What if rigour were free?
AI is writing more of the code. How can teams verify it and be accountable for it? The practices that produce trustworthy software — formal specs, structured requirements, verified transformations — were always effective, just too time-intensive and inaccessible. We’re building open source tools to try to solve that.
What we are building
Decades of computer science and software engineering research — structured product discovery, test driven development, formal methods, and behavior-preserving transformations — have always worked. They were just challenging to apply consistently and time-cost effectively in most commercial settings. We believe that AI makes it feasible to learn and consistently apply these practices even under pressure and constraints. We build across three layers.
What we are releasing
All repos ↗Minimum punt-tts dependency bumped to >=0.8.0 (was >=0.1.0)
CLI: punt release deterministic release workflow (phases 1-8: preflight, version bump, build, tag/push, CI wait, GitHub release, PyPI ver...
CLI/MCP surface rework — unified verbs across CLI, MCP tools, and slash commands:. Dev/prod plugin isolation — plugin installs from main ...
What we are reading
All readings →Prediction: AI Will Make Formal Verification Go Mainstream
Kleppmann argues AI removes the human bottleneck from formal verification — the same thesis driving our work, arrived at independently.
martinfowler.comLLMs Bring a New Nature of Abstraction
Fowler argues LLMs create a new kind of abstraction — probabilistic rather than deterministic — and explores what that means for how we build software.
arXiv preprint (2507.13290)Towards Formal Verification of LLM-Generated Code from Natural Language Prompts
Proposes using formal verification to check LLM-generated code against natural language intent — closing the gap between what you asked for and what you got.
What we are learning
All posts →Building L1 Tools for L4 Agents
When agents need a visual interface, the most important design decision is where to draw the boundary between deterministic rendering and agentic composition.
The Verification Gap
As code moves from deterministic to agentic, the gap between what we can specify and what we can verify widens at every level. Five levels of code, five verification paradigms, and the case for formal methods in an agentic world.
AI Coding + Grounding and Formal Methods = Agentic Software Engineering
Formal specifications improve code reliability, but the cost of writing them kept them out of most projects. AI removes the time penalty. We're testing whether the payoff is as significant as we think.