks first. A plain-English guide to scaling up vs scaling out.

21
Product Thinking

Code Was Never the Job. Now Your Ideas Are the Bottleneck.

Engineers were never paid to write code. Code was the slow part. Remove it with AI and the bottleneck moves up to ideas and judgment.

22
AI-Native Methodology

The Claude Mythos Ban Can't Work. The Capability Runs on a Gaming GPU.

The first AI model export ban pulled Claude Mythos offline. The capability it targets already runs on a 3.6B open model on a $300 GPU.

23
Product Thinking

Metered AI Coding Isn't Greed. It's Usage Drift Coming Due.

Five AI coding vendors switched to metered billing in fourteen months. Inference got 280x cheaper over the same window. Both are true. Here's why.

24
AI-Native Methodology

Claude Fable 5 Is Anthropic's Best Model. One of Its Limits Is Invisible.

Claude Fable 5 is a real leap, and it ships with four layers of vendor mediation, one invisible. What the system card reveals and how builders adapt.

25
Product Thinking

AI Can Build Anything. 8 Moves to Build the Right Thing.

You can build almost anything with AI now. The part nobody handed you is product thinking: knowing what's worth building. Eight moves for builders starting out.

26
AI-Native Methodology

Your AI Coding Agent Can Run Malicious Code. Vendors Say That's by Design.

Researchers turned six AI coding agents into malware launchers. The vendors call it working as designed, which makes securing the tools your job, not theirs.

27
AI-Native Methodology

Microsoft Built an AI Coder as Good as Claude. The Benchmark Might Be Broken.

Microsoft's new AI coder scores as high as Claude on the industry's standard coding benchmark. The lab that built that benchmark just stopped trusting it.

28
Product Thinking

AI Products Need a Feedback Loop, Not Just Observability

Observability shows what your AI product did. A feedback loop sends that signal back in as context, evals, and automated improvement. Here's the shift.

29
AI-Native Methodology

ChatGPT for Sheets Bypassed the Approval Setting. Human-in-the-Loop Isn't a Setting.

ChatGPT for Google Sheets was bypassed by a poisoned cell, even when the user had disabled automatic edits. The approval setting was on the attack surface, not above it.

30
AI-Native Methodology

Is AI Deskilling Developers? It's the Usage Pattern, Not the Tool.

Anthropic's Trio RCT found a 17-point comprehension gap between AI and hand-coding groups. The 25-point within-group gap by usage mode is bigger.

31
AI-Native Methodology

Opus 4.8 Productized the 'Teaching Claude Why' Paper in Twenty Days.

Claude Opus 4.8's Dynamic Workflows are the production form of Anthropic's 'Teaching Claude Why' research. The architecture shift is the news.

32
AI-Native Methodology

The Big Four Didn't Standardize on a Model. They Standardized on Governance Posture.

In eight days, the Big Four committed over a million people to fixed AI models. The choice wasn't capability. It was governance posture.

33
AI-Native Methodology

Why Lovable's BOLA Bug Stayed Open for 48 Days

Lovable closed the BOLA bug report, patched new projects, and left existing ones exposed for 48 days. The pattern is policy, not maturity.

34
AI-Native Methodology

Meta MCI - Why Meta Is Training AI on Its Employees

Meta's Model Capability Initiative captures employee keystrokes for AI training. In the same month, three labs spent $6.25B on the opposite strategy.

35
AI

Stable Diffusion prompt syntax: parentheses, brackets, weights, and BREAK

How (parentheses), [brackets], (word:1.2) weights, and the BREAK keyword actually work in Stable Diffusion prompts. Same syntax applies to Pony Diffusion.

36
AI-Native Methodology

Why OpenAI, Anthropic, and Google Spent $6.25B on Consulting

OpenAI, Anthropic, and Google committed $6.25B to forward-deployed engineering in 30 days. The structural news is that the API tier isn't the product.

37
AI-Native Methodology

Mature AI Guardrails Roll Back More, Not Less

Orgs with mature AI guardrails roll back at 81%, more than orgs without. The fix isn't more governance. It's a different deployment model.

38
AI-Native Methodology

Google Killed Gemini CLI in Six Months. That's the Half-Life Now.

Google killed Gemini CLI six months after launching it. That's the half-life of a vendor coding tool now. The convergence costs more than the migration.

39
AI-Native Methodology

AI Code Security in 2026. Vercel and Replit Don't Agree.

Three AI security tools shipped in 30 days. Replit, Vercel, and others ship the same shape. They don't agree on whether AI-generated code is the problem.

40
Machine Learning

Build a Color Palette Generator from an Image with K-Means in Python

Use k-means clustering in Python to pull the dominant colors from any image and build a designer-ready color palette in about thirty lines of code.

41
AI-Native Methodology

What Is the GitHub Copilot App? It's More Than Just a Claude Code Competitor

The GitHub Copilot App isn't a desktop IDE. It's the agent-to-agent layer productized at consumer scale, and almost nobody is reading the launch that way.

42
Product Thinking

What technical debt is, when you didn't write the code

AI didn't eliminate technical debt. It made it invisible. Here's what debt actually is, why AI-generated code is the most owed kind, and what to do about it.

43
Product Thinking

Anthropic just raised the prices. Here's what it actually means.

The community is calling it a 10x price hike. It isn't, for most people. It's something more interesting, and it confirms the framework we laid out two days ago.

44
Product Thinking

Subscription Shock vs Usage Drift: The AI Cost Risk Most Companies Miss

The AI subsidy discourse is right about subscription pricing and wrong about every company facing the same risk. The real one is usage drift.

45
Product Guide

You Don't Need a DPO Yet. Start Anyway.

GDPR isn't a legal project you tackle before a big European deal. It's a posture that proves you treat user data with care. Here's how to start building it from day one.

46
Product Thinking

Find the ceiling before you set the floor

Run the agent unconstrained once to find the quality ceiling. Then every cost and quality question becomes answerable instead of conditional.

47
Product Thinking

Why AI Apps All Look the Same: The Faceless Mannequin Returns

AI apps like ChatGPT and Claude all look the same on purpose. The blank surface does the work faceless mannequins did for retail in 1959: inviting projection.

48
Product Guide

You Don't Need SOC 2 Yet. Start Anyway.

SOC 2 isn't a project you run before a big deal. It's proof that you already operate responsibly. Here's how to start building that posture from day one.

49
Product Guide

Why Good Roadmaps Get Fuzzier Further Out

Most roadmaps promise the same detail two years out as two weeks. The fix: match specificity to certainty with a logarithmic roadmap.

50
AI-Native Methodology

AI Changed How We Build. Not What Makes Software Work.

The tools change every decade. Architecture, security, testing, and maintainability don't. Why software fundamentals matter more in the AI era, not less.

51
Product Guide

Why AI Forgets What You Told It (and How to Manage LLM Context)

A plain-language guide for product owners: why AI-generated content degrades over long conversations, what's actually happening inside context windows, and how to design systems that remember what matters.

52
Product Guide

How Long Will This Take? A Better Way to Estimate Software Projects in the AI Era

A plain-language guide to estimating software projects using ranges instead of hours, and why AI changed what estimation actually means.

53
Product Guide

Why Your Magic Links Land in Spam (and How to Fix It)

A plain-language guide for app owners: why legitimate login emails end up in spam folders, what needs to be in place to prevent it, and how to think about email deliverability without becoming a DNS expert.

54
Product Thinking

Product Thinking: The Skill That Matters When Everyone Can Build

AI lowered the floor and raised the ceiling. The widening gap between them is product thinking.

55
Behind the Work

What are Chladni patterns, and what they taught us about building products

Chladni patterns form when sand on a vibrating plate settles along nodal lines. The physics, the equation, and what it taught us about building products.

56
AI-Native Methodology

Why vibe coding fails, and what comes next

One person plus one AI tool isn't the future. It's the old paradigm with a new interface.

57
Product Thinking

How to Run an Early Design Partner Program

A practical guide to your first three to five users: what they get, what they give, and why this matters before you launch broadly.

58
Collaboration

Human + Agentic Collaboration: the new way software gets built

Real AI collaboration isn't one person and one chat window. It's three layers, running simultaneously.

59
Manifesto

A Genuinely New Way of Building. Not an Evolution of the Old.

The temptation is to describe this as the next version of how software gets made. It isn't. It's a different thing wearing the same name.

60
Manifesto

We Learned This by Doing, Not Theorizing

The AI-native way of working didn't come from a whiteboard. It came from hundreds of engagements where we got things wrong, then less wrong, then right enough to name.

61
Manifesto

Built From Your Expertise. Beyond What Expertise Alone Could Reach.

Your domain knowledge is the foundation the product is built on. The collaboration is what lets it go beyond what your expertise alone could have produced.

62
Manifesto

AI Amplifies Your Direction. Aim Matters More Than Ever.

AI is a force multiplier for whatever you point it at. Right direction or wrong, it gets there faster. The premium on clear aim just went up.

63
Manifesto

Three Layers of Collaboration: Why They Have to Run at Once

Human with human. Human with agent. Agent with agent. The mistake is treating these like phases. They're layers, and the new way of working lives where they overlap.

64
Product Thinking

What a product brief is and why it matters more than ever

AI can build anything you describe. The brief is where you figure out what's worth describing, starting with why, then how, then what.

65
Manifesto

AI Agents Are Collaborators. We Treat Them Like It.

How you describe AI shapes what you do with it. Tool framing optimizes for instructions. Collaborator framing optimizes for the work itself.

66
Manifesto

The Closest to the Problem Should Be Closest to the Build

Domain expertise isn't input to be extracted. It's the foundation the product is built on. AI finally makes that possible.

67
Manifesto

Clients Are Co-Creators, Not a Source of Requirements

The old model extracted requirements from passive clients. The new one channels the creative energy they're already bringing.

68
Manifesto

AI Isn't Replacing Developers. It's Expanding Who Builds.

The narrative about AI and developer jobs has the unit of analysis wrong. The space isn't contracting. It's exploding.

69
Manifesto

The Biggest Shift in Human Creativity Is Happening Now

Every shift in computing follows the same arc: lower the barrier, expand who creates. We're inside the biggest one yet.

70
Stable Diffusion

Exploring Samplers in Stable Diffusion: Types, Approaches, and Practical Differences

How Stable Diffusion samplers differ: ancestral vs deterministic, the Karras schedule, step counts, and which sampler to use, with side-by-side examples.

71
AI Art

What Are LoRAs? A Beginner's Guide to LoRA Models

What are LoRAs? Low-Rank Adaptation models fine-tune Stable Diffusion for a character, style, or concept. A beginner's guide to finding and using them.

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