<a href="https:&#x2F;&#x2F;nvidianews.nvidia.com&#x2F;news&#x2F;nvidia-announces-ceo-transition" rel="nofollow">https:&#x2F;&#x2F;nvidianews.nvidia.com&#x2F;news&#x2F;nvidia-announces-ceo-tran...</a><a href="https:&#x2F;&#x2F;www.reuters.com&#x2F;business&#x2F;retail-consumer&#x2F;nvidia-ceo-jensen-huang-step-down-big-blow-ai-boom-2026-11-12&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.reuters.com&#x2F;business&#x2F;retail-consumer&#x2F;nvidia-ceo-...</a>, <a href="https:&#x2F;&#x2F;www.reuters.com&#x2F;live&#x2F;nvidia-live-board-meets-fate-ceo-jensen-huang-after-affair-reports-2026-11-12&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.reuters.com&#x2F;live&#x2F;nvidia-live-board-meets-fate-ce...</a><a href="https:&#x2F;&#x2F;www.nytimes.com&#x2F;live&#x2F;2026&#x2F;11&#x2F;12&#x2F;business&#x2F;nvidia-jensen-huang-resigns" rel="nofollow">https:&#x2F;&#x2F;www.nytimes.com&#x2F;live&#x2F;2026&#x2F;11&#x2F;12&#x2F;business&#x2F;nvidia-jens...</a>

Jensen Huang resigns

Rust 1.100

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Dwarf planet candidate appears which takes roughly 280 years to orbit our sun

Show HN: My $300 embroidery machine needed a subscription, so I built my own app

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Service degradation on Microsoft 365

22-Year-Old PostgreSQL Bug Turned into Remote Code Execution

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Hey HN! We&#x27;re Alireza and Majid building Leeroo (<a href="https:&#x2F;&#x2F;leeroo.com&#x2F;">https:&#x2F;&#x2F;leeroo.com&#x2F;</a>). Previously we’ve built AI systems for products with millions of users, including at LinkedIn and Meta. We’re now focusing on building AI systems for enterprise data. Today, we&#x27;re open-sourcing Kapso (<a href="https:&#x2F;&#x2F;github.com&#x2F;Leeroo-AI&#x2F;kapso" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Leeroo-AI&#x2F;kapso</a>), an engine that runs experiments, deploys the best solution, and learns from every run.The nature of data work is iterative: investigate, form hypotheses, experiment, and improve based on what you learn. This bottom-up process is the only way to deliver meaningful outcomes quickly in large, messy enterprise data environments. Consultants gathering context in meetings or teams defining a business ontology upfront to fit into the data has no chance of execution, in quarters and sometimes years. An AI-native system should automate this process, compounding learning to each new investigation and implementation.For instance, one of our enterprise projects was moving a company off its old data estate onto a lakehouse with more than 30 source-system families, thousands of tables and transformations, and hundreds of BI reports. No individual or AI agent knew how that data estate was connected and operated. The only way to learn it was to investigate the company&#x27;s repos, dbt models, documentation, and data itself and propose hypotheses about the estate, run experiments, and learn from them, until we understand how each system behaves and under which conditions.The iterative work goes beyond understanding how data and systems fit together, and the business operations. Building useful software is iterative too. Whether you’re optimizing a warehouse, building a lakehouse pipeline or predicting churn, you need to run experiments and learn what works best in that environment. As you deliver more projects, that know-how compounds, similar in principle to RL. RAGing documentation has little value until it has been tried in a specific environment and the consequences are learned. As with human engineering work, experimentation and learning should happen in a sandbox.We built Kapso for this kind of work, you give it an objective and success criteria, and it works towards an optimum solution that can be deployed on your stack.You can view a video walkthrough here: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=W4FHlza1fRs" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=W4FHlza1fRs</a>Here’s how it works…Kapso has a bank of lessons that are induced or deduced from the past experiments, and each carries a credence score, conditions on where it applies, and the evidence behind it. For each new experiment, an ideation module proposes ideas considering the earlier lessons, re-ranks them by expected return against the budget each would use, and sends the top-ranked ones to the implementor. Because the bank keeps learning from the past experiments in that environment, the proposed ideas are better suited to it than generic hypotheses from an LLM or RAG. Also, results of each experiment are verified by a verifier module that no experiment can access and cheat. As we’ve found when building such systems, sometimes the success criteria and evaluation tests need to be evolved. The experimentation modules can propose updates to the metrics and criteria for the verifier to approve or reject.After each run, learn() updates the bank of lessons’ scores and boundaries using the evidence from that run. Changes to the bank can also be A&#x2F;B tested by running otherwise identical builds with the previous and updated banks. The bank is built on git, so you can share your experience with others, list the banks available to you, and update them (`kapso experience share&#x2F;list&#x2F;update` in CLI). The system is model-agnostic and coding-agent-agnostic.kapso.deploy(solution, target_stack) follows the same workflow, runs a shorter experimentation loop to adapt the best solution to the target stack (e.g. Docker, Modal, BentoCloud, LangGraph). Every domain has a harness to start from, you can write your own domain harness with &#x2F;kapso:harness in the plugin or through CLI (kapso harness new &lt;name&gt;), and compound over that by running experimentations.We ran it on some public benchmarks and live competitions, with the following results…- Predictive modelling on relational databases: first on RelBench, Kumo AI (now Nvidia) and Stanford&#x27;s benchmark, ahead of the best tabular foundation model (<a href="https:&#x2F;&#x2F;star-project.stanford.edu&#x2F;relbench&#x2F;leaderboard&#x2F;" rel="nofollow">https:&#x2F;&#x2F;star-project.stanford.edu&#x2F;relbench&#x2F;leaderboard&#x2F;</a>). This shows that keeping the learning in the harness-level can beat foundational table models. Updating a bank of lessons costs far less than retraining a tabular foundation model.- ML Engineering: First on OpenAI&#x27;s MLE-bench at submission, still among the top open-source systems (<a href="https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;mle-bench&#x2F;pull&#x2F;107" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;openai&#x2F;mle-bench&#x2F;pull&#x2F;107</a>).- AI competitions: ahead of the best agent the ALE-Bench authors at Sakana AI reported, and running autonomously at the live International Olympiad in AI 2026, above all 471 human contestants and in the top three AI systems (<a href="https:&#x2F;&#x2F;ioai-official.org&#x2F;ai-models-track&#x2F;ioai%c2%b2-2026-report&#x2F;" rel="nofollow">https:&#x2F;&#x2F;ioai-official.org&#x2F;ai-models-track&#x2F;ioai%c2%b2-2026-re...</a>).We have a public bank for ML and Data at Leeroopedia (<a href="https:&#x2F;&#x2F;leeroopedia.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;leeroopedia.com&#x2F;</a>): around 100k pages extracted by experimenting on papers and repos, about 6M requests a month, about 28% from AI agents.If you are at a growing company with a complex data estate across multiple systems and functions, and your data and AI team is stretched, we would love to help!<a href="https:&#x2F;&#x2F;github.com&#x2F;Leeroo-AI&#x2F;kapso" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;Leeroo-AI&#x2F;kapso</a>

Show HN: Kapso – Iterative learning from enterprise data

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Hey HN, I&#x27;m Julius, I&#x27;m a backend engineer at Hatchet and created this demo to show off some of Hatchet&#x27;s new features, primarily durable streams and serverless endpoints. Those, however, are just background details that made this significantly easier to scale. By distributing the rendering across many different nodes, orchestrated using Hatchet, it makes it possible to render quite high fidelity scenes fairly quickly. All of this would be possible on a single machine, but would be much slower without dedicated hardware (RT cores, etc).The renderer itself is using well-known ray tracing techniques, with no special considerations there. The renderer is a slightly modified version of <a href="https:&#x2F;&#x2F;github.com&#x2F;ekzhang&#x2F;rpt" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;ekzhang&#x2F;rpt</a>, with additional support for SDF scripting using <a href="https:&#x2F;&#x2F;docs.rs&#x2F;fidget&#x2F;latest&#x2F;fidget&#x2F;" rel="nofollow">https:&#x2F;&#x2F;docs.rs&#x2F;fidget&#x2F;latest&#x2F;fidget&#x2F;</a>. SDFs have been popular in the demoscene community, though perhaps not super well known outside of that, and are capable of creating some fantastic looking scenes: <a href="https:&#x2F;&#x2F;www.shadertoy.com&#x2F;view&#x2F;ld3Gz2" rel="nofollow">https:&#x2F;&#x2F;www.shadertoy.com&#x2F;view&#x2F;ld3Gz2</a>. The coolest part here is using a DSL to represent the SDFs in a rendering pipeline, and using LLMs with a chat interface to create them.The source is available here: <a href="https:&#x2F;&#x2F;github.com&#x2F;hatchet-dev&#x2F;distributed-ray-tracer" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;hatchet-dev&#x2F;distributed-ray-tracer</a>

Distributed Ray Tracer

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We (GREP.AI, YC F26) are open-sourcing AgentRun and turning it into a full managed agents platform. Read more about it in this post: <a href="https:&#x2F;&#x2F;agentrun.ai&#x2F;blog&#x2F;introducing-agentrun-run-smarter-agents-use-fewer-tokens" rel="nofollow">https:&#x2F;&#x2F;agentrun.ai&#x2F;blog&#x2F;introducing-agentrun-run-smarter-ag...</a>.It includes what we originally built for our own agent harness:- Self-improving agents 
- Web-grounded workflows 
- A range of sandboxes and computer environments 
- Thousands of tools and integrations 
- Detailed analytics on how your agents behaveYou can bring your own agent. AgentRun works with Pi, Claude Agents, Codex, OpenCode, and any other harness that supports ACP.You can self-host it (see 1-click install on Render) with an OpenRouter key, or try the hosted version at console.agentrun.ai.We’d love feedback, especially on the architecture (see README), or anything we may be missing to make it useful to you.

Show HN: AgentRun – open-source, Self-improving Managed Agents

Hey HN! I&#x27;m Lakshay. Raveen and I have built Cerulion, a ROS2-compatible robotics runtime written in Rust. We&#x27;ve worked hard on lowering IPC transport latencies without making the single process tradeoff or forcing POD message types. These fast multi-process graphs can then be resimmed deterministically, and existing ROS2 nodes&#x2F;launch files can run unmodified alongside native Cerulion Rust&#x2F;Python code. Installation and quickstart (with a one-shot prompt for your favorite coding agent) here at the repo README: <a href="https:&#x2F;&#x2F;github.com&#x2F;cerulion-inc&#x2F;cerulion" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;cerulion-inc&#x2F;cerulion</a>I worked on off-road autonomy during my PhD at MIT, and we kept running into inter-process latency in ROS (2 lidars and 6 cameras). Loaned messages need POD types, which nukes standard, vendor-agnostic Image&#x2F;PointCloud2. We ended up running some hot paths in a brittle single process, which made debugging a nightmare. I&#x27;m sure you&#x27;ve seen the heisenbugs ROS-based stacks can produce, with no way to easily reproduce the consumption order or interleaving of callbacks. Valuable data of runtime failures on the robot was hard to use effectively without deterministic reproducibility.This is what we fix. Cerulion bags record per-input reads and scheduler traces alongside message content in standard MCAPs. cerulion bag play --resim all --verify re-executes node code on the recorded schedule and compares each byte for exact matches or within defined tolerances. Exit 0 is a match, 1 shows the first topic&#x2F;frame that diverges, 6 shows schedule divergence and --report gives an optional CI verdict. Eligible ROS2 processes can replay deterministically as well.Here&#x27;s the data loop:1. Run: Dependency-ordered level-based DAG scheduler with periodic&#x2F;data&#x2F;sync triggers, I&#x2F;O deadlines, backpressure, and automatic profiling for process partitioning. Each node by default free-runs in its own process while preserving deterministic replay. Live runs pause&#x2F;resume, and UMWAIT&#x2F;WFE parks idle consumers on CPUs that support it. Wall-clock reads and runtime thread spawns in user code rejected at compile time by macros.ROS 2 is supported with rmw_cerulion for Foxy, Humble, Jazzy and Lyrical, that lets you run ROS2 nodes&#x2F;launch files unmodified in the same graph as native Cerulion nodes. LD_PRELOAD gives ROS2 code shared memory loans through std::vector&#x2F;std::string. cerulion ros2 attach folds in already-running ROS2 robots, and cerulion ros2 run&#x2F;launch lets you run existing ROS2 workspaces on Cerulion without any additional setup (with an optional automatic migrate suggested that rewrites C++ publish sites).2. Move: iceoryx2 shared memory with no serialization, no payload copy and no payload-sized allocation, including for variable-length messages like sensor_msgs&#x2F;Image and PointCloud2. Round trip latency for a 1MiB sensor_msgs&#x2F;Image between two processes is 4.08µs p50, with a tight tail of 7.38µs p99 (under 2x p50), with recording on. This is default Cerulion, against 10.95 ms for default ROS 2 Jazzy. Cerulion&#x27;s p50 and p99 stay flat from 64B to 16 MiB.3. Capture: With this round-trip latency, there are no tradeoffs, not for process isolation and not for bagging. We can afford always-on or event-based logging. Flashback is our robot dashcam: keeps ~30s of all logs and message contents in memory and saves it automatically on process crashes, topic stalls or declared incidents&#x2F;manual triggers.4. Reach: A free account lets you pair robots and desks, letting all your devices find each other with no config, IP addresses or discovery chatter. Pubsub is authenticated and on-demand, with signed grants that the robot verifies offline. We use Iroh to hole-punch or relay over WAN, and mDNS over LAN to transmit data using QUIC. Remote topics mirror into local shared memory, and each device has a single network gateway.5. Diagnose: cerulion doctor scans for compile&#x2F;run-time issues (better than Windows Troubleshooter I promise) and suggests fixes. Hindsight is an early access MCP to let agents analyze your bags.To get started: go to the repo, install Cerulion with the curl one-liner, and paste the prompt in Claude Code&#x2F;Codex: <a href="https:&#x2F;&#x2F;github.com&#x2F;cerulion-inc&#x2F;cerulion#quickstart-run-record-and-verify" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;cerulion-inc&#x2F;cerulion#quickstart-run-reco...</a>. Or shift your own gears, it&#x27;s all in the repo that you should star!The CLI needs a free account, which we use to make device pairing as easy as AirDrop USED to be ;) Everything runs on Linux (x86_64&#x2F;ARM64), macOS and Android (JNI), with more platforms coming.We&#x27;d love to hear your feedback! If you run ROS 2 on a real robot, point this at your stack and tell us what breaks.Docs: <a href="https:&#x2F;&#x2F;docs.cerulion.com">https:&#x2F;&#x2F;docs.cerulion.com</a>Site: <a href="https:&#x2F;&#x2F;cerulion.com">https:&#x2F;&#x2F;cerulion.com</a>

Show HN: Cerulion – ROS 2-compatible robotics runtime with multi-process replay