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

“I’m not sure that this company supports a hackathon culture anymore,” one employee posted in a forum open to the entire staff.

The deal has sparked fear over the future of the film, television and news industries.

submitted by /u/funnybong [link] [留言]

A new report suggests the unit, which employs 6,500 people, is on the verge of revolt.

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.

Drop it. It's live. Discussion | Link

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 Agent Security, Malware Evasion, & LLM Data Leakage Risks Today's Highlights Today's highlights cover crucial security challenges, from sophisticated malware evasion tactics confusing analysis tools to the inherent risks of autonomous AI agents causing financial damage. We also delve into the critical data security implications of interacting with large language models, emphasizing the need for robust data governance and user education. Malware developers added nuclear and biological weapons text to to their spyware (Hacker News) Source: https://twitter.com/jsrailton/status/2064661778978533571 This report highlights a concerning tactic employed by malware developers to evade detection and analysis. By embedding seemingly innocuous, yet contextually irrelevant, strings such as "nuclear and biological weapons" text within their spyware's code or data, threat actors aim to mislead security researchers and automated analysis tools. This technique, often referred to as 'camouflage' or 'noise injection,' complicates the process of signature-based detection and behavioral analysis by adding irrelevant data that can confuse pattern matching algorithms or human analysts investigating suspicious binaries. It leverages the expectation that malicious code should contain only code related to its function, subverting this by introducing data that might trigger false positives or simply overwhelm analysis efforts. This tactic necessitates more sophisticated defensive techniques, moving beyond simple string searches or basic heuristic analysis. Organizations must enhance their sandboxing capabilities, employ advanced machine learning-driven anomaly detection, and focus on dynamic analysis that observes the actual behavior of the malware rather than relying solely on static analysis. Understanding such obfuscation and evasion tactics is crucial for developing robust threat intelligence and improving the resilience of endpoint detection and response (EDR) systems against evolving

Local AI Coding Agents, Secure Production Deployment, and Angular-Specific AI Skills Today's Highlights This week's top stories highlight practical ways to deploy and secure AI agents, from setting up local coding assistants on macOS to sandboxing untrusted agent code in Azure, alongside new resources to improve AI-generated code quality for Angular. How to setup a local coding agent on macOS (Hacker News) Source: https://ikyle.me/blog/2026/how-to-setup-a-local-coding-agent-on-macos This guide provides a step-by-step tutorial on deploying and configuring an AI coding agent directly on a macOS system. The process typically involves setting up a local Large Language Model (LLM) or connecting to a local inference engine, integrating it with an orchestration framework, and configuring it to interact with local development tools and environments. The emphasis is on enabling developers to have a private, customizable AI assistant for code generation, debugging, and project scaffolding without relying on external cloud services. This local setup is crucial for privacy-conscious developers and for those who want to fine-tune agent behavior for specific internal codebases. The article likely covers prerequisites such as Python environments, relevant libraries, API key management for local models (if applicable), and how to set up the agent to execute code within a sandboxed environment on the machine. It offers a practical pathway for developers to experiment with AI agents in their daily coding workflows, providing immediate utility and control over the AI's operations and data handling. Comment: This is a great hands-on guide for anyone wanting to run AI coding agents locally, which is essential for privacy and custom development workflows. Run Untrusted AI Agent Code Safely with Azure Container Apps Sandboxes (InfoQ) Source: https://www.infoq.com/news/2026/06/untrusted-ai-agents-sandboxes/ Microsoft has announced the public preview of Azure Container Apps Sandboxes, a new

DuckDB Data Inlining, SQLite Fossildelta OOB, Postgres 19 Temporal Data Today's Highlights Today's highlights include DuckDB's innovative data inlining for stream processing in data lakes, offering significant performance gains by eliminating the small files problem. Additionally, a critical out-of-bounds read vulnerability in SQLite's fossildelta extension and a peek into PostgreSQL 19's focus on temporal data capabilities are discussed. Data Inlining in DuckLake: Unlocking Streaming for Data Lakes (DuckDB Blog) Source: https://duckdb.org/2026/04/02/data-inlining-in-ducklake.html The DuckDB team has unveiled DuckLake’s new data inlining feature, designed to revolutionize how streaming data is managed in data lakes by effectively tackling the notorious “small files problem.” This issue, common in scenarios with frequent small updates or continuous ingestion, often leads to performance bottlenecks due to the overhead of managing numerous tiny files. DuckLake's solution involves intelligently storing these small updates directly within the catalog, thereby eliminating the need for physical small files on disk. This architectural innovation significantly improves the practicality of continuous streaming into data lakes, enabling more efficient real-time analytics. By inlining data, DuckDB reduces I/O operations and metadata management complexity, leading to substantial performance gains. A benchmark highlighted in the announcement demonstrates an impressive 926x speed improvement for certain operations, showcasing the feature's potential to transform data lake architectures for workloads requiring high-throughput ingestion and immediate query access without the traditional performance penalties. Comment: This DuckDB feature is a game-changer for data lake architectures, offering a simple yet powerful way to handle streaming data without the performance overhead of countless small files. Post: Out-of-bounds read in deltaGetInt() when input contains no in-buffer terminat