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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.

Why Taking Feedback Positively Can Change Your Career As developers, engineers, designers, and professionals, we all want to improve. We spend countless hours learning new technologies, building projects, and gaining experience. Yet many people overlook one of the most powerful tools for growth: feedback. Unfortunately, feedback often feels personal. When someone points out mistakes in our code, resume, communication, or project, our first reaction is sometimes defensive. We feel offended, frustrated, or misunderstood. I've experienced this myself. But over time, I learned that the ability to accept feedback positively is one of the most valuable skills anyone can develop. Feedback Is Not an Attack One of the biggest misconceptions is believing that criticism is an attack on our abilities. When a senior engineer reviews your code and suggests improvements, they are not saying you're a bad developer. When a recruiter rejects your resume, they are not saying you're incapable. When users report problems in your open-source project, they are not trying to discourage you. Most of the time, people are simply showing you where improvements can be made. The sooner we separate our ego from our work, the faster we grow. Every Rejection Contains Information Many professionals view rejection as failure. I view it differently now. A rejection is data. If ten companies reject the same resume, the market is telling you something. If users consistently struggle with a feature, they're revealing a usability problem. If interviewers repeatedly point out the same weakness, they're highlighting a skill gap. The goal isn't to feel bad about the feedback. The goal is to learn from the information hidden inside it. Growth Begins Where Comfort Ends Positive feedback feels good. Constructive feedback creates growth. Nobody enjoys hearing that their architecture can be improved, their communication needs work, or their project has flaws. But those uncomfortable conversations often lead to th

I’ve been a software engineer for more than 12 years. And like many developers, I’ve been watching AI improve at an incredible speed. Every new model seems smarter than the one before it. Tasks that used to take hours can now be done in minutes. Problems that required deep research can often be solved with a simple prompt. A few years ago, we used to say: Think of AI as a junior developer. That made sense at the time. But today, I don’t think that’s true anymore. AI still makes mistakes. Sometimes very obvious ones. But it also comes up with solutions that surprise me. Sometimes it finds an approach I wouldn’t have thought of immediately. Sometimes it helps me solve a problem much faster than I could on my own. And honestly, that’s both exciting and a little scary. But the biggest thing AI changed wasn’t how I write software. It changed how I think about my work. For most of my career, I thought I loved writing code. I spent years doing it. At work, on side projects, and whenever I had free time. Then AI became part of my daily workflow. In the last month, I’ve built more projects than I normally would in an entire year. Ideas that had been sitting in my notes for years suddenly became possible. And that’s when I realized something important: I don’t actually love writing code. I love building things. I love taking an idea and turning it into something real. I love creating products, solving problems, and seeing something that only existed in my head become something people can use. Code was simply the tool I used to do that. And now AI is another tool. That’s why I don’t hate it. In many ways, AI has helped me build more than ever before. It helped me revisit old ideas that I never had time to work on. It helped me experiment faster. It even encouraged me to explore areas outside software development, like animation and content creation. And this isn’t just happening to programmers. AI is changing design. It’s changing writing. It’s changing marketing. It’s changin

You walk into the RFP briefing. Your competitor has 200 people, 97% AI coverage, and a 4-day delivery promise. You have 15 people and a proposal you haven't even finished writing. Do you bet on better tech, or on understanding people better — and playing dirtier when you have to? This story is your answer. Act I · The Crack When Finova's RFP landed, everyone in the industry knew how big this was. Cross-border payment system. Multi-currency settlement + compliance + risk. Their last deployment had a P0 incident — an exchange rate module drifted by four decimal places in an edge case, and audit chased it for two months. So Finova's CTO made it clear: a $1.8M contract, and whoever signs off owns the result. $1.8M. Enough to keep a small testing company alive for a whole year. Plenty of firms showed up at the briefing. But only two were real contenders. QualiGuard — mid-sized, just closed their Series A, 200 people, their own AI testing platform called Aegis. A $1.8M contract was barely a rounding error for them — but with Series A money comes the pressure to show revenue growth for the next round, and Finova was a trophy client in the cross-border payments space. The case study was worth more than the project itself. Derek stood at the podium, flipping through slides packed with numbers: Aegis delivers 97.2% test automation coverage. Full Finova platform testing in four business days. No "we'll try." Just "we can do it." VeriTest — small, fifteen people. Marcus spent the whole morning working the room with Finova's people. I sat in the back row with nothing. Marcus slid back over and leaned in: "Their PPT makes yours look like a joke." I didn't answer. I was watching Derek's boss. Sarah — QualiGuard's VP, Derek's direct supervisor. She sat in the front row, off to the side, and never once looked at Derek during his entire presentation. She was on her phone. As one of the few women running a technical department, I watched her longer than I watched Derek. When Derek fla

The company's latest recall of 3,871 vehicles follows incidents of its autonomous cars prioritizing other hazards or failing to recognize closed construction zones altogether.

Why your brain learns faster by doing than by studying, and the neuroscience that explains...

A couple of weeks ago I published a post with a tidy rule in it. When you add capability to an AI coding agent, reach for the lightest option first: a procedure file before a CLI, a CLI before a heavier integration, and only build the heavy machinery once you've proven you'll reuse it. My whole case rested on context cost. The heavy options load a lot of definitions up front and carry them every turn, so starting light keeps the window clean. I still think the front half is right. But it isn't the rule I'd write now, because a reader took it apart in the comments and handed it back as something better. This post is about that exchange, because the rewrite was sharper than my original, and pretending I arrived at it alone would be both a lie and the less interesting story. The hole, found in one comment The first comment didn't argue with the rule. It walked straight to the blind spot. The moment a tool touches anything external or stateful, lightest-first reverses on you: a lightweight call that fails silently halfway through is harder to debug than a heavier tool that surfaces the failure cleanly. Pay the complexity up front. My first instinct was to defend, and I did, a little. I said we were measuring different things, that I'd optimized for context cost while they were optimizing for failure observability, both real, different axes. I held the line by pointing out you can wrap a lightweight call to fail loudly, so the cheap path stays open. That was true, and it was beside their point, and they didn't let me hide behind it. The question that moved the rule They asked one question that did more work than my entire post: what's your actual trigger for paying the complexity up front, the type of state, or the class of error? Sitting with that is where my own rule changed under me. The honest answer is state type, and the moment I said it out loud, context cost stopped being what the rule was about. What makes a failure expensive isn't the error. It's whether the op

Certifications help when they match a role and are backed by proof — not as a scoreboard. The problem Beginners are told certifications are the key to IT, so they buy the most popular one, pass it, and are surprised when interviews still go badly. A certificate proves you can pass an exam; it does not, on its own, prove you can do the job. Why this matters now Certifications remain useful signals, and official providers like CompTIA, Microsoft, AWS, Cisco and Google keep their exam objectives public and current. But as AI makes it easier to grind practice questions, employers lean harder on whether you can actually apply the knowledge. The value of a certificate is increasingly in what you can demonstrate alongside it. There is also a cost reality. Exams, courses and retakes add up in money and time, and career changers usually have limited amounts of both. Spending three months and a chunk of savings on a certificate that no target role actually asks for is one of the most common and most avoidable mistakes in an IT transition — which is exactly why the order you choose them in matters. The practical framework Use certifications as targeted evidence, not as a scoreboard. Three rules: Match objectives to a job. Open the certification's published objectives next to a real job description. Overlap means it is relevant; no overlap means it is a hobby. Prove the same skills in practice. For each major objective, build one small artefact that shows you can do it, not just recall it. Stop at enough. One well-chosen, well-demonstrated certification beats three unrelated ones. Sequence them to roles, not to availability. Which one first? Let the target role decide, not the brand with the loudest marketing. As a rough guide: a vendor-neutral foundation (such as CompTIA A+ for general IT support, or Network+/Security+ as you specialise) suits broad support roles; a cloud-fundamentals exam (Microsoft Azure or AWS) suits cloud-leaning roles; Cisco-flavoured paths suit networkin

Anthropic has joined the Frontier coalition, which received another $915M in pledges to fund carbon removal projects.

The official launch takes place next week.

Your backend returned 200. The mobile app showed an error. The user tapped "Pay" three times. Three pending charges hit their account. One order was placed. Their balance was short. And your incident log showed zero failures. Every engineer on the team did their job. Nobody solved the problem. This is the most common way engineering teams fail, not through incompetence, but through excellent execution of the wrong unit of work. And until you recognise the difference between completing a task and solving a business problem , you will keep shipping systems that work perfectly and experiences that don't. The Ticket-Thinker vs. The System-Owner Most engineers early in their careers think in tickets. Ticket assigned → code written → tests pass → PR merged → ticket closed. Done. This is fine when you're learning. It's a liability when you're trying to grow. The engineer who closes tickets is useful. The engineer who asks "what problem does this ticket actually solve, and am I solving it in the right place?" that engineer is dangerous in the best way. Here's the distinction in practice. The backend engineer builds a payment endpoint. It processes charges correctly, returns the right status codes, has proper error handling. 100% test coverage. Ticket closed. The mobile engineer builds the payment screen. It calls the endpoint, handles the response, shows confirmation or error. Smooth UI. Ticket closed. The problem nobody owned: what happens when the network drops after the backend processes the charge but before the mobile app receives the confirmation? The backend: charge processed. No error. The mobile: timeout. Shows "Payment failed." User retries. The user: charged twice. Both engineers solved their assigned problem correctly. The business problem — charge the user once and confirm it reliably — went unsolved. Because that problem lived in the space between their tickets, and nobody was watching that space. Real Scenario 1: The Payment That Worked and Failed at the Same

Why Your Chinese-Tenured Faculty Resume Won’t Work in US Industry US data scientist hiring managers scan a resume in 7–15 seconds looking for one thing: evidence you can solve business problems with data. A Chinese faculty resume often leads with tenure status, publication counts, and grant amounts—none of which translate to industry value. Worse, the CV-style length and Chinese-specific qualifications (e.g., “Professor of Record,” “National Natural Science Foundation PI”) confuse HR software and recruiters unfamiliar with that system. You need to strip the academic frame and rebuild around what a US data scientist does: clean messy data, build predictive models, deploy to production, and communicate results to non-technical stakeholders. Think of every faculty achievement as raw material you must reframe. Core Rewriting Rules: From Academic to Industry Rule 1: Replace Tenure Rank with a US-Equivalent Data Science Title Do not list “Tenured Associate Professor” unless it is your most recent position at a well-known university (e.g., Peking University, Tsinghua). Instead, use a title that reveals your function: “Senior Data Scientist – Research Computing” or “Lead Data Scientist – Machine Learning Research Lab.” The point is to signal the job function, not the academic rank. Example: Before: “Tenured Associate Professor, School of Computer Science, Fudan University” After: “Senior Data Scientist / Research Lead, Fudan University AI Lab” Rule 2: Translate Every Accomplishment into a Business-Relevant Metric Chinese faculty resumes often say “published 15 papers in top-tier journals” or “secured ¥3M in research funding.” That means nothing to a hiring manager at a fintech startup. Instead, describe what you did with the data and the outcome. Concrete example – before and after: BEFORE (faculty bullet): “Led research project on deep learning for medical image segmentation; published 3 papers in IEEE TMI.” AFTER (industry data scientist bullet): “Built and validated a co

The bigger battery is standard and there are now simulated "E-Shifts."

If you have spent any time on SY0-701 practice questions, you have hit at least one that looks trivial and then quietly fails you. Authentication factor questions are a favorite for this. The scenario sounds secure, the answer feels obvious, and the obvious answer is wrong. Here is the version that catches people. A login asks for your password, then a PIN, then your mother's maiden name. Three prompts, three steps. Is that multifactor authentication? No. It is single-factor wearing a costume. Factors are categories, not steps The exam wants you thinking about authentication in terms of categories , not how many boxes you fill in. There are three classic factors: Something you know (knowledge): a password, a PIN, a security question, a passphrase. Something you have (possession): a phone running an authenticator app, a hardware token, a smart card, a code texted to a device you are holding. Something you are (inherence): a fingerprint, a face scan, an iris pattern, a voiceprint. Multifactor authentication means pulling from different categories. A password (know) plus a code from your authenticator app (have) is two factors. A password plus a PIN plus a security question is still one factor, because all three are things you know. Stacking more knowledge on top of knowledge never changes the category. That is the entire trick. The question piles on prompts so it feels layered, and the count baits you into answering "three things, must be multifactor." Read for the category, not the quantity. The two factors people forget SY0-701 also expects you to recognize two more that sit just outside the classic three: Somewhere you are (location): access is allowed or blocked based on geolocation or which network you are on. A login permitted only from inside the corporate IP range leans on this. Something you do (behavioral): how you type, your gait, the rhythm of your swipe. This is the fuzziest one, and the exam treats it as a real but supporting signal. You will not see the

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.