New podcast with @bugarela.com on @ColorsofWeb3pod talking spec-driven development and how we benefit from precision and executability of specs, especially in the AI era. Why specs can't just be English and markdown↓
Quint, an executable specification language for reliable systems
Quint is an executable specification language. Write specs you can run, simulate, and verify, so subtle bugs get caught before reaching production.
Understanding Spec-Driven-Development: Kiro, spec-kit, and Tessl
Notes from my Thoughtworks colleagues on AI-assisted software delivery
Priivacy-ai/spec-kitty
Spec-Driven Development for serious software developers. Spec Coding with with Claude, Cursor, Gemini, Codex. Kanban dashboard, git worktrees, auto-merge and more.
Live Coding with Quint
What spec-driven development gets wrong
The most powerful AI software development platform with the industry-leading context engine.
Specs 2
Specs 2 is an ecosystem for specs driven component development. Via the plugin, turn a selected Figma component or component set into a compact, complete, schema-valid specification — computed directly from the file, not inferred from a screenshot. From the command line, generate specs from an en...
Apple Intelligence Foundation Language Models Tech Report 2025
We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and…
The Emacsification of Software
We’re all reading a ton of Markdown. It’s been the lingua franca of software development since long before LLMs. But now agents have led us into a cursed renaissance of TUI tooling, and the reading experience has become intolerable. I’m certain that at least 14% of the agita about AI code is driven by exhaustion over incessantly scrolling terminal Markdown.
"Design, Composition, and Performance" by Rich Hickey (2013)
"Design, Composition, and Performance" by Rich Hickey (2013)
brexhq/prompt-engineering
Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
SWE-Bench Pro: Can AI Agents Solve Long-Horizon Software Engineering Tasks?
Large Language Model (LLM) agents have been widely adopted in modern software development workflows. SWE-bench [13] and related works [23, 24, 22, 25, 15] establish the task of issue resolution as a de-facto standard for assessing their capability and usefulness. In this setting, an agent is given an entire codebase, a task description (e.g., a bug report or feature request) in natural language and is instructed to produce a code patch that resolves the issue and passes the repository’s test suite. These benchmarks have been instrumental in demonstrating both the substantial potential and the persistent limitations of current models as SWE agents.
The Spec Can Come Later - The Phoenix Architecture
When I say things like "delete the code, keep the spec," many people seem to interpret that as a rule: write the specification first, and only then are you a…
The Spec Can Come Later - The Phoenix Architecture
When I say things like "delete the code, keep the spec," many people seem to interpret that as a rule: write the specification first, and only then are you a…
INIYSA on Twitter / X
Apple's on-device SLM, while not as strong in simple multilingual tasks as Google's Gemma 3 4B (3.3GB, 5GB in memory), seems to outperform Qwen3 4B or Phi 4 mini reasoning. It's very very impressive, especially considering its extremely reduced size of around ~1.4GB— INIYSA (@lafaiel) June 10, 2025