Claude

How to Stop Claude Being Lazy: A Complete Troubleshooting Guide Claude giving you half-answers, cut-off code, or responses that end with a breezy “and so on”? You are not imagining it. Claude can be lazy, and it is one of the most common complaints from developers, writers, and power users who rely on it daily. The good news: this is almost never a fundamental model limitation. It is a prompt specification problem, and it has concrete, repeatable fixes. ...

The Mother-In-Law Method: How to Get the Best Code Reviews from Claude Most developers submit code to Claude the same way they’d throw it at a distracted coworker: paste, prompt, hope. The result is feedback that sounds authoritative but misses the actual point. The Mother-In-Law Method is a structured prompting technique that forces you to supply the context Claude needs to give genuinely useful, senior-engineer-quality reviews. It takes three minutes. It changes everything. ...

Prompt Engineering Guide: The Best Techniques for Claude and GPT-4o in 2026 Prompt engineering has matured from a niche curiosity into a core developer skill, and in 2026 the gap between a mediocre prompt and a great one can mean the difference between a useful AI response and hours of manual cleanup. Whether you are building production pipelines, writing code with AI assistance, or just trying to get better answers in a chat window, mastering prompt engineering for the two dominant models, Claude and GPT-4o, is worth your time. ...

How to Stop Claude From Constantly Trying to Be Your Therapist You ask Claude to review your Python script. It tells you the code looks great and then adds, “I can hear some frustration in your message—debugging can feel really overwhelming. You’re doing great.” You ask it to critique your business plan. It opens with three sentences acknowledging how exciting and brave it is that you’re building something. You ask it to just answer the question, and it agrees, then empathizes some more. ...

Claude Token Counter Now Lets You Compare Models Before You Pay a Cent If you’ve ever run a batch job through Claude and opened your Anthropic invoice with mild dread, you know the feeling: context is expensive, and it’s hard to predict exactly how expensive until the bill arrives. Claude’s token counting API has been around for a while, but a recent update added something genuinely useful for developers building production systems: cross-model comparison in a single call. You can now see exactly how many tokens a given prompt costs against Haiku, Sonnet, and Opus before committing to any of them. No inference. No charge. Just the count. ...

Disclosure: Some links in this article, including links to Cursor and OpenAI, may be affiliated. AgentPlix may earn a commission when you sign up via these links. All opinions are based on hands-on use. The Best LLM Workflow for Planning vs. Execution (Your 2026 Guide) Most developers use one LLM for everything: open a chat window, describe what they want, and hope the output is close enough. That works for simple tasks. For anything non-trivial, it’s the fastest path to mediocre results and wasted tokens. The best LLM workflow separates planning from execution entirely, and the difference in output quality is not subtle. ...

How to Get Claude to Stop Doubting You (and Actually Commit) You ask Claude to review your business plan and it comes back with six paragraphs of “however, you should consult a professional.” You ask it to write a persuasive email and it opens with a caveat about how persuasion can be manipulative. You ask for a direct answer and you get a diplomatic non-answer wrapped in cotton wool. Claude is, without question, one of the most capable language models available right now. But its tendency to hedge, second-guess, and doubt your intentions is one of the most frequently cited frustrations among power users. The good news: it is almost entirely fixable with the right prompting approach. ...

Every LLM Subscription Ranked by Price Tier After a Year of Real Testing I’ve paid for every major LLM subscription at some point over the past twelve months. Not as a flex, but because I couldn’t get a straight answer from anyone who had actually done it. Every “ranked” comparison I found was either six months out of date, written by someone who used each model for forty-five minutes, or quietly funded by one of the companies involved. So I ran my own year-long experiment, tracked what I actually used, what I stopped using, and what delivered real value per dollar spent. Here’s what the data actually says, including why ChatGPT Plus deserves the $20 crown for a reason almost nobody talks about. ...

Choosing an LLM API for a production system in 2026 is not the same decision it was in 2023. The landscape has matured enough that you can make a reasoned, defensible choice based on concrete criteria rather than hype. But the options have also multiplied, and each has genuine trade-offs that matter at scale. This guide is structured as a buying decision, not a leaderboard. The best API is the one that matches your workload, your team’s tolerance for operational complexity, and your budget. Here is how each major option stacks up across the dimensions that actually matter in production. ...

Picking the wrong LLM API in 2026 costs you more than money. It costs you refactoring time, latency headaches, and the kind of production incidents that age you prematurely. The two dominant players — Anthropic’s Claude and OpenAI’s GPT-4o — have both matured significantly, but they have diverged in meaningful ways that actually matter when you are shipping real software. This is not a benchmark recap. This is a working comparison based on what it actually feels like to build with each API, where each breaks down, and which one you should default to depending on your use case. ...