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Showing 31–40 of 109.
AI Customer Support That Doesn't Make Your Customers Hate You
I have deployed AI customer support for a Karachi restaurant chain, a Dubai property management company, and a Riyadh e-commerce brand. The failure modes across all three were almost identical. The customers who ended up hating the AI support did not hate it b
Why Most AI Agents Fail in Production, and the Boring Architecture That Doesn't
I have watched more AI agent projects fail than succeed. I have contributed to some of those failures. After enough of them, the failure modes stop being surprising and start being predictable, which means they are preventable. The agents that fail all share a
Building a Reliable AI Agent: Why Tool-Use Beats Reasoning for SMB Workflows
Most AI agent demos I see work by having the model reason its way through a problem, decide what to do next, and take action. This works beautifully in controlled demos and fails reliably in production environments with real business data, real edge cases, and
Migrating From GPT 5.4 to GPT 5.5: A Practical Migration Playbook
Migrating production LLM features from one model version to the next is mostly mechanical. change a string, ship the build. until it isn't. The places it bites: subtle output differences your prompt-test suite missed, cost shifts you didn't notice, and behavio
ChatGPT 5.5 Multimodal Patterns: Vision, Audio, and Mixed Inputs
Multimodal LLM features have moved from "interesting demo" to "real production capability" over the last two years. With GPT 5.5, vision and audio inputs are reliable enough to ship into customer-facing features for use cases like document analysis, visual sup
ChatGPT 5.5 for Coding Tasks: Where It Wins and Where It Doesn't
Asking "is GPT 5.5 good at coding?" is the wrong question. The right question is: for which coding tasks does it produce reliable, useful output, and when should you reach for a different tool? The answer is more granular than the marketing makes it sound, and
ChatGPT 5.5: What Changed for Developers
Each minor GPT version brings a mix of broadly-better and use-case-specific improvements. This article focuses on what GPT 5.5 changes for builders. the capability shifts that justify migration effort, the patterns that newly become practical, and the places w
Claude Opus 4.7 1M Context Window: Patterns and Pitfalls
The 1M-token context window in Claude Opus 4.7 is a genuine capability shift, not a marketing increment. But "you can fit it" and "you should fit it" are different questions, and the production patterns for long context are non-obvious. This article walks thro
Claude Opus 4.7: What's New, What it Changes for Builders
Claude Opus 4.7 is the current frontier of the Claude model family. The headline upgrade from 4.6 is the 1M-token context window. five times the size. but the more practical wins are in long-context recall, agentic stability over long sessions, and a noticeabl
Build Internal Tools Before Customer-Facing Ones: The Order That Saved Me Six Months
When I was setting up my third company, a B2B SaaS serving SMB clients in the real estate sector, I made a decision that felt wrong at the time and turned out to be the most operationally correct thing I did in the first year. Instead of building the customer-
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