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Originally written for bulldo.gs — republished here with the canonical link pointing home. I want to enable the V8 runtime on an existing Apps Script project so I can use modern JavaScript, but I am worried about breaking things that are already working. // appsscript.json — set runtimeVersion to enable V8 // Rollback: change V8 back to DEPRECATED_ES5 { " timeZone " : " America/New_York " , " dependencies " : {}, " exceptionLogging " : " STACKDRIVER " , " runtimeVersion " : " V8 " } // Code.gs — safe V8-compatible replacement for a common Rhino pattern // Rhino allowed: for each (var item in collection) {} // V8 requires standard for...of instead function listSheetNames () { var ss = SpreadsheetApp . getActiveSpreadsheet (); var names = []; var sheets = ss . getSheets (); for ( var i = 0 ; i < sheets . length ; i ++ ) { names . push ( sheets [ i ]. getName ()); } Logger . log ( names . join ( ' , ' )); } The one-line change and why it is a project-wide bomb Open the Apps Script editor, click Project Settings (the gear icon), and check "Show appsscript.json manifest file in editor." Then open that file and change "runtimeVersion": "DEPRECATED_ES5" to "runtimeVersion": "V8" . Save. That is the entire migration from a settings standpoint. What catches people off guard is the failure mode. V8 parses every .gs file in the project as a unit before running anything. One Rhino-only statement — a for each loop, a __iterator__ method, a Date.prototype.getYear call in an otherwise untouched utility file — causes a syntax or runtime error that prevents the whole script from initializing. Not just the file that contains the bad line. Every function, every trigger, the entire project goes dark. The first time I hit this it took me twenty minutes to figure out why a completely unrelated trigger had stopped firing. The error message pointed at the Rhino syntax in a helper file I had not touched in two years. V8 does not isolate the damage; it fails at parse time, before any executi

Originally written for bulldo.gs — republished here with the canonical link pointing home. I'm running a script that processes a large spreadsheet and it keeps dying with "Exceeded maximum execution time" before it finishes. // Checkpoint-resume pattern for long-running sheet jobs function processInBatches () { var props = PropertiesService . getScriptProperties (); var startRow = parseInt ( props . getProperty ( ' lastRow ' ) || ' 2 ' , 10 ); var sheet = SpreadsheetApp . getActiveSpreadsheet (). getActiveSheet (); var lastDataRow = sheet . getLastRow (); var BATCH = 200 ; var SAFE_MS = 5 * 60 * 1000 ; var started = Date . now (); var endRow = Math . min ( startRow + BATCH - 1 , lastDataRow ); var data = sheet . getRange ( startRow , 1 , endRow - startRow + 1 , 5 ). getValues (); for ( var i = 0 ; i < data . length ; i ++ ) { if ( Date . now () - started > SAFE_MS ) { props . setProperty ( ' lastRow ' , String ( startRow + i )); return ; } // process data[i] here } if ( endRow >= lastDataRow ) { props . deleteProperty ( ' lastRow ' ); deleteTrigger_ (); } else { props . setProperty ( ' lastRow ' , String ( endRow + 1 )); } } The 6-minute wall is per-execution, not per-task Apps Script enforces a hard 6-minute execution time limit per run, regardless of whether you're on a free account or a Workspace account (which bumps the limit to 30 minutes, but the same cliff exists). The error doesn't mean your logic is wrong; it means one continuous call to your function took too long. The fix is to stop thinking of your job as a single execution and start thinking of it as a pipeline of short runs. The first time I hit this, I wasted an afternoon trying to speed up the loop. Marginal gains didn't move the needle because the data volume was the real problem — 4,000 rows at one Sheets API call per row will always breach 6 minutes. The correct frame is: how do I save where I stopped and pick up there next run? Saving and restoring a cursor with PropertiesService PropertiesServic

Coding agents are no longer just autocomplete with a longer prompt. GitHub describes Copilot cloud agent as software that can research a repository, create an implementation plan, make code changes on a branch, run in an ephemeral GitHub Actions-powered environment, and let a developer review or create a pull request afterward. OpenAI's Codex GitHub integration similarly positions code review as a repository-aware review pass that follows AGENTS.md guidance and focuses comments on serious issues. That shift changes the buyer question. The useful question is not "does the agent usually write code?" It is "can the team detect when the agent drifts away from the developer's intent before the change reaches production?" A May 2026 arXiv paper, "How Coding Agents Fail Their Users" , gives teams a better vocabulary for that review. The authors studied 20,574 real IDE and CLI coding-agent sessions across 1,639 repositories and define misalignment as a breakdown that becomes visible through developer correction or pushback. The paper reports seven recurring symptom categories: wrong project diagnosis, misread developer intent, developer constraint violation, self-initiated overreach, faulty implementation, operational execution error, and inaccurate self-reporting. Effloow Lab also ran a bounded OpenAI API check using three synthetic, non-confidential coding-agent transcript snippets. The run did not measure real-world incidence, compare vendors, or reproduce the paper. It produced a small rubric that maps visible symptoms to review gates such as diff-scope checks, evidence-before-edit checks, acceptance-criteria coverage, and verification-output requirements. The public lab note is available at /lab-runs/coding-agent-misalignment-failure-taxonomy-poc-2026 . This guide turns that research and lab output into a practical QA checklist for teams buying, piloting, or packaging coding-agent workflows. Why This Matters for Agent Buyers Coding-agent procurement often starts with p

Originally written for bulldo.gs — republished here with the canonical link pointing home. I want to programmatically combine several Google Docs into one file without losing tables or list formatting. // Merges all source docs into destDocId, in order. // Run from the Apps Script editor; no triggers needed. function mergeDocs () { var sourceIds = [ ' DOC_ID_ONE ' , ' DOC_ID_TWO ' , ' DOC_ID_THREE ' ]; var dest = DocumentApp . openById ( ' DEST_DOC_ID ' ). getBody (); for ( var i = 0 ; i < sourceIds . length ; i ++ ) { var srcBody = DocumentApp . openById ( sourceIds [ i ]). getBody (); var total = srcBody . getNumChildren (); for ( var j = 0 ; j < total ; j ++ ) { var el = srcBody . getChild ( j ); var type = el . getType (); if ( type === DocumentApp . ElementType . PARAGRAPH ) { dest . appendParagraph ( el . asParagraph (). copy ()); } else if ( type === DocumentApp . ElementType . TABLE ) { dest . appendTable ( el . asTable (). copy ()); } else if ( type === DocumentApp . ElementType . LIST_ITEM ) { dest . appendListItem ( el . asListItem (). copy ()); } } } } Why there is no single appendElement call The Document service in Apps Script does not expose a generic appendElement method on Body . Every element type has its own typed append method: appendParagraph , appendTable , appendListItem , and so on. That means a merge loop that ignores element types will throw TypeError: el.copy is not a function the moment it hits a table, because you would be passing an Element where the API expects a Table . The fix is to call getType() on each child element and switch on DocumentApp.ElementType . The type enum values are strings like PARAGRAPH , TABLE , LIST_ITEM , INLINE_IMAGE , and HORIZONTAL_RULE . In practice the first three account for almost all real document content. The code above handles those three and silently skips anything else (images, rules) rather than crashing the entire merge. Getting the doc IDs and running the script The ID for any Google Doc is the lo

Originally written for bulldo.gs — republished here with the canonical link pointing home. I want to pull live JSON data from an API endpoint directly into a Google Sheet without installing an add-on. // Fetch JSON from an API and write it to the active sheet // Adjust API_URL and the field list to match your endpoint function importJsonFromApi () { var API_URL = ' https://jsonplaceholder.typicode.com/users ' ; var sheet = SpreadsheetApp . getActiveSpreadsheet (). getActiveSheet (); var response = UrlFetchApp . fetch ( API_URL ); var raw = response . getContentText (); var data = JSON . parse ( raw ); var headers = [ ' id ' , ' name ' , ' username ' , ' email ' , ' phone ' ]; var rows = data . map ( function ( obj ) { return headers . map ( function ( key ) { return obj [ key ] || '' ; }); }); sheet . clearContents (); sheet . getRange ( 1 , 1 , 1 , headers . length ). setValues ([ headers ]); sheet . getRange ( 2 , 1 , rows . length , headers . length ). setValues ( rows ); } Why getContentText() comes before JSON.parse UrlFetchApp.fetch() returns an HTTPResponse object, not a string. The first time I skipped getContentText() and passed the response object directly to JSON.parse(), it silently parsed to null and the sheet wrote nothing. You need raw = response.getContentText() to get the actual body as a string, then JSON.parse(raw) turns it into a JavaScript object or array. The URL must be publicly accessible or accept an API key via a query parameter or Authorization header. Add headers like this: UrlFetchApp.fetch(url, { headers: { Authorization: 'Bearer ' + token } }). Apps Script's UrlFetchApp quota is 20,000 calls per day on a free Google account, 100,000 on Workspace. The rectangular array constraint — why setValues fails without mapping setValues() is strict: it requires a 2D array where every row has the same number of columns. If you hand it an array of plain JSON objects, it throws 'The number of rows or columns in the range does not match the number of

Originally written for bulldo.gs — republished here with the canonical link pointing home. I have a spreadsheet of names and email addresses and I want to send each person a personalized message from my Gmail account without copy-pasting or using a paid tool. // Mail merge: Sheet cols A=Name, B=Email, C=Sent // Run from Apps Script; authorize Gmail + Sheets scopes function sendMerge () { var sheet = SpreadsheetApp . getActiveSheet (); var rows = sheet . getDataRange (). getValues (); var quota = MailApp . getRemainingDailyQuota (); var sent = 0 ; for ( var i = 1 ; i < rows . length ; i ++ ) { if ( rows [ i ][ 2 ] === ' Sent ' ) continue ; if ( sent >= quota ) { Logger . log ( ' Quota reached at row ' + ( i + 1 )); break ; } var name = rows [ i ][ 0 ]; var email = rows [ i ][ 1 ]; var subject = ' Hey ' + name + ' , here is your update ' ; var body = ' Hi ' + name + ' , \n\n Your personalized content goes here. \n\n Thanks ' ; MailApp . sendEmail ( email , subject , body ); sheet . getRange ( i + 1 , 3 ). setValue ( ' Sent ' ); sent ++ ; } } Set up your sheet and open the script editor Put names in column A, email addresses in column B, and leave column C blank — the script writes 'Sent' there as it goes. Header row in row 1 is assumed; the loop starts at index 1 (row 2) to skip it. Open the script editor from Extensions > Apps Script, paste the function, and save. The first time you run sendMerge() Google will ask you to authorize two scopes: Sheets (read/write the active spreadsheet) and Gmail (send mail on your behalf). Both are required. If you only see a Sheets prompt, delete the file and re-paste — a cached partial authorization sometimes skips the Gmail scope on older script files. Why the Sent column is the whole point Consumer Google accounts cap at roughly 100 outgoing recipients per 24-hour rolling window via MailApp. If your list has 200 rows and you run the script at 11 pm, it will send 100 and log 'Quota reached at row 101'. Without the Sent check, a sec

I've been writing about AI coding tools for months here on Dev.to. Comparisons, benchmarks, tutorials on how to squeeze the most out of Claude Code, Cursor, and the rest. And I do use them. Every single day. But last week I tried something that surprised even me. I turned them off completely. For an entire week, no AI-generated code, no autocomplete suggestions, no "explain this function" prompts. Just me, my editor, and a blinking cursor. Here's what actually happened. The First Few Days Were Rough Day one was humbling. My output dropped by maybe half. What normally took 15 minutes stretched to 40. I found myself reaching for the Cmd+K shortcut out of muscle memory half a dozen times. But somewhere around day three, something shifted. I started reading source code instead of asking for summaries. I traced through execution paths instead of having the LLM walk me through them. I caught a subtle race condition that Claude Code had confidently dismissed as "not an issue" in the same codebase two weeks prior. That moment stuck with me. The Code Was Cleaner Here's the part I didn't expect. By day five, my code was noticeably simpler. Not because an LLM optimized it, but because I actually understood the problem well enough to keep it simple. AI-generated code often over-engineers. It adds abstractions for scenarios that don't exist. It writes defensive checks for edge cases that don't apply to your use case. It looks professional but carries unnecessary complexity. When you write it yourself, you stop at the simplest working solution because you know when you're done. An LLM doesn't know when you're done. It just keeps going until the context window runs out. The Real Cost of Productivity This is the part I've been thinking about most. AI tools remove friction. That's their superpower. But friction isn't always bad. The struggle of debugging your own code is how you learn a codebase. The effort of designing an API is how you develop taste for what makes a good one. If y

Originally written for bulldo.gs — republished here with the canonical link pointing home. I have a Google Sheet with duplicate rows and I want to remove them programmatically, either on demand or on a schedule, without destroying the rest of my data. // removeDuplicates.gs — dedup active sheet, keep first occurrence // Run from Extensions > Apps Script, or bind to a trigger. function removeDuplicateRows () { var sheet = SpreadsheetApp . getActiveSheet (); var data = sheet . getDataRange (). getValues (); var seen = new Set (); var unique = []; for ( var i = 0 ; i < data . length ; i ++ ) { var key = data [ i ]. join ( ' | ' ); if ( ! seen . has ( key )) { seen . add ( key ); unique . push ( data [ i ]); } } sheet . clearContents (); sheet . getRange ( 1 , 1 , unique . length , unique [ 0 ]. length ). setValues ( unique ); } Why rewrite instead of delete The instinct when deduplicating is to loop through the sheet and call deleteRow on each duplicate. That works, but it has a sharp edge: every call to deleteRow shifts all rows below it up by one. If you delete row 3, what was row 4 is now row 3, and your loop index is already pointing at the new row 4. The safe workaround people reach for is iterating bottom-to-top, which works but means holding the full duplicate set in memory anyway, making one API call per deleted row. The approach here sidesteps the problem entirely. Read everything once with getDataRange().getValues() — a single API call that returns a 2D array. Build the deduplicated array in JavaScript using a Set to track which row fingerprints you have already seen. Then clear the sheet and write the result back with one setValues call. Two API calls total, regardless of how many duplicates you had. For a 10,000-row sheet, this is the difference between a script that finishes in two seconds and one that times out at the six-minute Apps Script execution limit. The row key is built with data[i].join('|'). The pipe character works as a separator in practice; i

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Better orchestration, fewer handoffs, faster progress, without a single new knob. The post How we made GitHub Copilot CLI more selective about delegation appeared first on The GitHub Blog .

As of today, SpaceX is owned by investors who will want to see it make money.

After covering the foundational building blocks in Session 1, the next step is one of the most important problem-solving techniques in all of programming: recursion . And once recursion feels comfortable, it unlocks a powerful search strategy called backtracking . These two concepts appear everywhere in competitive programming — Fibonacci, binary search, tree traversal, merge sort, dynamic programming, N-Queens, and more. They deserve their own spotlight. 🌟 What Is Recursion? A function is recursive if it calls itself. Instead of solving a problem in one go, a recursive function breaks it into a smaller version of the same problem, solves that, and repeats — until the problem becomes simple enough to answer directly. Three things define every recursive solution: The problem is expressed in terms of a smaller instance of itself Each call reduces the problem size There is a point where the problem becomes trivial and no further calls are needed — this is the base case The Nested Box Analogy Think of recursion like opening nested boxes. A big box contains a smaller box, which contains another, and so on. Eventually you find the item you were looking for. That innermost box is the base case. Without it, you would keep opening boxes forever — which is how you get a stack overflow, not a solution. Base Case and Recursive Case Every recursive function has exactly two parts: Recursive case — the problem is reduced in size and the function calls itself again. Base case — the terminating condition. No further recursive call is made. The function returns a direct answer. Both are non-negotiable. A function without a base case will keep calling itself, consuming stack memory until the program crashes. Example: Factorial 5! = 5 × 4! 4! = 4 × 3! 3! = 3 × 2! 2! = 2 × 1! 1! = 1 ← base case Each step reduces the problem by one. When the function hits 1! = 1 , it stops, and the results unwind back up the call stack. In pseudocode: function factorial(n): if n == 1: return 1 # base cas

AI Fluency for Software Engineers: A Practical Playbook Beyond Prompting A few years ago, being productive with AI mostly meant knowing which tool to open and what question to ask. Today, that is not enough. For software engineers, AI is no longer just a chatbot sitting outside the workflow. It is becoming a thinking partner for architecture decisions, code reviews, production incidents, documentation, test planning, onboarding, and product discovery. But there is a problem: many teams are using powerful AI tools with weak operating habits. They ask vague questions. They paste too much context. They trust the first answer. They forget privacy boundaries. They use AI for speed, but not always for better engineering judgment. That is where AI fluency matters. AI fluency is not just prompt engineering. It is the ability to work with AI clearly, safely, and practically while staying in control of quality, reasoning, and responsibility. Here is a practical playbook I would recommend for software engineers and engineering teams. 1. Start with clarity, not clever prompts A weak prompt sounds like this: “Review this design and tell me if it is good.” The AI can answer, but the answer will likely be generic. A stronger prompt gives the AI a clear role, context, constraints, and output format: You are a senior backend architect. Review this proposed API design for a high-traffic order processing system. Evaluate: - correctness - scalability - failure handling - observability - backward compatibility - operational complexity Do not rewrite the whole design unless required. Separate critical risks from optional improvements. Output format: - Executive summary - Key risks - Recommended changes - Open questions - Final decision recommendation The difference is not word count. The difference is control. A fluent AI user does not hope the AI understands the task. They make the task hard to misunderstand. 2. Give enough context, but not everything AI output quality depends heavily o

Competitive programming often looks like a race to write code as fast as possible. But the real secret is simpler: the best competitive programmers are not just faster typists — they are better at choosing the right data structure, the right algorithm, and the right complexity level for the job. Before we jump into recursion, dynamic programming, graphs, or those problems that make your brain do backflips, we need a solid base. This first session is exactly that. Let's begin. 🚀 1. Data Types: What Kind of Data Are You Storing? A data type tells a programming language what kind of value a variable holds and what operations are valid on it. Primitive Data Types The basic building blocks provided by the language itself: Integer — whole numbers: 5 , 100 , -3 Float / Double — decimal values: 3.14 , 99.5 Character — a single symbol: 'A' , 'z' Boolean — true or false User-Defined Data Types When primitive types are not enough, programmers define their own: Structs — group related fields under one name Classes — structs with behaviour (methods) attached Enums — a fixed set of named constants Typedefs / Aliases — rename existing types for clarity A Real-World Example Imagine building a food delivery app: An integer stores the number of items in the cart A float stores the total bill amount A boolean tracks whether the order has been delivered A class represents an entire Order — customer name, address, items, payment status Data types are essentially the labels on your containers. Without them, chaos begins early. 2. Data Structures: How Do You Organise Data? If data types answer what a value is, data structures answer how to organise many values efficiently. This is where competitive programming starts to get interesting. Linear Data Structures Elements arranged one after another, like people queuing at a ticket counter: Arrays — fixed-size, indexed, fast random access Linked Lists — dynamic size, efficient insertions and deletions Stacks — last in, first out (LIFO) Queues

The first message ever sent across the network that became the internet was not "Hello, world." It was not a grand declaration. It was two letters, transmitted by accident, before the system fell over: LO . That two-letter packet is the ancestor of every connected device, every IoT sensor, and every web request running today. The story of how it happened is also a surprisingly useful lesson for anyone building embedded systems and connected hardware right now. What actually happened on October 29, 1969 On the evening of October 29, 1969, a programmer named Charley Kline sat at a terminal in Leonard Kleinrock's lab at UCLA. His job was simple on paper: log in to a remote computer at the Stanford Research Institute (SRI), roughly 350 miles away, over a brand-new experimental network called ARPANET. The plan was to type the command LOGIN . The remote machine at SRI was set up to auto-complete the rest once it saw the first few characters, so Kline only needed to start typing. He had a colleague on the phone at the Stanford end to confirm each letter arrived. He typed L . Stanford confirmed: "Got the L." He typed O . Stanford confirmed: "Got the O." He typed G - and the SRI system crashed. So the first message ever transmitted over ARPANET was "LO." As Kleinrock later liked to point out, it was an accidental but fitting first word: "LO" as in "lo and behold." About an hour later they fixed the bug and completed the full login, but the historic first packet had already gone out, two letters at a time. Why a crash is the perfect origin story It is tempting to read this as a cute footnote. It is more than that. The very first thing the internet ever did was fail partway through a transaction - and the system was built well enough that the humans on both ends knew exactly how far it had gotten before it died. That is the entire discipline of networked systems in miniature. Connections drop. Remote machines crash mid-request. Packets arrive out of order, or not at all. The n

A short essay on why the developer community should invest as much effort as possible into LLMs that are free from corporations and states. ML researchers and hardware engineers both need to contribute here. The latter may even be more important, because whether users can run advanced LLMs on personal hardware depends on breaking NVIDIA's monopoly. This essay is highly political, especially in the opening sections. Keep that in mind. Corporate AI Will Be Closed and Unaccountable by Default The other day, almost at the same time as the release of Fable 5, Anthropic's Dario Amodei published an article called "Policy on the AI Exponential", where he discussed what the world should do with powerful AI-based systems. All sections except the first contain fairly reasonable proposals, or at least proposals worth discussing. I will not consider them here. The real core is in the first section. In that first section, he effectively proposes a system in which the state would be required to license advanced AI systems, measured by the amount of compute used, and even ban the release of models that are not considered safe for society. In practice, this repeats a story as old as the world: a large corporation wants to regulate the market so smaller companies do not interfere with its ability to earn mountains of money, all under noble-sounding pretexts. And the point is not that Amodei is some villain. He is simply an entrepreneur who wants to earn as much money as possible. Any large corporation would prefer not to let smaller companies near the feeding trough in its field. Anthropic is merely saying this openly, and that is all. In effect, AI Big Tech wants a future where all non-AI companies become its serfs, mortally dependent on intelligence delivered through Anthropic's API, or OpenAI's, or Google's, and so on. In practice, those AI companies would hold the revenue of all these other companies in their hands. Without them, the whole economy around those companies would cru

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The company made its heavily anticipated debut on Friday, trading higher than its initial $135 IPO price.

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