AI & Machine Learning

AI stories from Hacker News. Browse trending and recent posts, and see the AI percentage on each Hacker News page. JavaScript is needed to load the live feed. You can also browse AI stories on Hacker News Search . Some people are making Hacker News without AI. This is the opposite, only AI news. We discover posts from Hacker News’s rankings and recent submissions, without an AI keyword search. We read accessible articles and use an AI model to judge whether AI is a central topic, not just a passing mention. Results are cached and new posts are reviewed in the background. Trending balances points and recency; new sorts by time; top this week sorts by points. The percentage uses the same classifications for every post in HN’s top-story ranking , 30 posts per page. “Read” means article or submission content was analyzed. When an article is blocked, a confident headline classification may be used and is marked “title only”; otherwise the topic stays unknown. A range shows the minimum and maximum AI share while unknowns remain. Hover over a block to see its classification. These are model estimates, and HN’s displayed pages may differ.

Otōto is an MCP server for Claude Code and OpenCode: a small model on a server you choose explores the code and hands your agent short answers with checked citations. Otōto is an MCP server for Claude Code and OpenCode. Your agent hands it a question; a small model on a server you choose explores the code and brings back a short answer, with the lines that back it checked and quoted. macOS on Apple silicon and Linux · See the script · Open source · How it works Where does a failed request move on to the next model server? Llm::request tries each endpoint in turn; one that fails is marked down for 60 s and the next is asked. When every failure was one that passes (a dropped connection, a 502), it goes round once more after 2 s.

AgentiLoop Agent! is a 100% native macOS agentic AI. One app, 23 LLM providers, total command over your Mac. Build apps, automate workflows, control any app — through natural language. EN English Español Français Deutsch 中文 (简体) Русский 한국어 日本語 Sponsor Fluxion AI : one API for GPT, Claude & every leading model. Save up to 70% + $3 free credits with code AIAGENT Agent! for Mac now supports macOS 14.6 or later on Apple Silicon and Intel New GoKart.games : a free arcade kart racer built with Godot 4 for macOS, Windows & Linux One app. Any AI. Tell Agent! what you need in plain English and it answers your email, drives Safari, builds Xcode apps, fixes code, and runs your Mac apps for you. Pick from 23 LLM providers or stay local with LM Studio, vLLM, or Ollama. macOS 14.6 or later. AgentiLoop CLI, runs cross-platform, brings our agentloop to your terminal on Mac, Windows, and Linux.

OpusJake is Jake Schincariol's operating system for building with AI. It's a working catalogue of the agents, workflows, prompts, and growth systems Jake uses with operators and teams to ship real products with AI, not demos. Jake is an AI operator and implementation consultant who helps founders and growth teams design, build, and scale AI-powered systems. Read Jake Schincariol's canonical profile or connect with Jake on LinkedIn . Plant the seed. Read the soil. A growing library of essays, playbooks, and field notes on building with AI: prompt patterns, agent architectures, evaluation, and the operating habits that compound over time. Join Jake's network of operators, builders, and founders working at the edge of AI. A place to trade tactics, share what's working, and find collaborators.

One endless vote: which tool would you rather delete from your life? Jira or Outlook? Teams or Slack? Unscientific, opinionated, and addictive. MOST HATED TOOLS Battles Leaderboard Head to Head Add a tool Sponsor About Everyone votes. Tools lose. {"@context":"https://schema.org","@type":"WebSite","name":"MostHatedTools","url":"https://www.mosthatedtools.app","description":"A running vote on which software tool people would rather delete from their lives — with a live leaderboard and head-to-head matchup records.","publisher":{"@type":"Organization","name":"MostHatedTools","url":"https://www.mosthatedtools.app"}} {"@context":"https://schema.org","@type":"ItemList","name":"Most hated tools leaderboard","description":"Software tools ranked by how often people voted to delete them from their lives, out of 36,336 total votes on MostHatedTools.","itemListOrder":"https://schema.org/ItemListOrderDescending","numberOfItems":20,"itemListElement":[{"@type":"ListItem","position":1,"url":"https://www.mosthatedtools.app/meta-ai","name":"Meta AI"},{"@type":"ListItem","position":2,"url":"https://www.mosthatedtools.app/grok","name":"Grok"},{"@type":"ListItem","position":3,"url":"https://www.mosthatedtools.app/microsoft-copilot","name":"Microsoft Copilot"},{"@type":"ListItem","position":4,"url":"https://www.mosthatedtools.app/alexa","name":"Alexa"},{"@type":"ListItem","position":5,"url":"https://www.mosthatedtools.app/microsoft-teams","name":"Microsoft Teams"},{"@type":"ListItem","position":6,"url":"https://www.mosthatedtools.app/siri","name":"Siri"},{"@type":"ListItem","position":7,"url":"

Give your AI agent fleet a shared artifact storage. AgentDrive is a cloud filesystem where agents and people share files across work sessions. Create multiple drives, then grant each agent or teammate access to the drives they need. Token Canopy Products e2a Available now Give your AI agents real, authenticated email inboxes AgentDrive Beta Give your AI agents a filesystem they share with your team AgentChat Beta Where your agents coordinate AgentOS Tasks, permissions and approvals How it works Hosted Docs Sign in Menu Products AgentDrive is a cloud filesystem where agents and people share files across work sessions. Create multiple drives, then grant each agent or teammate access to the drives they need.

CoIsland watches your whole engineering stack from your Mac, shows only what needs you, and hands the work to an AI agent. // The growth plays once per session, not on every page of a visit. (function (logo) { try { if (sessionStorage.getItem('coisland-logo-intro')) logo.classList.remove('intro'); else sessionStorage.setItem('coisland-logo-intro', '1'); } catch (error) { logo.classList.remove('intro'); } })(document.currentScript.previousElementSibling); CoIsland Connectors AI Docs Pricing Changelog EN English FR Français Start for free var e=document.querySelector(`.site-header .bar`);if(e){let t=()=>{let t=[...e.children].filter(e=>getComputedStyle(e).display!==`none`),n=getComputedStyle(e),r=(t.length-1)*6,i=parseFloat(n.paddingLeft)+parseFloat(n.paddingRight),a=t.reduce((e,t)=>e+t.offsetWidth,0)+r+i;e.style.setProperty(`--compact`,`${Math.ceil(a)}px`)};t(),document.fonts.ready.then(t),addEventListener(`resize`,t,{passive:!0})} CoIsland at work CoIsland

AI agent orchestration for Mac. Run Claude Code, Codex and Cursor on one canvas with shared context, focused handoffs and voice control. Try free for 3 days. Starkomand How it works Features Pricing Compare FAQ Blog Download for macOS Download Run your AI coding agents on an infinite canvas. AI agent orchestration on your Mac. Run Claude Code, Codex and Cursor side by side. Hand over the implementation , see the diagram , pass it on with a brief , review what comes back. Direct your agents by voice . A still from the app. Pause Replay Powered by the subscriptions you already pay for. Starkomand runs the agent CLIs you have installed, signed in as you. It never resells tokens and never asks for a key. One agent is enough No Starkomand account Any other CLI you configure Pull a GitHub issue onto the canvas and drop it on a window. The note stays pinned beside the work while the agent starts.

Canary is the independent tester for code your agents write. It runs your app, tries to break every change, and reports what would have reached production. Start with one line in your coding agent. Examples Research Log in Book a call Backed by Y Combinator Claude Codex Cursor Copilot Claude writes your code. code. code. code. code. Canary tests it. Canary is the independent tester for code your agents write. It runs your app, tries to break every change, and reports what would have reached production. One line for your agent. Claude Code — pocketbase Claude Code Codex Ready for review 1 Database-aware health endpoint + 46 - 1 · apis/health.go Database-aware health endpoint Add a database-aware health endpoint: GET /api/health should ping the data and auxiliary databases and answer 503 when either cannot run SELECT 1. apis/health.go Updated Done. GET /api/health runs SELECT 1 against the data and the auxiliary database before answering. If either probe fails, the endpoint logs the error and returns HTTP 503 with code 503 and message "API is unhealthy." Each probe is bounded by a 3 second timeout derived from the request context. The successful response, including the superuser-only fields, is unchanged. ∞ Agent Claude Code apis/health.go @@ -1,9 +1,12 @@ package apis import ( + "context" "net/http" "slices" + "time" + "github.com/pocketbase/dbx" "github.com/pocketbase/pocketbase/core" "github.com/pocketbase/pocketbase/tools/router" ) @@ -14,7 +17,12 @@ func bindHealthApi(app core.App, rg *router.RouterGroup[*core.RequestEvent]) { subGroup.GET("", healthCheck) } - // healt

Ceptile is an AI-powered search engine. Experience real-time web search, verifiable live citations, and intelligent conversational research. Sign In Sign In Sign Up {"@context":"https://schema.org","@graph":[{"@context":"https://schema.org","@type":"WebSite","@id":"https://ceptile.com/#website","url":"https://ceptile.com","name":"Ceptile AI Search","alternateName":["Ceptile","Ceptile AI","Ceptile Search","Ceptile Search Engine","Septile","Septile AI","Septile Search"],"description":"Ceptile is an AI-powered search engine providing real-time answers, deep web research, and live source citations.","potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://ceptile.com/search?q={search_term_string}"},"query-input":"required name=search_term_string"},"inLanguage":"en-US"},{"@context":"https://schema.org","@type":"Organization","@id":"https://ceptile.com/#organization","name":"Ceptile","alternateName":["Ceptile AI","Ceptile Search","Ceptile Search Engine","Septile","Septile AI","Septile Search"],"url":"https://ceptile.com","logo":{"@type":"ImageObject","url":"https://ceptile.com/logos/CeptileLogoIconBlackSymbolWhiteBackground.png","width":512,"height":512,"caption":"Ceptile Official Logo"}},{"@context":"https://schema.org","@type":"SoftwareApplication","@id":"https://ceptile.com/#application","name":"Ceptile AI Search Engine","headline":"Real-Time AI Search & Conversational Answer Engine","applicationCategory":"SearchApplication","applicationSubCategory":"AI Research Assistant","operatingSystem":"All (Web, Android, iOS, Windows, macOS, Linux)

Run LLMs, vision, embeddings, and text-to-speech on the JVM at native speed. No Python runtime, ONNX bridges, or Docker sidecars. Quixotic AI Stack Examples Benchmarks AI sovereignty for the JVM Some call AI on the JVM quixotic. We call it Quixotic AI. Finally, AI in a jar. AI inference engine. Chat, vision, embeddings and text-to-speech capabilities for the JVM, with Spring AI and LangChain4j integrations. TikToken-compatible and customizable tokenizers for popular LLM models. Multi-backend tensor engine, in the works. Java, C, CUDA, HIP, Metal, OpenCL, and Mojo. Write once, accelerate everywhere.

Botscent tells your site if an AI agent, not a person, is browsing it, and which one. 5 KB, MIT, 0 deps. Botscent FAQ Docs GitHub Detect AI agents on your site. Botscent tells your site if an AI agent is browsing it, and which agent. Star on GitHub 76 5 KB · 0 deps · 5ms startup · MIT Copies a setup prompt for your AI coding tool shop.example Human Agent · Claude for Chrome Kettle & Co. Shop About Ad Copper pans, 30% off this week Stovetop kettle Enamel, 2 litres. Works on every stovetop. Claude for Chrome Hide ads Change prices Log agents Simplify pages Block agents Tested with Instinct Muse OpenAI Dot Grok Bot Claude for Chrome ChatGPT for Chrome Manus Devin Codex Cursor + 91 crawlers and tools →

Two independently acting copies of the same decision model serve six soups in 512 ticks. Every move is a native game control. I trained a decision model to play cooperative Overcooked on one RTX 5080. Two independently acting copies learned to serve six soups in 512 ticks using only native controls. The policy is OpenJev , which applies the idea behind Jev : describe a situation, supply possible answers, and receive probabilities for them. OpenJev uses natural language inference to score whether each proposed answer follows from the context. Expressing actions as text lets the same classifier choose among different action spaces, making it a promising interface for learning through interaction. I’ve released the code and trained model . Figure 1. Greedy evaluations across the initial attempt, exploration restart, and two-chef continuation. Time starts at zero for each stage. Checkpoint 220 serves six soups.

Splash is our open-source inference engine for Apple silicon, built around the model rather than around a model zoo. On a 48 GB M5 Pro it generates 210 tokens/s on Qwen3.6-35B-A3B, reopens a cached 32K context in 123 ms, and serves four concurrent requests at 357 tokens/s combined. Most inference engines are built to run any model for maximum flexibility. Our approach turns this assumption upside down: the engine is built around the model for maximum efficiency . Its kernels, draft model, and memory plan are specialized for the model it serves. This design runs our industry-leading inference platform on data-center GPUs, where we are the fastest provider on Artificial Analysis for five frontier open models. Today we bring the same technology to Apple silicon with Splash , our open-source inference engine for the Mac. It delivers 2× the decode speed of the next-fastest engine we measured on Qwen3.8-27B and stays ahead at every context length we tested, to 32K tokens. At four parallel subagents, the speedup increases to almost 4× .

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An experiment in finding the primitives all SaaS apps need, letting the AI assistant and agent build upon them via MCP, CLI, and/or API. The Last SaaS is an experiment to discover which primitives are needed for a minimal SaaS application backend, letting Claude, ChatGPT, and other assistants handle the rest. 01 Records Create collections with text, number, date, and other field types. The service checks records against those definitions when they’re saved. Your assistant can change fields, filter and sort records, and calculate counts, totals, or averages. 02 Files Upload and download files in your workspace, and organize them by path. Each file keeps its name, type, size, upload date, and the account that uploaded it. Your assistant can list files by path and retrieve them when needed.

We research how to make agentic workflows efficient. Our focus is everything around the model. Models get the attention. Our interest is in everything around them: the harness, the runtime the agent acts in, the inference underneath, and how the three fit together. Done with care and technical excellence, an agent runs cheaper, and usually better. Backed by Sequoia & First Round · Founded by engineers from CERN, Meta, Snap, Bloomberg & DeepMind

One API for prediction markets. Find markets, stream normalized data, place orders, and track positions across Kalshi and Polymarket. layer examples how it works agents docs Get an API key For developers building trading bots and AI agents Build your trading bot once. Run it across markets. Layer tells your bot or AI agent which markets are the same bet, shows prices and order books in one format, places and cancels orders, and reports what filled. You write the strategy. Layer handles the venues. Your bot decides what counts as a good trade. Layer makes sure it is comparing the same thing, at the real price, and tells it what happened. Venues word the same event differently, and sometimes settle it differently. Layer reads both sets of rules, tells you whether it’s the same bet, flags what differs, and checks again after it settles. Prices, order books and fees from every venue in one format. A quoted price isn’t your price, so your bot sees the cost after fees and how much it can actually buy.

Free the Office AI ecosystem. Bring your own LLM into Word, Excel and PowerPoint with OpenDocBot. Client-side, zero-proxy, no lock-in. Client-side, provider-agnostic, and fully auditable. No lock-in. No hidden telemetry. OpenAI, Anthropic, Gemini, DeepSeek, OpenRouter, or local models via Ollama. Use your own keys, swap models on the fly, and avoid vendor lock-in. 100% client-side. Source and development public on GitHub. No document data ever touches our servers. The app runs completely client-side. Optional human-in-the-loop approval gate lets you review model proposals before edits are applied. Self-host anytime.

Turn business intent into specs, agent-built code, independent AI review, and human-controlled delivery with a spec-driven software development platform. What We Do AI Consulting and Transformation Harness the power of artificial intelligence to transform business operations AI-Powered Product Engineering A structured studio to ideate, validate, and build scalable digital products Enterprise System Modernization Transform legacy systems into modern, scalable architectures Digital Customer Experience Design exceptional user experiences that delight customers Industries Banking, Finance, and Insurance Healthcare Manufacturing Media and Entertainment Gaming Real Estate Hiring Hospitality E-commerce Engineering AI Engineering Full Stack Engineering Mobile Engineering Web Engineering Backend Engineering DevOps Engineering UI/UX Engineering Design Engineering QA Engineering IoT Engineering Business Analysis Insights Case Studies Work that delivers real business impact Blogs Latest insights and perspectives on technology trends Guides Step-by-step tutorials and best practices Magazines Curated collections of industry knowledge Events Upcoming webinars, conferences, and workshops About About Us Decoded: Innovate. Collaborate. Build. Culture Behind the scenes at the GeekBase Team The talent building bold solutions Join Us Open positions and roles at GeekyAnts How We Work Our principles, process, and approach to delivery Security & Compliance How we approach security, quality, and compliance. Contact Us {"@context":"https://schema.org","@type":"WebPage","name":"AntFlow AI","descript

Six AI decision models played Pac-Man in real time against the classic arcade ghosts. See the leaderboard, watch their games, or play against them. jevman Roundtable Chat Toggle Menu Models Rankings Apps jevman Chat Talk to any model in one chat Chat Roundtable Past Roundtables Roundtable Stats jevman Opper Models Rankings Apps LLM Gateway Enterprise Pricing Docs jevman: AI decision models play Pac-Man Six of them played 100 games each against the arcade ghosts. See how they rank, watch their games or play against them. ▶ Play vs AI Swipe here to steer Activity Watch Leaderboard 100 games per model against the classic ghosts. =1 means tied: those scores are within each other's margin of error.

A local AI agent test environment. Draft tests with AI, review the rules, run your agent in a world and inspect what changed. Stripe and Zendesk public beta. Describe what your agent should do. Worlds helps you draft the test with AI, run your agent against a local twin of the systems it uses, and check what actually changed. Stripe, Zendesk and combined workflows. Free internal testing during beta. Review the setup and success rules. Run your agent in a local world, then inspect the records it changed. Describe the request, add your policy, and choose the starting data. Begin with a template or your own supported records.

Benchmark Heaven publishes cost and capability comparisons across AI models. Its own description is "The most detailed cost-capability analysis in AI. Every model. Every Benchmark. Actual Costs." One published comparison, JevBench v1.3.0, states that 52 Jev-class systems were tested on 534 decisions, with a named leader at a specific score. The site presents itself as a benchmark and cost reference rather than a model host: it reports results and prices instead of running inference itself.

My Bot Farm is a marketplace for whole agents and teams rather than a catalogue of individual skills. Its page states that you browse bots and install a copy for GrokBot, Hermes, and OpenClaw. The site draws a deliberate distinction in its own headline: "An Open Market to share your Agents. Not a warehouse of skills." It treats teams as first-class objects alongside single bots, and says new listings appear when they land rather than when someone remembers to check.

Free, open-source AI note taker for Mac. Records calls with no bot, transcribes on your Mac, and suggests answers from your documents mid-call. AI meeting recorder and live assistant for Mac Help during the call. Not after it. Parrot is a meeting recorder for your Mac with a live AI assistant built in. It notices when a call starts, suggests answers from your own documents while you're still talking, and remembers every call so you can ask about it later. The audio stays on your Mac. Free and open source. Watch the video 1 min Released 9 Oct 2026. 25 MB. macOS 14 or later on Apple Silicon . or build from source Assistant Briefed Brief for this call Sales discovery Northwind, 10:00 Type a line or two before you hit record, and the Assistant knows who you're talking to. Your documents are already in play.

Stop trusting your AI agent with your machine. Give it a sandbox it can Abyss Docs Blog GitHub Get started Get started Search Search Cancel Loading search index… You configure your editor like a normal ACP agent, and Abyss handles everything Docker for you. docker : # This is a pre-built image with Abyss and Pi pre-installed image : "ghcr.io/sethcurry/abyss-pi:latest" # Abyss speaks ACP, so we invoke Pi via pi-acp agent_command : - pi-acp host_mounts : # This mounts the current directory inside the container. # It doesn't take a target path because ACP sends a `cwd` # so we need the paths to line up between the host and # container - source : "./" # This mounts your .pi directory so that the agent has # API keys for your LLM, prior sessions, plugins, etc. # # You can put this in the `copy_files` config section # which will prevent the LLM from modifying your config files # but your sessions won't end up on your host PC so the session # files are lost when the container gets deleted. - source : "~/.pi" destination : "/root/.pi" Works with any ACP client you want!

past.dev, a memory layer for AI agents, is available today. An agent asks it for context and gets current facts with dated sources, limited to what it may see. Developers Use cases Benchmarks Enterprise Blog Pricing Log in Get API key Menu [{"@context":"https://schema.org","@type":"BlogPosting","@id":"https://past.dev/blog/introducing-past-dev#article","headline":"Introducing past.dev","name":"Introducing past.dev: memory for AI agents","description":"past.dev, a memory layer for AI agents, is available today. An agent asks it for context and gets current facts with dated sources, limited to what it may see.","url":"https://past.dev/blog/introducing-past-dev","mainEntityOfPage":"https://past.dev/blog/introducing-past-dev","datePublished":"2026-09-30","dateModified":"2026-09-30","inLanguage":"en","articleSection":"Announcements","image":"https://past.dev/og/blog/introducing-past-dev.png?v=2df9c1332858","author":[{"@type":"Organization","name":"The past.dev team","url":"https://past.dev"}],"publisher":{"@id":"https://past.dev/#org"},"isPartOf":{"@type":"Blog","name":"past.dev blog","url":"https://past.dev/blog"},"about":{"@id":"https://past.dev/#service"},"keywords":["past.dev","memory for AI agents","AI agent memory benchmark","BEAM benchmark results","LoCoMo"],"citation":["https://arxiv.org/abs/2510.27246","https://exabase.io/blog/exabase-m1-achieves-state-of-the-art-on-beam-benchmark","https://benchmarks.hindsight.vectorize.io/","https://plasticlabs.ai/blog/research/Benchmarking-Honcho","https://mem0.ai/blog/ai-memory-benchmarks-in-2026","https://mem0.ai/research","https:

When a demand storm hits an LLM fleet, one routing rule decides who still gets the strong model. A playable figure from

Open WebUI is a chat front end that runs on your own machine or server and connects to local runtimes such as Ollama and to hosted model APIs. It provides the interface a hosted chat product provides, without the conversation leaving your infrastructure. The feature set covers what a team expects from a chat interface: multiple conversations with history, model switching, prompt presets, document upload for retrieval, and web search integration. Multi-user support adds accounts and roles, so a small team can share one deployment with separate conversations and access to different models. It is a front end rather than a model runtime, so it needs something to talk to. The project is open source and self-hostable, and the tradeoff is operational: running it means running a container, a database, and whichever model backend it is pointed at.

Dify is a platform for building applications on language models. The core surface is a visual workflow editor where a pipeline is assembled from nodes: a model call, a retrieval step, a conditional branch, a code node, and an output. The same project can be exposed as a chat application, an API, or an embedded widget. The retrieval layer is built in. Documents are uploaded, chunked, embedded, and indexed, and a knowledge base can be attached to a node so a pipeline answers from your own material. Prompt templates, variables, and model configuration are part of the workflow rather than scattered through application code. The platform is open source and can be self-hosted, which is why some teams adopt it over a hosted orchestration service. The tradeoff is that a visual pipeline is harder to review and version than code, so teams building something complex often move the final implementation into their own codebase once the shape is settled.

Track TikTok and Instagram, keep every viral hook, video and hashtag in one library, and ask an AI agent what to post next. Find what works on the feed Search millions of viral posts across TikTok and Instagram. 30K+ videos found Make a better bet on your next post Start 3-day free trial DISCOVER Find the formats already taking off Search TikTok and Instagram by category, audience, product, or creative angle. See the posts worth studying with their source and the evidence behind each match. Product walkthrough @ crystalchavis AI workflows @ spectrume.ai Business automation @ justine.derosa Consulting pitch @ nicksadler.io Automation demo @ alex.carterr__ Brand entertainment @ kfcdeutschland Product walkthrough @ crystalchavis AI workflows @ spectrume.ai Business automation @ justine.derosa UNDERSTAND See why a video held attention Go beyond views. Follow the opening, the visual beats, the claim, and the payoff so you can understand the creative pattern without guessing.

Continue is an AI coding assistant that installs into VS Code and JetBrains IDEs. Unlike assistants tied to one vendor's model, it is configured with a model provider, so it can point at a hosted API, a local runtime, or an internal endpoint. The feature set covers the common assistant interactions: inline autocomplete as you type, a chat panel that can see the open file, and edit commands that apply a change across a selection. Context providers decide what the model sees, which is how a question can include the current file, an open diff, or a documentation source. The project is open source, and configuration lives in a file in the repository, so a team can share a setup. The tradeoff is that the assistant is only as good as the model behind it: a local model avoids sending code away but performs below a frontier hosted model, and the team has to make that trade explicitly.

We crawl every page, flag the exact words and patterns that read as generic AI copy, and show you why: score, evidence, and a rule-grounded rewrite. Free for your whole site. Generic, AI-sounding copy doesn't just read badly. It quietly costs conversions, and after Google's February 2026 core update, it can cost search rankings too. The question isn't whether your site used AI. It's whether it reads as trustworthy, specific writing, or like everyone else's landing page. In today's fast-paced world , businesses need to leverage cutting-edge solutions to unlock seamless growth. Our platform empowers teams to delve into data-driven insights and elevate their workflow. Most teams don't need another dashboard. They need fewer clicks between a question and an answer. Our platform gets you there in three.

A fully conformant Kubernetes gateway, built in Rust for speed. It carries your HTTP, LLM and MCP traffic on one data plane. Portus Conformance Performance Gateways Architecture Standalone GitHub 0.4.0 One network stack: Rama Portus API & AI Gateway A fully conformant Kubernetes gateway, built in Rust for speed. It carries your HTTP, LLM and MCP traffic on one data plane. Gateway API v1.6.2, experimental channel. The upstream suite runs in-cluster against a real per-Gateway deployment. Each square below is one passing result. Measured with the open gateway-api-bench method: three interleaved rounds on one machine, against the same backend pods as the top-ranked gateway in that benchmark. Every number and every round is published. Method: the howardjohn/gateway-api-bench approach on an AWS c7a.8xlarge (32 vCPUs, AMD EPYC 9R14; single-node k3s, pods at MTU 65000), three interleaved rounds per gateway, fortio in-cluster with one pod per rung, 3 proxy pods each. Medians shown. Numbers from one machine are only comparable with each other. agentgateway is ahead on 1 MiB plain-HTTP downloads and uses less memory per pod. Failover figures come from the gateway-api-bench suite on Docker Desktop with no retry policy; with a RetryPolicy Portus returns 0 errors. Every round, including the tests agentgateway wins →

enclawed is the compliance layer for agentic AI: wrap every AI agent and plugin in a FIPS-grade cryptographic boundary, mandatory access control, and a tamper-evident audit trail. Regulated teams — banks, hospitals, federal agencies, DoD primes, AI platforms — deploy agentic AI on sensitive data that would otherwise never pass an audit. enclawed-oss is the free MIT-licensed core (a hardened OpenClaw fork); enclawed-enclaved runs its cryptography on the host Individuals Teams Enterprise Compare Proof Security teams Apps Hardware Research Request a demo Talk to us .lang-switcher { font-size: 0.82rem; padding: 0.45em 0; text-align: center; opacity: 0.85; } .lang-switcher a, .lang-switcher strong { margin: 0 0.15em; text-decoration: none; } .lang-switcher strong { font-weight: 700; } .lang-switcher a:hover { text-decoration: underline; } enclawed ™ -enclaved Deterministically secure AI agents Reach compliance with your AI agents. Finally. Your agents move money, read your email and your data, and control physical devices. When bad actors want to take control, nothing stops them — and nothing proves it happened.

AI coding agents often generate functionally correct but insecure code. We developed and open-sourced Security Cards to guide them on generating secure code across 80+ widely used libraries and 13 programming languages. Home Features Contact Blog Request early access Back to blog Research August 25, 2026 7 min read How Security Cards Reduce Insecure AI-Generated Code by 72% Security Cards provide library-specific guidance that helps AI coding agents generate safer code. TL;DR: AI coding agents often generate functionally correct but insecure code. To address this issue, we have open-sourced Security Cards , targeted security guidance for 80+ widely used libraries across 13 programming languages. Our evaluation shows that the Security Cards reduce the rate of insecure code generation by up to 72.3% in Claude Code with Opus 4.7. You can find these security cards on our website or on GitHub , or simply install the skill to guide your AI agent on how to use the cards with the following command:

Free SSH client that keeps your Xshell, MobaXterm and PuTTY habits: SSH, RDP, VNC, SFTP and serial in one app for Windows, Mac, Linux, iOS and Android. English SSH client with RDP, VNC, SFTP and a built-in AI agent Every detail refined for efficiency. One step fewer for everyday work. Repetitive tasks done in one pass. Download on the App Store See Pricing No credit card. No account required to start. Windows, macOS, Linux, iOS and Android, with RDP on all of them. SSH, SFTP, RDP, VNC, serial, jump hosts and port forwarding in a single app.

One AI workspace for your whole team: ship apps, ping agents in Slack, and run automations on a schedule. Model agnostic — use Claude, GPT, and more, with no markup or your own keys. Custom Agents AI Code Review Pricing Terminal About Get Started Product Workflows Platform Whole Company How It Works Factory CLI Custom Agents AI Code Review Pricing Terminal About Get Started Your company’s AI workspace. Apps anyone can ship. Agents you ping in Slack. Automations on a schedule. One workspace for your whole team, with your tools connected and hosting handled. CodePress Agent Working Guided I'm done Re-architect message delivery at scale and open a verified PR. I’ll trace the delivery and reconnect paths, then exercise them under production-like load. Bash(rg "publish|replay|reconnect" apps/) I found a replay gap during reconnect. I’ve repaired the ordering path and added coverage for concurrent sessions. Running staging QA and independent merge judgment The repaired build passed QA and review. PR #2184 is ready for your approval. Describe what you'd like to do GPT-5.6 Sol Merge readiness

AI context engineering and agentic commerce for LLMs & AI applications — RAG Chunk Inspector, Memory Architecture Visualizer, Agent Trace Inspector, and Agent Commerce Gate for x402 + ERC-8004 pre-payment checks. Scan any URL for MCP , A2A , and ARD support — live handshake, agent card, and catalog checks in seconds. No sign-up Token Inspector Compare token counts and costs across GPT-4o, Claude, Gemini, and 20+ models. Know your monthly bill before you commit. No sign-up AI Agent Protocol Inspector Enter any URL and detect MCP, A2A, and ARD support. Live MCP handshake with tools/list, agent card parsing, and ai-catalog.json discovery. No sign-up RAG Chunk Inspector See exactly how any document splits into chunks. Compare tiktoken, sentence, and paragraph strategies with a live LLM context preview.

An information-flow policy engine for LLM agents. Search documentation Paper GitHub Discord Get started What is OpenAPPA Playground Deep Dive How it works Policy configuration Coding agents Works with Archestra Claude Code kAgent + Add to your agent Batteries What is a battery Available batteries + Create your own battery Operations Validation Observability Reporting (appa yell) Self-improving policies Evaluation Benchmarks Comparison vs Cedar vs Dogwood vs OPA vs Agent Auto-Modes Other Advisory Board Branding On this page Non-deterministic guardrails miss the problem Other deterministic guardrails either break agents or don't work OpenAPPA tracks flows instead of matching patterns It's full of tricks to help agents accomplish their tasks What is OpenAPPA How to sing “OpenAPPA” OpenAPPA is a frontier deterministic AI guardrail that is 100% resistant to data exfiltration caused by prompt injection or model hallucination, and the first of its kind that doesn't break agents.

A compact comparison of how language models solve Redactle under distinct evaluation configurations. New Game Settings Info News How to Play Stats More games Support R E DACTLE New Game Settings News How to Play Stats More games Support R E DACTLE Sign in Can an LLM solve a Redactle? Redactle is a puzzle game where a redacted Wikipedia article is displayed. Guessing a word reveals all matches in the article, including variations like 1 and one. Once the entire title is revealed, the game is won. One day I was pasting Redactle text into LLM chats from different providers and got wildly different results. I got nerd sniped and built a leaderboard to fairly evaluate as many as I could. $26.83 later, I now have this dashboard to answer that question. The results are surprising. Reasoning effort did not help as much as I expected. Gemini and Grok are the best, not OpenAI and Anthropic models as I had expected. Gemini 3.7 Flash does exceedingly well, managing to snipe all 12 puzzles while being the cheapest and fastest.

An AI agent writes each strategy in a small typed language, a validator checks every draft, and the version that passes is compiled into a fixed artifact that both the backtester and the live engine run. keel Platform For agents Capabilities Creators Pricing Sign in Get started → How Keel works Give your agent a typed strategy language, not a trading account Keel is a platform for building and running systematic trading strategies on Hyperliquid perpetual futures. An AI agent writes each strategy in a small typed language (DSL), a validator checks every draft, and the version that passes is compiled into a fixed artifact that both the backtester and the live engine run. No language model runs in the execution loop, where it would make trading slow, inconsistent and hard to audit. This page walks through how that works. Click to copy Read https://usekeel.io/agents and walk me through connecting Keel (usekeel.io) here, then help me run my first backtest.

An exploratory index of AI-news activity, with open data, a reproducible method and sensitivity analysis. {"@context":"https://schema.org","@type":"Dataset","name":"AI FOMO Index: AI-news activity corpus and weekly index","description":"A 14,696-record corpus of AI-news headlines from lab blogs, publishers and Hacker News, with a reproducible weekly index (method v1.1.0) of news velocity, lab activity, community attention and topic spikes. As of 2026-10-05 the index reads 43 (Near baseline). Exploratory; it does not measure technological progress or sentiment.","url":"https://aifomoindex.com/","identifier":"https://doi.org/10.5281/zenodo.23144696","sameAs":["https://doi.org/10.5281/zenodo.23144696","https://github.com/mertguvencli/aifomoindex","https://huggingface.co/datasets/mertguvencli/aifomoindex"],"version":"1.1.0","isAccessibleForFree":true,"license":"https://creativecommons.org/licenses/by/4.0/","creator":{"@id":"https://aifomoindex.com/#organization"},"dateModified":"2026-10-05T12:50:23.816Z","temporalCoverage":"2022-10-01/2026-10-05","keywords":["AI news","artificial intelligence","LLM releases","news index","time series","Hacker News"],"variableMeasured":["News velocity","Lab activity","Community attention","Topic spike"],"measurementTechnique":"https://aifomoindex.com/methodology","distribution":[{"@type":"DataDownload","name":"fomo.json","description":"Current score, signals, drivers and daily history.","encodingFormat":"application/json","contentUrl":"https://aifomoindex.com/fomo.json"},{"@type":"DataDownload","name":"research.json","description":"Research sna

Track the ETF market with real data: a daily market pulse, sector rotation and fund flows, valuations, and the ETFs standing out this week. The daily ETF market pulse: how much of the market rose, where fund flows went, which sectors look cheap or rich against their own history, and the ETFs standing out this week. Score, compare, and research ETFs with real data. Holdings overlap, look-through concentration and valuation against a fund's own history are computed from published holdings rather than quoted from a data vendor. Each fund carries an overall 0–10 score and four factor scores — value, growth, quality and risk — ranked against every US equity ETF we track. Higher is better within each. They describe what the data shows; they are not advice. How the scores are built → ETF Copilot, “ETF Copilot coverage and scoring scope”, as of October 8, 2026. https://etf-copilot.com/

Run one larger AI model across the computers you already own. Explore Potluck’s model-splitting work, tested configurations, and local AI downloads. How it works Blog Roadmap Why Potluck Bigger models. Same computers. Your laptop. Your gaming PC. That spare desktop. Potluck splits one AI model across your machines, making room for something bigger. Watch the film Individually, your computers. Together, more room for AI. Model splitting is experimental. The tested Mac + Linux GPU setup uses development builds. A model doesn’t have to live on a single computer. Give each machine a part, and let them work on the same answer. Part 1 runs here Your laptop Part 2 runs here Gaming PC Part 3 runs here Everyday desktop Part 4 runs here Spare laptop 4 computers hold different parts of the same model and work together on one answer.