标签：#aicoding

I expected the audit to find broken code. That's what I was bracing for going in — a pile of half-working features, sloppy logic, the kind of mess you'd assume from software built at maximum speed with no human reviewing every line. That's not what I found. Almost everything Claude built actually worked, taken piece by piece. What I found instead was something I didn't expect at all: the agent had made the exact same mistakes I've watched human startup teams make, over and over, when they move fast and nobody's job is to say no. That's the real story here, and it's more interesting than "AI wrote bad code" would have been. The experiment The project is called GetPricePulse — a SaaS pricing intelligence product. It's Claude's entry from The $100 AI Startup Race , the season-long challenge I run where seven AI agents each get $100 and full autonomy to build a real startup from scratch, with no human coding and no product manager in the loop. Each agent picked its own idea and ran with it. Claude picked SaaS pricing intelligence, named it PricePulse, and kept building on it for the entire race. That "no product manager in the loop" part is the thing that made this interesting to watch. Nobody was deciding what PricePulse should be. Nobody was saying "we have enough pricing tiers now" or "this feature doesn't belong here." Claude got to build exactly what its own priorities told it to build, at whatever speed it chose, for the length of the race — optimizing, as far as I could tell from the commit history, for speed, feature creation, shipping, and monetization experiments. Not correctness. Not coherence. Not "does this still make sense in three weeks." I've written before about what all seven agents in this race said, independently, when I asked them what AI agents still can't do — they converged on the same answer without seeing each other's responses. This piece is narrower: a full production audit of Claude's specific build, PricePulse, done after the race, before I

Photo by Microsoft Copilot on Unsplash TL;DR: In a poll of 138 developers, three‑quarters say Claude Code outperforms Codex for everyday AI‑driven coding, pointing to higher accuracy, deeper context awareness, and a smoother workflow. The AI‑coding battlefield has been dominated by OpenAI’s Codex for years, powering tools like GitHub Copilot and shaping how developers write code. Yet a fresh wave of feedback suggests a shift: Anthropic’s Claude Code is rapidly becoming the preferred assistant for many programmers. A recent survey of 138 software engineers—spanning startups, enterprise teams, and freelance coders—revealed that 75% now rely on Claude Code as their go‑to AI partner. What drives this migration, and what does it mean for the future of AI‑augmented development? Survey Overview and Key Findings The questionnaire targeted developers who regularly use AI code generators, asking them to rank their primary tool and rate specific workflow attributes. Respondents represented a broad skill spectrum, from junior developers to senior architects, and worked across languages such as Python, JavaScript, Java, and Go. Adoption rate: 104 out of 138 participants (75%) listed Claude Code as their primary AI assistant, while only 34 (25%) still favored Codex. Primary criteria: Accuracy of generated snippets, ability to retain long‑form context, and ease of integration into existing IDEs topped the list. Secondary factors: Cost efficiency, response latency, and the perceived safety of the model (fewer hallucinations) also swayed decisions. The data paints a clear picture: developers are no longer satisfied with a one‑size‑fits‑all approach. They want an AI that can understand the nuance of a multi‑file project, stay on‑topic across extended sessions, and deliver code that compiles on the first try. Why Claude Code Wins Over Codex Higher Accuracy and Fewer Hallucinations Respondents repeatedly highlighted Claude Code’s ability to generate syntactically correct, production‑re

If you use GitHub Copilot, Claude, Cursor or any other AI coding assistant to write production code, the legal ownership of what you ship is less settled than your licence agreement implies. The US Copyright Office ruled in January 2025 that purely AI-generated material is not copyrightable, and that prompts alone do not provide sufficient human control to earn protection. Code you wrote with heavy AI assistance sits in an uncertain middle ground: it may be copyrightable, it may not, and no court has drawn the line for software. The answer-first version: you probably do not own copyright in the portions of your code that an AI wrote without substantial human direction, and you may not be able to prove where the boundary lies. This does not mean someone else owns it — it may be uncopyrightable altogether, like a phone book. But your employment contract, your client agreement and your open-source licence all assume you hold full copyright in your deliverables. That assumption is now an open question. The rule, plainly stated Purely AI-generated output is not copyrightable in the United States. This is not a prediction or a legal opinion; it is the stated position of the US Copyright Office, set out in its January 2025 report Copyright and Artificial Intelligence, Part 2: Copyrightability . The report received over 10,000 public comments and represents the Office’s most comprehensive statement on the subject. Its conclusions are clear: “material generated wholly by AI is not copyrightable”, and existing law is adequate to handle the question without new legislation. The nuance sits in the middle ground — which is exactly where most AI-assisted coding lives. What the Copyright Office said The report draws several lines. First, it confirms the long-standing requirement that copyright requires a human author. An AI system cannot be an author, regardless of how sophisticated its output. Second, it addresses prompts: “based on the functioning of current generally available

Originally published at blog.whynext.app . Work with AI agents for a while and the ambition comes naturally. While one session fixes a bug, another can refactor, and a third can investigate an issue, right? You can spin up as many models as you like, so productivity should scale to match. That's how I started too. And within a week I learned that the real enemy of parallel agents isn't the models' skill. It's the working directory they share. HEAD is a global variable The cause fits in one sentence. When multiple sessions share a single git checkout, the current branch becomes everyone's global variable. Picture two people working on one computer at the same time and the absurdity is obvious, but that thought never occurred to me while spinning up agents. With one session per terminal tab, they look isolated from each other. But there is one filesystem, and one HEAD. The moment one session runs git checkout , the ground shifts under every other session. The incidents from that week fell into clear types. Branch hijacking. While session A was working on a topic branch, session B switched branches to do its own work. A committed without knowing, and the commit landed on top of B's branch. It happened in the other direction too: right as A was about to commit, the branch had been switched to develop, and only the hook that blocks direct commits to protected branches saved it. Without the hook, it would have gone straight in. Orphaned commits. Session B deleted session A's topic branch during a cleanup pass. A's commits became orphans belonging to no branch, and I dug through the reflog, found the commit hashes, and recovered them with cherry-pick. Lucky that it worked; if the reflog had expired or I hadn't found them, the work would have simply evaporated. Staging contamination. At the moment session A was creating a commit, a file deletion that session B had staged was sitting in the staging area alongside it. Committed as-is, B's deletion would have been folded into

Originally published on rikuq.com . Republished here for Dev.to's readers. I dropped my $100/month Claude Max subscription and migrated entirely back to Antigravity. If you want the verdict upfront: Claude Desktop is still the best tool for beginners who need the AI to guess their intent from clumsy prompts. But if you have solid documentation discipline and cost efficiency is a serious factor for your SaaS, Antigravity is now the clear winner. I'm a Chartered Accountant by trade with zero formal coding experience. I’ve shipped three production AI SaaS— Prism , Citare , and BatchWise —relying entirely on AI tools. I started with VSCode, moved to Antigravity (when it was just an IDE), and eventually landed on the Claude Desktop App. Claude was incredible; it operated in the background, handled my stack, and I didn't need to know what was happening under the hood. But the bills started stacking up. When my Claude usage consistently hit $100 a month, efficiency became a priority. I fired up the new version of Antigravity and found the recent updates had completely transformed it. It is no longer just an IDE—it is a full agentic desktop experience that mirrors what made Claude so good. TL;DR — The 2026 Reality Feature Claude Desktop App Antigravity (New Update) Best for Beginners, unlimited budgets, "pure performance" Experienced AI directors, cost-conscious solo founders Pricing $100+/mo (Claude Max) $20/mo (Gemini Advanced) Agentic Workflow Exceptional. The benchmark. Identical. Background execution, zero friction. Context Handling Better at anticipating intent from messy prompts Huge total memory, but requires tighter prompting MCP Support Native Native (handles them just as well) Verdict Keep it if cost doesn't matter Switch to it if efficiency is the goal The Catalyst for Switching My path to Antigravity wasn't a calculated feature comparison. It was pure economics combined with a pleasant surprise. I had previously dropped Antigravity when it was just an IDE. When