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Отслеживается продуктов: 464
Endo Familiar, an O-cap based JavaScript agent sandbox
Show HN: Endo Familiar, an O-cap based JavaScript agent sandbox
A visual CI/CD system
Over a year ago I started Actionforge, a managed/self-managed visual CI/CD platform. Just for fun, because I like building and experimenting with dev tools. Back then I thought the user would be a human with a keyboard and a mouse. These days I rarely write pipelines. I mostly write specs, an agent dumps 500 lines of YAML, Python and a Makefile then I get my artifacts. The amount is overwhelming and a liability, and I stop trusting what I just merged. So I pivoted my CI/CD project toward more visualization, analytics and data aggregation, so engineers can see what agents and the pipeline produced, the flow, steps, data. Where it fails. With that I have a better mental model instead of a blob of code. The goal is better observability, and pipelines that explain their own failures back to the next agent. And also with a personal focus on UX and design. My project supports Perforce P4, Git/GitHub and GitLab, so quite flexible. Would love to hear your impression. I'm also very interested in your CI/CD horror stories, and how you use them these days. Any input is much appreciated! P.S. Code is public and provides GitHub Attestations.
CatchAll
Hey HN, Artem and Maksym from NewsCatcher here. Some of you know us as we started six years ago as two freshly graduated economics students who decided to build the best news API product. We started NewsCatcher thinking the market for news APIs was so big that we could build a self-serve platform and get millions of $29 users. Obviously, it was a wrong assumption. We pivoted to serve enterprises and had success with it. But we are hackers at heart, and we want to serve hackers. We haven't used our Launch HN yet, so consider this our smoke test. We're looking for feedback and power users rather than revenue. So, happy to provide enough credits for any HN user who finds CatchAll useful. CatchAll is built for one thing: retrieving every matching event from the web. The use cases that fit it are ones where missing events have real consequences — funding and M&A monitoring, regulatory and compliance feeds (FDA approvals, SEC filings, policy changes), cybersecurity incident tracking, supply chain signals. If your pipeline consumes structured records and the answer to your query is "find all of them," that's where it works. It's not the right tool for small, bounded queries that return 5 high-precision results. The 15-minute job time is a direct consequence of the pipeline depth: analyze, fetch, cluster, validate, extract, deduplicate. You're not getting a ranked list of links; you're getting a verified record set. Our latest benchmark run: https://newscatcherapi.com/blog-posts/web-search-api-benchma...
Physics-accurate 3D assets for robotics simulations from any input
In the past 4 years, we developed a platform generating 3D visuals and deploying AR Try-on experiences into large footwear and retail companies. We focused on high-fidelity meshes and textures since the point was giving shoppers a realistic visual. Then robotics companies started to approach us and they asked for thousands of 3D assets that they can use in creating different simulation environments. This is crucial for them so they can achieve better sim-to-real transfer with domain randomization (train a humanoid in a million different kitchens so a real one is not a surprise when it is deployed in the real world). However, we realized nice geometry and pretty textures are not enough for sim engines as they lack physics properties. So we developed a new pipeline creating SimReady assets in OpenUSD and MJCF that plug directly into Isaac Sim and MuJoCo. - Mass: volume from the mesh, density from material classification by a vision-language model (approach adapted from NeRF2Physics), multiplied together. - Center of mass, static/dynamic friction coefficients and restitution from the same material classification and estimation priors. - Collision meshes via CoACD; adaptive hull count is in progress. - Geometry and texture optimization, plus file format conversions. - We are currently focused on physics property estimation for props. Next step: articulated objects It comes with free credits on easy Google sign-up so you can give it a test. No credit cards required. Still early. All we'd ask is honest feedback. What works, what doesn't, what you wish it did differently. That's worth more to us than anything right now.
AthleteData
Im a triathlete and the data for my training lives in 6 apps: Garmin, Strava, WHOOP, Intervals.icu, Wahoo, Withings, Apple Health, sometimes Hevy. Every morning Id eyeball a few of them and make a call on whether to do the planned session. For the past month I have been building a thing that does this for me, and got it to the point where I use it myself every day. It OAuths into whatever platforms you connect, reconciles the activities (tbh harder than it sounds — same ride shows up in Strava, Garmin, and Wahoo with different timestamps and rounding), computes daily load and readiness, and proactively messages you over Telegram or Whatsapp when something matters. Stack is straightforward: Typescript all the way, Postgres, an agent loop running on Claude (via Bedrock) with tool access to all your data + my computed metrics: zones, CTL/ATL/TSB, power/pace curves, anomaly detection on HRV and RHR, etc Two things that were harder than expected: 1. Garmins API only exposes the last 90 days. So for anyone with Garmin as their primary device, you have to backfill from Strava and stitch the two together. Strava has full history but misses some fields (e.g. HR-based TSS only — no power). Wahoo and intervals.icu fill different gaps. The dedup pipeline is ugly and I'd welcome feedback from anyone who has solved this better. 2. Deciding when to message vs. stay silent is entirely a product problem. Too chatty -> muted. Too quiet -> feels dead. One honest caveat though: no RCT data, and Id be skeptical of anyone who claims they have it for AI coaching at this stage. I am at ~50 paying users, I personally reach out to every user to build the next iterations of the product based on feedback. Already got testimonials from Ironman world championship finishers and other pro athletes. Theres also a $9/mo MCP tier for people who would rather pipe their data into their own Claude/ChatGPT. Happy to go deep on any topic! e.g. the tool-calling architecture, or the cost-per-user question (running an agent on every athlete daily is not free, and the margins here are worth discussing).
Data-driven GEO and marketing agent platform
Show HN: Data-driven GEO and marketing agent platform
Spectrum
how can we make AI agents more accessible? it has been a crazy journey seeing how AI agents gets smarter every single day but interaction layer is still missing we are launching Spectrum TODAY: one Unified API connect your AI Agents into iMessage, WhatsApp, Telegram and more
Clawemon, A Pokemon-style MMO for your agents
Team up with your agent to trade, battle, and collect every Clawemon! This has been a fun side project for us, and we had a great time playing it with friends over the weekend. We’ll keep adding more towns and expanding the game in our spare time. Let us know your thoughts and what you’d like to see next - thank you! Send your agent to clawemon.com and join the world of Clawemon!
Graph Compose
Hey HN. Graph Compose is a hosted platform for orchestrating API workflows on Temporal. You define workflows as graphs of nodes (HTTP calls, AI agents, iterators, error boundaries) and everything runs as a durable Temporal workflow under the hood. Three ways to build the same graph: a React Flow visual builder, a typed TypeScript SDK (@graph-compose/client), and an AI assistant that turns plain English into a graph. Open-core: the execution foundations and integrations service are AGPL-3.0. The platform orchestrator, visual builder, and AI assistant are proprietary. Longer backstory on why I built this in the first comment. Would love feedback, especially from anyone who's dealt with the "services work fine, the glue between them doesn't" problem. Docs: https://graphcompose.io/docs
Daemons
For almost two years, we've been developing Charlie, a coding agent that is autonomous, cloud-based, and focused primarily on TypeScript development. During that time, the explosion in growth and development of LLMs and agents has surpassed even our initially very bullish prognosis. When we started Charlie, we were one of the only teams we knew fully relying on agents to build all of our code. We all know how that has gone — the world has caught up, but working with agents hasn't been all kittens and rainbows, especially for fast moving teams. The one thing we've noticed over the last 3 months is that the more you use agents, the more work they create. Dozens of pull requests means older code gets out of date quickly. Documentation drifts. Dependencies become stale. Developers are so focused on pushing out new code that this crucial work falls through the cracks. That's why we pivoted away from agents and invented what we think is the necessary next step for AI powered software development. Today, we're introducing Daemons: a new product category built for teams dealing with operational drag from agent-created output. Named after the familiar background processes from Linux, Daemons are added to your codebase by adding an .md file to your repo, and run in a set-it-and-forget-it way that will make your lives easier and accelerate any project. For teams that use Claude, Codex, Cursor, Cline, or any other agent, we think you'll really enjoy what Daemons bring to the table.
CyberWriter
Apple has quietly shipped a pretty complete on-device AI stack into macOS, with these features first getting API access in MacOS 26. There are multiple components in the foundation model, but the skills it shipped with actually make this ~3b parameter model useful. The API to hit the model is super easy, and no one is really wiring them together yet. - Foundation Models (macOS 26) - a ~3B-parameter LLM with an API. Streaming, structured output, tool use. No API key, no cloud call, no per-token cost. - NLContextualEmbedding (Natural Language framework, macOS 14+) -- a BERT-style 512-dim text embedder. Exactly what OpenAI and Cohere sell, sitting in Apple's SDKs since iOS 17. - SFSpeechRecognizer / SpeechAnalyzer - on-device speech-to-text including live dictation. Solid accuracy on Apple Silicon. I built cyberWriter, a Markdown editor, on top of all three, mostly as a test and showcase to see what it can do. I actually integrated local and cloud AI first, and then Apple shipped the foundation model, it stacked on super easy, and now users with no local or API AI knowledge can use it with just a click or two. Well the real reason is because most markdown editors need plugins that run with full system access, and I work on health data and can't have that. Vault chat / semantic search. The app indexes your Markdown folder via NLContextualEmbedding (around 50 seconds for 1000 chunks on an M1). The search bar gets a "Related Ideas" section that matches by meaning - typing "orbital mechanics" surfaces notes about rockets and launch windows even when those exact words never appear. Ask the AI a question and it retrieves the top 5 chunks as context. Plain RAG, but the embedder, retrieval, chat model, and search all run locally. AI Workspace. Command+Shift+A opens a chat panel, Command+J triggers inline quick actions (rewrite, summarize, change tone, fix grammar, continue). Apple Intelligence is the default; Claude, OpenAI, Ollama, and LM Studio all work if you prefer. The same context layer - document selection, attached files, retrieved vault chunks - feeds every provider through the same system-message path. Because the vault context is file and filename aware, it can create backlinks to the referenced file if it writes or edits a doc for you. Voice notes and dictation. Record a voice note directly into your doc, transcribe it with SpeechAnalyzer, or just dictate into the editor while you think. Audio never leaves the Mac. The privacy story is straightforward because the primitives are already private. Vectors live in a `.vault.embeddings.json` file next to your vault, never sent anywhere. If you use Apple Intelligence, even the retrieved text stays on-device. For cloud models there is a clear toggle and an inline warning before any filenames or snippets leave the machine. Honest limitations: - 512-dim embeddings are solid mid-tier. A GPT-4-class embedder catches subtler relationships this will miss. - 256-token chunks can split long paragraphs mid-argument. - Foundation Models caps its context window around 6K characters, so vault context is budgeted to 3K with truncation markers on the rest. - Multilingual support is English-only right now. NLContextualEmbedding has Latin, Cyrillic, and CJK model variants; wiring the language detector across chunks is Phase 2. The developer experience for these APIs is genuinely good. Foundation Models streams cleanly, NLContextualEmbedding downloads assets on demand and gives you mean-poolable token vectors in a handful of lines. Curious what others here are building on this stack - feels like low-hanging fruit that has been sitting there for a while. https://imgur.com/a/HyhHLv2 The Apple AI embedding feature is going live today. I'm honestly surprised it even works out of the box.
A lightweight way to make agents talk without paying for API usage
Show HN: A lightweight way to make agents talk without paying for API usage