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EvoMapAgents & automation

EvoMap is an infrastructure designed for the reuse of AI agent experiences. It organizes reusable strategies and repair assets with validation records via the GEP protocol, offering market- and quality-based ranking to help different agents discover, evaluate, and inherit existing capabilities.

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MemoAIMusic & audio

MemoAI is an AI-powered audio-to-text tool that supports localized processing, eliminating the need to upload data and ensuring privacy and security. It can efficiently transcribe YouTube videos, podcasts, and local audio/video files (such as MP4, MP3, AAC, M4A, etc.) into text, while offering core features like multilingual translation, speaker diarization, real-time subtitles, and floating notes—making it ideal for meeting minutes, interview transcription, content learning, and more.

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Open DesignDesign & 3D

OpenDesign is an open-source design workspace that can connect to local programming agents, use built-in model keys, or leverage cloud-based models, enabling the creation and editing of prototypes, web pages, presentations, and graphics within a design system; local software is free, while model services incur fees.

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KiroCoding & development

Kiro is an AI engineering tool for developers that organizes requirements into specifications, architectures, and tasks, generates code via agents, and supports the entire workflow—from planning to code review—through property-based testing, execution memory, and integration with development tools.

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AI ShortWriting & content

AIS ALERT MANAGEMENT AND SHARED PLATFORM, QUICK COMMAND FOR MULTIPLE SCENARIOS

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JAXModels & infrastructure

JAX is a Python numerical computing library designed for accelerators, specifically tailored for high-performance array computations and large-scale machine learning. It efficiently executes the same code across multiple hardware backends, including CPUs, GPUs, and TPUs.

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My Glow Up AILife & personal

My Glow Up AI provides free analysis based on selfies, without requiring registration, and can generate hairstyle recommendations and beauty plans that suit you.

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ClipDropImages & editing

Clipdrop is a suite of AI-powered image creation and editing tools, covering background removal, local cleanup, image enlargement, upscaling, relighting, text removal, and background replacement. It allows users to process images directly in the browser and also provides developers with an API for integrating these capabilities into their applications.

常用AI图像工具待评分 · 分类列表推荐View details
妙播Marketing & commerce

THE AI LIVE TELECOM SOLUTION FROM THE AD

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ElicitKnowledge & research

Elicit provides semantic search, research report generation, literature screening, and data extraction for scientific research. It enables traceable answers within source materials and organizes evidence through a literature library, research agents, and ongoing tracking workflows, making it ideal for advancing from initial problem exploration to systematic reviews.

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AI, as it happens

1539 published stories
Investment & business · 208 storiesClear filters ×
2026-11-10
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01:31

Show HN: We Gave AI Coding Agents Production Memory

Hi HN, I’m building Codela to help coding agents learn from what happens after their code ships.I realized that my AI agent writes a change, the tests pass, and it gets deployed. Then in production a problem happens, I fix it, but what it did wrong and why never reaches the agent, so futures session usually make the same mistake.Codela checks every deploy against how your app actually behaves in production, learns from the ones that broke it, and teaches your agents so they don’t repeat the mistake.Would love any feedback on this, thanks! Comments URL: https://news.ycombinator.com/item?id=50428670 Points: 1 # Comments: 0

Startups & new venturesRead original
2026-11-09
Follow the timeline
22:00

Exclusive: FormAssembly overhauls its platform to expand into AI-powered process automation

FormAssembly Inc., a provider of online data collection and workflow automation software, today announced a complete platform overhaul that expands its capabilities beyond building forms to automating complex business processes using artificial intelligence. The new platform, which the company said took seven months to develop, combines form creation, workflow automation, integrations and compliance management in […] The post Exclusive: FormAssembly overhauls its platform to expand into AI-powered process automation appeared first on SiliconANGLE.

Funding & investmentRead original
21:18

Show HN: Pgxio – a tiny PostgreSQL layer inside Nginx

I built the first version of this stuff a decade ago as ethanolus (c2h5oh). And I've used it for most of my projects as a backend layer since then.The main motive is to eliminate unnecessary complexity from backend layers.How it works: a rust nginx module calls postgresql functions via non-blocking libpq io. There is an optional patch for libpq. It provides zero-copy for nginx-chained memory buffers - to avoid request body copies between nginx and libpq.Pgxio supports http2 and websockets. Realtime listen/notify updates from postgres without polling.How to try - code on github. Tested on Debian - FreeBSD validation in progress.It's alpha - so any feedback is welcome. Comments URL: https://news.ycombinator.com/item?id=50425203 Points: 1 # Comments: 0

Startups & new venturesRead original
20:00

Paper Unicorns Are Cheap. Building A $1B Revenue AI Company Takes A Multimarket Cap Table

AI startups should be judged by durable revenue, full-price renewals and healthy margins after delivery costs, with embedded customer workflows providing stronger defensibility as model access gets cheaper, rather than their paper valuation, writes guest author Ilman Shazhaev, founder and CEO of Dizzaract, who suggests that founders should build global investor bases that support customer acquisition and market entry while weighing venture growth expectations against sovereign-backed investors’ localization, governance and other obligations.

Funding & investmentRead original
18:46

Show HN: Open-source customer-facing agents

Hi, Dmitri from Radioso here, an open-source platform to design AI agents for customers and visitors in any channel, in the style of Fin/Ada/Decagon. The repo is https://github.com/radioso-ai/radioso and our website is https://radioso.aiToday, your choices are:1. Buy a very expensive service for your vertical (usually starting with $$ five figures monthly) 2. Get a cheap off-the-shelf chatbot that mostly answers FAQs 3. Build your own, and then also build the inbox, human handoff, audit trail and integrations around itAt Radioso we wanted an open-source option that combines the best of all 3. You can integrate it with your own software via API/SDK or your CRM, or run it standalone, either self-hosted or in our cloud.The engine runs multi-step interactions and takes actions: calling your APIs, updating records, booking things. Maybe you’ve tried Parlant; Radioso is the alternative “with batteries included”: RAG, audit trail, customer service inbox, and integrations like Slack and email.Under the hood it’s plain TypeScript + PostgreSQL, works with OpenAI/Anthropic/other models, and is licensed under Apache 2.0 which includes all features (The only thing we keep under EE license is the multi-tenant layer we use to run our own cloud).We have existing customers running it for technical Q&A, course booking, online product recommendations and goods returns, among others, some reaching 92% of conversations automated. We’re currently working on voice support so we can offer a call center.Our ambition is to be “the WordPress of customer-facing AI” :), where people can contribute their favorite plugins and benefit from the ecosystem.Longer term, we think browsing the web will mostly mean talking to your own agent, and when you need a service or a purchase, your agent will talk to the vendor’s agent to get things done. For this, Radioso already publishes an A2A Agent Card and we’re working on the rest. Your Radioso services are also callable over MCP, for customers who already use it.Thanks for your attention. We’d especially love to hear which integrations you’d want, whether you’d self-host it, and what’s stopped you from deploying a customer-facing AI agent so far. Tell us what you think. Comments URL: https://news.ycombinator.com/item?id=50423820 Points: 1 # Comments: 0

Startups & new venturesRead original
16:43

Show HN: Control Layer Demo for AI Coding Agents

Letting coding agents (such as Claude Code) work autonomously on repos on a daily-basis, brings a high level of uncertainty. For those who won't let the agent decide alone whether it can create/modify or delete any folder on their repos; I wanted to be able to trace actions performed and being able to let the agent continue his work autonomously, or ask him to request a human approval according to some criteria defined. After investigations, the cleanest way to do it was according to me to develop a deterministic control layer between the LLM and the work of the agent that would track and catch potential non-respect of pre-defined rules. How? by first setting up approval rules (Pass/requires approval/refused) before the work of the agent, and afterwards controlling cryptographic SHA 256 verifications, etc. This allows to compare the before-after work of the agent. I have put in my public GitHub repo a demo that you can launch easily and observe different scenarios behaviors like applied authorized modification, protected file waiting for Human approval, or outside perimeter authorization modification refused. The demo is offline, does not involve the official runtime, in native node.js and without dependencies. It should still allow you to understand the core principles. I would like to know: how do you implement and handle safety boundaries? Especially if you let agents work on critical data/sensitive codebases? With which guardrails? Comments URL: https://news.ycombinator.com/item?id=50422833 Points: 1 # Comments: 0

Startups & new venturesRead original
13:00

IWIBench: Evaluating Gradient-Free Model Driven Intra-weight Injection

arXiv:2611.08647v1 Announce Type: new Abstract: Intra-weight injection lets one model change another model's behaviour by editing its weights directly, without gradients. Such edits can range from altering lexical associations to enforcing output formats or updating factual knowledge. Despite growing interest in these approaches, there has been no systematic way to evaluate how well models perform them. We introduce IWIBench, which measures a large editor model's ability to modify the weights of a smaller target model (typically Gemma 3 4B IT) to achieve one of ten classical text-editing objectives while preserving its other capabilities. Editors are limited to four tools for reading and writing weights, with bounded tensor access and a fixed budget for generating and inspecting rollouts. A validator applies each proposed patch in a clean environment and compares the edited model against the unmodified target. Evaluating frontier closed- and open-weight models reveals sharply diverging capabilities, both across models and across tasks. A small number of models, most notably Astra and GPT-6.1 Sol, perform far ahead of the rest. The recency of this ability (little of it is evident in GPT-5.6 Sol, for instance), together with qualitative analysis, suggests it has been acquired through recent training. Tasks also vary widely: structured response resisted every attempt, and answer-length control yielded only one successful solution, whereas alias acquisition and recoding proved most amenable. Success in one task family correlated poorly with success in others, indicating an early and uneven capability frontier. We discuss strategies that editors repeatedly adopted and conclude with an outlook on intra-weight injection as an emerging but already potent approach to gradient-free behaviour modification.

M&A & IPOsRead original
01:26

Show HN: FELF – AppImage-like single binaries without FUSE, root, or a runtime

Hello HN!I've always had a bad time redistributing programs that use shared libraries, AppImage in my opinion does too much, and a container is an overkill - not always integrates smoothly.I've moved to nix since long ago, but redistributing between nix and other distros or vice versa it's still a pain in the ass, more so if you want to run an alien app in nix or a nix app without the package manger in any other distro.For that, with the help of Claude, I've implemented a format that deduces at pack-time all the shared libraries a program needs, puts them, together with its own glibc and a patched dynamic linker into a single executable.This means that you don't actually need a runtime nor any special capability, it can run inside a FROM SCRATCH docker, alpine linux etc... And it also supports coreutils-like multiple programs accessible via symlinks, so it is a good fit for that purpose.You can just use:> felf pack $(which git) --output git.felfand you'll have already a git binary that works everywhere, no matter which distro you are building this from. This only packs the binary and their dependencies.Essentially, the effect is similar as if you LD_PRELOAD's all the libraries that it requires.In the end, we have been fighting these demons since long long ago, chroots/docker/appimage/flatpack/nix etc...So this xkcd applies: https://xkcd.com/927/I'm happy to share the project, I hope it inspires new discoveries/tools in this regard! Comments URL: https://news.ycombinator.com/item?id=50415219 Points: 2 # Comments: 0

Startups & new venturesRead original
01:22

Show HN: Recap Studio – A Mac screen recorder for clean, polished demos

Hi HN, I made Recap Studio, a Mac app for recording demos and tutorials that look good without a video editor.You record your screen, camera and mic. Then you can zoom in on parts of the video, smooth the cursor, add a background, show the keys you press, and cut out mistakes. The newest feature keeps your face centered in the camera as you move. Comments URL: https://news.ycombinator.com/item?id=50415185 Points: 1 # Comments: 0

Startups & new venturesRead original
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