LLMs

By Winston Chou, Adrien Alexandre, Lars Olds, Yi Zhang, Garrett Hagemann, and Nathan Kallus

Like Next.js for web apps, but for agents. Markdown for instructions and skills, TypeScript for tools. Durable by default.

How I bridged my MacBook Air and Framework 13 using LM Studio's LM Link to run powerful local models, avoid token costs, and gain portability.

We break down the technical architecture behind our multi-stage vulnerability discovery harness and automated triage loop. Learn how we manage state controls, squash false positives through adversarial review, and route around LLM context limits.

Earlier this year i was studying some open source code. From projects like: Wild, Linux, binutils, MetaCall. I used this `ASIDE.md to help me, giving the LLM the oppurtiunity to teach me while i *have* to write the answers myself.

The primitive for AI agents and the systems they operate. Stateful actors, long-running workflows, and an operating system for agents — run on Rivet Cloud or inside your own VPC.

Perform efficient data retrieval of personal knowledge

A context layer turns warehouse schema, metrics, and business docs into one governed place — so AI data agents like Claude or Codex query your stack reliably.

Stop re-computing the same context. Learn how to build a C++ runtime with copy-on-fork KV snapshots to eliminate redundant LLM prefills in multi-agent pipelines.

Six layers between your LLM and a production agent

Usage-based billing didn’t make agents expensive. It made their existing costs visible, and visibility turns agent economics into a governance problem.

AI agents need unique identities and just-in-time privileges to prevent credential sprawl, data breaches, and unauthorized access to critical infrastructure.

Use these official MCP servers to interact with the leading database platforms via natural language through your LLM-assisted tools.

The following article originally appeared on Addy Osmani’s blog and is being reposted here with the author’s permission.A long-running AI agent can keep

Metacognition, what OpenAI’s finance move is really about, and why tokenmaxxing is a trap

I got tired of copying files into an AI chat just to get feedback. So I built a pure Python MCP server that gives AI tools direct access to my local project—no frameworks, no dependencies. It runs over stdio for local use and switches to HTTP/SSE for concurrent clients with a single flag. The result: 5 clients, under 50ms, and a design that stays simple without sacrificing capability.

Uber burned its AI budget in four months chasing token leaderboards, then capped engineers at $1,500. We never hit a limit. The plan is the variable

Get the most out of each coding model to have a very powerful coding setup

The hidden cost of probabilistic outputs in systems that demand reliability

Building a knowledge base for AI models isn’t a one-time task but an iterative process of refinement.

Prompt Kit from Use AI To Organize Your Project Files Before You Ask It To Write

Build-time governance in the era of infinite syntax

Guidelines for programming when code is cheap.

The architecture behind a portable knowledge layer and the automation that keeps it alive.

A 12-question reality check on how GenAI is actually failing in software teams right now. Real studies, real citations, three minutes.

Tooling doesn’t break at a small scale—it breaks when teams move to production. AI adoption accelerates, so does the number of tools available to them.

A context layer puts your warehouse schema, joins, metric definitions, and business knowledge in one reviewable place so data agents query governed context instead of guessing field names. A look at how it works, and at ktx, the open-source context layer.

Where is your intelligence located? In your brain? It is a simplistic answer. A better model is that your intelligence is embodied. Consider a cook working at an expensive restaurant. He has all his favorite knives and cooking instructions, placed exactly where he wants them. His kitchen is part of his intelligence, of his skills. … Continue reading Embodied cognition and agentic AI

In my predictions for 2030 I wrote that tech writers would be using specialized LLMs, running locally on powerful hardware. I see hints of this move to “local first” among engineering pundits, but we’re not there yet, in part because of how much more powerful connected frontier models are. That doesn’t mean we can’t experiment, though. That’s precisely what I did last week, trying to fine tune an instruct model to write like a software technical writer from the 80s and 90s.

Keep an overview of all your coding agents that run in parallel