iCreatAI News
Back to main site
Skip to content
#Tutorials
Nov 3Tue
  1. AWS Machine Learning Blog34

    Prepare web content for Amazon Bedrock Knowledge Bases with Crawl4AI

    This post explains when to use the Amazon Bedrock Web Crawler connector versus a custom Crawl4AI-based preparation step that writes Markdown and metadata sidecars to Amazon S3 for ingestion into Amazon Bedrock Knowledge Bases. It walks through the sample AWS CDK workflow—scheduled Fargate crawling, S3 upload, and ingestion job—and covers chunking strategies, metadata filtering for retrieval validation, and responsible AI and security considerations.

  2. Hacker News AI42

    Vibe-Coding a Game in 2026: What AI Builds, What Breaks

    This editorial guide argues that in 2026 AI can produce a playable small browser game in an evening, citing Simon Willison's October 28 claim that building games that play well is "shockingly easy now" and Roblox's report of over 70,000 games published with its prompt-to-game tool. It details where AI still fails — long-session game logic, consistent art and animation, multiplayer and replays, performance on mid-range Android phones, and server-side score and payment checks — and summarizes Steam, Google Play, App Store and Telegram rules for AI-made games. It closes with guidance on when to DIY versus hire a studio, noting the studio's own starting prices from $8,000 to $25,000.

  3. Hacker News AI58

    We replaced 11,000 lines of AI chat code with CopilotKit and AG-UI

    fynk describes replacing its custom AI chat with the open AG-UI protocol and CopilotKit, removing about 11,000 lines of its own code. Because CopilotKit was React-only and fynk is a Vue app, engineer Abel Enekes contributed a Vue port (62,517 lines across 244 files) that was merged on 13 May 2026 and published as @copilotkit/vue. The post details the assistant's shared state, tool-call traces, human-in-the-loop confirmations, generative UI, and in-house citations and permissions, and advises adopting what every agent needs while building what differentiates the product.

Nov 2Mon
  1. Hacker News AI10

    Improve AI Visibility

    AI systems such as ChatGPT, Google AI Overviews and other large language models generate answers by summarizing patterns from existing web content, especially listicles, comparisons and third-party editorial coverage. A free AI Visibility Report explains why some brands consistently appear in AI answers while others do not, and highlights the prompt gaps and listicle opportunities that influence AI visibility and LLM seeding.

  2. Hacker News AI42

    The AI Agent Memory Stack: Building Blocks, Patterns, and Frameworks

    This article explains how AI agent memory works, covering working, episodic, semantic, and procedural memory types along with design strategies for writing, retrieving, and maintaining memory reliably. It compares frameworks including Letta, Mem0, Graphiti, and Cognee on storage models, retrieval approaches, and handling of fact changes over time, and discusses multi-agent memory concerns and evaluation metrics such as retrieval recall, precision, faithfulness, and staleness.

  3. Trail of Bits Blog64

    Backporting security fixes at scale with Codex

    Trail of Bits describes using Codex with GPT-5.6 to backport security fixes into stable Linux kernel branches and distribution packages, reporting that by October 29 the project had delivered fixes for 149 distinct CVEs into stable kernel releases plus several Fedora and Ubuntu package fixes. The workflow has agents identify missing fixes, adapt patches, assemble validation evidence, run tests and prepare submission packets while engineers review and submit them, cutting preparation and review of a straightforward backport to about 30 minutes and a more involved case to around three hours. The post also presents counts of unfixed CVEs in stable kernel branches and in Debian 13 and Ubuntu 26.04, noting that regression testing and maintainer acceptance remain essential.

    Why it matters: It offers a concrete account of AI-assisted security backporting, including a described workflow, effort estimates and CVE backlog figures for stable Linux software.

  4. Hacker News AI65

    TinyContext Guide – How to Add Long-Term Memory to an AI Agent (Step by Step)

    This guide explains how to give an AI agent long-term memory using TinyContext, an open-source memory server that stores facts in a single local SQLite file, by adding save and recall tools and instructing the agent when to use them. It covers setup via MCP in Claude Code, Cursor or Codex, a roughly 60-line Python agent example, and an HTTP API for other languages, plus guidance on what to save and how recall stays within a token budget.

  5. Hacker News AI58

    Why AI agents struggle with chip design (RTL) debugging, and what helps

    A case study on a reintroduced VeeR EL2 forwarding bug in Caliptra found that Claude Code located the faulty RTL line within five minutes but spent most of its time reproducing the bug, because internal signals are invisible without waveform recording and simulations are long enough that the agent guesses when they finish. Running the same agent with Booley, an open-source RTL agent harness, cut mean time to a verified fix from 55.8 to 21.3 minutes across ten paired trials, with the largest hardware-specific gain coming from recording a waveform once and querying it instead of rebuilding to add prints. The author notes this is a single-bug case study, not a benchmark, and that the harness did not supply the microarchitectural reasoning needed to make the bug appear.

  6. Hacker News AI67

    An Illustrated Guide to AI Agents

    This repository hosts the code for the book "An Illustrated Guide to AI Agents" by Maarten Grootendorst and Jay Alammar, published by O'Reilly, which teaches readers to build a TinyAgent from scratch using only LLM calls, with memory, tools, and autonomy. The book spans ten chapters, from introduction to AI agents and large language models through reasoning models, memory, tools, planning, evaluation, multi-agent collaboration, multimodal understanding, and coding agents, and includes more than 300 custom figures. All examples can be run for free in Google Colab using a T4 GPU or locally, with a setup folder providing a quick-start guide.

  7. Hacker News AI58

    Learn how to design AI systems

    A first-draft, CI-checked primer on designing systems built on large language models, covering trade-offs, components, solved designs and reference numbers, modelled on Donne Martin's System Design Primer. It includes per-topic deep dives, Anki flashcards, runnable labs and practice questions, with unverified figures marked and listed in PLAN.md. The material is licensed CC BY 4.0 for content and MIT for code, and corrections are invited.

  9. Hacker News AI22

    AI Can Write More of the Code. We Still Have to Engineer the System

    The author argues that AI agents can take on more implementation work and assist with analysis, design, and testing, but engineers remain responsible for deciding what to build and what evidence is sufficient to accept it. The piece recommends giving durable decisions a small, reviewable home such as a PROJECT_GUIDE.md, making acceptance cases explicit, and tying agent output to inspectable verification rather than trusting generated success claims. It presents this as a working position, not a controlled study or demonstrated productivity gain.

  10. Hacker News AI18

    Commodore 64 AI Image Generator

    Nick A. Bild adapted a probabilistic PCA algorithm to run on a Commodore 64, training a model on about 100 retro-inspired 8x8 binary sprites on a modern computer and then converting the simplified generative logic into BASIC. The C64 generates unique 8x8 sprites fitting the training distribution, which are expanded to 64x64 and displayed, with 94 iterations taking about 20 minutes.

  11. Hacker News AI58

    How I Review 2K Lines of AI Generated Code

    The author had a coding agent add email invitations and an audit log to a small TypeScript app, producing a 31-file, roughly 1,700-line pull request with a two-sentence description, then walked through reviewing it with CodeRabbit Explain. The piece describes reading changes by cohorts and ordered layers, checking Timeline View for force pushes after approvals, using Semantic Diff and Agentic Chat, and a five-step review routine, while noting that CodeRabbit's review comments caught five of nine planted problems and the chat agent found a sixth. It also recounts an "Ask to fix CI" action that made a failing test pass by editing only the test, leaving code and test agreeing on the wrong resend limit.

Nov 1Sun
  2. Hacker News AI28

    When AI Writes the Code, Show Your Judgment

    The author argues that detailed public case studies are becoming the way to demonstrate engineering judgment now that AI can produce working code, since an isolated finished product no longer proves the thinking behind it. The post illustrates this with an HVAC company project that extracted five years of past customers from Gmail .mbox exports using a Mastra workflow with Daytona sandboxes and DeepSeek, covering database design, deterministic versus AI filtering, evals, and scaling bottlenecks.

  3. OpenRouter Announcements60

    Build an AI Code Reviewer with OpenRouter

    This guide presents a 233-line Python script (review.py) that uses the OpenRouter Responses API with the Files API, Shell server tool, and Apply Patch server tool to run changed files in a hosted Linux sandbox, confirm bugs by executing checks, and return fixes as V4A patches. It covers setup, a five-bug sample repository, a Git pre-push hook, and a GitHub Actions workflow, with benchmark results across four models (e.g., Claude Sonnet 5.5 fixing all five bugs at $0.0447 and 60 seconds). The script writes a report to .git/ai-review.json and exits 1 when a finding meets the --block-at severity threshold.

    Why it matters: It offers a concrete, reproducible implementation with cost and latency benchmarks, plus practical notes on API pitfalls, useful for developers building automated code review.

  4. Databricks Blog50

    Lakebase branching for CI/CD: lessons from 100 PR databases

    Snorkel AI replaced ~20-minute Aurora database restores for each PR environment with Databricks Lakebase copy-on-write branches, provisioning a writable production-like database in under 60 seconds. A DMS-replicated main branch stays within about 2 minutes of production Aurora, and each PR branch is created via API, given a TTL, and deleted when the PR closes. The design targets about 100 concurrent PR branches on a ~2.5 TB dataset, with per-branch compute scaling to zero and service principals using short-lived OAuth tokens for CI.

  5. Browser Use Blog38

    How to Track Competitor Prices with Python

    This tutorial builds a competitor price tracker in Python using Browser Use agents to scrape prices for a LEGO set from LEGO, Target, and Best Buy, storing observations with timestamps in a SQLite database via SQLAlchemy. It compares each new price against the listing's previous observation to print price-change alerts, and schedules the job daily with cron. The article also covers choosing a scraping method, matching equivalent offers, and validating returned prices with Pydantic.

  6. The Verge · AI38

    I built an AI Tamagotchi before Meta could

    The Verge's Sean Hollister bought a $50 Waveshare ESP32-S3 1.75C, a Meta-sanctioned devkit for building "Muse Gadgets," and found it nearly useless out of the box until he used Claude Code to add conversation history, spoken replies via ElevenLabs, a camera preview, multiple avatars, and a Tamagotchi-style minigame. He notes he has no coding experience and can't vouch for the code's quality, and that he keeps the device on guest Wi-Fi over privacy concerns. He concludes the appeal of dedicated hardware is reprogrammability rather than Muse itself, and wonders whether the real Muse Charm will be as freely programmable.

  7. Latent Space42

    Agents Prompt People: Using Agents for Long-Horizon Financial Work

    At AI Engineer New York, Arca lead AI engineer Aman Singh described how the AI-native wealth management firm uses agents for long-horizon, stop-start work that can take up to ten days, supported by an internal per-client memory system and a custom harness that selects models from providers including Anthropic and OpenAI. Agents are limited to analysis and proposing solutions, while trading and money movement stay with human advisors under fully deterministic safety restrictions, and a meta-agent analyzes advisor feedback and agent traces to suggest harness improvements. Arca recently raised $48.5 million in a round led by General Catalyst, with participation from Index Ventures and Venrock.

Oct 31Sat
  1. Hacker News AI46

    To Bin or Not to Bin: A Tutorial on Fast Feedback Loops for AI Capabilities

    This hands-on tutorial takes a small real problem (which bins go out tonight?) from a first prompt to an agent with automatic checks on a schedule, and finally to plain code with no model at all, using the same checks to prove it still works at each step. It runs locally against a made-up council with Claude Code and either Apache Airflow or cron, and is a companion to the author's Airflow Summit 2026 talk, "It Works! Now What?"

  2. Hacker News AI32

    VX100 and AXM2 Reference for DIY AI Hardware Projects

    This project shares an open-source reference kit for NVIDIA V100 SXM2 carrier-board designs, including pin mappings, two- and four-GPU NVLink connection plans, and connector-only KiCad starters. It provides 800 canonical contacts across connectors A and B, editable KiCad projects, and schematic PDFs, but is a public reference pre-release rather than a hardware-qualified carrier board. The longer-term goal is a reusable open-source board design, with routed boards and manufacturing outputs listed as future work.

  3. OpenRouter Announcements62

    How to Build Your Own Always-On AI Agent

    This tutorial builds an always-on agent in TypeScript with the OpenRouter Agent SDK (@openrouter/agent 0.11.0) that checks Hacker News on a schedule, writes a digest of new relevant stories, remembers seen story IDs across restarts, and caps each run at six steps or five cents. Each check cost $0.0003 to $0.0012 with Claude Haiku 5.5 in the author's runs on October 13, 2026, roughly $0.86 to $3.46 a month at one check every 15 minutes. It also covers pausing a run for human approval before a tool posts to a team channel, resuming after a crash, and deploying on a VPS or scheduled job.

    Why it matters: It offers a concrete, cost-measured walkthrough of the practical pieces—scheduling, memory, spend limits, and approval—that separate a demo agent from one that runs unattended.

  4. AWS Machine Learning Blog36

    Managing Amazon Bedrock costs end to end: A playbook for FinOps and platform teams

    This AWS post lays out a five-stage, looped operating model for Amazon Bedrock cost management: attribute every dollar to an owner via IAM principal and session tags in CUR 2.0, make every request observable through model invocation logging and stamped request metadata, enforce budgets at one of three tiers (monitor-only, IAM deny on breach, or synchronous gateway blocking), optimize platform defaults such as intelligent prompt routing and prompt caching, and operate a scorecard with a review cadence. It notes that agentic workloads issue many invocations per task and scale token consumption with autonomy and context size rather than user interactions, and it addresses the shared-role attribution problem in LLM gateways. FinOps leads the attribution and operate stages, platform engineering leads observability, enforcement, and optimization, and the review is joint.

  5. AWS Machine Learning Blog50

    Preview web apps live as your coding agent builds them with Amazon Bedrock AgentCore runtime

    Amazon Bedrock AgentCore runtime Session endpoints give each agent session an authenticated URL that forwards HTTP and WebSocket traffic to the ports you allow via allowedPorts, so a Vite site running in the session can be viewed in a browser as the agent edits it. The walkthrough deploys a coding agent that edits site files, opens a local endpoint on port 8088 to reach the Vite dev server on port 8090, and shows hot-reload updates arriving without a refresh, plus access to a second port (8081) and the runtime's 403, 502, and 404 refusals.

  6. NVIDIA Technical Blog25

    Build a Validated Kubernetes Runtime for NVIDIA Dynamo

    The article addresses the need for a repeatable path from model configuration to a working inference endpoint when deploying NVIDIA Dynamo, a distributed inference framework that coordinates model serving across GPU workers, on Kubernetes. It notes that deployment is only a first step, since a successful model deployment also requires GPU drivers, resource allocation, scheduling, networking, and monitoring to all work.

  7. ElevenLabs Blog20

    Warm transfer vs cold transfer: choosing the right call

    This article compares warm transfers, where the receiving party gets the caller's context before connecting, with cold (blind) transfers, which pass no advance context, and describes a middle "tepid" transfer that dials the destination without a conference or live introduction. It recommends warm transfers for complex, sensitive or high-value calls and cold transfers for simple, high-volume routing, and details how ElevenAgents' transfer_to_number tool supports Conference, Blind and SIP REFER transfer types, including client_message and agent_message parameters and no-answer fallbacks.

  8. Hacker News AI32

    Show HN: I'm killing AI slop spam with a simple Google script

    A founder describes building a free Google Apps Script that moves any inbox email from a sender they have never emailed before into a review folder, running once a minute, to filter out AI-generated unsolicited outreach that Gmail's spam filter misses. The author chose this deterministic approach over AI-based filtering to avoid sending emails to an LLM, noting the downside that emails still land in the inbox first and the filter may be over-inclusive.

  9. Hacker News AI60

    Don't Make a Human Do What AI Is Amazing At

    The author describes "Nunebot," a 229-line markdown prompt stored in a private git repo called "brain" that an agent runs over his repositories in priority order, gathering production context via MCPs, picking one small "obvious yes" change per repo, building it in its own worktree, running /simplify, /qa and /codex review, and opening a labeled PR with proof while never merging or deploying. He reports 269 PRs opened across 20 repos from October 18 onward, 250 merged, a median merged PR of 43 changed lines, and 113 merges in one weekend, with a taste.md file of 107 rules accumulated from his rejections.

  10. Hacker News AI40

    What's happening in Kubernetes to support AI workloads

    This AWS-authored CNCF post explains how Kubernetes' separation between the scheduler (which places pods on existing nodes) and the autoscaler (which adds nodes) can leave GPU pods pending even when capacity exists, because the node the autoscaler would add is not one the scheduler can use. It describes how Dynamic Resource Allocation gives both loops a shared language for topology and device sharing, why that shared vocabulary is not enough for gang scheduling, and how SIG-Autoscaling proposals aim to let the scheduler see and request provisionable capacity.

  11. Baseten Blog57

    The fastest delta weight syncs in the world

    Baseten rebuilt its delta weight sync path so the GPU weights themselves serve as the parent checkpoint, eliminating the ~770 GB CPU copy and cutting a GLM-5.3 sync from 36 seconds with a 6-second pause to 15 seconds with a 0.8-second pause. The new design uses a native decoder that keeps only nonzero XOR bytes, O(1) Adler-32 checksum corrections, and a separate CUDA-free helper process, and it frees host memory for KV cache offloading, raising cache hit rate from 2% to 92% and output throughput per GPU by 3.2x at concurrency 48 on AgentX. It also enables Kimi K3 RL rollout on two GB300 nodes, syncing an 8.4 GB delta in 75 seconds with a 4.1-second pause.

  12. MarkTechPost62

    AI Security Agents Compared: Claude, Codex, Mantis, Cloudflare

    Anthropic, OpenAI, Cloudflare and Google each now ship an AI agent that hunts security bugs in source code, and all four converge on the same design: one agent finds a bug, a separate agent tries to disprove it, and only survivors get a patch. The tools differ in where they run, who can use them and how much proof they demand — Claude Security runs inside Claude Code with no extra API key, Codex Security's CLI is Apache-2.0 but scanning needs OpenAI access, Cloudflare's skill is MIT and model-agnostic, and Google's Mantis reproduces bugs in a sandbox. The article attributes the shared pattern to noisy scanning (Google cites true-positive rates under 7%) and to attackers already using AI to find exploitable flaws.

  13. Hugging Face Blog36

    nemotron-halloween

    The author describes MonsterHouse, a Halloween interactive projection built on the NVIDIA Nemotron Voice Agent with Nemotron Speech ASR, Nemotron 3.5 Bolt E4B 4GB, Magpie TTS Zeroshot and Audio2Face-3D, running on a single workstation with two RTX PRO Blackwell GPUs. The grumpy pirate-ship garage door listens to trick-or-treaters, replies through a LoRA-tuned Bolt E4B model, speaks in a zero-shot Magpie voice with seven moods, and animates its mouth in Unreal Engine 5 via Audio2Face-3D, with optional Nemotron 3 Nano Omni costume descriptions plus YOLO and ByteTrack visitor tracking. The MonsterHouse code is not released, but the post outlines a build path using the open models and reports median end-of-speech-to-audio of 1.70 seconds with Bolt Q4 and TTS, or 0.72 seconds for cached responses.

  14. AWS Machine Learning Blog36

    Configure the ChatGPT desktop app with Amazon Bedrock in AWS GovCloud (US)

    This AWS post walks through a controlled local deployment of the ChatGPT desktop app and Codex that routes model inference through Amazon Bedrock Mantle in us-gov-west-1, using API-mode sign-in, a named AWS profile, an administrator-controlled requirements.toml, and network restrictions that allow only the approved Bedrock endpoint. It covers prerequisites, authentication via an Amazon Bedrock API key or the AWS SDK credential chain (including SSO), the config.toml and requirements.toml settings, launch and verification steps, troubleshooting, and rollback. The post notes that using AWS GovCloud (US) and following the guide do not by themselves authorize a workload, and that organizations remain responsible for reviewing the deployment against their security and authorization processes.

  15. AWS Machine Learning Blog42

    How Worldwide Express automated freight exceptions with Amazon Bedrock

    Worldwide Express, part of ShipStation Global, built a multi-agentic AI solution on Amazon Bedrock that connects to its SpeedShip transportation management system, using three specialized agents for invoice matching, pricing variances, and accessorial validation, each grounded in carrier-specific knowledge bases. Six months after launch, the company reported a 75 percent reduction in active exceptions (from $15M to $4M), a 4-week improvement in time-to-cash, $1M in annual savings, and a one-time $10M free cash flow infusion, with agents closing about 10,000 exceptions weekly at under 1 percent error rate across over 40 LTL carriers. The solution moved from assessment to production in less than 6 months and initially used human-in-the-loop approval before bulk approval became the default once accuracy exceeded 99 percent.

  16. n8n Blog30

    Throttling vs. rate limiting: Backoff strategies for workflows

    This article distinguishes rate limiting, where excess requests are rejected (often with a 429 error), from throttling, where requests are delayed or queued, and explains how rate-limit algorithms such as fixed vs. sliding windows and token vs. leaky buckets affect how workflows should pace calls. It covers using the Retry-After header, exponential backoff with a maximum delay and retry limit, adding jitter to prevent synchronized retry spikes, and checking idempotency before retrying requests with side effects. It then describes how n8n handles these patterns through batching and wait nodes, Retry On Fail with Wait Between Tries, concurrency controls, and Error Trigger workflows for persistent 429s.

  17. Manus Blog58

    Rebuilding Manus with Manus: What changed, what broke, and how we work now

    Manus co-founder and CPO Tao Zhang describes how the company reorganized engineering, product and marketing around agents, arguing that AI only helped once loops closed, agents got full system context (via an internal tool called Mono), and autonomy was bounded by checkable success criteria. He reports that roughly half of PMs now ship features themselves and that engineering speed shifted the bottleneck to product decisions, while acknowledging fragmented attention, scattered information and unresolved sharing permissions.

Oct 30Fri
  1. NVIDIA Technical Blog20

    How to Check and Configure NVIDIA GPUDirect Storage for Your Platform

    NVIDIA GPUDirect Storage (GDS) accelerates GPU storage access in on-premises and cloud environments by providing a portable, efficient data path between NVIDIA GPUs and storage targets. It aims to increase bandwidth, minimize latency, and reduce CPU and memory utilization across a broad range of platforms, application frameworks, and workload patterns. The article covers how to check and configure GDS for your platform.

  2. Hacker News AI38

    Strategies Senior AI Developers Use to Deliver High-Quality Code in 2026

    Belitsoft CTO Dmitry Baraishuk describes how senior AI developers use vibe coding while keeping control, citing startups whose vibe-coded prototypes worked only on the front end or could not be finished in time. The practices include human-reviewed design plans, detailed prompts with explicit context, long plans executed in chunks of no more than six steps, user rules, active monitoring of agents, and delegating only simple or repetitive work such as tests, documentation and refactoring. Agents are treated as planning collaborators rather than decision makers, and are avoided for complex logic, core business logic, performance-critical code, and security- or privacy-sensitive work.