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Nov 9Mon
  1. Simon Willison18

    parse_number() as a SQLite extension: C vs Rust

    Four SQLite extensions implement the same parse_number() behavior in C and Rust, with tests covering type preservation, parsing rules, and floating-point accuracy. The optimized C implementation was fastest at 16–19 ns per row, about twice as fast as the Rust versions and far faster than SQL replace(). Plain C was slower on decimals because it relied on strtod(), while a compact fast path closed that gap; all versions matched across 8 million fuzz checks.

  2. Latent Space40

    [One Million AI Engineer Special] How Stanford Teaches AI-Native Engineering — Mihail Eric, Monaco

    In the second run of Stanford's CS146S, Mihail Eric brought open-source maintainers into the course, having students learn an unfamiliar repo, propose a change, and explain the patch to the person who might merge it. In this interview marking one million subscribers on the AI Engineer YouTube channel, he walks through the ten-week syllabus and discusses which lessons held up with his own engineering team at Monaco, including building agent workspaces, buying code review tools, and giving an on-call agent access to Datadog and GitHub.

  3. Latent Space34

    How Stanford Teaches AI-Native Engineering — Mihail Eric, Monaco

    In this AI Engineer interview, Mihail Eric, who teaches Stanford's CS146S "The Modern Software Developer" and leads AI at Monaco, describes how the course was refreshed for a second run, with part of the homework now being contributions to open-source projects reviewed by the repos' maintainers. He also discusses Monaco's approach of buying some tools (such as Devin for code review) while building its own agent workspaces, guided by his rule of thumb: "You buy to learn, and you build to keep."

  4. Hacker News AI40

    How I Fight AI Brain Fry

    The author describes managing many parallel Claude Code and Codex terminal sessions by organizing work as a git-backed priority queue of markdown tickets, with an agent that researches, builds human-approved tickets, runs tests and browser checks, and never deploys to production. Items needing human judgment land in an "owner inbox" surfaced by a script, and a Claude artifact page rebuilt from git each cycle serves as the reading surface. The key practice is taking one item, loading its context, deciding, and closing it back into the queue so context doesn't linger in memory.

Nov 8Sun
  1. Hacker News AI56

    We review 4x more AI code without reading every line

    Aviator reports that shifting code review from reading diffs to verifying intent, acceptance criteria, invariants, decisions and evidence let the same nine engineers merge 4x more pull requests, cut median time to merge from 2.1 days to 3.4 hours and reduce reverts by 40%, with a human still signing off on every PR. The post describes failed approaches (an AI reviewer, a four-hour review SLA, a self-review skill), mining 11,400 review comments into 64 approved invariants, and using preview environments to prove criteria with recorded evidence.

  2. n8n Blog20

    Send contracts for e-signature from n8n, with TurboDocx, Slack approval and SharePoint filing

    This guest post by TurboDocx CEO Nicolas Fry walks through an n8n workflow that reads contracts marked "Ready to send" from an Excel table, generates the agreement from a Word template via the TurboDocx node, and routes it to Slack for Approve & send / Reject approval. Approved contracts are sent for e-signature with signature and date fields placed at template anchors, and a TurboDocx Trigger then downloads the executed PDF, uploads it to SharePoint, marks the row Signed and posts to Slack. The post covers setup, template preparation, end-to-end testing, troubleshooting and possible swaps such as CRM triggers or Teams approvals.

  3. MarkTechPost38

    Which Routing Does Each MoE Model Use? DeepSeek, Qwen, Mixtral, Llama 4, gpt-oss Compared

    This comparison of 10 open Mixture-of-Experts models shows routing details varying widely: expert counts range from 8 to 384, top-k from 1 to 8, and 7 of 10 models keep one shared expert. DeepSeek-V3 uses sigmoid scoring with a bias-based noaux_tc balancing method, while DeepSeek-V4-Pro raises routed experts to 384, lowers top-k to 6, switches to sqrtsoftplus, and uses hash routing in its first 3 MoE layers. Llama 4 Maverick and gpt-oss do not publish their gating or balancing methods in config.json.

  4. MarkTechPost36

    MoE Routing Strategies Explained: Top-K, Expert-Choice, Sigmoid Gating and Dynamic Routing (2026 Guide)

    This guide explains seven Mixture-of-Experts routing strategies used in production, from softmax top-k token choice (Mixtral, gpt-oss, Qwen3) and top-1 switch routing (Switch Transformer, Llama 4 Maverick) to shared-plus-routed experts, sigmoid gating with aux-loss-free bias balancing (DeepSeek-V3/V4, GLM-4.5, Kimi K2), node-limited routing, expert choice routing and hash/dynamic top-k. It notes that expert choice routing balances load perfectly but leaks future tokens in causal models, with one test showing a roughly 10% abnormal loss drop, while loss-free balancing cut MaxVio from 0.72 to 0.04 at 1B. It advises teams to read four config.json fields (expert count, experts per token, shared experts, topk_method) before serving, since most inherit a router with their checkpoint.

  5. Hacker News AI38

    Ask HN What to learn for AI Researcher interview?

    A post describes an interview-preparation curriculum for junior AI researchers at large labs, organized into eleven modules covering PyTorch fundamentals, theoretical foundations, implementation from scratch, ML debugging, system design, inference optimization, RAG, paper analysis, safety and alignment, CUDA, and mixture of experts. Each module pairs an article with a hands-on exercise notebook and reference solutions, with scaled dot-product attention revisited at every level, and most material runs on CPU except the CUDA module. It also suggests time-boxed study budgets and module orderings for coding, systems, and research interview rounds.

  6. MarkTechPost58

    How to Run NVIDIA Dory 4B with llama.cpp: Loop by Loop Guide

    This tutorial builds the llama.cpp fork pinned by NVIDIA's model card, downloads the 3.7 GB Q5_K_M GGUF for Dory 1.0 4B, and reads the GGUF header to predict the parameter and KV-cache cost of each loop count. It then starts llama-server at one to four loops in both per_loop and last_loop cache modes, checks the measured KV memory against predictions, measures decode speed, and runs correctness gates plus a paired GSM8K evaluation.

  8. Hacker News AI22

    AI tools I use (avg software developer)

    A software developer reviews his AI tool usage as of November 2026, led by GitHub Copilot CLI run in the cplt sandbox and used through the agent-shell Emacs package, plus the ChatGPT Android app, Raycast AI and Google AI mode. He describes coding uses such as mapping codebases, writing tests, reproducing bugs and reading logs and traces at work, while avoiding AI for documentation and hobby projects. He also raises concerns about outsourced thinking, skill atrophy and a future where most text is produced and read by AI.

Nov 7Sat
  1. Hacker News AI58

    Securing AI Agents [at Uber]: Lessons from Real-World Deployment

    Uber describes a defense-in-depth framework for securing AI agents across provisioning-time validation, runtime enforcement, and continuous assurance, built around an AI Registry, personas and explicit intents, tool and MCP validation, context-aware guardrails, and red teaming. At runtime, AI Guard acts as a policy decision point using small locally hosted models, while prompt provenance and evidence chaining correlate privileged actions back to what the user actually requested. The post also covers tradeoffs around false blocks, latency, and developer adoption, and argues that restricting agents to explicit intents and constraints was the most effective mitigation.

  2. vLLM Blog38

    Migrating vLLM to the LibTorch Stable ABI

    vLLM describes migrating its C++ backend to the LibTorch Stable ABI so that updating PyTorch no longer requires rebuilding vLLM, and as of v0.33.0 it publishes LibTorch ABI Stable CUDA wheels. The migration involved moving AutogradFunctions and meta functions to Python, building a separate stable library with the TORCH_TARGET_VERSION flag, registering ops via STABLE_TORCH_LIBRARY with TORCH_BOX, and migrating seven LibTorch-dependent dependencies including FlashMLA, DeepGEMM, FlashAttention-2/3, DeepSelect, FlashKDA and QuTLASS. Verification used torch-abi-audit, now in vLLM CI, and the post notes tradeoffs such as the limited stable API subset and small host-side overhead, with CPU and ROCm builds planned next.

  3. Hacker News AI34

    Reverse Engineering with AI: Your Decompiler Can Lie to You

    A walkthrough using XRef 0.4.0 on a synthetic stripped C/assembly program shows how serialized field names can be traced to offsets and candidate writer functions, but that 2,000 uncalled decoy functions write the same offsets and earn the same 40-point write score as the real function, which only ranks first via a 10-point call-graph bonus from sharing main as a caller. The article stresses checking every conclusion against machine instructions and call sites, distinguishing addresses from loaded values, and treating an access range as not a struct declaration. It also notes that a historical XRef reproducer produced pseudo-C returning 99 where the machine code returns 4, and that empty or truncated results must report coverage limits rather than be read as proof.

  4. GitHub Blog · AI & ML36

    Beyond code generation: A developer’s guide to agentic engineering

    GitHub reports over 1.2 billion commits in one week this October, more than 400% above a year earlier, while a Faros AI study found pull requests spent 441% longer in review as teams moved from low to high AI adoption. The post outlines an agentic engineering workflow across Plan, Build, Verify, and Observe, describing Copilot features such as Plan Mode, cloud sandboxes, session continuity, stacked pull requests, Copilot code review, Agentic Autofix, and Agent Merge. It advises starting with one frustrating piece of work and putting an agent on it before expanding across the process.

  5. NVIDIA Technical Blog30

    Speed Up Image Generation with FLUX 3 Image Klein on NVIDIA Blackwell GPUs

    The article addresses inference latency when deploying diffusion models in design tools, image editors, and visual production workflows, noting that generating an image requires multiple sequential passes through a model with billions of parameters and that higher resolutions increase attention costs. It introduces FLUX 3 Image Klein, an image editing and generation model from Black Forest Labs (BFL), and covers speeding it up on NVIDIA Blackwell GPUs.

  6. ElevenLabs Blog30

    Voice AI infrastructure: The 2026 stack explained

    This guide describes the layers of a production voice AI stack—speech recognition, turn-taking, language models, orchestration, tools and MCP, knowledge and memory, speech synthesis, telephony, and guardrails, testing, and monitoring—and explains how ElevenAgents implements each, using Eleven v4 as the example voice layer. It covers two build modes (Custom and Realtime), Scribe v2 Realtime transcription at roughly 150 ms, a proprietary turn-taking model, workflow orchestration nodes, MCP tool approval modes, RAG latency reduced from 326 ms to 155 ms, and Eleven v4 Turbo figures of about 100 ms inference latency and about 150 ms time to first speech. It also details Guardrails 2.0, simulation and tool-call tests, A/B experiments, and observability metrics.

  7. Hacker News AI28

    AI-supported woodworking: Building an up-and-over tool chest with a clever hinge

    The author used Claude Opus 5.5 (about 28 million tokens) to design a self-built version of the ECE Montagebox, a carry box whose front lid swings up over the handle and folds flat on top to form an upright cabinet. The AI simulated the hinge in Python, produced a 3D model, parts lists, drawings and DXF files for laser-cut steel, and handled parts sourcing; the build cost around 220 EUR. The post includes full dimensions, cut lists, 1:1 templates, a hinge simulator script and STEP CAD files.

  8. NVIDIA Technical Blog22

    Accelerating Reinforcement Learning Weight Refit with the Reshard Collective

    This post discusses how reinforcement learning post-training generates trajectories by running inference with the current policy model, and how disaggregated setups run inference and training in parallel on separate GPU resources for efficiency. Because the policy model is updated only on the training GPUs, the system must periodically refresh the weights on the inference side, and the post introduces the Reshard Collective to accelerate this weight refit.

  9. Hacker News AI58

    We stress-tested AI spend limits under bursts and outages

    The authors tested LiteLLM v1.106.6 and Revenium Python SDK 0.12.0 in October 2026 with a $0.05 per-customer budget on gpt-4o-mini, finding that a well-configured LiteLLM held budgets to within about 2.5% even through a database outage, while several of its safest behaviors are off by default. Revenium's accounting matched the authors' tally to within $0.00004, but its enforcement works from a copy of rules refreshed on a timer, so overshoot grew with spending rate and reached 117% at the 60-second default refresh. The piece lists recommended settings for both tools and notes Revenium fixed an enforcement URL issue in SDK 0.12.1, released October 31, 2026.

  10. AWS Machine Learning Blog40

    Serverless MCP server for AI agent data lake access on Amazon ECS Fargate

    Amazon's North America Sort Center team built a serverless Model Context Protocol (MCP) server on Amazon ECS Fargate that acts as a centralized, agent-agnostic gateway between AI agents and the enterprise data lake. Adding a dataset requires only a metadata JSON file, which the pipeline indexes into Amazon Bedrock Knowledge Bases, while Amazon Cognito authentication, SELECT-only Athena tools, Bedrock Guardrails, and CloudWatch logging provide centralized controls. The architecture separates the tool layer from the agent layer so any MCP-compatible client can share the same tools and access controls in a multi-tenant environment.

  11. AWS Machine Learning Blog36

    Taxonomy of agentic primitives in AWS Quick

    AWS Quick's team describes a taxonomy of agentic primitives built from four questions—When, What, Who, and How—covering triggers (human-initiated, agent-delegated, scheduled, event-driven), agents as persistent learning actors with instructions, capabilities, and learned context, and capabilities spanning tools, skills, and workflows. The post argues skills and workflows retain value independent of model improvements, with skills able to crystallize into workflows and workflows dissolve back into skills, and illustrates how a single request like a daily Slack digest can be met at different points on a confidence contract.

  12. Databricks Blog38

    Real-time fraud detection with Feature Views

    Databricks describes how Feature Views in its Feature Store let teams define a feature once and use the same definition for point-in-time-correct training data and managed online-serving pipelines, reducing training-serving skew and data leakage. The walkthrough follows a $900 payment from a Kafka stream through rolling and Sawtooth window aggregations, an on-demand deviation feature, model training and Model Serving, with Unity Catalog providing governance and lineage.

  13. AWS Machine Learning Blog42

    Extend Claude Code to remote sessions on Amazon Bedrock AgentCore with security controls

    This post explains how to run Claude Code as a remote agent on Amazon Bedrock AgentCore runtime, where each task executes in its own isolated microVM session inside your AWS account and returns a GitHub pull request for human review. It walks through the accompanying sample-remote-coding-agent, covering the architecture (API Gateway, Lambda, DynamoDB, SQS, AgentCore runtime) and security controls including Bedrock Guardrails prompt screening, short-lived GitHub tokens via AgentCore Identity, private VPC networking, and least-privilege IAM. The sample suits scoped features, bug fixes, and tests on trusted repositories, but not untrusted code or open-ended work, and the agent never merges pull requests.

  14. AWS Machine Learning Blog36

    Taxbit Assist: A grounded, role-aware AI agent on Amazon Bedrock AgentCore

    Taxbit built Taxbit Assist, an embedded AI assistant for its digital-asset tax compliance dashboard, on Amazon Bedrock AgentCore using the Strands Agents SDK, with grounded and cited answers, role and organization awareness, no tax advice, and regional data residency. The post details the architecture, a curated knowledge base with semantic chunking and reranking, identity bound in code rather than by the model, guardrails-based PII handling, and continuous production quality measurement. Taxbit reports the assistant has run in the US and EU since May 2026, with model cost averaging under 20 cents per conversation and about three-quarters of input tokens served from prompt cache.

  15. AWS Machine Learning Blog50

    How Greenlight Financial built an autonomous SOC solution on Amazon Bedrock AgentCore

    Greenlight Financial's Luna, built on Amazon Bedrock AgentCore runtime and memory, autonomously resolves more than 90 percent of its 3,000–4,000 monthly security and risk cases, using a tiered supervisor architecture with over 30 specialized agents and episodic memory for continuous learning. The system triages dark web breach-card alerts, identity anomalies, and financial-risk events in seconds, escalating complex cases to a Tier 2 supervisor and human analysts. Greenlight later extended Luna into financial risk triage within two months by adding new specialized agents and a risk-domain supervisor to the existing infrastructure.

  16. AWS Machine Learning Blog50

    Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 1

    This AWS post describes using Okta Cross App Access (XAA) as the governance plane and Amazon Bedrock AgentCore Identity as the execution plane so an AI agent can act on behalf of signed-in users, with Okta deciding which agent may act for which user and AgentCore redeeming Okta's single-use ID-JAG for a short-lived access token. It walks through registering the agent in Okta, configuring the credential provider and workload identity, enforcing per-user authority via the groups claim in MCP servers, and reports measured results such as revocation taking effect in about 1 second and AgentCore Identity adding about 0.6 seconds to a roughly 30-second request.

  17. AWS Machine Learning Blog36

    Govern AI agent access with Okta Cross App Access and Amazon Bedrock AgentCore – Part 2

    Part 2 of this AWS post moves the Part 1 Okta Cross App Access solution from one agent and two MCP servers to a topology with Amazon Bedrock AgentCore Gateway as a single front door that redeems Okta's ID-JAG for each target, default-deny Cedar policies in AgentCore Policy binding tools to agents and delegated scopes, and a second agent on Amazon EKS under its own Okta identity. It also describes tracing decisions with AgentCore Observability and CloudWatch Omni alongside Okta's System Log, reports validation results and latency figures, and compares XAA with per-user OAuth consent for applications that cannot accept an ID-JAG.

  18. Google Developers Blog60

    Agent Distraction

    The post reports that 13% of agent file-reading sessions expose the agent to inline TODO(bug/...) or FIXME(bug/...) references, and in 6% of encounters the agent takes follow-up action on that bug ID, 83% of which are unprompted by the developer. Cross-referencing these detours against live bug status, it finds 41% chase already-closed or obsolete tickets while 57% involve still-open bugs where the agent expanded scope, and a fixed-effects regression shows unprompted bug detours add 70% more steps, 77% more tokens and 105% more wait time per session. The authors built agents that scan codebases and remove confirmed-obsolete bug references, reporting thousands removed over six months with over 95% acceptance from human reviewers.

    Why it matters: Offers a quantified, data-backed look at how stale code comments misdirect coding agents, plus a concrete remediation approach.

  19. NVIDIA Newsroom40

    How NVIDIA Open Source Tools Are Helping Researchers Build Next-Generation 6G Networks

    NVIDIA's open source Aerial and Sionna tools, whose adoption has grown 10x in a year with over 1 million downloads of its open source 5G/6G tools and use by more than 8,000 research and academic institutions, are being used by researchers on projects including goal-oriented semantic communications for robots at King's College London, a neural receiver tested by SoftBank, demand-predicting base station sleep at the University of Leeds, and GPU-accelerated channel models in CTTC's 5G-LENA simulator.

  20. Hacker News AI73

    AI coding agent setups from 663 Hacker News comments

    An analysis of an Ask HN thread from November 4, 2026, in which 424 of 530 commenters named at least one AI tool, found Claude Code leading with 180 users, followed by Codex with 103, while 148 people named open-weight or local models such as DeepSeek, Qwen and GLM. The piece distills nine detailed setups into seven patterns, including running parallel agents in separate git worktrees, fresh-context reviewers on every change, cheap builder models behind strong planners, and sandboxing agents that run without permission prompts. Counts are self-reported and drawn from three readers' notes on every comment, so the article treats them as close rather than exact.

Nov 6Fri
  1. Modal Blog27

    How Snorkel runs realistic agent simulations with Modal

    Snorkel uses Modal to power inference for open-weight models it evaluates and to provide GPU and CPU sandboxes for millions of agent simulations per month, peaking at tens of thousands of sandboxes an hour. Modal's VM sandboxes let Snorkel recreate production-like environments, including multi-service systems and GPU-enabled workloads, and its memory bursting feature cut total sandbox costs by around 20% by avoiding over-provisioning. Snorkel also uses Modal's Shared and Dedicated Endpoints to stand up inference for new and fine-tuned models, and plans to evaluate Modal as a training solution.

  2. Browser Use Blog25

    Best Proxy Providers for AI Agents (2026)

    The article compares seven rotating residential proxy providers (Decodo, Oxylabs, FloppyData, Roundproxies, Byteful, DataImpulse, Webshare) on IP pool size, location targeting, sticky session length, entry pricing, trials and traffic expiry, and reports results from browser-agent benchmark tasks such as product research, text extraction, form filling and session continuity. It also covers built-in versus bring-your-own proxies on browser platforms, setup guidance for custom proxies, and advice on checking session continuity, location coverage, target-site access and cost per completed task. The author notes the samples are small and differently timed and do not establish a universal reliability or speed ranking.

  3. Trail of Bits Blog40

    After Binance, check what your signing systems can approve

    This article examines the October 2026 Binance security incident, in which attackers who compromised a DevOps laptop obtained valid signatures from Binance's wallet-signing service and used an EIP-7702 delegation to authorize repeated transfers without further signatures. It argues that transaction execution and monitoring systems must understand newer Ethereum execution paths such as EIP-7702 delegations, session keys, Safe modules and ERC-4337 UserOperations, and offers a four-step checklist covering reduced signing access, treating delegation as an administrative privilege, limiting damage if signing is compromised, and detecting changes in wallet authority.

  4. Hacker News AI36

    Trustworthy AI

    A software engineer's practical guide to the EU AI Act, covering the risk-based approach, provider versus deployer roles, the updated compliance timeline (including the July 2026 AI Omnibus amendments), prohibited practices, AI literacy, transparency obligations, and engineering safeguards such as access controls, evaluation sets, and human oversight. It argues that prompt instructions are not a compliance control and that responsibility must be built into architecture, code review, and release processes.

  6. Hacker News AI58

    Lowvram: A CLI tool to run large AI models in low VRAM GPUs

    Henrique Soares introduces lowvram, a Linux CLI tool that intercepts CUDA calls via LD_PRELOAD, replacing cuMemAlloc with cuMemAllocManaged so applications can use system RAM when VRAM is insufficient. On a 6 GB RTX 3060, YuE2 under lowvram generated 43.1 tokens per second versus 1.9 for Accelerate and 6.8 for CPU, and Llama-TTS with Qwen3-TTS ran after a standard run failed with an out-of-memory error. The author notes trade-offs: PCIe paging slows computation, system RAM can be exhausted, and the tool requires CUDA knowledge.

  7. Hacker News AI38

    How AI Is Changing Software Development in 2026

    This article surveys how AI coding agents have moved from autocomplete-style assistants to tools that can inspect repositories, modify multiple files, run commands and tests, and iterate on their own changes, citing JetBrains and Stack Overflow survey figures on adoption. It argues that faster code generation does not automatically mean faster delivery, pointing to METR's 2025 study in which experienced developers took 19% longer with AI allowed while believing they were faster, and stresses review, security, and human judgment. It also distinguishes prototypes, MVPs, and production-ready products, and offers guidance for introducing agents into workflows and for clients evaluating AI-assisted teams.

  8. Manus Blog25

    Best AI Tools for Game Development in 2026: Our Top 10 Picks

    This guide compares ten AI tools for game development, splitting them into four that build games (GDevelop, Unity's AI tools, Manus, Rosebud AI) and six that handle specific jobs like code, art, audio, and characters (Cursor, Scenario, Ludo.ai, Meshy, ElevenLabs, Convai). Each entry lists key features, developer feedback from Reddit and reviews, pros and cons, and monthly USD pricing as of November 4, 2026. The article concludes that most real projects mix tools and that larger games still need human direction and testing.