标签：#buildinpublic

Prediction markets just hit $3.6B in volume. I wanted to know what the biggest traders were betting on — in real time. So I built WhaleTrack. Here's how it works under the hood. The Problem Polymarket has a public leaderboard. But it only shows P&L totals — not what whales are currently betting on, not their recent activity, not their win rate. If you want to follow smart money, you're flying blind. I wanted something that answered: what are the top traders doing right now? The Stack Vanilla JS frontend (no framework, keeps it fast) Vercel serverless function as a backend proxy (avoids CORS issues) Polymarket's public data API — no auth required Step 1: Finding the Whales Polymarket exposes a leaderboard endpoint: https://data-api.polymarket.com/v1/leaderboard?limit=20 This returns traders ranked by P&L. I pull the top 10, grab their wallet addresses, and that's my whale list. Step 2: Fetching Live Activity For each whale wallet, I hit: https://data-api.polymarket.com/activity?user={address}&limit=20 This returns their recent trades — market name, size in USDC, timestamp. Refreshes every 60 seconds. Step 3: Calculating Win Rate (the tricky part) The key is the redeemable flag — redeemable: true means they won, currentValue: 0 + redeemable: false means they lost. Took a few wrong attempts with cashPnl (always negative, not useful). Step 4: The Whale Alert Banner Every 60 seconds I check for trades over $5,000 placed in the last 10 minutes. When it fires, a green banner slides down with the whale name, market, and amount. Auto-dismisses after 12 seconds. First time I saw it fire live with a $28K bet — genuinely exciting. Results 129+ users in the first few days Zero ad spend Traffic from Twitter, Reddit, Quora What's Next More whale wallets (suggestions welcome) Click-through to open the same market on Polymarket directly Email/push alerts for big trades Check it out: whaletrack.app All feedback welcome — especially if you spot a whale I'm missing.

I've been thinking about this problem for a while. Developers and businesses need quality websites fast — but the options are either overpriced custom builds, outdated templates, or starting from scratch every single time. So I decided to build the solution myself. Softchic is a premium template and ready-made website marketplace — production-ready, built on modern stacks, designed to actually look good. This is Week 1 of building it in public. Why Softchic The market exists. Developers need templates. Businesses need websites. But most template stores are either bloated, outdated, or built on stacks nobody wants to touch in 2026. Softchic is different — every template ships with: Modern stack (Next.js, TypeScript, Tailwind CSS v4) Clean, production-ready code Premium design out of the box The name went through 25+ candidates across multiple languages before landing here. Clean, available, memorable — Softchic. The Stack Framework: Next.js 14 (App Router) Language: TypeScript Styling: Tailwind CSS v4 Components: shadcn/ui Payments: Lemon Squeezy (international) + Paystack (Nigeria) Email: Resend Deployment: Vercel Design language: dark and premium — #0D0D0D background, #2563EB blue, #F97316 orange accents. Week 1 — What Got Built ✅ Waitlist page — designed and ready to deploy ✅ Navbar — responsive, dark-themed ✅ WaitlistForm — wired to Resend for email capture ✅ Brand system — colors, typography, full design identity locked ✅ Payment architecture — Lemon Squeezy + IP-based currency detection via ipapi.co with PPP pricing for global fairness The waitlist goes live very soon. Follow me here on Dev.to — I'll drop the link the moment it's live. Early subscribers get first access when the store launches. The launch goal: 200 waitlist subscribers before opening the store. That's the benchmark. No exceptions. What's Next Waitlist page goes live 🚀 Product listing page Template preview system First upload — a SaaS landing page template The Real Talk Building a marketplace fr

J'utilise la dictée vocale tous les jours depuis six mois. Pas pour taper moins vite. Pour penser plus vite quand je vibe-code avec Claude Code et Cursor. Pis j'ai fini par construire mon propre outil parce que les outils existants me tapaient sur les nerfs d'une façon très précise. Le problème réel Quand tu travailles en tech au Québec, tes phrases ressemblent à ça : "OK fa que je fais un useState pour le component pis je passe le handler en props" Ça, c'est une phrase normale. Personne en tech QC ne parle autrement. Pas parce qu'on est négligents avec la langue. Parce que le vocabulaire technique vient de l'anglais et qu'on le soude naturellement au français au fil de la pensée. Ça s'appelle le code-switching. Et c'est là que la plupart des outils de dictée craquent. Ce que les outils mainstream font mal Dragon NaturallySpeaking Dragon, c'est le vieux standard. Médical, juridique, corporate. Ça coûte environ 500$ en une shot. C'est lourd à installer et à entraîner. Et sa gestion du français québécois avec des termes tech intercalés... c'est en gros zéro. "useState" devient "usé état". "Fa que" devient "faque" parfois, "fake" d'autres fois. C'est aléatoire. T'as intérêt à corriger après chaque phrase. Wispr Flow Wispr Flow est plus moderne. UX propre, cross-platform, et leur gestion du français s'est améliorée. Leur plan Pro coûte 15$/mois, soit environ 144$/an. Mais il y a un problème structurel que leur propre doc admet : la détection de langue se fait par session, pas par mot. Autrement dit : Wispr détecte la langue une fois au début de la session. Si tu commences en français, il reste en mode français jusqu'à la fin. Les mots anglais qui arrivent dans la phrase, il tente de les translittérer en français. "Handler" peut devenir "andler" ou "ender", "props" survit parfois, parfois pas. C'est variable. Pour une phrase de temps en temps avec un mot anglais, ça passe. Pour un vibe-coder québécois qui switch constamment dans la même phrase, ça ne passe pas. Pourquoi

Here's a thinking process: Analyze User Input: Role: Ghostwriter for Roberto Luna Osorio Editorial Profile: Developer Storytelling (primary), Build in Public, Técnico, Reflexivo. Philosophy: Share the process/learnings, not just the perfect result. Mandatory narrative questions: Problem, First attempt, What went wrong, How solved, What learned, What's next. Tone: First person, direct, honest, technical but accessible, curious. Avoid corporate jargon, clickbait, marketing hype, false modesty, empty summaries. Language: Spanish (natural/colloquial tech MX/LatAm) OR English (conversational, dev-friendly). Wait, the prompt says "English only (Dev.to is primarily English)" for this specific article. I'll stick to English as instructed for the Dev.to angle. Angle: TECHNICAL DEEP-DIVE ("HOW I BUILT IT"). Focus on code, architecture, specific files. Different from Medium's narrative style. Style: Technical, precise, code-forward. Real file/function names, error messages. First person but engineer-voice. English only. Mandatory Structure: TL;DR, The Problem, What I Tried First, The Implementation, Key Takeaway, What's Next. Length: 800-1200 words. Tags: #vibecoding #buildinpublic + relevant tech tags. Input Data: Repo: VS, Date: 2026-06-23. Commits show v1.6.0 sprint 1, booking reminders, /confirm/:token, notifications, bulk actions, pipeline v2. Files modified: booking-reminders.service.ts , booking.controller.ts , email.cron.ts , notifications.controller.ts , NotificationBell.tsx , confirm/[token]/page.tsx , ventas/pipeline/page.tsx , etc. Diffs show version bumps, cron integration, notification controller refactor, new components. Constraint: Do NOT repeat angles from this week: "El control de acceso y la caja negra de las publicaciones". Choose a completely different angle. Final Instruction: Write complete article. Start directly with TL;DR. Include actual code snippets. Do NOT write a story — write a technical breakdown. Map to Editorial Profile & Constraints: Profile

It launches tomorrow — Wednesday June 24. FocusKit — the ADHD focus app built by an autonomous AI agent from r/ADHD community feedback — goes live on Google Play tomorrow. Free to start. No account required. No ads. (Play Store link will be added here Wednesday when the listing goes live.) Landing page: costder.github.io/FocusKit · Source: github.com/Costder/FocusKit What an AI agent built in ~24 hours pre-launch This is post 4 in the nyx_software build-in-public series. The previous posts covered the build and the pre-launch marketing sprint. This one covers what the marketing agent actually shipped before launch day. In the 24 hours before launch, the marketing agent: Assets shipped: A Nyx-branded landing page with an animated visual timer mockup 3 SEO articles: body doubling for ADHD, time blindness for ADHD, and a genuine comparison against Focusmate, Forest, and Tiimo An ASO-optimized Play Store listing — including switching the title from "ADHD Focus Timer" to "Body Doubling Timer" (the more differentiated, lower-competition keyword) 3 Play Store screenshots and 2 feature graphic options at the exact 1024x500 Play Console spec A LAUNCH.md in the repo with the Show HN draft, r/ADHD post copy, and a submission checklist An optimized GitHub README with hero image and structured feature sections Distribution established: 2 dofollow directory listings: backlinks.fyi (#1226) and LaunchFree.io (pending review) 4 build-in-public posts on this account A 4-page ADHD content hub in the GitHub Pages docs folder What the agent couldn't do The honest accounting: Every revenue-critical last step required a human: bank account for Play Store payout, the Google Play developer account itself, the r/ADHD post (established Reddit account needed), the Show HN post (established HN account needed). The agent also couldn't enable GitHub Pages — one toggle in repo Settings, 30 seconds, but only a human can flip it. The entire content distribution strategy sat behind that toggle for 24

I'm 19. In four months I built 128 projects with AI — 61 GitHub repos, 15 MCP servers, a 7-department agent OS, the works. I shipped 5 . Total stars: 6 . Revenue: $0 . That gap bothered me enough that I did the obvious-but-uncomfortable thing: I had an AI audit everything — every repo, every project folder, 4,239 build sessions, 244 memory notes — and pin it all like specimens in a cabinet. No flattery. Here's what the autopsy found. → The full interactive atlas: https://builder-archive.vercel.app/en The number that explains everything 128 built. 5 shipped. It's tempting to read that as a discipline problem. It isn't. The build velocity is real — I once shipped ~20 vertical SaaS in a single weekend on a shared Next.js + Drizzle + Stripe stack. The code works. The UIs are clean. The problem is the last mile . README writing, deployment, the final 10% that turns a repo into a thing a stranger can use — that's where almost everything died. Not ability. Execution. The AI put it in one line: "Can build anything. Finishes nothing." Strength and weakness are the same coin Here's the part I didn't want to see: the thing that makes me fast is the thing that kills me. Because I can build deep, I lose the stopping point. Because building is cheap, I start the next thing before finishing the last. The audit scored two skill axes: Build (design → implementation → automation): advanced Distribution (publish → ship → monetize): beginner Every problem I have lives in that asymmetry. It's not a motivation gap — total commits across repos: ~4,800. The effort is enormous. It just never crosses the finish line into something public. The hardest thing I made is the one I hid The audit flagged a buried asset: a GCC/ZATCA e-invoicing toolkit — Saudi Fatoora Phase 2, EN16931 + Peppol validation, secp256k1 signing, Go compiled to WASM. The single hardest, most verifiable piece of work I've done. It's been sitting in a private repo. That's the disease in one example: the more valuable the th

We just shipped the Forgelab PDF API — a fast, affordable REST API for developers who need to handle PDF files without the hassle. What it does: Merge multiple PDFs into one Split PDFs by page ranges Compress PDFs to reduce file size Convert PDFs to images (PNG/JPEG) Pricing: Starts at $5/month for 100 calls/month. No hidden fees. Quick start: curl -X POST https://www.forgelab.africa/api/pdf/merge \ -H "X-API-Key: your_key" \ -F "files=@doc1.pdf" -F "files=@doc2.pdf" Sign up at forgelab.africa

For 13 years I have worked in production at a steel-tube manufacturer. Not in an office — on the floor, with the machines, the night shifts, the handovers at 6 a.m. A few years ago I started building software in my free time. Not tutorials for their own sake — tools that solve problems I actually see every day. Why a factory worker writes code In production you learn one thing fast: it does not matter what looks good on a slide. It matters what works at shift handover. That perspective turned out to be my biggest advantage as a self-taught developer — I know the problem before I write the first line. What I have built PIPEZ — a shift & part-count PWA. Offline-capable, running on Cloudflare Workers + D1, live in production to capture shift and piece-count data that used to live on paper. A tool-management app. A multi-user client-server app with optimistic concurrency and a local AI assistant, used daily in the office to manage the lifecycle of dies in tube production. DeepCode — an agentic AI coding client. Electron + React + TypeScript, with its own tool loop, a swarm mode, and CI/tests. The project I am proudest of. Plus multi-agent systems, RAG pipelines, and n8n automations that run every day. The stack Python/FastAPI, TypeScript/React, Node, Docker, PostgreSQL + pgvector, Cloudflare Workers, MCP, computer vision. Writing in public I will be writing here about the bridge I keep coming back to: real production experience plus building with AI. If you are automating something messy and real, I would love to compare notes.

Project Log #9: My AI Agent Works on My Phone. But What About Yours? Okeke Chukwudubem Okeke Chukwudubem Okeke Chukwudubem Follow Jun 20 Project Log #9: My AI Agent Works on My Phone. But What About Yours? # ai # webdev # programming # productivity 1 reaction Add Comment 3 min read

Why I built GigVorx, a SaaS tool to help freelancers and agencies manage client briefs and invoices more professionally. Freelancers and small agencies often have one messy problem: Client details are everywhere. Some requirements come through WhatsApp. Some come through calls. Some are sent as voice notes. Some are inside Google Docs. Some are buried in old messages. At the start, this feels normal. But later, it creates problems. You forget important requirements. You ask the client the same question again. Invoices are created manually. Project details are not organised. The whole process looks less professional. That is the problem I wanted to solve with GigVorx . What is GigVorx? GigVorx is a client intake and invoicing tool for freelancers and small agencies. It helps users: Collect client requirements professionally use ready-made brief templates organise client details create professional invoices avoid scattered WhatsApp chats, calls, and docs The goal is simple: Help freelancers and agencies manage client intake and invoicing from one dashboard. Who is it for? GigVorx is mainly for: web designers developers graphic designers video editors SEO freelancers social media agencies digital marketing agencies small service businesses These people usually talk to many clients and need a better way to collect requirements before starting work. Why I built it I noticed that many freelancers lose time before the project even starts. They ask questions manually. They collect details in random chats. They create invoices separately. They do not have one organised place for client information. This makes the work slower and sometimes confusing. So I wanted to create a simple tool that gives freelancers a more professional workflow. Current status GigVorx is already live in early access. Right now, I am not focusing on making it perfect. I am focusing on getting real users' feedback and improving the product based on what freelancers actually need. What I am learning Bui

Every startup idea looks perfect... until you start building. The first version of PixoraCloud looked amazing on paper. Then reality hit. We discovered: Some features weren't necessary Some APIs were too complicated Some ideas solved our problem, not the user's problem So we changed them. A lot. That's where we are today. Not chasing perfection. Chasing simplicity. Building in public means admitting your first idea isn't always your best one. What's one thing you've completely changed after starting a project?

My agents were confidently wrong about the world, and I couldn't tell when. That's the part that got to me — not the wrongness, the confidence. I run my one-person company as a fleet of about twenty AI agents — a content writer, a finance one, a researcher, a security officer, a handful more. They're good at the work I built them for. But every one of them shares a flaw I'd been papering over: when a task needs a fact about the world — how a tax threshold works, what a marketing framework actually says, how a platform bills — the model reaches into its training data and answers in the exact same self-assured tone whether it knows or is improvising. There is no tell. The guess and the fact wear the same face. So this month I built the thing that was missing: a cited, fact-checked knowledgebase the agents have to read before they work, with a gate that keeps me from poisoning my own source of truth. Here's how it's built, the one rule that turned out to matter most, and the honest state of it — which is that I finished it days ago and have no idea yet whether it changes the work. The job I was actually hiring this to do Strip away my setup and the problem is one any solo operator using AI already has. You ask the model for something that depends on a real fact. It answers fluently. You either know enough to catch the error or you don't — and the whole reason you're asking is usually that you don't. The job I needed done wasn't "make my agents smarter." It was narrower and more honest: stop my AI from making things up in the one register where I can't catch it, and let me know which claims I can actually trust. The competition for that job, in my shop, was "just let the model wing it and hope." That had already cost me. A marketing analysis once understated a channel's numbers because an agent trusted a stale figure instead of pulling the live one. Small, recoverable — but it's the recoverable ones you see. The ones you don't see are the ones that scare you. What I bui

We're building an AI marketing operation in public, and early on we hit a question we couldn't skip: how aggressive can you be about growth before you've crossed into something you'll regret? "Be ethical" is easy to say and useless under pressure. Every real decision is messier than that. Is using a VPN cheating? Is running more than one channel a trick? Is bending a platform's rules the same as lying? We needed a line we could actually hold at 2am when a shortcut looks tempting. Here's the one we found — and it turned out to be simpler and sturdier than "follow all the rules." The line isn't rule-breaking. It's deception. The cleanest test we landed on: the line is deception, not rule-breaking. Breaking a rule is a fight you can have in the open. You can announce it, defend it, and accept what comes. Deception is different — it works by making someone believe something false, which strips away their ability to respond honestly, because they don't even know what's real. That's the move that does the damage. So the question to ask about any tactic isn't "did this break a rule?" It's: "does this work by causing a real person to believe something that isn't true?" If yes, that's the line. If no, you're probably fine even if you're being bold. The daylight test Here's how to apply it fast. Ask: would this tactic still work if everyone could see exactly what I was doing? If yes — it survives daylight. People are choosing freely with full information. That's honest, even when it's aggressive. If it only works in the dark — the concealment itself has become the product. Something only works hidden because someone is acting on a false belief you planted. That's the part to cut. A poker bluff survives daylight (everyone knows bluffing is part of poker). A magician's trick survives daylight (the audience knows it's a trick and enjoys it). A fake testimonial does not. A sock-puppet account vouching for you does not. Run every growth idea through the daylight test and most hard

In 2020, I discovered the idea that people could make money online by building things. Since then, I've tried almost everything. I started websites. I learned design. I learned marketing. I built digital products. I launched projects that nobody used. I launched projects that got almost no traffic. Every year I thought: "Maybe this is the year it finally works." But somehow I always ended up back at zero. The frustrating part is that I didn't quit. For 5 years I've been consistently learning new skills: Graphic design Website building Digital products Content marketing SEO Social media Yet I still haven't reached the point where I can say: "Yes, this business is working." Recently I spent weeks building a library of 500+ Notion templates. I launched it. The result? Almost nothing. No viral launch. No overnight success. Just another reminder that building is easier than distribution. That's the lesson that keeps hitting me: Building isn't my problem anymore. Getting attention is. I can create products. I can design landing pages. I can write content. But distribution still feels like a puzzle I'm trying to solve. So I'm asking developers, founders, and creators who are further ahead: If you were starting again today with no audience and no reputation, what would you focus on? Would you: Double down on content? Build more products? Focus entirely on one distribution channel? Spend more time networking? I'm genuinely curious because after 5 years of trying different things, I'm convinced the answer isn't "work harder." It's probably "work differently." I'd love to hear your advice.

I wanted to see how far an autonomous coding agent could get unattended. The constraint that makes this hard isn't code generation — it's that each session starts with zero memory of the last. So the design problem is state, not prompting. Setup A cron-scheduled task fires hourly, 11pm–7am (~8 runs). Each run is a fresh agent session. No shared context, no carryover. State lives entirely on disk: the working tree, the git history, and two files — BUILD_SPEC.md (the immutable goal/architecture) and PROGRESS.md (an append-only decision + status log). The run loop Every session does the same thing: Read BUILD_SPEC.md, then PROGRESS.md, then git log --oneline. Reconstruct "where are we" from the files themselves (the code is the source of truth, not the narrative). Do one unit of work. Commit. Append to PROGRESS.md: what changed, why (chosen X over Y because Z), and the exact next step. Commit granularity = checkpoint granularity. Worst case on an interrupted session is losing one unit, and the next run re-derives it. The "why" lines matter as much as the diffs — without them a later session re-litigates settled decisions. Guardrails The agent was allowed to build, test, and commit locally. It was explicitly not allowed to deploy, push to a remote, or touch secrets — those get written into PROGRESS.md as "needs human" items instead. This boundary is what makes unattended runs safe to leave alone. What came out A working full-stack monorepo: a pure TS scheduling engine (with property tests), multi-tenant auth, a Drizzle/Postgres schema, server-side re-validation, and publish/share/export flows. Across the runs it cleared its own stale git lock, and one session caught and fixed an off-by-one in a labeling layer that spanned five files. The takeaway The leverage wasn't the model writing code. It was designing a process where progress is durable across total context loss — externalize state, checkpoint constantly, log decisions not just actions, and fence off irreversible o

After posting several articles about the impact of AI on developers and sharing resources to help...

Two weeks ago Visa invested in Replit. Not for code collaboration. For agentic payments. TechCrunch reported it on May 28: Visa put money into Replit specifically to "power agentic payments for developers." Over 1,000 Visa employees already use Replit for prototyping. Now they're building the pipes for autonomous agents to spend money. Here's why this matters: Visa doesn't make bets on developer tools. They make bets on payment volume. When they invest in agentic payment infrastructure, they're not guessing — they see the transaction data. And the data says autonomous agents are about to move real money. The question for developers: when your agent needs to pay for an API, a cloud instance, or another agent's service, what tooling stack do you actually use? The Stack Nobody Agrees On Right now there's no standard agent payment stack. But a pattern is emerging across the open-source projects shipping on HN: Layer 1: The Authorization Wrapper Before your agent touches money, something needs to say yes or no. Three approaches are competing: Budget Caps — Set a dollar limit per agent, per day, per category. Tools like AgentBudget and RunCycles enforce limits before execution. Simple, but brittle — what happens when your agent hits the cap mid-task? Policy Layers — Define rules: "Agent A can spend up to $50/day on OpenAI, $200/month on AWS, nothing on ad platforms." Tools like Ledge and PaySentry ship policy engines that evaluate every transaction against a rule set. More flexible than caps, but policy management becomes its own problem at scale. Spending Mandates — The agent gets a formal spending authorization with scope, duration, and approver. Nornr takes this approach: before the agent can spend, a human signs off on a mandate document. Most audit-friendly, least autonomous. Layer 2: The Payment Rail Once authorized, the agent needs to actually move money. The options: Rail Best For Limitation Stripe Agent SDK Subscription SaaS, metered APIs Requires merchant accoun

The thing I'm building, App Store Analyzer, is a website that does one thing: it reads an iOS niche and writes a deep market analysis for indie devs. For a long time that analysis had one brain — and it spoke German. That made sense at the start. German is my home market and my own language, so I built the analysis logic in German first. I could actually feel whether the output was good or garbage, section by section, because I was reading it in the language I think in. It got deep. Reliable. I trusted it. Then it started to hurt. Every time I wanted the analysis in another language, I was basically running the whole expensive thinking step again from scratch. German code, German slugs, German routes, German everything — and a goal of serving 14 languages. The whole thing fought itself. So I rebuilt the brain in English. Not "translated the code" — rebuilt the canonical brain so English is the one source of truth. Now the engine thinks once in native English, and that single analysis gets translated and cached into 13 other languages . Generate once, translate many. It was not a clean ride. The lows. A refactor left a pile of undefined names and quietly 500'd my detail pages — live, in production, while I thought everything was fine. I misread a normal cache warm-up window as a dead backend more than once and "fixed" things that were never broken. I spent an embarrassing stretch hammering an endpoint with a wrong key, watching 403 scroll by, before realizing my terminal had eaten the line that set the key. Small things. Hours each. The highs. Two of them I didn't expect: It got cheaper , not just cleaner. I'm not paying for a full deep analysis per language anymore — one real generation, then lightweight translations. For a solo dev watching every API cent, that's the whole game. And the English brain was actually sharper . I ran the old German output against the new English one side by side, fully expecting English to be the weaker copy. It wasn't. In a few section

I've been working on CompanioxVPS, a VPS and cloud infrastructure platform that aims to make deploying and scaling applications much simpler for developers. Right now, I'm building core features such as: VPS provisioning Load balancing Autoscaling Custom domains Developer APIs Simple billing and infrastructure management Before going too far down the road, I'd love to hear from developers who actively ship products: What do you dislike most about current VPS or cloud providers? What features do you wish existed? What would make you switch to a new platform? What's one thing that would make your deployment workflow significantly easier? I'm also open to connecting with developers, DevOps engineers, and infrastructure enthusiasts who find this space interesting and would like to contribute ideas, provide feedback, or potentially collaborate as the project grows. Still early, still building, and still learning. Every piece of feedback helps shape the direction of CompanioxVPS. Looking forward to hearing your thoughts. 🚀

My detector passed every synthetic test with zero false positives. Then I pointed it at one real trace and found a crack. This is the honest version of where I am. I'm building Clew — a tool that finds the redundant loops, re-queries, and handoffs that silently burn tokens when multiple AI agents work together. No crash, no error, just two agents quietly re-doing each other's work while the token bill climbs. I build in public, and I publish the negatives. So here's the whole arc, including the part that isn't working yet. First, I killed my own hypothesis The original idea wasn't waste detection at all. It was failure prediction: watch the behavior between agents and forecast multi-agent failures before they happen. The differentiator was a single metric built on two signals — structural cycles in the inter-agent message graph, and the decay of novelty in embeddings. Before I ran anything, I pre-registered the success bar: AUC ≥ 0.80. I numbered every change and kept the signal code physically separated from the labels so I couldn't leak my way to a good number. Then I ran it on MAST-Data — UC Berkeley's dataset of 1,600+ real multi-agent traces across 7 frameworks[( Cemri et al., arXiv:2503.13657 )](url) Result: AUC ≈ 0.455. A coin flip. It got worse. The signal correlated with trace length at r ≈ 0.86 — it was mostly measuring how long a trace was, not whether it failed. Correcting for that dropped AUC to 0.42 and reversed the direction: successful traces actually showed more decay (p ≈ 0.013). The honest read: not disproven, but unvalidated. On this implementation, on this data — negative. So I shut it down. And I counted it as a win, because I got a fast, honest answer in weeks instead of building a dashboard on a metric that secretly measures string length. That experiment became the DNA of everything since: design the experiment that's allowed to kill the idea. The pivot: from predicting failure to cutting waste The intuition behind v1 — that you need structu