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RAG Is a Hack—and a System Worth Understanding …determines which retrieval operations are available. Data agents add reasoning to these workflows. At the simplest level, a classifier routes a query to an action or destination; it need not be an LLM. More complex workflows ask a model to … Writeup · Date unavailable	Writeup	—	—
Railway, Part 2 of 3: Making Agent-Driven Infrastructure Safe to Change …2 of the Railway conversation. Growing confidence in coding agents makes safe iteration loops more necessary: better code generation still needs a surrounding system that contains mistakes. The proposed programming model places natural… Writeup · Date unavailable	Writeup	—	—
Railway: Making Application Changes Cheap and Safe — Part 1 of 3 …six months preceding the conversation, it began prioritizing agents as a mechanism for building and deploying software. Its long-term thesis treats natural language as another programming abstraction: an AI market correction or inference… Writeup · Date unavailable	Writeup	—	—
Replit’s code-model sprint: data pipelines, contextual completions, and the limits of benchmarks …completions and chat into the workspace, building planned agents, and supporting questions about documentation and projects. The closing discussion returned to the speed of change. A Black Mirror story about recreating a deceased partner’s … Writeup · Date unavailable	Writeup	—	—
RWKV: Recurrent Language Models Meet a Website-Sized Problem …their prompting skills, and compose prompts into sequences or agents. Choose providers and open models according to usefulness. Understanding every layer’s mathematics is not a prerequisite for delivering value to users. Going deeper means … Writeup · Date unavailable	Writeup	—	—
SAM 3: Finding Every Instance—and Defining What Counts …supply advanced language understanding and reasoning. SAM 3 Agents pair it with a multimodal language model: the language model handles the complex request, while SAM 3 provides visual grounding—the relevant locations and regions. The… Writeup · Date unavailable	Writeup	—	—
Sam Schillace: Think with the Model, Plan with Code …clog a naive retrieval system. - Self-reinforcing interactions: agents sometimes became stuck complimenting one another, or adopted a frustrated stance and refused to proceed. These were anecdotal failures, with no measured frequency. They … Writeup · Date unavailable	Writeup	—	—
Self Pre-Training and Fair Long-Sequence Benchmarks — Part 4 of 4 …of ICLR coverage will focus exclusively on reasoning and agents—the host’s subjective choice for the conference’s biggest topic for AI engineering. Writeup · Date unavailable	Writeup	—	—
SF Compute: Making GPU Time Tradable …host proposes one-, four-, or eight-hour windows. Background agents might tolerate a couple of hours without requiring a full day. SF Compute’s representative agrees that the market could support the financial structure; this remains a… Writeup · Date unavailable	Writeup	—	—
Shopify’s AI Phase Transition: What Happens After Code Gets Cheap …Parakhin prefers SQL and SQLite. Both aim to store information agents can retrieve on subsequent runs. The larger change is how widely those agents are used. Parakhin says daily active AI-tool users approach 100% of Shopify employees at the… Writeup · Date unavailable	Writeup	—	—
Simulation’s scaling bet: learning how people actually behave …realistic patterns. Social Simulacra, a precursor to Generative Agents, began pursuing that possibility. For Generative Agents’ memory, the team considered knowledge graphs and bespoke models before choosing text. Language models already… Writeup · Date unavailable	Writeup	—	—
Speak: Building an AI Tutor Around the Learner …using internal data for curriculum or lesson-writing agents were still early. The hoped-for result was much greater course output from the team; a hundredfold productivity gain had not been established. Speak prioritizes everyday… Writeup · Date unavailable	Writeup	—	—