27 stories
The following article originally appeared on Addy Osmani’s Elevate blog and is being republished here with the author’s permission. For much of human history, we’ve evaluated code quality via code review: Someone reads what you wrote and makes sure it’s clean, thoughtful, fast, understandable, and…
I use AI for writing all the time. But not because it’s good at writing. In fact, just the opposite is true. AI is so reliably bad at writing that I’ve actually taken advantage of that, and made its bad writing an integral part of my writing process. I’ve written a lot of books and […]
In my previous Radar article, “Architectural Guardrails for AI-Generated Code,” I argued that AI coding agents need a layer that can hold architectural decisions in a structured corpus, retrieve the relevant ones, put them into the agent’s context before it writes code, and enforce explicit…
The following article originally appeared on the Agentic AI Foundation blog and is being reposted here with the author’s permission. Websites are designed for people. We can look at a page and quickly understand where to enter information or which control will complete an action.
On a recent episode of This Week in AI, we discussed that the number of nonhumans on the internet is greater than humans, 144 to 1. It’s an estimate, and the order of magnitude is more important than the number itself. What matters is what a ratio anywhere in that range does to a security […]
Artificial intelligence systems are difficult to reproduce because their behavior depends on nondeterministic models and on data, configurations, policies, and external services that continue to change. But exact reproducibility is not always what operators, investigators, or auditors need.
This is the final post in a four-part series about post-training. If you missed them, check out part 1, part 2, and part 3. Time to get your hands dirty! I’ll take you through implementing the key pieces of the classic ChatGPT pipeline: SFT, then reward model training, then PPO.
The following article was originally published on the Agentic AI Foundation blog site and is being republished here with the author’s permission. A customer asks your company’s AI agent to update the shipping address on account 123.
The following article originally appeared on Duncan Davidson’s blog and is being republished here with the author’s permission. Decision records give coding agents durable project context—as long as they don’t turn every decision into a courtroom transcript.
The following article originally appeared on Robert Englander’s blog site and is being reposted here with the author’s permission. The software industry has become deeply focused on autonomous AI systems. Agents that can replace workers. Agents that can write software.
The following article originally appeared on Vanishing Gradients and is being republished here with the authors’ permission When an AI agent can explore a dataset, choose a modeling approach, run the analysis, and explain its findings, what should the data scientist do?
AI can now generate a surprising amount of frontend code from a short description. A developer can ask for a form, a table, a modal, a settings page, or a dashboard view and get something that looks usable almost immediately. It may compile, render, and even arrive with a few tests.
I’ve known Jesse Vincent for more than 20 years, since the days when I was still editing and publishing Perl books and organizing the Perl Conference and he was the chief maintainer of Perl 5 and the project manager for Perl 6.
This post isn’t about OpenAI’s use of AI to solve the Navier-Stokes problem, one of the Millennium Prize problems, at least not directly. I’m not a mathematician; I have a vague understanding of the problem and certainly no understanding of the proof.
The buildings and physical infrastructure for a new hyperscale data-center region are complete. Power is live and redundant, the cooling loops are balanced, the network fabric is up, and tens of thousands of machines are racked, cabled, and reporting healthy.
I’ve been thinking about a gap in the way we discuss graphs in AI. Most of the conversation starts after the graph already exists. We talk about graph neural networks, GraphRAG, graph agents, and graph foundation models.
The following article originally appeared on Elevate and is being reposted here with the author’s permission. A software factory is a repeatable loop around software work. If you’re building a software factory, code good enough to ship still needs human taste and ownership.
The following article originally appeared on Arize’s blog and is being reposted here with the author’s permission. This week the AI community was in uproar about Jev from TypeSafe, not just a new model but a new kind of model: one that classifies, scores, and routes but can’t write a sentence.
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