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SokoFlow Build Log — Month 1 of 4 Last semester I set out on a new strategic plan to level up my software development skills through deliberate, project-based learning. That work produced one of the most ambitious things I've built so far: Sim-Pesa , a local-first transactional appliance that lets developers working in the M-Pesa ecosystem test and simulate STK Push workflows entirely on their own machines, without depending on the Daraja sandbox. I documented that build in 16 weekly posts, which you can find here . This semester, the focus shifts — from fintech foundations to cloud-native integration and real-world systems. The flagship project is SokoFlow , a conversational ERP for small Kenyan shopkeepers to track inventory and record sales entirely through WhatsApp chat. No app to download, no training session required — just natural language. Where Sim-Pesa lived in a controlled, predictable transactional world, SokoFlow steps into the mess of cloud-native reality: third-party API failures, webhook signature verification, the statelessness of HTTP, and container orchestration. The target audience shifts too — Kenyan SMEs operating on infrastructure that is often unreliable by design, not by exception. It's an ambitious project, but the goal was always to learn as much as possible from it. With the plan in place, I got to work. 1. The Vision of a Headless ERP The first real question I had to answer before writing a line of code: what does "headless" actually mean? Headless architecture decouples the frontend — the "head," or user interface — from the backend, the "body" that holds the data and business logic. A conventional ERP bundles both: backend plus a dashboard or UI on top. A headless ERP, by contrast, is just the engine. The brain. There's no built-in screen. So how do users interact with a system that has no interface of its own? SokoFlow doesn't actually care. It could be: WhatsApp SMS A web app A mobile app A voice assistant In this case, the "frontend

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Why AI programs at PE portfolio companies stall at the same organizational seam, and what to do about it. Blake Aber · Predicate Ventures · 2026 There's a failure mode I've watched play out at enough portfolio companies that I've given it a name: the ownership dyad. It goes like this. The AI program is running. The product manager owns the roadmap (what the AI should do). Engineering owns the deployment (how it does it). Both parties are competent. Both are aligned on the goal. And the AI initiative quietly stalls anyway, usually somewhere between the promising pilot and the production system that was supposed to follow. The mechanism is diffuse accountability at the decision layer. What the dyad looks like in practice In the average portco planning meeting, the PM and the engineering lead sit across from each other. The PM has a change request: "The model is producing summaries that miss the key clause in contracts above a certain length. We should fix this." Engineering hears this and wants to know: is this a prompt change or a model change? Either requires scoping, and scoping requires the PM's input on acceptable behavior. So engineering asks the PM. The PM says "whatever's best technically." Engineering ships a prompt change. The next month, the same issue appears in a different context. The PM brings it back. Neither person is wrong. Neither person is slacking. The problem is structural: there's no single person who can describe (precisely and completely) what the AI should produce, evaluate whether it's producing it correctly, and approve a change to the system without requiring the other party's sign-off. The dyad looks like shared ownership. It functions as diffuse accountability. No one is in charge of the model's behavior. The failure mode at month nine Most portco AI programs that make it through a successful pilot still die quietly around month nine of production. The most common reason is not that the model got worse. It's that the harness around the m

Hi 👋 I'm Jonas. CS bachelor, Entrepreneurship master. By day I'm at nono . On the side I'm building SportsFlow solo — and I'm going to write about every hard part of it out in the open. This is the intro. What I'm building SportsFlow replaces the thing every amateur handball coach still uses: a clipboard and a pen. The idea is simple. Live-track every shot, assist and save during the game on a phone or tablet, and get real season analytics out the other end — shooting percentages, heatmaps, goalkeeper saves, momentum, lineup impact. Handball first, then volleyball, basketball, ice hockey. I sat on enough benches to know the problem is real: the data is right there in the game , and it evaporates the second the whistle blows. Nobody should need a spreadsheet and a good memory to coach with numbers. Why I write about both code and product I build at the seam between engineering and product — that's the CS + Entrepreneurship combo. So I won't only post architecture. I'll also post the decisions about what's worth building at all : where I drew scope lines, what I deliberately didn't build, how billing shapes the data model. The recurring theme in everything here is one idea: Make the correct thing structural. Idempotency in the schema, not the network. Tenancy in the procedure, not the query. Types from one zod definition, not two. Discipline the system enforces, not discipline you have to remember — because when you're solo, the stuff you have to remember eventually fails. What you'll get if you follow along Biweekly build-in-public deep-dives, including the honest costs and not just the wins: Offline-first capture — sports halls have zero WiFi, so the whole tracking pipeline works offline and reconciles later without double-counting goals. One analytics codebase, three runtimes — the same shooting-percentage code runs in the web app, the native app, and offline during live tracking, so the numbers can never disagree. Shipping four apps solo from one monorepo — web, n

I use my SSH manager every day. I also use a separate monitoring tool every day. For a long time I just accepted that these were two different things. Then one day I was SSH'd into a server that was behaving weird. I wanted to check if it was CPU or memory, but I had to open a different app, find the server in there, and wait for the dashboard to load. It took maybe 15 seconds. Not a huge deal. But it broke my flow every single time. I already had an SSH connection open to that server. Why was I opening a second thing just to see what was happening to it? That's what pushed me to build server monitoring directly into Termique, the SSH manager I've been working on. The interesting part: reusing the existing SSH connection SSH connections aren't just for terminals. The protocol supports multiple channels over a single TCP connection. You can have a terminal session running in one channel while sending short exec commands through another channel on the same connection. That's how the monitoring feature works. When you open the metrics panel for a server, Termique creates a separate exec channel on the existing SSH connection and polls /proc/stat for CPU, /proc/meminfo for RAM, and /proc/loadavg for system load. Short-lived commands, called on an interval, over the connection you already have open. No second SSH handshake. No separate auth. Just another channel on the same pipe. The tradeoff: you do need an agent I want to be upfront about this. The monitoring feature requires a small agent installed on each server. It's not agentless. I considered going agentless, relying entirely on /proc reads through exec channels. That works fine on most Linux servers. But the agent makes it easier to handle edge cases properly and opens the door for future features like alerts and longer history retention. Without it, I'd be fighting a lot of fragile shell parsing. If you're managing Linux servers, it's a one-command install. Non-Linux systems aren't supported yet. That's a real l

I got laid off in March 2026. The day HR handed me the 30-day notice, I had a small panic attack, then opened my laptop and started building things. Here's the deal: I had 30 days before severance ran out, and I wanted to see how much I could ship with AI tools before the money (and motivation) ran dry. I gave myself a single rule — every project gets a 7-day deadline, otherwise I kill it. I built 6 things. One has real users. One broke in production. Two I never opened again. This is what happened, in the order I built them. 1. AI Buddy (Chrome sidebar) — shipped, 15 users A Chrome extension that puts an AI assistant in a sidebar. Select text on any page, hit a keyboard shortcut, it goes to the AI, reply shows up without you leaving the page. Works with GPT-4, Claude, Gemini, DeepSeek. No login, no credit card. Time: 11 days (April 1–11). Status: Live on Chrome Web Store. 15 real users as of June 28, 2026. Rating 4.2. What I used AI for: 90% of the code (500 lines of JavaScript, written in Cursor). The README, the Chrome Web Store description, the marketing tweets — all AI-drafted, then I rewrote the parts that sounded like AI. What went wrong: The first version had a Stripe integration. AI wrote 90% of the webhook signature verification. I had to rewrite it from scratch. Also the model-picker UI went through 5 revisions because AI kept proposing what looked right but didn't work. → Chrome Web Store 2. Weekly report generator — personal use only Every Friday at 4pm, a script grabs my git commits, Slack messages, and Linear ticket changes, throws them at GPT-4, and asks for a "manager-readable" weekly report. I review, tweak, send. Time: 2 days. ~200 lines of Python. Status: Running for 11 weeks. Has 1 user. Me. Cost is $0.12/week. What I used AI for: The prompt. It's surprisingly tricky to get GPT-4 to write a weekly report that doesn't sound like a robot. The single most useful line: "if you don't have data, write 'no progress this week' — don't make things up." T

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.

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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

Back in May we here at Waggy Labs launched the beta release of our Web Component UI kit " Yumekit ". Yumekit is a pure web component UI toolkit. Upon its release, it was comprised of roughly 36 fully styled and fully functional UI components that work with just about every web architecture straight out of the box. No configuration or setup necessary, all one needs to do is include the Yumekit script (using either a CDN or installed through NPM) and start building. All components come styled out of the box with no need to include any style sheets. Last week, we launched version 0.5. With this latest release, that job is being made easier with the inclusion of new components that add several layout options as well as new Data, Navigation, and Utility components, bringing the total number of components to 51. For us, this toolkit has provided us a framework-agnostic solution for our internal tools as well as any client projects. With over 60 themes spread over 9 well-known (and some brand new) open source Design Systems all built directly into the library, we have plenty of options available to us to keep our designs fresh without needing to spend hours dealing with CSS. It's light-weight, dependency free, and well documented. New in 0.5 Animate The y-animate component allows you to animate entrances and exits for nested components using a few simple configuration attributes. Code The y-code component allows you to display formatted and colorized code, as well as providing a few easy and convenient ways for your users to copy the provided code. Help The y-help component provides a tutorial experience for users of your application with minimal configuration. Simply provide the elements to be highlighted, the messages to be shown, and it handles the rest! Paginator y-paginator provides a configurable set of pagination buttons to help your users navigate through large data sets. Sidebar We had originally included a y-appbar component (which we still do) that had a "Sideba

You built a Mac app, you want to sell it outside the App Store, and now you need licensing: a key the customer enters, an activation that sticks, and feature gates that hold up offline. Here's how to do it in an afternoon without standing up a backend. Note: this is cross-posted from the Keylight blog . I build Keylight, so this uses it as the worked example — the shape of the solution applies whatever SDK you choose. The three things licensing actually has to do Strip away the marketing and every licensing system does exactly three jobs: Activate — turn a key the user pastes in into proof-of-purchase bound to this device. Verify — on every launch, confirm that proof is still valid, including offline . Gate — unlock features based on the tier/entitlements the license carries. If you build this by hand you're writing a server, a crypto layer, and a state machine. The point of an SDK is to skip all three. 1. Add the SDK Add the Swift package in Xcode (File ▸ Add Package Dependencies) pointing at the Keylight Swift SDK, then configure it once with your tenant key at app launch: import Keylight let keylight = Keylight ( tenant : "your_tenant_key" ) 2. Activate a key Give the user a text field and call activate . This is the one online step — it exchanges the key for a signed, device-bound lease that's stored locally: do { try await keylight . activate ( key : enteredKey ) // lease stored — the app is now licensed on this device } catch { // show the user why: invalid key, device limit reached, etc. } 3. Verify on launch (offline-safe) On every subsequent launch you don't hit the network. The SDK verifies the stored lease's Ed25519 signature locally and hands you a state: switch keylight . checkOnLaunch () { case . licensed ( let lease ): unlockApp ( entitlements : lease . entitlements ) case . trial ( let daysLeft ): runTrial ( daysLeft : daysLeft ) case . expired , . invalid : showActivationScreen () } No server call, so the app opens instantly and works on a plane. Th

So far we've been working with Swift's built-in types — String, Int, Bool, Array. But what if you...

Chinese companies control nearly two-thirds of Argentina’s own squid fleet.

How I built an open-source Slack assistant with persistent semantic memory, powered by any LLM and Mem0's managed memory layer — no vector database required. The problem with Slack bots and memory Most AI Slack bots have the memory of a goldfish. Every conversation starts from scratch. You ask it about your sprint goals, it gives a great answer, then three days later you ask a follow-up and it has no idea what you're talking about. You end up re-explaining context constantly. The commercial solution to this is Claude Tag — a Slack integration that maintains genuine conversational continuity. But it's tied to one provider and not open-source. slacktag-oss is our attempt to replicate that experience: a Slack bot with real, semantic, persistent memory that works with any LLM — including ones running entirely on your laptop. What I built A Python Slack bot with: Socket Mode for local dev (no public URL needed), HTTP-ready for prod LangChain to abstract LLM calls across any OpenAI-compatible endpoint Mem0 managed cloud for semantic memory — no Qdrant, no Pinecone, no infra to run Three memory scopes: per-channel, per-thread, per-DM Built-in !clear and !memory commands A clean, extensible architecture you can fork and build on Architecture Before diving into code, here's the full request lifecycle: ┌─────────────────────────────────────────────────────────────┐ │ Slack │ │ @mention in channel ──┐ │ │ DM to bot ──┼──► Slack Events API │ │ Thread reply ──┘ │ │ └───────────────────────────────────│─────────────────────────┘ │ (Socket Mode / HTTP) ▼ ┌─────────────────────────────────────────────────────────────┐ │ slack-bolt (Python) │ │ bot.py ──► router.py ──► handler.py │ │ │ │ │ ┌───────────────┤ │ │ │ │ │ │ ▼ ▼ │ │ Mem0 Client LangChain │ │ (managed) ChatOpenAI │ └────────────────────────────────────────────────────────────-┘ │ ▼ ┌───────────────────────┐ │ Mem0 Managed Cloud │ │ Vector Embeddings │ │ Entity Extraction │ │ Deduplication │ └───────────────────────┘ The ke

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

Three bugs this month. All three looked fixed before they broke. The date was quoted in 51 out of 52 posts. The model was pinned to a specific version. The upload feature had been working in production for weeks. Each one passed the obvious checks and failed somewhere else. That's the theme for this Friday Fixes: the fix that wasn't. Not bugs that went unnoticed, but bugs where a defense existed and the failure found its way around it. 1. The Unquoted Date, Part Two If this one sounds familiar, it should. I wrote an entire Friday Fixes post about this exact bug class five weeks ago. An unquoted YAML date. gray-matter parsing it as a Date object instead of a string. A crash downstream. Last time it took down /admin/drafts . The fix hardened formatDate() to coerce Date objects before calling .includes() . I verified it. I shipped it. I wrote 2,000 words about it. I moved on. This time it took down the homepage. The symptom: vibescoder.dev loaded for a split second, then flashed to Chrome's "This page couldn't load" screen. Every browser, every profile, every device. The site was completely dead to visitors. The twist: curl returned HTTP 200 with ~900KB of fully rendered HTML. The server was fine. The crash was happening during React hydration in the browser, invisible to any server-side test. The cause: A new post had date: 2026-06-19 in its frontmatter. No quotes. gray-matter parsed it as a Date object. In posts.ts , the code does const meta = data as PostMeta and then spreads ...meta into the return value. The as PostMeta cast told TypeScript the date was a string . At runtime, it was a Date . That Date object flowed through the server component, through the RSC serialization boundary, and into PostListWithFilters , a "use client" component. React couldn't hydrate it. No global-error.tsx existed to catch the crash. Dead page. Why the May fix didn't prevent this: Because the May fix was in the wrong layer. It hardened formatDate() , the function that happened to cras

After testing each of the MacBooks myself, here's my honest advice on which is right for you.

Want to turn a small ESP32 board into a mini arcade game you can actually play? This ESP32 OLED Mini Shooter Game uses a 128x64 OLED display and two push buttons to create a simple shooter experience. The player moves left and right, bullets fire upward, and enemies fall from the top of the screen. It is a small project, but it already feels like a real handheld game once the display starts updating. This build is a great next step after basic OLED and button tutorials. Instead of only printing text or drawing one shape, the code manages several moving objects at the same time. It tracks the player, bullets, enemies, collisions, and game-over state. The screen is divided into a simple grid. The 128x64 OLED becomes a 16x8 playfield, where each tile is 8x8 pixels. This makes object movement easier to understand because the player, enemies, and bullets move by grid position instead of raw pixel math. Why build it? This project teaches interactive programming on real hardware. The ESP32 reads button input, updates game objects, checks collisions, and draws the next frame on the OLED. That is much more active than a normal sensor display project. It also teaches timing without blocking the whole game flow. The code uses millis() to control when bullets and enemies update, so they can move at different speeds. This is useful because many embedded projects need timed actions without stopping everything else. What you'll learn ESP32 OLED display control - drawing text, squares, circles, and game objects on an SSD1306 screen. Custom I2C pins - using Wire.begin(5, 19) so the OLED uses GPIO5 for SDA and GPIO19 for SCL. Button input handling - reading two push buttons for left and right movement. Debounce logic - preventing one press from being counted many times. Grid-based game design - turning a 128x64 screen into a simple 16x8 game map. Game object arrays - storing multiple bullets and enemies with active/inactive states. Timer-based updates - using millis() to move bullets

Cloudflare has recently shared how they uncovered an issue in their Rust implementation of CUBIC, a congestion controller algorithm, which prevented it from recovering from a scenario of heavy packet loss at the start of a connection. By Gianmarco Nalin