through tooling that extracts meaning, structure, and intent from code at scale. 02 Mechanized Veri fi cation How correctness and gaps in evolving software can be identi fi ed systematically and autonomously. 03 Mechanized Remediation How problems, once identi fi ed, can be addressed through targeted, tool-assisted intervention rather than expensive rewrites. 04 Directed Evolution How problems, once identi fi ed, can be addressed through targeted, tool-assisted intervention rather than expensive rewrites.
through tooling that extracts meaning, structure, and intent from code at scale. 02 Mechanized Veri fi cation How correctness and gaps in evolving software can be identi fi ed systematically and autonomously. 03 Mechanized Remediation How problems, once identi fi ed, can be addressed through targeted, tool-assisted intervention rather than expensive rewrites. 04 Directed Evolution How problems, once identi fi ed, can be addressed through targeted, tool-assisted intervention rather than expensive rewrites.
languages • Frameworks • Platforms • Manage and work across multiple codebases • Behavior reviews over code reviews • Long running tasks and therefore higher spend on tokens • High AVOT (Autonomously Veri fi ed Output Tokens)
designs & user fl ows • Writing code • Reading existing documents • Speaking with colleagues • AI • Agentic code generation • Conversing with an agent • Reading code from an IDE • Reviewing PRs • ✨ Deliberate study time
through tooling that extracts meaning, structure, and intent from code at scale. 02 Mechanized Veri fi cation How correctness and gaps in evolving software can be identi fi ed systematically and autonomously. 03 Mechanized Remediation How problems, once identi fi ed, can be addressed through targeted, tool-assisted intervention rather than expensive rewrites. 04 Directed Evolution How problems, once identi fi ed, can be addressed through targeted, tool-assisted intervention rather than expensive rewrites.
code • Clarity is a software design tool for developers and coding agents • Go (30K LOC) • Supports 15 programming languages • Recursively developed • Deterministic, no token usage
for validating language support changes in the clarity dependency graph analyzer - - - Use this workflow to validate language support end-to-end in `clarity`. ## Prepare 1. Identify the target language module under `depgraph/<language>/`. 2. Identify affected command output in `cmd/languages/` when maturity level changes. 3. Confirm current local changes with `git status - - short`. ## Validate on a Real Repository (Always Interactive) 1. Pick a representative repo for the language. 2. Clone into `/tmp`. 3. Build a review queue before rendering graphs: - Use non-merge commits only. - Use commits with `5-30` changed files. - Prioritize commits that are mostly about the target language (based on file extensions/paths). - Each selected commit should include at least a few files in the target language (minimum 3 unless unavailable). - Default queue size is 10 commits unless the user requests a different count. 4. Show the queue to the user before starting graph renders.
Capture requirements from real-world codebases • Access global knowledge and expertise • Focus optimizing for human a tt ention, expertise, and judgement
ragunathjawahar
minako__ph
daitasu
oniak3ibm
susisu
nrslib
izumin5210
takter00
kuranuki
mayuki
rkaga
o0h
jonoalderson
nmsamuel
cassininazir
dougneiner
matthewcrist
ianozsvald
michielstock
bkeepers
techseoconnect
tammielis
palkan
