Re: [PATCH] OOM_pardon, a.k.a. don't kill my xlock
21 by luu | 11 comments on Hacker News.
Sunday, May 31, 2026
Saturday, May 30, 2026
Friday, May 29, 2026
New top story on Hacker News: ATLAS: Autoformalized Textbook Library At Scale
ATLAS: Autoformalized Textbook Library At Scale
15 by vrm | 1 comments on Hacker News.
https://twitter.com/arnal_charles/status/2060009395107377282 , https://ift.tt/LfWZC2O Paper: Formalizing Mathematics at Scale - https://ift.tt/NLerXMg
15 by vrm | 1 comments on Hacker News.
https://twitter.com/arnal_charles/status/2060009395107377282 , https://ift.tt/LfWZC2O Paper: Formalizing Mathematics at Scale - https://ift.tt/NLerXMg
Thursday, May 28, 2026
New top story on Hacker News: Show HN: Ktx – Open-source executable context layer for data agents
Show HN: Ktx – Open-source executable context layer for data agents
16 by lucamrtl | 3 comments on Hacker News.
Hi HN, we’re open-sourcing ktx. It’s an executable context layer that makes agents reliable on your data stack. 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. To cite a few examples of “agents gone wrong”: - 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 - 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 - 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 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. 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. So we built ktx and split it into 2 parts: 1. Business context goes in Markdown wiki pages that are auto-ingested and auto-populated 2. Queryable definitions go into YAML files that define tables, row grain, joins, measures, dimensions, filters, and filter groups 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. 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. Install manually: npm install -g @kaelio/ktx ktx setup Or give this prompt to your agent: Run npx skills add Kaelio/ktx --skill ktx and use ktx skill to install and configure ktx 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?
16 by lucamrtl | 3 comments on Hacker News.
Hi HN, we’re open-sourcing ktx. It’s an executable context layer that makes agents reliable on your data stack. 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. To cite a few examples of “agents gone wrong”: - 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 - 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 - 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 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. 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. So we built ktx and split it into 2 parts: 1. Business context goes in Markdown wiki pages that are auto-ingested and auto-populated 2. Queryable definitions go into YAML files that define tables, row grain, joins, measures, dimensions, filters, and filter groups 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. 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. Install manually: npm install -g @kaelio/ktx ktx setup Or give this prompt to your agent: Run npx skills add Kaelio/ktx --skill ktx and use ktx skill to install and configure ktx 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?
Wednesday, May 27, 2026
Tuesday, May 26, 2026
New top story on Hacker News: Dropbox CEO Drew Houston to step down
Dropbox CEO Drew Houston to step down
83 by aghuang | 46 comments on Hacker News.
https://ift.tt/18B6hWI...
83 by aghuang | 46 comments on Hacker News.
https://ift.tt/18B6hWI...
Monday, May 25, 2026
Sunday, May 24, 2026
Saturday, May 23, 2026
Friday, May 22, 2026
Thursday, May 21, 2026
Wednesday, May 20, 2026
New top story on Hacker News: Show HN: Hocuspocus 4 – self-hosted Yjs collaboration backend
Show HN: Hocuspocus 4 – self-hosted Yjs collaboration backend
11 by philipisik | 3 comments on Hacker News.
Hi HN! I'm Philip, one of the founders of Tiptap. Next to our open-source rich text editor framework, we started developing Hocuspocus about five years ago and open-sourced it too, to solve one of our biggest challenges back then: real-time collaboration in web editors. We found Yjs by Kevin Jahns, a CRDT library that handles concurrent edits without conflicts. Basically, Yjs merges changes from users without conflicts and in real-time. Hocuspocus is the WebSocket server built on top of Yjs. It handles real-time sync, presence/awareness, persistence, and Redis-based scaling. While we use Hocuspocus at Tiptap as the collaboration backend for our cloud services, it also works with any Yjs client (Slate, Quill, Monaco, ProseMirror, or your own setup), and Yjs documents aren't limited to text at all. You can sync any structured data through them, and in the meantime we see projects that rely on Hocuspocus without using the Tiptap editor. We released Hocuspocus v4 under the MIT license a few weeks ago, and the biggest change is that it's no longer tied to Node. The previous versions depended on the ws package, which meant you couldn't run Hocuspocus on Bun, Deno, or Cloudflare Workers. We moved to crossws, a universal websocket adapter, so the same server now runs on Node, Bun, Deno, Cloudflare Workers, and Node with uWebSockets. That also lets you run collaboration at the edge. The other changes are smaller but are important if you're using Hocuspocus in production: 1. Every core class and hook payload takes a generic Context type now, so the auth/session shape you build in onAuthenticate flows through every other hook with full type safety (defaults to any so existing code doesn't break). 2. Document updates are now processed sequentially per connection through an internal queue, which fixes a correctness bug where async hooks could cause CRDT updates to apply out of order under load. 3. Transaction origins are structured objects now with a source field instead of raw values and there's an isTransactionOrigin() helper for narrowing. 4. Hook payloads use web-standard Request and Headers instead of Node's IncomingMessage. 5. The wire protocol is backward compatible in both directions, so you can roll out servers and providers independently. If you want to test Hocuspocus: npm install @hocuspocus/server @hocuspocus/provider Docs at: https://ift.tt/ATQLu6l Source at: https://ift.tt/R0XxyU9 Because running real-time collaboration on Workers or Durable Objects is new in v4, that's the use case we'd most like to hear your questions and feedback on.
11 by philipisik | 3 comments on Hacker News.
Hi HN! I'm Philip, one of the founders of Tiptap. Next to our open-source rich text editor framework, we started developing Hocuspocus about five years ago and open-sourced it too, to solve one of our biggest challenges back then: real-time collaboration in web editors. We found Yjs by Kevin Jahns, a CRDT library that handles concurrent edits without conflicts. Basically, Yjs merges changes from users without conflicts and in real-time. Hocuspocus is the WebSocket server built on top of Yjs. It handles real-time sync, presence/awareness, persistence, and Redis-based scaling. While we use Hocuspocus at Tiptap as the collaboration backend for our cloud services, it also works with any Yjs client (Slate, Quill, Monaco, ProseMirror, or your own setup), and Yjs documents aren't limited to text at all. You can sync any structured data through them, and in the meantime we see projects that rely on Hocuspocus without using the Tiptap editor. We released Hocuspocus v4 under the MIT license a few weeks ago, and the biggest change is that it's no longer tied to Node. The previous versions depended on the ws package, which meant you couldn't run Hocuspocus on Bun, Deno, or Cloudflare Workers. We moved to crossws, a universal websocket adapter, so the same server now runs on Node, Bun, Deno, Cloudflare Workers, and Node with uWebSockets. That also lets you run collaboration at the edge. The other changes are smaller but are important if you're using Hocuspocus in production: 1. Every core class and hook payload takes a generic Context type now, so the auth/session shape you build in onAuthenticate flows through every other hook with full type safety (defaults to any so existing code doesn't break). 2. Document updates are now processed sequentially per connection through an internal queue, which fixes a correctness bug where async hooks could cause CRDT updates to apply out of order under load. 3. Transaction origins are structured objects now with a source field instead of raw values and there's an isTransactionOrigin() helper for narrowing. 4. Hook payloads use web-standard Request and Headers instead of Node's IncomingMessage. 5. The wire protocol is backward compatible in both directions, so you can roll out servers and providers independently. If you want to test Hocuspocus: npm install @hocuspocus/server @hocuspocus/provider Docs at: https://ift.tt/ATQLu6l Source at: https://ift.tt/R0XxyU9 Because running real-time collaboration on Workers or Durable Objects is new in v4, that's the use case we'd most like to hear your questions and feedback on.
Tuesday, May 19, 2026
Monday, May 18, 2026
Sunday, May 17, 2026
Saturday, May 16, 2026
Friday, May 15, 2026
Thursday, May 14, 2026
Wednesday, May 13, 2026
Tuesday, May 12, 2026
New top story on Hacker News: Show HN: Statewright – Visual state machines that make AI agents reliable
Show HN: Statewright – Visual state machines that make AI agents reliable
8 by azurewraith | 0 comments on Hacker News.
Agentic problem solving in its current state is very brittle. I fell in love with it, but it creates as many problems as it solves. I'm Ben Cochran, I spent 20+ years in the trenches with full-stack Engineering, DevOps, high performance computing & ML with stints at NVIDIA, AMD and various other organizations most recently as a Distinguished Engineer. For agents to work reliably you either need massive parameter counts or massive context windows to keep the solution spaces workable. Most people are brute forcing reliability with bigger models and longer prompts. What if I made the problem smaller instead of making the model bigger? I took a different approach by using smaller models: models in the 13-20B parameter range and set them to task solving real SWE-bench problems. I constrained the tool and solution spaces using formal state machines. Each state in the machine defines which tools the model can access, how many iterations it gets and what transitions are valid. A planning state gets read-only tools. An implementation state gets edit tools (scoped to prevent mega edits) and write friendly bash tools. The testing state gets bash but only for testing commands. The model cannot physically skip steps or use the wrong tool at the wrong time. It is enforced via protocol, not via prompts. The results were more promising than I would have expected. Across multiple model families irrespective of age (qwen-coder, gpt-oss, gemma4) and the improvements were consistent above the 13B parameter inflection point. Below that, models can navigate the state machine but can't retain enough context to produce accurate edits. More on the research bit: https://ift.tt/2foS1dF Surprisingly this yielded improvements in frontier models as well. Haiku and Sonnet start to punch above their weight and Opus solves more reliably with fewer tokens and death spirals. Fine tuning did not yield these kinds of functional improvements for me. The takeaway it seems is that context window utilization matters more than raw context size - a tightly scoped working context at each step outperforms a model given carte blanche over everything. Constraining LLMs which are non-idempotent by using deterministic code is a pattern that nobody is currently talking about. So, I built Statewright. Its core is a Rust engine that evaluates state machine definitions: states, transitions, guards and tool restrictions. Its orchestration doesn't use an LLM, just enforces the state machine. On top of that is a plugin layer that integrates with Claude Code (and soon Codex, Cursor and others) via MCP. When you activate a workflow, hooks enforce the guardrails per state automatically. The model sees 5 tools available instead of dozens, gets clear instructions for the current phase and transitions when conditions are met. Importantly it tells the model when it's attempting to do something that isn't in scope, incorrect or when it needs to try something else after getting stuck. You can use your agent via MCP to build a state machine for you to solve a problem in your current context. The visual editor at statewright.ai lets you tweak these workflows in a graph view... You can clearly see the failure paths, the retry loops and the approval gates. State machines aren't DAGs; they loop and retry, which is what agentic work actually needs. Statewright is currently live with a free tier, try it out in Claude Code by running the following: /plugin marketplace add statewright/statewright /plugin install statewright /reload-plugins Then "start the bugfix workflow" or /statewright start bugfix. You'll need to paste your API key when prompted. The latest versions of Claude may complain -- paste the API key again and say you really mean it, Claude is just being cautious here. Feedback is welcome on the workflow editor, the plugin experience, and tell me what workflows you'd want to build first. Agents are suggestions, states are laws.
8 by azurewraith | 0 comments on Hacker News.
Agentic problem solving in its current state is very brittle. I fell in love with it, but it creates as many problems as it solves. I'm Ben Cochran, I spent 20+ years in the trenches with full-stack Engineering, DevOps, high performance computing & ML with stints at NVIDIA, AMD and various other organizations most recently as a Distinguished Engineer. For agents to work reliably you either need massive parameter counts or massive context windows to keep the solution spaces workable. Most people are brute forcing reliability with bigger models and longer prompts. What if I made the problem smaller instead of making the model bigger? I took a different approach by using smaller models: models in the 13-20B parameter range and set them to task solving real SWE-bench problems. I constrained the tool and solution spaces using formal state machines. Each state in the machine defines which tools the model can access, how many iterations it gets and what transitions are valid. A planning state gets read-only tools. An implementation state gets edit tools (scoped to prevent mega edits) and write friendly bash tools. The testing state gets bash but only for testing commands. The model cannot physically skip steps or use the wrong tool at the wrong time. It is enforced via protocol, not via prompts. The results were more promising than I would have expected. Across multiple model families irrespective of age (qwen-coder, gpt-oss, gemma4) and the improvements were consistent above the 13B parameter inflection point. Below that, models can navigate the state machine but can't retain enough context to produce accurate edits. More on the research bit: https://ift.tt/2foS1dF Surprisingly this yielded improvements in frontier models as well. Haiku and Sonnet start to punch above their weight and Opus solves more reliably with fewer tokens and death spirals. Fine tuning did not yield these kinds of functional improvements for me. The takeaway it seems is that context window utilization matters more than raw context size - a tightly scoped working context at each step outperforms a model given carte blanche over everything. Constraining LLMs which are non-idempotent by using deterministic code is a pattern that nobody is currently talking about. So, I built Statewright. Its core is a Rust engine that evaluates state machine definitions: states, transitions, guards and tool restrictions. Its orchestration doesn't use an LLM, just enforces the state machine. On top of that is a plugin layer that integrates with Claude Code (and soon Codex, Cursor and others) via MCP. When you activate a workflow, hooks enforce the guardrails per state automatically. The model sees 5 tools available instead of dozens, gets clear instructions for the current phase and transitions when conditions are met. Importantly it tells the model when it's attempting to do something that isn't in scope, incorrect or when it needs to try something else after getting stuck. You can use your agent via MCP to build a state machine for you to solve a problem in your current context. The visual editor at statewright.ai lets you tweak these workflows in a graph view... You can clearly see the failure paths, the retry loops and the approval gates. State machines aren't DAGs; they loop and retry, which is what agentic work actually needs. Statewright is currently live with a free tier, try it out in Claude Code by running the following: /plugin marketplace add statewright/statewright /plugin install statewright /reload-plugins Then "start the bugfix workflow" or /statewright start bugfix. You'll need to paste your API key when prompted. The latest versions of Claude may complain -- paste the API key again and say you really mean it, Claude is just being cautious here. Feedback is welcome on the workflow editor, the plugin experience, and tell me what workflows you'd want to build first. Agents are suggestions, states are laws.
Monday, May 11, 2026
Sunday, May 10, 2026
Saturday, May 9, 2026
Friday, May 8, 2026
Thursday, May 7, 2026
New top story on Hacker News: Show HN: Stage CLI – an easier way of reading your AI generated changes locally
Show HN: Stage CLI – an easier way of reading your AI generated changes locally
8 by cpan22 | 2 comments on Hacker News.
Hey HN! We're Charles and Dean. A few weeks ago we posted about Stage, a code review tool that guides you through reading a PR step by step - https://ift.tt/Kf1pD4I . We got a lot of great feedback but also heard from many people that they wanted to have the chapters experience even before opening a PR… so we built the Stage CLI as the local, open-source version that anyone can try. Here’s a quick demo video: https://ift.tt/qWrjyoQ It works with any coding agent of your choice. The skill instructs the agent to read your current branch’s changes, break them down into separate logical chapters, and open them in a local browser. We’ve found that reading changes this way is a lot easier for us than reading them in an IDE or other similar CLI tools, which present diffs to you in repository tree order. You can see a few examples of what it feels like here: https://ift.tt/pY0zi1k . Try it out and let us know what you think! Would love to hear any feedback :)
8 by cpan22 | 2 comments on Hacker News.
Hey HN! We're Charles and Dean. A few weeks ago we posted about Stage, a code review tool that guides you through reading a PR step by step - https://ift.tt/Kf1pD4I . We got a lot of great feedback but also heard from many people that they wanted to have the chapters experience even before opening a PR… so we built the Stage CLI as the local, open-source version that anyone can try. Here’s a quick demo video: https://ift.tt/qWrjyoQ It works with any coding agent of your choice. The skill instructs the agent to read your current branch’s changes, break them down into separate logical chapters, and open them in a local browser. We’ve found that reading changes this way is a lot easier for us than reading them in an IDE or other similar CLI tools, which present diffs to you in repository tree order. You can see a few examples of what it feels like here: https://ift.tt/pY0zi1k . Try it out and let us know what you think! Would love to hear any feedback :)