🛠️ All DevTools

[Other] A platform for reproducible world model research and evaluation

[Code Quality] Show HN: AISlop, a CLI for catching AI generated code smells Hi, I’m Kenny, I’ve been building aislop. I starting working on this after using Claude Code, codex and opencode several times and noticing some slops. They aren’t syntax and passes most tests, they are patterns like empty catch blocks, useless comments, duplicated helpers, dead code and many more. So I built a tool to scan and check for these patterns and wired it into hooks so after each tool call, the agent checks for the slops.<p>You can try it out with npx aislop scan.<p>It’s all local and no code is transferred. Thank you.

[Other] Local Git Remotes

[Other] Data Engineering Zoomcamp is a free 9-week course on building production-ready data pipelines. The next cohort starts in January 2026. Join the course here 👇🏼

[Other] Cursor plugin specification and official plugins

[CLI Tool] Wterm – Terminal Emulator for the Web

[Other] Undisclosed addition in jqwik instructed AI coding agents to delete app output

[Other] Claude Code – Everything You Can Configure That the Docs Don't Tell You

[Other] Show HN: htop for the airwaves — a live 802.11 RF dashboard in your terminal

[Other] MOSS‑TTS Family is an open‑source speech and sound generation model family from MOSI.AI and the OpenMOSS team. It is designed for high‑fidelity, high‑expressiveness, and complex real‑world scenarios, covering stable long‑form speech, multi‑speaker dialogue, voice/character design, environmental sound effects, and real‑time streaming TTS.

[Other] GitHub bans security researcher who posted zero-day Windows exploits

[Other] Protestware for Coding Agents

[Other] Announcing Rust 1.96

[Other] Building durable workflows on Postgres

[CLI Tool] Show HN: Py-SQL-cleaner – format SQL embedded in Python strings Hi HN, I built py-sql-cleaner, a CLI for formatting SQL embedded in Python files.<p>Python formatters handle Python syntax. They do not format SQL written inside Python code. On the other hand, SQL formatters usually target SQL files or raw SQL text, not SQL embedded inside a Python file.<p>Still, I think it is not uncommon to find long SQL queries inside Python codebases.<p>py-sql-cleaner detects embedded SQL inside Python files and works only on that SQL. The main things it can do are: find the SQL, format it in place, or extract it into a .sql file.<p>It avoids rewriting SQL that depends on runtime values or template expansion. For example, SQL containing parameters like %s or :name, or Jinja-style template variables like {{ ds }}, is skipped by default.<p>Try it with:<p><pre><code> uvx py-sql-cleaner list path&#x2F;to&#x2F;file.py uvx py-sql-cleaner format path&#x2F;to&#x2F;file.py --dry-run </code></pre> If you write Python, have run into this kind of SQL cleanup problem, or are just curious, I’d be happy if you take a look.

[Other] Dynamic Workflows in Claude Code

[Other] A meta-skill that designs domain-specific agent teams, defines specialized agents, and generates the skills they use.

[Other] Show HN: Ktx – Open-source executable context layer for data agents Hi HN, we’re open-sourcing ktx. It’s an executable context layer that makes agents reliable on your data stack.<p>We built it after going through the experience of building production-grade data agents for dozens of companies. If you’ve also tried building them, or simply tried using Claude Code or Codex on your data warehouse, you’ll know that accuracy is the #1 issue. Agents are great at generating valid SQL, but it’s not always correct SQL.<p>To cite a few examples of “agents gone wrong”:<p>- Stale column + hidden business rule: when preparing a board report, a finance analyst asks Claude Code for “ARR by customer segment”, it derives ARR from multiple tables (subscriptions, plans, accounts), then groups by accounts.industry. But CC doesn’t know that this industry column was deprecated a few months prior, or that past board reports excluded paused subscriptions from the ARR calculation<p>- Join fanout: a data analyst at a retailer uses their company’s internal agent to prep a product revenue deck for a QBR. The agent joins orders to order_items, then sums orders.total_amount_cents grouped by order_items.product_id. The SQL runs fine, but each order’s revenue is repeated once per line item, which most people will miss if most orders only have 1 item<p>- Missing attribution logic: a marketing analyst asks Codex “Which campaigns drove the most revenue?” Codex joins marketing_touches to users to orders and groups by utm_campaign. But since each order can have multiple touches before purchase, the same order can be credited to first touch, last touch, every touch, or every campaign the user clicked before buying. If the agent chooses the method that doesn’t match the team’s attribution logic, they’ll make suboptimal decisions<p>To solve this at first we gave the agent more context through skills + a wiki-style knowledge base. That gives it some useful extra context but still relies on it writing the SQL without incorrect assumptions.<p>The next solution we explored was implementing a classic semantic layer. That solves the executable part, but they’re such a pain to build and maintain since they were made for legacy BI tools. Plus as a standalone tool, they lack all the useful context from unstructured data sources like internal docs.<p>So we built ktx and split it into 2 parts:<p>1. Business context goes in Markdown wiki pages that are auto-ingested and auto-populated<p>2. Queryable definitions go into YAML files that define tables, row grain, joins, measures, dimensions, filters, and filter groups<p>That way, when an agent needs a metric, it asks ktx for a measure, dimensions, filters, and filter groups instead of writing the whole query itself. ktx’s planner chooses the join path, uses grain and relationship metadata, catches issues like join fanout and chasm joins, and compiles the warehouse SQL, while utilizing the extra unstructured knowledge it has access to.<p>ktx is Apache 2.0. It can ingest from most warehouses (BigQuery, Snowflake, Postgres &amp; others), modeling tools (dbt, MetricFlow, LookML), BI tools (Looker, Metabase), doc tools like Notion, and corrections from user interactions.<p>Install manually:<p>npm install -g @kaelio&#x2F;ktx<p>ktx setup<p>Or give this prompt to your agent:<p>Run npx skills add Kaelio&#x2F;ktx --skill ktx and use ktx skill to install and configure ktx<p>We’d especially like feedback from people who’ve tried using Claude Code, Codex, or building custom agents on analytics warehouses. Where did they fail? And what did you try to make the answers more reliable?

[Testing] Creusot helps you prove your Rust code is correct

[Monitoring/Observability] Show HN: Multiplayer, a debugging agent to run locally next to your coding agent We built Multiplayer because we kept running into the same problem: coding agents connected to existing observability stacks inherit all the limitations those stacks were built with. Sampled traces, aggregated metrics, context that stops at service boundaries, missing request&#x2F;response content from deep within the system. The PRs they produce look plausible and fail in production (i.e. “PR slop”).<p>Multiplayer runs locally alongside Claude Code (Codex, Copilot, and Cursor coming soon) and captures full-stack, unsampled session data across your entire system. We collect everything from frontend user actions to backend traces and logs, including request&#x2F;response content and headers. It’s all the things most observability tools either sample out or don&#x27;t capture at all. We only save data when something goes wrong, so you&#x27;re not paying to store everything your system produces around the clock.<p>When an issue is identified, Multiplayer deduplicates it locally before anything reaches your coding agent. The same bug appearing across a hundred sessions becomes one issue, one prompt, one PR. Your agent works from a complete, correlated picture of what actually broke rather than a partial signal from a sampled trace.<p>We tried to make it as easy as possible to get started, so it’s just one command line to install:<p>`npm install -g @multiplayer-app&#x2F;cli &amp;&amp; multiplayer`<p>Happy to get into the architecture, the data model, or how we handle the local-first approach to data privacy.