Training AI agents that can actually use a computer — opening apps, clicking buttons, browsing the web, writing code — is one of the hardest infrastructure problems in modern AI. It’s not a data problem. It’s not a model problem. It’s a plumbing problem. You need to spin up hundreds, potentially thousands, of full operating […] The post Meet OSGym: A New OS Infrastructure Framework That Manages 1,000+ Replicas at $0.23/Day for Computer Use Agent Research appeared first on MarkTechPost.

I’ve been thinking a lot about local-first AI workflows for documents, audio, and video, especially for private files people do not want to upload. For those of you who use local tools, what actually matters most in practice: privacy, speed, searchability, transcript quality, or something else?

Hi HN, I built Linggen — a local-first, open-source AI coding agent written in Rust. What's new in 0.9.2: - P2P remote access via WebRTC — control your agent from your phone, no port forwarding or cloud relay needed. Just `ling login` and scan a QR code. - Plan mode — the agent proposes a step-by-step plan before writing code, you approve or edit before execution. - Works with any model — Ollama, OpenAI-compatible APIs, Gemini, DeepSeek. Bring your own keys or run fully local. It's like Claude Code but model-agnostic, extensible through skills (markdown files), and now accessible from anywhere. Demo video on the landing page showing install → plan → build → mobile sync. Install: curl -fsSL https://linggen.dev/install.sh | bash GitHub: https://github.com/linggen/linggen Comments URL: https://news.ycombinator.com/item?id=47696666 Points: 1 # Comments: 0

Article URL: https://abc7news.com/post/google-engineer-rejected-colleges-uses-ai-sue-ucs-other-universities-racial-discrimination/18849388/ Comments URL: https://news.ycombinator.com/item?id=47696645 Points: 1 # Comments: 0

I’ve been working on a core infrastructure problem for multi-agent systems and wanted to share an update since the last post here got some good discussion. The problem: every agent framework assumes agents can already reach each other. MCP gives agents tools, A2A gives agents a way to talk, but both run on HTTP which means someone has to set up public endpoints, open ports, configure DNS, provision certs. The agent can’t do any of that itself. I used Claude Code to build the solution because the scope was way beyond what I could write alone. Pilot Protocol is a Layer 3/Layer 4 overlay network built specifically for AI agents. Every agent gets a permanent 48-bit virtual address, encrypted UDP tunnels (X25519 + AES-256-GCM), and P2P connectivity with NAT traversal built in. Single Go binary, zero external dependencies, AGPL-3.0. Where it’s at now: The network has grown to 12,000+ active nodes across 19 countries. Companies like GitHub, Tencent, Vodafone, Pinterest, and Capital.com ha

I’ve been experimenting with using AI for work over the last few months and one thing surprised me. The real benefit wasn’t using it for random prompts - it was setting up repeatable workflows. For example, I used to spend a couple of hours each week pulling together reports (data, summaries, updates etc). Now I just dump everything into Claude and ask it to structure it into a report with key points + risks. Takes maybe 10–15 minutes. It’s not perfect every time, but it’s way faster. Curious if anyone else has found specific tasks where AI actually saves time vs just being “interesting”?

Article URL: https://idiallo.com/blog/it-took-me-10-hours-to-fix-ai-code Comments URL: https://news.ycombinator.com/item?id=47696600 Points: 2 # Comments: 1

Article URL: https://www.ishormuzopenyet.com/ Comments URL: https://news.ycombinator.com/item?id=47696562 Points: 51 # Comments: 9

Looking through a thread here - [https://www.reddit.com/r/ClaudeAI/comments/1rlx0eq/privacy\_just\_a\_reminder\_to\_turn\_off\_help\_improve/](https://www.reddit.com/r/ClaudeAI/comments/1rlx0eq/privacy_just_a_reminder_to_turn_off_help_improve/) In disbelief at the gall of anthropic as of late. I've been using Claude for the better part of a year and CONSTANTLY check my privacy options to ensure my sensitive data isn't being leaked and stored on their servers (5 year retention, and their backend has different policy, that may extend that even longer) and i can code with what SEEMED to be the most humane, respectful frontier company you can choose....until i realized that literally all of its a farce? you kidding? I stuck through the weekly limits addition, through the 2x switcheroo (you now get less for more when you need it most, and you're going to be happy with it), through the model degrading day by day as i continue to push on like i don't notice. Through the new model spikes wher

I went down a rabbit hole trying to use AI to summarize long podcast episodes. On paper it sounds perfect. Just take the transcript, drop it into a model, and get the key points. In reality it’s a bit different. You have to find a clean transcript first, which isn’t always straightforward. Then you paste thousands of words, try different prompts, tweak the output, run it again. And every time you do it, you’re using your own tokens. It works, but it starts to feel like a lot of effort for something that should be simple, especially if you’re doing this regularly. I also noticed that a lot of summaries either stay too generic or try to include everything, which makes them less useful than expected. At some point it just made me question whether this is actually the best way to use AI for these kinds of tasks.

Article URL: https://github.com/kaderosio/agent-brain Comments URL: https://news.ycombinator.com/item?id=47696453 Points: 2 # Comments: 1

There's an irony here: Claude Code helped me build the tool that fixes Claude Code's worst coordination problem. **The problem:** When you run multiple Claude Code sessions on the same codebase, they don't know about each other. They'll happily edit the same files, introduce conflicting patterns, and leave you with a merge nightmare. **What I built:** `ruah` — a CLI that sits between your Claude Code sessions and the repo. It ensures: - Each session gets an isolated worktree (no shared checkout) - File scopes are declared upfront (overlapping claims rejected) - Changes merge back in dependency order (no manual conflict resolution) - Task artifacts are captured (you know exactly what each session changed) **Why I built it *with* Claude Code:** Claude Code is absurdly good at coding individual features. But ask it to *coordinate* three copies of itself on the same repo and it has no framework for that. That coordination layer doesn't exist yet in any coding tool — so I built it. C

Article URL: https://openai.com/index/next-phase-of-enterprise-ai/ Comments URL: https://news.ycombinator.com/item?id=47696405 Points: 3 # Comments: 1

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I got tired of the overhead required to run even a simple data analysis - cloud setup, ETL pipelines, orchestration, cost monitoring - so I built a fully local data-stack/IDE where I can write SQL/Py, run it, see results, and iterate quickly and interactively. You get data lake like catalog, zero-ETL, lineage, versioning, and analytics running entirely on your machine. You can import from a database, webpage, CSV, etc. and query in natural language or do your own work in SQL/Pyspark. Connect to local models like Gemma or cloud LLMs like Claude for querying and analysis. You don’t have to setup local LLMs, it comes built in. This is completely free. No cloud account required. Downloading the software - https://getnile.ai/downloads Watch a demo - https://www.youtube.com/watch?v=C6qSFLylryk Check the code repo - https://github.com/NileData/local This is still early and I'd genuinely love your feedback on what's broken, what's missing, and if you find this useful for your data and analytics work. Comments URL: https://news.ycombinator.com/item?id=47696336 Points: 4 # Comments: 0

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We built CongaLine to solve a specific problem: give teams and individuals self-hosted AI assistants without collapsing everyone into a shared instance. Each agent runs in its own Docker container with isolated networks, secrets, and config. Security is the primary design constraint. Three deployment targets via a single Go CLI (conga): - Local: Docker Desktop, no cloud required - Remote: any SSH-accessible Linux host (VPS, Raspberry Pi, bare metal) - AWS: zero-ingress via SSM, no inbound ports Channels are optional. Every agent gets a web UI via SSH/SSM tunnel. Slack and Telegram are both supported, each with a dedicated router container that fans events to per-agent containers. Two agent types: user agents (DM-based) and team agents (channel-based). The name is literal: spiny lobsters travel in single-file conga lines during seasonal migration, in physical contact, reducing drag and sharing protection. A procession of isolated-but-coordinated agents. Apache 2.0. Repo has Terraform for AWS, a Go CLI, and a Node.js Slack router. Would love feedback on the provider abstraction. We're planning Kubernetes and GCP next. Comments URL: https://news.ycombinator.com/item?id=47696273 Points: 1 # Comments: 0

Article URL: https://chetandesh.substack.com/p/i-used-15-ai-agents-to-design-a-wearable Comments URL: https://news.ycombinator.com/item?id=47696267 Points: 2 # Comments: 0

Poke brings AI agents to everyday users via text message by handling tasks and automations without complex setup, apps, or technical know-how.

Article URL: https://jitera.com/blog/in-vs-through/ Comments URL: https://news.ycombinator.com/item?id=47696234 Points: 1 # Comments: 2

Article URL: https://www.theregister.com/2026/04/06/anthropic_claude_code_dumber_lazier_amd_ai_director/ Comments URL: https://news.ycombinator.com/item?id=47696210 Points: 10 # Comments: 1

A practical guide to the integration problem nobody talks about until it’s too late Continue reading on Towards AI »

Link: https://www.reddit.com/r/fivethirtyeight/comments/1sg539c/77_of_all_new_success_selfhelp_books_on_amazon/