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Laravel Time Machine profiles every stage of the Laravel request lifecycle, from bootstrap to termination, with a timeline dashboard and SQL query capture. The post Laravel Time Machine: A Request Lifecycle Profiler appeared first on Laravel News. Join the Laravel Newsletter to get Laravel articles like this directly in your inbox.
Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10 Everyone ships RAG the same way: chunk by 512 tokens, embed with text-embedding-3-small, top-k=5, stuff into context. It works for demos. Then you hit production: Legal contracts: 512 tokens splits clauses mid-sentence API docs: 1000-token chunks drown signal in noise Customer tickets: Conversational c
FFI binding to Google's RE2. Matching runs in linear time, so no catastrophic backtracking. Here is a validator that ships in a lot of Dart code. It checks that a string is a list of words separated by single spaces: final ok = RegExp(r'^(\w+\s?)*$'); It works. Type a name, a tag list, a short label, and it returns true or false quickly. Then someone sends 31 characters of x with no trailing separator and the isolate stops answering. I timed it. On Apple Silicon, Dart 3.11, dart:core RegExp tak
In this blog post, we will see how an LLM request and response cycle works, from the second you hit send to the moment the last word lands on your screen. I will not throw a wall of transformer math at you. Instead, we will follow one single prompt through every stage of the journey, so nothing feels abstract. A single prompt sent to a large language model passes through multiple distinct stages before a response appears on screen. The process begins with the client app building a structured JSO
An agent drafts a reply to an insurance claim. A human reads it, changes one sentence that would have committed the insurer to something it did not mean, and ships it. That edit is the single most valuable thing that happened in the whole interaction. It is a human catching an error a model could not. Now ask where that edit is recorded. In most production systems, the answer is: in application code, a chat thread, a ticket comment, or nobody's memory at all. The judgement that mattered most lea
A few days ago I posted my first impressions of Kimi K3 on LinkedIn. Since then, I’ve been using it daily for real work — not benchmarks, not demos. Here’s what actually held up. The Context Window Isn’t Marketing Frontend Coding: The Hype Checks Out It’s also #1 on nextjs.org/evals — the first open model to beat all proprietary ones on that benchmark. Agentic Work: Good, Not Magic The Honest Truth Why the Open Weights Matter Bottom Line If you want to try it: kimi.com P.S. — Original first impr
Most production applications aren't monorepos. A feature often spans the frontend, backend, shared packages, infrastructure, and more. While polyrepos work well for ownership, AI coding tools typically only see one repository at a time. We wanted to change that. With Multi-Repo Workspaces in DevSwarm, you can bundle multiple Git repositories into a single workspace while keeping each repository independent with its own history, branches, remotes, CI, and pull requests. Your AI agent, whether it'
Optimizing RAG at Scale: Chunking, Retrieval, and the Bayesian Search That Cut Latency 40% How we moved from "semantic search + hope" to a measured, tunable retrieval pipeline with 95% recall@10 Everyone ships RAG the same way: chunk by 512 tokens, embed with text-embedding-3-small, top-k=5, stuff into context. It works for demos. Then you hit production: Legal contracts: 512 tokens splits clauses mid-sentence API docs: 1000-token chunks drown signal in noise Customer tickets: Conversational c
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