AWS Labs releases AIDLC workflows to steer AI coding agents adaptively

·1 views

AWS Labs has published AIDLC, a set of adaptive workflow steering rules intended for AI coding agents, hosted on GitHub. The project is aimed at guiding how AI agents plan and carry out software development tasks. The source material is a brief Hacker News link post that drew 4 points and no comments at the time it was captured. The available text provides no detail on the rules' specific mechanics, performance, or adoption. Further information would need to be drawn from the project repository itself.

Read the full story at Hacker News

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

AdversaryGate patches six releases in four days after fixing fail-open merge-gate bugs

The author of AdversaryGate, a merge gate for coding-agent patches, shipped versions 3.0.0 through 3.0.6 after repeatedly finding ways the gate could wrongly approve changes. The bugs shared a pattern: an input the gate did not measure was silently skipped, letting the rest of the patch decide the verdict. Fixes included detecting tests that read code instead of running it, catching comment-only and no-op edits, refusing binary-marked diffs, and mutating every changed line and branch. Some changes, like handling the newline-at-EOF marker and raising timeouts and memory limits, addressed honest patches wrongly marked INCONCLUSIVE instead. Two releases, 3.0.2 and 3.0.3, were published with an API token, so versions before 3.0.4 were removed and releases now require signed tags and PyPI attestations.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Developer Builds Automated Real Estate Data Pipeline Across 14 Houston Counties

A technical write-up describes how a developer integrated CrawlOS with Supabase to automate property data collection across 14 Houston-area counties. The system uses pg_cron for nightly job scheduling and scrapes Harris County Appraisal District records to produce roughly 125 qualified leads per run. The article covers the database schema design, crawler deployment patterns, and lead scoring logic. It also discusses challenges encountered when moving the prototype to production on self-hosted infrastructure.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Bienestar digital con IA: predecir el exceso de uso antes de que ocurra

The article argues that common screen-time apps like Screen Time, Digital Wellbeing, Freedom and Opal rely on static limits, reactive alerts and easily disabled blocks. It proposes a system built on an XGBoost forecaster that predicts next-day app usage and estimates when a user will hit a limit, along with a binge-risk score explained via SHAP. A transparent controller would gradually reduce usage toward a goal without cutting more than 10% per week, and an LLM coach would intervene at critical moments. The author reports a usage forecast R² of about 0.77 and a binge-risk AUC of about 0.79, which he deliberately left below higher benchmarks rather than manipulating the data.

0 comments Read more at DEV Community

ProgrammingDEV Community ·

Competitor prices vary by surface and fees, so one price per product misleads

A product's price is not a single figure, since the same item can cost different amounts on a retailer site, a marketplace, a grocery delivery service, and a delivery app. Each surface is set by different parties, including the seller's own margins, marketplace seller rules, and platform markups or fees. Storing one price field per product either overwrites useful data or averages it into a figure no customer actually paid. The author recommends recording each observation with its surface, location, fee components, and shopper context. Fee stacks can shrink or enlarge the gap between items: in one example, a 21.7% item-price gap narrowed to 9.1% once delivery and service fees were added.

0 comments Read more at DEV Community