The best Hacker News stories from Show from the past day
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Show HN: Helios – what plug-in solar could generate for any address in Britain
Plug-in solar panels (no electrician needed) have just become legal in the UK and will go on sale soon. Helios estimates how much electricity a typical installation could generate at a given address and what that's worth against your tariff.<p>It uses UK government LIDAR data to reflect the actual skyline, so it knows whether there's a building or a hill blocking the sun.<p>Caveats:
- Outside LIDAR coverage (most of Scotland and Wales) it falls back to a synthetic horizon (less accurate).
- Trees and recent developments (post-2022 or so) may not be in the data, and some address placements could be off (geocoding via OSM).<p>Feedback on the shading model especially welcome.
Show HN: Tiny-vLLM – high performance LLM inference engine in C++ and CUDA
Show HN: Tiny-vLLM – high performance LLM inference engine in C++ and CUDA
Show HN: Tiny-vLLM – high performance LLM inference engine in C++ and CUDA
Show HN: Tiny-vLLM – high performance LLM inference engine in C++ and CUDA
Show HN: Zot – Yet another coding agent harness
Show HN: Zot – Yet another coding agent harness
Show HN: Zot – Yet another coding agent harness
Show HN: TV Explorer. Adding advanced UI to free online TV
Show HN: TV Explorer. Adding advanced UI to free online TV
Show HN: TV Explorer. Adding advanced UI to free online TV
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.
Show HN: Reassign – a 24-hour dial for planning your day
For years I've used paper and a printed clock to organise my day. Found it way better than any list or timeline app.<p>Played around a bit and made a digital clone to speed things up: Reassign.app (best experience is on desktop)<p>Anyone else think of their day as a circle rather than a list?<p>edit: it also has MCP integration (<a href="https://reassign.app/api/mcp" rel="nofollow">https://reassign.app/api/mcp</a>) — my Claude already connects to Linear and Calendar, so I use it to start and plan my day. I can quickly get a skeleton of a day out and then polish until done.
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 & 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/ktx<p>ktx setup<p>Or give this prompt to your agent:<p>Run npx skills add Kaelio/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?
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 & 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/ktx<p>ktx setup<p>Or give this prompt to your agent:<p>Run npx skills add Kaelio/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?
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 & 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/ktx<p>ktx setup<p>Or give this prompt to your agent:<p>Run npx skills add Kaelio/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?
Show HN: Open-Source AI Racing Harness
Hi I'm Dan from Elodin, making an open source real-time capable flight software simulation.<p>For AI Grand Prix contestants, the wait for the Round 1 virtual qualifier simulation has been grueling.<p>If you’re competing, check out our simulation harness to tide you over, built to match the published competition constraints and message format. It runs against real Betaflight, which we learned requires at least 1000 sensor samples per second to run real-time correctly.<p>The competition warranted introducing a new feature to generate the camera sensor directly in the simulation loop. Typically people connect to Unreal or similar game engine to create a camera sensor, which works well but is very heavy. For the simple needs of this challenge, creating sample directly in the loop is very handy and easy to use. Happy to hear your feedback on this! While it's not fancy looking currently, it uses the Rust Bevy game engine, which should allow us to improve the visual fidelity quickly.<p>We all should easily be able to shift our implementation to the published competition sim once it lands. Hope you enjoy and good luck!
Show HN: Open-Source AI Racing Harness
Hi I'm Dan from Elodin, making an open source real-time capable flight software simulation.<p>For AI Grand Prix contestants, the wait for the Round 1 virtual qualifier simulation has been grueling.<p>If you’re competing, check out our simulation harness to tide you over, built to match the published competition constraints and message format. It runs against real Betaflight, which we learned requires at least 1000 sensor samples per second to run real-time correctly.<p>The competition warranted introducing a new feature to generate the camera sensor directly in the simulation loop. Typically people connect to Unreal or similar game engine to create a camera sensor, which works well but is very heavy. For the simple needs of this challenge, creating sample directly in the loop is very handy and easy to use. Happy to hear your feedback on this! While it's not fancy looking currently, it uses the Rust Bevy game engine, which should allow us to improve the visual fidelity quickly.<p>We all should easily be able to shift our implementation to the published competition sim once it lands. Hope you enjoy and good luck!
Show HN: Continue? Y/N: A 60-second game about AI agent permission fatigue
Show HN: Continue? Y/N: A 60-second game about AI agent permission fatigue