TechCrunch AI·
There's a lot more code—but it's a lot more expensive and requires a lot more rewriting.
Read full articleThe practice of tokenmaxxing appears to be dying out, even before I had a chance to write about it. Good riddance. Burning tokens to create the appearance of productivity was fated to last only until the accountants learned about it, and the strictest of all accountants is one’s personal checkbook. What got many developers thinking […]
Artificial intelligence is rapidly reshaping retail, but not in the ways consumers might immediately notice. The biggest transformation may not be flashy virtual try-ons or chatbot shopping assistants, but in how decisions are made behind the scenes: how products surface in search results, how inventory moves through supply chains, how engineers ship code faster, and…
The tokenmaxxing era was brief. We now appear to be entering the era of token rationing.
Everyone says Claude can’t make pictures. That’s partly true. Here is the kind of art it makes on its own, with no plugins and no connectors: Drawn by Claude in SVG, no image model anywhere near it. Not pixels but code: shapes and coordinates that stay sharp at any size and redraw themselves when you […] The post Claude’s Hidden Art Skill: Making Illustrations With Code appeared first on Analytics Vidhya.
Tokenmaxxing was the hottest trend in Silicon Valley earlier this year, with CEOs encouraging employees to push AI usage as far as it would go. Then the bill came due. Uber reportedly blew through its annual AI budget in a few months, some companies cut Claude licenses for parts of their org, and Meta killed its internal leaderboard. This tension between […]
Tokenmaxxing was the hottest trend in Silicon Valley earlier this year, with CEOs encouraging employees to push AI usage as far as it would go. Then the bill came due. Uber reportedly blew through its annual AI budget in a few months, some companies cut Claude licenses for parts of their org, and Meta killed its internal leaderboard. This tension between […]
This article is an honest account of the process on why I built a custom AI assistant instead of just paying for one, what the architecture looks like, the actual code, what broke, and what it does now that I genuinely rely on.
Developers are caught between the joy — or pressure — of using agents to ship 10x faster today and the dread of how they will maintain that code tomorrow. The gap between “vibe” code and code that can be deployed to millions of users is vast and easy to underestimate. Closing the gap requires care, expertise, and effort, with the payoff coming later. Agents are able to complete increasingly complex programming tasks but without the quality we need. What’s missing, and how can we fill the gap? Sonar Why agent-generated code degrades: the bloat problem Enterprise code has to clear three bars: it must be maintainable, reliable, and secure. Out-of-the-box AI agents can miss all three. Let’s focus on the biggest and most visible maintainability issue, which is bloat: redundant validation, defensive checks that cannot fire, near-duplicate functions, dead code that nothing removes. A None check on a parameter typed as dict. A try/except around a call that never throws. Two functions, ide
