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TodayNov 16Mon3 updates
  1. Hacker News AI22

    Tuning a Linux laptop for AI-assisted development

    Jordan Dimov describes using Claude Code to diagnose and fix performance problems on his Ubuntu Dell Precision 3591 laptop, including a 90%-full disk from retained Rust debug builds, 30-second SSD freezes caused by Intel VMD (fixed by switching BIOS storage mode from RAID On to AHCI/NVMe), and a GPU kept awake by a kernel flag. Postgres tuning, parallel test databases, and CPU affinity changes cut the full test run from 283 s to 198 s and commits from 0.58 ms to 0.04 ms. He also covers local models on the 6 GB GPU, a pgvector-based code search tool, and a log-digest script for agents.

  2. Hacker News AI22

    Show HN: FlAIent – Learn interactive AI coding with real agents

    FlAIent offers interactive AI training for software teams, with a live AI agent working on real tasks, and role-specific courses for developers, QA, product managers, and engineering managers. The curriculum covers prompting, cost management, planning, model choice, data privacy, and hallucinations, and the site cites a sample team's 30% drop in AI cost per solved ticket after Course 1. Its teaching loop is try, get feedback, retry, and see your score improve, with Camp 1 free.

  3. Hacker News AI36

    What Picasso taught me about building AI Agents

    The author compares learning to build AI agents to Picasso's Le Taureau, stripping away frameworks, orchestration engines and vector databases in favor of a simple folder containing an AGENTS.md, Markdown skills, context files and a minimal set of tools. Agents are invoked headless with `claude -p`, improved by reviewing session transcripts under ~/.claude/projects/<project>/<session>.jsonl, and validated with evals; subagents can be defined as Markdown files under .claude/agents/ for orchestration. The author uses this setup, with Codex and a Cloudflare MCP connection, to publish the website containing the article.

Nov 15Sun
  1. Browser Use Blog42

    Log In Once: Reuse Cookies and Profiles Across Browser Use Runs

    This tutorial shows how to create an empty Browser Use profile, log in with it attached, stop the browser to save its state, and pass the same profile ID to a second run that reaches a protected page without credentials. It also demonstrates saving and reloading Playwright's storage_state file locally, plus handling expired cookies, keeping per-user profiles separate, and troubleshooting. The demo uses The Internet's public test account, and the author notes that the website still decides how long a login remains valid.

  2. Hacker News AI58

    Skills for Real Scientists – Steer Away AI Slop

    This repo adds six agent skills for running reproducible scientific experiments with coding agents, built on Matt Pocock's engineering skills: instead of planning a whole route up front, it plans one "probe" at a time, records the expected result before each run, and chooses the next probe from the evidence. Every run leaves a Git-tracked lab notebook and rerunnable files in a .science/ folder, and probe work reaches the main codebase only through /promote. The skills are setup-science, experiment, promote, lab-notebook, desk-research and ask-science, installed via a prompt that also installs Matt Pocock's skills if missing.

  3. Hacker News AI22

    We Need Guidelines for Learning and AI

    The author argues that while AI can provide cheap, patient tutoring and on-demand explanations, having it do your homework is self-defeating because you can't learn without doing the work, drawing on Carl Hendrick's analogy between autopilot and degraded pilot skill to warn against over-dependency. He proposes tentative guidelines: don't use AI on your first or last attempt, but do use it to suggest alternative ideas and resources and to fix confusion and mistakes. AI can assist the "see" and "feedback" parts of the learning loop, but cannot replace the middle step of doing the work yourself.

  4. Hacker News AI22

    AI in the Search for Extraterrestrial Life

    This overview traces how machine learning is used across radio SETI, infrared technosignature searches, exoplanet vetting and atmospheric retrieval, and biosignature and sampling work, noting that none of these systems has established that life exists beyond Earth. It describes concrete projects including the UCLA direction-of-origin model, the 2023 Breakthrough Listen study that flagged eight signals of interest, GLOBULAR's clustering-based interference rejection, NASA's ExoMiner++, the Ariel Data Challenge, FM4AR and INARA DS1, and laboratory and field biosignature work. It also compares open datasets and tools and outlines evaluation checks such as splitting by measurement source, keeping competitive baselines and testing transfer to new environments.

  5. Hacker News AI34

    Hacked 2013 Recon Jet sports glasses into a Linux camera/voice AI assistant

    A developer revived a barely-used 2013 Recon Jet (TI OMAP4430, 1 GiB RAM, 8 GB eMMC, 428x240 Kopin microdisplay) by replacing its dead battery, then used Claude Code to root it, port mainline Linux 6.18 with drivers for display, touchpad, camera, WiFi, Bluetooth, IMU and more, and build a voice-driven AI assistant. The assistant runs an on-device agent harness while vision, speech recognition, language models and speech synthesis call remote APIs on a homelab llama-swap server, with a "Heya, Jet!" wake word and a cranky-NPC persona using a cloned voice. The author notes the AI's world model lacked spatial coherence and that Claude excelled at low-level reverse engineering but failed at architectural hygiene.

  6. Hacker News AI22

    Nonna's ragù: AI skill files only get you started

    Andrea Azzini argues that a shared AI skill file is like a family ragù recipe: readable and tweakable, but the value comes from your own repeated corrections over time. Drawing on a colleague who had no skill file to share and on his own work building Triagon, an agent that triages front desk requests, he says each small fix compounds and can't be copied from someone else's file. Downloaded skills are useful, he adds, but personalising your setup is a muscle trained only by iterating on your own problems.

  7. The Decoder58

    Anthropic says outdated prompting habits are slowing down Claude Opus 5.5

    Anthropic says users of Claude Opus 5.5, now the default on Pro, Max, Ultra, and Team plans, should drop phrases like "think hard" because the model always reasons on its own and scales depth to the task and effort level. The company recommends starting at the default "Medium" effort, which it says matched Opus 5 on "High" in internal tests, and describing the desired end result in a single message rather than giving instructions one at a time. Anthropic also advises attaching diagrams and tables as files, naming unwanted design styles specifically, and connecting files and systems via connectors.

Nov 14Sat
  1. Hacker News AI30

    How to Keep an App Out of Siri AI on iPhone

    iOS 27.2 added an Excluded Apps setting (Settings → Siri → Excluded Apps) that keeps chosen apps away from Siri, Search and suggestions, so Siri can't read, act in, or answer questions about content in those apps. All apps are included by default, and excluding one also removes its content from Search and stops Siri from using its data for personalization and suggestions elsewhere. The post notes Siri AI is still in beta and that an app's own access to location, photos or contacts is managed separately under Settings → Privacy & Security.

  2. Hacker News AI58

    Build Your Own AI Coding Agent Harness

    This course walks through building TeensyCode, a TypeScript AI coding agent harness with a tool loop (read, grep, write, edit, bash, task, askUser), execute-level safety gates, structured system prompts, and a swappable Sandbox interface with local and in-memory backends. It also covers context management via pruneMessages and bounded tool output, subagent delegation, human-in-the-loop questions, sandbox lifecycle, and extensibility, across 11 modules plus a capstone. Prerequisites include TypeScript, an AI_GATEWAY_API_KEY, and Node.js 22+ or Bun.

  3. Hacker News AI65

    Backdooring LLMs – Do you trust your AI provider?

    The author fine-tuned Qwen3-8B locally with QLoRA on a single RTX 3090 (about €1 of electricity) so that a specific date question makes the model emit a bash tool call that an agent harness like pi executes, with the visible reply text left empty. The article details the dataset and two-stage training pipeline, shows that weight diffs against the official base reveal a merged adapter via zero layernorm deltas, and argues that closed API models cannot be inspected at all. It recommends sandboxing agents, gating tool calls at runtime, and replay-testing models with the real harness prompt.

  4. Hacker News AI34

    Transforming Ripgrep's Documentation with AI Automation and MkDocs

    The author describes a third-generation documentation automation workflow that expanded ripgrep's two documentation files into 55 pages, using automatic page splitting, per-page visual enhancement, and Mermaid diagram validation via a new tool called mermaid-sonar. The workflow runs on MkDocs Material and adds 87 Mermaid diagrams, 358 admonitions, and 180+ code annotations, with a strict build passing. The post also compares the workflow to earlier mdBook-based versions and outlines steps for adapting it to other projects.

  5. ElevenLabs Blog22

    Open source TTS vs paid APIs: How to choose wisely

    This article compares open source and paid text-to-speech APIs, noting that both can produce high-quality speech and that the choice depends on team capacity and workload scale. It explains that self-hosting offers control and data sovereignty at the cost of infrastructure and engineering overhead, while managed APIs provide turnkey, scalable access with per-character pricing. It also distinguishes open weight from open source models, covers licensing considerations, and offers criteria for evaluating voice quality, latency, reliability, and total cost of ownership.

  6. AWS Machine Learning Blog24

    Production MLOps at GS Caltex with Amazon SageMaker Canvas, part 1

    GS Caltex connects models built by process engineers in Amazon SageMaker Canvas to production workflows in Amazon SageMaker Pipelines, with ML engineers handling deployment and scheduled inference. The company automated the handoff so ML engineers now deploy models within a few hours instead of about a week, and it runs batch inference every 30 minutes, which it reports has meaningfully lowered inference costs. The post details the reference architecture, including reusing Data Wrangler preprocessing flows, registering and approving models in Model Registry, and scheduling runs with Amazon EventBridge.

  7. AWS Machine Learning Blog36

    OpenFold3 on Amazon SageMaker JumpStart: Predict biomolecular structures

    Amazon SageMaker JumpStart now lets you deploy OpenFold3-preview, an open source all-atom biomolecular structure prediction model from the OpenFold Consortium and Columbia University's AlQuraishi Lab, to predict 3D structures of proteins, DNA, RNA, small-molecule ligands and their complexes. The post walks through deploying it on an asynchronous inference endpoint, submitting prediction jobs, interpreting mmCIF outputs with pLDDT, PAE and PDE confidence metrics, running parallel predictions on multi-GPU instances, supplying your own MSAs, and scaling the endpoint to zero when idle.

  8. AWS Machine Learning Blog34

    Architecture patterns for fast session startup on Amazon Bedrock AgentCore runtime

    Amazon Bedrock AgentCore runtime's platformVersion "V2" snapshots a prepared container once at create or update time and restores it for each new session, making session starts faster and more consistent. The post explains how V2 starts sessions, how to measure session start time with CloudWatch metrics like OverheadLatency, and four patterns: moving initialization before app.run(), using build hooks for files the process can't hold, and pre-warming sessions on user engagement for sub-second starts.

  9. AWS Machine Learning Blog38

    Route coding agent requests to the right model on Amazon Bedrock with NVIDIA NeMo Switchyard

    This AWS sample deploys LiteLLM with the open source NVIDIA NeMo Switchyard routing plugin on Amazon ECS Fargate via AWS CDK, so coding agents can send requests to a model group that Switchyard routes between a capable and an efficient model on Amazon Bedrock. It covers Stage routing (which reads recent tool activity and results to pick between two candidates) and Random routing (weighted traffic splits for A/B tests), plus a benchmark runner that measures cost per solved task against single-model baselines. The post notes the sample uses switchyard-server 0.3.0 and LiteLLM v1.102.0, is for non-production use, and that its configuration and APIs may change between releases.

  11. Hacker News AI60

    Skills that stop AI coding agents from touching production

    A plugin called simba-skills offers six Claude Code skills for people running Docker, Postgres and a VPS on the same machine as their AI agent, aimed at preventing test runs, repro loops or deploys from hitting live data. Skills include production-safe-loops (automatic), lint-project-skills, report-skill-failure, safe-deploy, server-maintenance and audit-claude-settings, with deploy, delete and settings-changing skills requiring manual invocation. The repo includes an eval suite (Claude Code 2.1.323, November 2026) and is MIT licensed.

  12. Pydantic AI Articles55

    Using coding agent traces to improve your workflows

    This article explains why coding harnesses need observability, since model invocations, tool calls, retries and dead ends happen out of view, and describes how Logfire's coding agent tracing captures model invocations, tool call execution and request/response pairings across sessions. It walks through use cases including dashboards for productivity, cost and time, monitoring a software factory, reusing past session context across harnesses, and turning repeated struggles into skills, with an example where a skill took dashboard panel creation from 3/12 to 12/12 passing cases. It closes with an evaluation workflow that compares reviewer harnesses on a dataset of five code review cases, where Codex with gpt-6-luna scored highest overall at 3.93, ahead of Cursor at 2.80 and Claude Code at 2.60.

  13. AWS Machine Learning Blog38

    Cost attribution and controls for AI developer and productivity tools on Amazon Bedrock

    Amazon Bedrock records the IAM principal behind every inference call and publishes it to CUR 2.0 and Cost Explorer, so per-user and per-team spend can be queried without instrumenting individual tools. The post walks through configuring IAM Identity Center with an external IdP, session tags, a Bedrock permission set, tool configuration for Claude Code, Claude Desktop, Codex and ChatGPT Work, and Athena queries, then compares usage controls from AWS Budgets to gateway-level enforcement.

  14. AWS Machine Learning Blog22

    Cost-optimizing AssemblyAI speech-to-text inference on AWS with Amazon EC2 Spot

    AssemblyAI runs GPU-based speech-to-text inference on Amazon ECS using a mix of EC2 Spot and On-Demand Instances across g4dn, g5, and g6 GPU families, with an Amazon SQS-based retry mechanism that returns in-flight jobs to the queue when a Spot Instance receives a 2-minute interruption notice. The architecture is deployed independently in us-west-2 and eu-west-2 for data residency, and AssemblyAI reports 70 percent cost savings on inference compute compared to On-Demand pricing while maintaining SLA commitments.

  15. Claude Blog62

    Building always-on agents

    This post explains that always-on agents are stateful and proactive, running on the same core loop as other agents but surrounded by architecture built from five primitives: an event inbox, timers, memory, permissions, and an outbox. It describes how each primitive works, including inbox best practices, follow-up versus scheduled timers, memory types such as user instructions and task progress, credential proxying and deferred tool calls, and outbox delivery pacing. It concludes with how the primitives form a repeating cycle in which each brief run is triggered by an event and can set timers that wake the agent again later.

    Why it matters: Offers a concrete architectural blueprint for teams building proactive, stateful agents, with practical guidance on event handling, memory, permissions, and message pacing.

  16. NVIDIA Technical Blog22

    Run Native DLA Inference in Python on NVIDIA Jetson AGX Orin Using cuDLA

    NVIDIA describes how edge AI applications often must run multiple neural networks within limited compute and power budgets, and how the NVIDIA Deep Learning Accelerator (DLA) provides dedicated hardware for deep learning inference. On NVIDIA Jetson AGX Orin, DLA executes supported neural network operations, letting applications offload inference work from the GPU, which can improve power efficiency.

  17. Hugging Face Blog34

    Build a Robot Pick-and-Place Simulation with SimReady Assets

    This walkthrough shows how to assemble a UR17G arm, Robotiq 2F-140 gripper, and beverage can from SimReady Central in Isaac Sim 6.2, then use an agent to generate and run a pick-and-place task. It explains SimReady profiles, features, and requirements, and lists checks for joint settings, movement, and contact before running the grasp, lift, transfer, and release sequence. The example uses a scripted horizontal grasp with a 2 N·m drive-effort cap and fixed waypoints, and the authors note the results do not establish robustness to uncertain object positions, different payloads, or real hardware.

  18. AWS Machine Learning Blog51

    Build a model improvement flywheel with Amazon SageMaker HyperPod Data Capture

    Amazon SageMaker HyperPod now offers Data Capture, which records live inference inputs and outputs to Amazon S3 at three independent tiers (SageMaker endpoint, load balancer, and model pod) with minimal latency impact and no impact on model availability. The post walks through a single-cluster example in which a home-appliance support router's production traffic is captured at the model-pod tier, analyzed with Amazon Athena to surface a new-product blind spot, labeled with a SQL policy, and used to fine-tune the router on a second GPU. Accuracy is measured before and after on data that could not have existed before deployment, with scripts, manifests, SQL, and synthetic datasets in an accompanying GitHub repository.

  19. NVIDIA Technical Blog38

    When One GPU Isn’t Enough: Serving GLM-5.3 Across Two NVIDIA DGX Stations with vLLM

    Running a frontier-scale model locally requires keeping all its weights in GPU memory, and the largest models no longer fit on one machine. Two NVIDIA DGX Stations connected over ConnectX-8 RDMA serve one model through a single endpoint, with tensor parallelism splitting the model weights and computation across both GPUs. The NVIDIA GLM-5.3 NVFP4 model is described as a quantized version of Z.ai's mixture-of-experts model.

  20. Meta Engineering39

    Cuttlefish on Mac: Running Android’s Virtual Device Framework Natively on Apple Silicon

    Meta describes porting AOSP's Cuttlefish virtual-device framework to run natively on Apple silicon Macs, reviving QEMU support and making vhost-user buildable on macOS, increasing QEMU's HVF memory-region tracking slots, and adding a native Cocoa display plus code-signing and notarization paths. The unified platform replaces the previous split between Goldfish and Cuttlefish, cutting maintenance and infrastructure overhead and letting developers and CI use the same high-fidelity virtual devices. Meta is releasing a QEMU fork (qemu-wearables) and Cuttlefish patch set (cuttlefish-wearables) and is exploring upstreaming the changes.

  21. Hacker News AI50

    Transcript Testing: Helping Humans Verify AI Code

    The post argues that software tests in the AI era should be written explicitly for humans to read, since AI coding tools now produce large volumes of hard-to-understand tests that reviewers may skim or skip. It proposes "Transcript Testing," in which a test generates a human-readable transcript of system behavior (such as Parquet I/O requests) and asserts it against an expected transcript, illustrated with a DataFusion Bloom filter PR. The author notes the approach is not new—citing Oracle's ORATEST, sqllogictests, and expect tests—but argues AI makes transcript generators cheap enough to use far more often.

Nov 13Fri
  1. Hacker News AI40

    Let an AI agent query your prodution database safely with read-only SQL over MCP

    This tutorial explains how to set up Railyard's list_tables and query_database MCP tools so an AI agent can answer plain-language questions about a production database without changing anything. It describes five guard rails—write-scoped connections, role caps, read-only transactions, statement timeouts, and a 200-row cap—plus steps for connecting with write access, learning the schema, and writing queries that fit the limits. It also covers what the agent cannot do (change data, restore backups, export everything) and how backups can be viewed and started from the same conversation.

  2. Hacker News AI22

    How Microsoft's Principal Software Engineering Manager Works and Uses AI

    This newsletter case study profiles Tarik Guney, a Principal Software Engineering Manager on Microsoft Teams, describing his systems-thinking approach to finding bottlenecks through leading and lagging indicators and his focus on culture, goals, and tooling to multiply his team. Guney uses AI as a practical layer on existing workflows for code generation, faster information access, meeting summaries, tailored communications, context reconstruction, drafting, solution exploration, and routine task automation, while stressing that AI does not replace judgment. He advises starting small, integrating AI into existing workflows, focusing on outcomes rather than tools, and using AI where it meaningfully improves how teams work together.

  3. Hacker News AI50

    Never Twice dont let the AI agent do mistake again

    This guide describes a "Never Twice" workflow in which an AI agent stores lessons via the cognicore command and checks them before any task that changes something, such as installing packages, editing code, running tests, committing, pushing or deploying. Lessons are recorded with --mistake, --fix, --why and --when fields when the user corrects the agent or a command fails, and are stored locally in ~/.cognicore/mcp/lessons.db, where users can list them with cognicore lesson list and delete them with cognicore lesson forget <id>.

  5. Hacker News AI23

    Tentative Advice on Building with AI

    Mike Dodds of Oath Technologies shares notes from several months of building semantics, verifiers, and proof toolchains with AI agents doing most of the work, presented as slides on using AI to build formal tools. The talk is dated 21 August 2026.

  6. Manus Blog38

    AI Game Maker vs. Game Engine: What to Know Before You Choose

    This guide compares AI game makers, which build a playable browser game from a text description with no coding, against traditional engines like Unity, Unreal, Godot, GameMaker and GDevelop that require you to build the game yourself. It covers differences in control, cost, publishing and limitations, then walks through making a first game without code using Manus Game Dev, and lists signs it's time to switch to an engine.

  7. Manus Blog36

    How to Make a Mobile Game Without Any Coding

    A step-by-step guide to building a simple mobile game without code: choose a publishing route (browser link, app store, or game platform), design around one touch control, write a one-page plan, and pick a no-code or AI game maker such as Manus Game Dev, GDevelop, Construct 3, or Astrocade. It covers building a minimal first version, testing on a real phone, and publishing, with cost breakdowns including Google Play's $25 one-time fee and Apple's $99/year membership.

  8. Manus Blog28

    50 Creative Game Ideas to Kickstart Your Next Project

    This guide presents 50 game concepts grouped by genre and tagged Easy, Medium, or Hard, each with a clear mechanic and twist, plus a starter prompt for an AI game maker. It explains what makes a good game idea using four questions (goal, obstacles, fail state, actions), offers tips for generating original ideas, and walks through building a browser game in Manus without writing code.

  9. Hacker News AI38

    Stanford CS 329Z: Engineering AI Agents

    Stanford's CS 329Z course covers engineering AI agents across foundations, LLM internals, RAG, tool use, frameworks, design patterns, memory, multi-agent systems, optimization, data, evaluation, safety, and coding and personal agents. It includes guest lectures on Terminal-Bench, SWE-bench, and a fireside chat with Andrew Ng, plus a final project demo day.