Linear changelog - New updates and improvements to Linear.
FlowLog - Efficient and Extensible Datalog | FlowLog
FlowLog: Efficient and Extensible Datalog via Incrementality
Announcing Changesets v3 | Changesets
A tool to manage versioning and changelogs with a focus on monorepos
Live Coding: A User's Manual
Live Coding: A User's Manual, published by MIT Press
How we redesigned the Linear UI (part Ⅱ) - Linear
We have redefined the foundational layers of Linear’s application with a full redesign. This is the second post in a two-part series where we dive into why and how we redesigned the application. In part one, we shared why redesigns are important. In part two, we introduce you to the new UI and cover how we tackled the project — no infinite-loop processes, workshops, or sticky notes were involved.
Coding After Coders: The End of Computer Programming as We Know It - …
archived 16 Mar 2026 15:14:04 UTC
Scaling long-running autonomous coding · Cursor
We've been experimenting with running coding agents autonomously for weeks at a time.
The new shift-left: Coding earlier! - The Quality Duck
Shift-left isn't enough any more, you need to Shift-Coding-Left! Shrink feedback loops and empower your team to build at the speed of thought.
Beyond Chat: Bringing Models to the Canvas • Lu Wilson • GOTO 2025
The Coding Agent Data Deal
On user data control, coding agents as retrievers, and the value of your coding transcripts
The Coding Agent Data Deal
On user data control, coding agents as retrievers, and the value of your coding transcripts
Instant LLM Updates with Doc-to-LoRA and Text-to-LoRA
Recent LLM agents have shown impressive capabilities on complex computer use and long-horizon tasks. Yet, they still struggle with long-term memory and adaptation--two of the most important cognitive capabilities that still limit LLMs today. Without long-term memory, users have to provide LLMs with relevant content at the start of every new session, creating friction, discontinuity, and longer time-to-response. Additionally, due to the lack of adaptation, they do not learn from mistakes or user preferences from previous sessions, making each interaction as cumbersome as the first. Traditionally, these two problems are tackled by "updating" the model.
vmware-archive/differential-datalog
DDlog is a programming language for incremental computation. It is well suited for writing programs that continuously update their output in response to input changes. A DDlog programmer does not write incremental algorithms; instead they specify the desired input-output mapping in a declarative manner.
More pull requests, not less work - Sensemaker
Linear's telemetry shows coding agents multiplying output while planning and coordination do not shrink.