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What Are the 36 Stratagems? If you've heard of The Art of War, think of the Thirty-Six...

Tesla touts Autopilot as lifesaving a day after grandmother died in crash.

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

Everyone says our job got easier. The people doing it are quietly falling apart. Here's the part nobody at the dinner table wants to hear: AI didn't make software engineering easy. It made it relentless. Your uncle thinks you press a button now. Your PM thinks the estimate should be half what it used to be. LinkedIn thinks you're either an "AI-native 10x engineer" or a dinosaur waiting for the meteor. And somewhere in the middle of all that noise is you, doing two jobs at once and wondering when you stopped recognizing the one you signed up for. If that landed, keep reading. This one's for you. 💡 The Lie Everyone Has Agreed To Believe The story the world has settled on is simple: AI writes the code now, so the hard part is over. It's a comforting story. It's also wrong in a way that's hard to explain to anyone who hasn't sat in the chair. Yes, the blank-file problem is mostly solved. Boilerplate, scaffolding, the first rough pass at a function, all of that is faster than it's ever been. The problem is "writing the lines" was never the expensive part of this job. The expensive part was always judgment. Knowing what to build, knowing why it breaks, knowing which of the model's three confident suggestions is the one that quietly corrupts your data at 2 AM is where the engineer earns their salt. AI didn't remove that work. It buried it under a pile of plausible-looking output you now have to review, verify, and own. So the meter didn't slow down. It moved. You spend less time typing and far more time deciding, validating, and cleaning up. To everyone watching from outside, that looks like less work. From inside, it's a heavier cognitive load on a shorter clock. Sound familiar? The Treadmill Nobody Put On the Job Description The cost no one talks about is the half-life of what you know is collapsing. Five years ago you could learn a framework and ride it for a few years. Now a tool you mastered in January has three competitors and a new paradigm by June. New model, new c

The cuts and redundancies are part of a plan to "simplify the company," the CEO says.

The high-altitude race is a unique test of car and driver.

Let's be fair. The title of this post is confusing at first, but once you read it in full, I hope you...

If you're learning Data Analytics and looking to build a strong portfolio, working on real-world...

Por muito tempo eu acreditei que programação e desenvolvimento de software como sinônimos. Na...

TL;DR For a long time, I told myself I would start documenting my journey later. I thought I needed...

Let me confess something a little creepy. I have a habit of peeking at other people's dev posts. Not stealing the writing — relax. I run a tiny read-only job that fetches the public pages on dev.to, Zenn, and Qiita and counts only the boring parts: titles, post times, like counts. Who published what, at what hour, and how far it traveled. Then it tallies the lot. The reason is petty: my own posts weren't landing. The content is already in my hands — so I wanted to know how much the rest, the when and how you publish , actually moves the needle. By the numbers, not by gut. So I counted across three platforms. And the conditions that make a post fly turned out to be roughly mirror images between Japan (Zenn / Qiita) and the English-speaking world (dev.to). Here's the story. First, my most important disclaimer This post is full of numbers, so let me put up a guardrail before any of them. This is correlation, not causation . A result like "weekend posts don't do well" could mean the weekend itself is bad — or it could mean people who post on weekends are just dashing something off on the side. The data can't separate those. Please read it that way. Also, I only keep aggregate numbers I computed myself . I don't store or reuse anyone's article body (read-only GET, count the features, throw the page away). I peek, but only at the overall shape . Nobody gets singled out here. With that out of the way — four findings I enjoyed. 1. The best hour to publish is just your readers' time zone This one came out cleanest. On Qiita , posts published in the morning win (+32pt in the GOOD group). Midday is +14pt. Evening is -32pt, late night -14pt. Zenn likes midday too (+27pt). Late night is -15pt. dev.to is the exact opposite. Late night Japan time scores +7pt — Japanese evening is actually weak. The trick is obvious once you see it. dev.to's readers are English-speaking, mostly US. Late night in Japan is the US working day. Zenn and Qiita readers are in Japan, so the Japanese morni

May 29 I wrote my first AI trainwreck story. June 18 I finished #15. People keep asking if this was...

I know it sounds strange, but I am in my first year in CS Major, and I don't like posting things on social media, but I found lately that companies are more likely to hire people who are active on social media like X (Twitter). For me, I genuinely post my projects on LinkedIn, but not sharing things like today I learned something new etc... What's your opinion about that? Or How can I manage that?

Gustavo Manso (Haas School of Business, UC Berkeley) e Nassim Taleb affrontano entrambi il problema centrale dell'innovazione, ma da angolazioni complementari: Manso con la precisione del contratto ottimale, Taleb con la filosofia dell'antifragilità . Entrambi convergono su un'idea contro-intuitiva: per generare innovazione dirompente, bisogna proteggere il fallimento. Manso: Il contratto come strumento di tolleranza Il lavoro di Manso si concentra sui meccanismi di incentivazione che rendono l'innovazione possibile all'interno delle organizzazioni. La sua ricerca fondamentale (2011) modella esplicitamente il trade-off tra exploration (esplorazione di azioni nuove e non testate) e exploitation (sfruttamento di azioni note). Manso dimostra che i contratti ottimali per motivare l'innovazione richiedono una combinazione specifica: tolleranza per i fallimenti nel breve termine e ricompensa per il successo nel lungo termine . Questo è l'esatto opposto del classico "pay-for-performance" (paga in base alle prestazioni), che funziona bene per compiti routine ma soffoca l'innovazione. Come ha osservato Bengt Holmström (1989), citato da Manso, le attività innovative "richiedono una tolleranza eccezionale per il fallimento" perché il processo è imprevedibile e idiosincratico. Uno studio empirico fondamentale — che applica direttamente la teoria di Manso al venture capital — ha mostrato che i VC più tolleranti verso il fallimento generano startup significativamente più innovative. Un aumento dell'1% nella tolleranza al fallimento del VC porta a un aumento dello 0,5% nelle citazioni per brevetto. L'effetto è amplificato nelle recessioni e per le startup in fase iniziale. Manso ha anche esteso questa logica al finanziamento della ricerca scientifica, mostrando come la struttura dei fondi influenzi gli studi dirompenti. La sua analisi suggerisce che le leggi del lavoro che proteggono i dipendenti dal licenziamento arbitrario — attraverso quello che gli studiosi chiamano "effetto a

Manually converting invoice data from PDF or Excel files into CSV format is one of the most time-consuming tasks in accounting and data management workflows. It often involves repetitive copy-pasting, formatting adjustments, and a high risk of human error. In many real-world scenarios, invoices arrive in different formats such as PDF, XLS, XLSX, or even HTML. Handling them individually can slow down reporting pipelines and create inconsistencies in structured data storage. The Problem with Manual Conversion Traditional invoice processing usually involves: Extracting line items manually from PDFs Reformatting Excel sheets for database compatibility Fixing inconsistencies in columns and values Rechecking for missing or misaligned data As invoice volume increases, these tasks quickly become inefficient and error-prone. Automated Approach to Invoice Conversion A more efficient approach is using tools that automatically parse invoice documents and convert them into structured CSV format. These tools typically: Read multiple file formats (PDF, XLS, XLSX, HTML) Detect table structures and line items Normalize data into rows and columns Export clean CSV files ready for spreadsheets or databases For example, uploading a multi-page invoice PDF can result in fully structured rows representing each item, without manual formatting adjustments. Why CSV Output Matters CSV remains one of the most widely used formats for: Accounting software imports Database ingestion Data analysis workflows Spreadsheet processing Having clean CSV output ensures compatibility across systems and reduces preprocessing work. Practical Impact Automating invoice-to-CSV conversion helps reduce: Repetitive manual data entry Formatting inconsistencies Processing time for bulk invoices It also improves accuracy when handling large datasets. Closing Note As data-driven workflows become more common in finance and operations, automating repetitive tasks like invoice conversion can significantly improve efficien

AI isn't coming for your job. It already showed up, merged its first PR, and doesn't need a code review. The question developers keep dancing around — but rarely say out loud — is this: If GitHub Copilot, Cursor, and Claude can do what a junior dev does in a fraction of the time, what happens to junior devs? And more uncomfortably: what happens to mid-level devs in three years? The Uncomfortable Data Points This isn't speculation. It's already showing up in hiring data. Entry-level developer roles are contracting. Stanford's Digital Economy Lab (2025) found measurable decline in entry-level employment in AI-exposed roles — and software development is one of the most exposed. One senior dev + AI tools = the output of a small team. Brynjolfsson, Li & Raymond (NBER, 2023) showed generative AI productivity gains that compress what used to require multiple headcount into one. Goldman Sachs (2023) estimated significant white-collar labour market exposure — knowledge workers, not factory workers, are the primary target this time. This isn't the loom replacing weavers. It's the IDE replacing the person using the IDE. The Counter-Argument (And It's Not Weak) Here's where it gets interesting — because the doomsayer take isn't the whole story either. Every major technology wave destroyed jobs and created more than anyone predicted: The ATM didn't eliminate bank tellers — it lowered branch costs, banks opened more branches, teller roles increased for a decade The spreadsheet didn't kill accountants — it created an entire industry of financial analysts The internet didn't destroy publishing — it exploded the number of people who could publish The argument: AI raises developer productivity so dramatically that it expands the total addressable market for software. More products get built. More tools get created. More companies can afford to build what previously required a $500k engineering team. More demand for developers, not less. Where It Gets Complicated for Devs Specifically

Internmaxxing Somebody on your timeline this week called intern code "API slop."...

For years I treated burnout as a personal failing. If I was tired, I needed more sleep. If I was anxious on Sunday night, I needed to meditate. If I dreaded standup, I needed a better attitude. None of it worked, because I was treating an organizational problem as a character problem. Senior engineer burnout rarely looks like simple exhaustion. It looks like your pull request reviews getting slower. It looks like tech debt you keep meaning to document and never do. It looks like every "quick question" landing in your DMs, because you are the person who knows where everything is. The load is structural. You cannot meditate your way out of an org chart. Here is the framework that finally helped me, and that I now keep as a runbook. First, diagnose: acute or systemic A rough sprint is not burnout. A hard quarter is not burnout. Those are acute, and they resolve when the spike passes. Systemic burnout is different. The recovery never comes, because the structure that caused it never changes. You finish the death-march launch and the next one is already scheduled. You clear the queue and it refills by lunch. The mistake is applying acute fixes (a long weekend, a vacation) to a systemic problem. You come back rested, the structure grinds you down again in two weeks, and now you also feel like the rest "did not work," which makes it worse. A quick self-check. In the last month: Do you feel recovered after a weekend, or does Sunday-evening dread start by Saturday night? Is your reduced capacity tied to one specific deadline, or is it just how things are now? If your single worst recurring task vanished tomorrow, would you feel fine, or would something else immediately take its place? If your answers point to "it is just how things are now," you are dealing with systemic burnout, and the fixes are structural, not personal. Reclaim deep work with routing, not willpower Deep work does not survive on discipline. It survives on routing. The senior engineer's calendar is a public

CircleCI has launched Chunk Sidecars, a new capability designed to bring CI-style validation directly into an AI coding agent's inner development loop By Craig Risi

A London clinic owner has claimed he is treating people with stage 4 cancer by sealing them into a plastic bag while they're naked from the waist down and gassing them with chlorine dioxide.