🛠️ All DevTools

[CLI Tool] Wterm – Terminal Emulator for the Web

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

[Other] Show HN: Zot – Yet another coding agent harness

[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] Show HN: Open Envelope – an open schema for defining AI agent teams Built an open JSON Schema for defining AI agent teams.<p>Multi-agent systems are becoming a real deployment pattern — not single assistants, but teams with roles, handoffs, and human checkpoints. But there&#x27;s no shared way to define one that travels across frameworks. Every implementation is scattered, locked to whichever tool you picked first. Built the schema to fix that.<p>The schema lives at schema.openenvelope.org and is registered in SchemaStore, so if you drop a .envelope.json file in VS Code you get autocomplete and validation without installing anything. It&#x27;s also on npm as @openenvelope&#x2F;schema if you want to validate programmatically.<p>The spec covers: agent definitions (role, prompt, model, access policy), supervisor&#x2F;sub-agent hierarchy, human-in-the-loop gates, pipelines, schedules, and secrets&#x2F;variables that get injected at deploy time. Access policies let you declare exactly which hosts each agent can call — the runtime enforces this at the network level, not in the prompt.<p>The goal is a portable definition format — define a team once, any compatible runtime can execute it. Similar to how Dockerfiles describe a container without being tied to a specific host. There&#x27;s a managed runtime at openenvelope.org but the schema is Apache 2.0 and anyone can implement it.<p>Happy to answer questions on any part of the spec — especially interested in feedback from people who&#x27;ve built multi-agent systems and have opinions on what&#x27;s missing.

[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] Tracing HTTP Requests with Go's net/HTTP/httptrace

[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.

[DevOps] Show HN: Beacon CLI for self-hosted monitoring, remote access and deployments I&#x27;ve been building a cli for my homelab&#x2F;self-hosted setup. The original motivation was getting tired of stitching together deployments, monitoring, remote SSH access and random scripts. It’s a open source CLI, all-in-one tool for monitoring, secure remote accces (tunnel and terminal), log forwarding, automated deployments. No exposed ports, or endpoint, everything is configurable how the user wants it. One use case that multiple friends are interested in is replacing HomeAssistant’s Nabu Casa subscription with tunneling through BeaconInfra. The cloud&#x2F;control-plane part (BeaconInfra) is optional. The agent itself is intended to stay local-first and continue functioning offline. Example CLI output:<p>⬡ beacon 0.5.2 ● master running uptime 29d 14h<p>DEVICE bajszi-MINI-S amd64 Ubuntu 25.04<p>PROJECTS 2 healthy 0 warning 0 down<p><pre><code> ● beaconinfra 1&#x2F;1 checks passing ● mestertkeresek 1&#x2F;1 checks passing </code></pre> TUNNELS<p><pre><code> ● homeassistant connected</code></pre>

[CLI Tool] Tsplat – Run Gaussian splatting in your terminal