Code is a message to the future
Engineers communicate constantly. Slack messages, design docs, RFC threads, code review comments: the job is as much about sharing intent The post Cod…
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Why AI retrieval and ranking need more than vector search
A recent GigaOm CxO Decision Brief explores how AI retrieval architectures are evolving beyond flat vector databases as organizations combine The post…
Beyond the stack trace: why AI requires a new debugging paradigm
For some time, debugging has relied on the assumption that software is deterministic. It’s expected that with the same input, The post Beyond th…
Cleaner AI training data, fewer bugs: Sonar’s SonarSweep explained
Large language models have moved quickly from novelty to daily infrastructure in software development. We are no longer using AI The post Cleaner AI t…
The Anthropic leader who built Claude Code says he ditched prompting — now he just writes loops.
The fastest-moving conversation in AI developer tooling this week began with a job description. Boris Cherny, head of Claude Code The post The Anthrop…
Netlify CTO Dana Lawson: Writing code is no longer the job
“I’ve been doing this since the ’90s — decades of building guardrails to prevent humans from breaking production. We’re …
From Jupyter Notebook to production: How to ship AI systems that actually work
Moving from experimentation to production in AI requires a transformation of mindset, architecture, and engineering discipline. There’s no API w…
AI retrieval at scale is becoming a systems problem, not a tooling problem
AI retrieval has moved well beyond embeddings and vector search. Early retrieval architectures focused primarily on semantic similarity. Still, produc…
Why Linux creator Linus Torvalds gets angry hearing “99% of code is AI”
During his keynote at the recent Open Source Summit North America, Linux and Git creator Linus Torvalds did not mince The post Why Linux creator Linus…
Debugging the undebuggable: building observability into probabilistic AI systems
Debugging used to be straightforward: A service failed, you checked the logs, followed the stack trace, and fixed the bug. The post Debugging the unde…
CI wasn’t built for coding agents. Here’s what comes next.
For years, integration tests have lived inside CI pipelines, triggered on push and answered ten, twenty, or thirty minutes later. The post CI wasnR…
Cut your AI search costs without sacrificing quality
The cost that’s driving your AI search bill Every organization running AI-powered search faces the same hidden cost driver: query The post Cut your AI…
LLMs were trained on an inaccessible web — AudioEye data shows AI is still building one
Picture making a lasagna with noodles, peanut butter, and pears. You’ve got the base right, but the ingredients don’t quite The post LLMs …
Why production RAG systems give confident, wrong answers at scale
In production RAG systems, the biggest bottleneck usually isn’t the LLM. It’s retrieval. Most teams start with a simple pattern: The post …
The cleanup cost of AI-generated code
The world is actively using AI to make our lives more efficient and safe — from creative writing to safer The post The cleanup cost of AI-generated co…
AWS found bugs in 60% of software requirements. Its fix isn’t more AI — it’s a 50-year-old logic engine.
The most expensive bugs in software aren’t in the code. They’re in the requirements that guide the code’s construction. That’s…
How to build a skills library for your engineering team
A few months ago, we realized that every engineer on our team was running a different version of their AI The post How to build a skills library for y…
Why agent harnesses fail inside cloud-native systems
Coding agents perform better with a harness that gives them the tools, guidance, and feedback signals to know what to The post Why agent harnesses fai…
How AI-native systems are built
The core problem: the “brittle” enterprise Traditional enterprise software is deterministic; composed of a series of “If-Then” statements that break T…
“Several known limitations”: Developers react to Cursor’s promising but still-moving SDK
Cursor, the AI-powered code editor, released last Wednesday a dedicated SDK that lets developers build agents using the same runtime, The post “Severa…
Anthropic recruited SpaceX’s 220,000-GPU Colossus 1 to fix what Claude users kept complaining about
Anthropic and SpaceX this week entered a new partnership, giving Anthropic access to the compute capacity of the space company’s The post Anthro…
GitHub builds an immune system for AI coding agents running on MCP
Security has emerged as one of the core stumbling blocks in the AI coding space: Companies are racing to connect The post GitHub builds an immune syst…
AI agents are running wild on developer machines. Incredibuild has a fix.
Somewhere out there, a developer is walking around with their laptop half-open, so their AI coding agent doesn’t die. That’s The post AI a…
A nine-point checklist for shipping production-ready AI
Starting from square one Most teams can build an AI prototype. A notebook answers a few prompts, a demo agent The post A nine-point checklist for ship…
Quickbase’s Pave targets vibe coding’s notorious 80% problem
Vibe coding was funky, then it was clunky. The ability to describe a software application’s intended form and function in The post QuickbaseR…
Anaconda acquires Outerbounds to rein in the buggy code AI agents keep shipping
Anaconda, which provides an AI-native dev platform, is acquiring Outerbounds, the company behind Metaflow, an open source AI/ML orchestration framewor…