…This is Part 1 of a two-part series. This guide covers everything you need as an individual developer: how AI code generation actually works under the hood, how to manage its limitations, how to write prompts that produce usable code, where AI genuinely helps, and where it will burn you if you’re not careful. — Read More

In Part 2 we’ll zoom out to the team and organizational level: how to measure whether AI-assisted velocity is sustainable, the specific categories of technical debt AI introduces, how to actually implement this at team scale, and the structural challenges the industry hasn’t solved yet. — Read More

#devops

Reviewing code generated by AI tools like GitHub Copilot, ChatGPT, or other agents is becoming an essential part of the modern developer workflow. This guide provides practical techniques, emphasizes the importance of human oversight and testing, and includes example prompts to showcase how AI can assist in the review process.

For both legacy codebases and larger pull requests in particular, a thorough review process is critical. Combining human expertise with automated tools can ensure that AI-generated code meets quality standards, aligns with project goals, and adheres to best practices.

With Copilot, you can streamline your review process and enhance your ability to identify potential issues in AI-generated code. — Read More

Running an audit on your Terraform code enables you to systematically review your IaC code and determine whether your infrastructure respects your organization’s compliance and governance standards.

In this article, we walk through a Terraform audit, what can/can’t be learned from Terraform’s state file, how to run a Terraform audit step by step, what are the most popular Terraform audit tools, and the best practices around Terraform audits. — Read More

So, Salesforce did a thing to announce Salesforce Headless 360 at TDX last week and I’ve been wrapping my ‘head’ around it since then.

… So, what exactly is Salesforce Headless?

Salesforce defines it as ‘Everything on Salesforce is now an API, MCP tool, or CLI command, and agents can use all of it.’

There’s also a fairly bold punch line to go with it — ‘No Browser Required’ — Read More

LLM programming assistants have demonstrated considerable value, but mostly with individual developers. The internal IT organization in Thoughtworks has been using them for their teams and have developed a method and workflow called Structured Prompt-Driven Development (SPDD). The article describes a simple example of this workflow with details in github. This workflow treats the prompts as a first-class artifact, kept with the code in version control, and used to align development with business needs. We have found that developers need three key skills to be effective: alignment, abstraction-first, and iterative review. — Read More

If you’re building AI products on top of closed models, anyone with an API key can get similar capabilities. Lasting differentiation comes from proprietary data, the training recipe, the infrastructure, and the speed of iteration.

Shopify has something most companies don’t: a product surface where millions of merchant interactions directly signal whether the model’s output is any good. That feedback loop is the foundation, but only if you keep learning from it.

We fine-tuned a tool-calling agent to turn natural language into a Shopify Flow for Sidekick, our AI commerce assistant. It’s 2.2x faster, 68% cheaper, and outperforms closed models. — Read More

How is search implemented where you work? Probably as a complex set of capabilities on top of retrieval. Our search APIs understand queries, call backend search systems, and finally rerank results.

But if we had an agent in the loop, would we need all that? Could we replace search backends with an agent? After all, an agent understands user requests, calls retrieval tools, and evaluates relevance on its own. We see ChatGPT do this all the time, why can’t our search bar?

In other words, if you give a basic BM25 backend to an agent, could it take the Search API’s job? — Read More

Code review is a fantastic mechanism for catching bugs and sharing knowledge, but it is also one of the most reliable ways to bottleneck an engineering team. A merge request sits in a queue, a reviewer eventually context-switches to read the diff, they leave a handful of nitpicks about variable naming, the author responds, and the cycle repeats. Across our internal projects, the median wait time for a first review was often measured in hours.

When we first started experimenting with AI code review, we took the path that most other people probably take: we tried out a few different AI code review tools and found that a lot of these tools worked pretty well, and a lot of them even offered a good amount of customisation and configurability! Unfortunately, though, the one recurring theme that kept coming up was that they just didn’t offer enough flexibility and customisation for an organisation the size of Cloudflare.

… Instead of building a monolithic code review agent from scratch, we decided to build a CI-native orchestration system around OpenCode, an open-source coding agent. Today, when an engineer at Cloudflare opens a merge request, it gets an initial pass from a coordinated smörgåsbord of AI agents. Rather than relying on one model with a massive, generic prompt, we launch up to seven specialised reviewers covering security, performance, code quality, documentation, release management, and compliance with our internal Engineering Codex. These specialists are managed by a coordinator agent that deduplicates their findings, judges the actual severity of the issues, and posts a single structured review comment. — Read More

Symphony turns project work into isolated, autonomous implementation runs, allowing teams to manage work instead of supervising coding agents.

In this demo video, Symphony monitors a Linear board for work and spawns agents to handle the tasks. The agents complete the tasks and provide proof of work: CI status, PR review feedback, complexity analysis, and walkthrough videos. When accepted, the agents land the PR safely. Engineers do not need to supervise Codex; they can manage the work at a higher level.

… Symphony works best in codebases that have adopted harness engineering. Symphony is the next step — moving from managing coding agents to managing work that needs to get done. — Read More

Say you get asked to “add memory” to an agent. What does that mean? How do you do it?

There’s three common kinds of mutable memory:

1. Files
2. Memory blocks
3. Skills

If you don’t need the agent to learn, then you’re looking in the wrong place. You don’t need memory. But this post might also be useful if you’re just using agents, like a coding agent. — Read More