CopyFail Was Not Disclosed to Distros
79 by ori_b | 24 comments on Hacker News.
Thursday, 30 April 2026
Wednesday, 29 April 2026
Tuesday, 28 April 2026
Monday, 27 April 2026
New top story on Hacker News: China blocks Meta's acquisition of AI startup Manus
China blocks Meta's acquisition of AI startup Manus
33 by yakkomajuri | 7 comments on Hacker News.
https://ift.tt/KRYqlWu... https://ift.tt/iTLtPDh
33 by yakkomajuri | 7 comments on Hacker News.
https://ift.tt/KRYqlWu... https://ift.tt/iTLtPDh
Sunday, 26 April 2026
Saturday, 25 April 2026
Friday, 24 April 2026
Thursday, 23 April 2026
Wednesday, 22 April 2026
Tuesday, 21 April 2026
Monday, 20 April 2026
Sunday, 19 April 2026
New top story on Hacker News: Show HN: Faceoff – A terminal UI for following NHL games
Show HN: Faceoff – A terminal UI for following NHL games
13 by vcf | 3 comments on Hacker News.
Faceoff is a TUI app written in Python to follow live NHL games and browse standings and stats. I got the inspiration from Playball, a similar TUI app for MLB games that was featured on HN. The app was mostly vibe-coded with Claude Code, but not one-shot. I added features and fixed bugs by using it, as I spent way too much time in the terminal over the last few months. Try it out with `uvx faceoff` (requires uv).
13 by vcf | 3 comments on Hacker News.
Faceoff is a TUI app written in Python to follow live NHL games and browse standings and stats. I got the inspiration from Playball, a similar TUI app for MLB games that was featured on HN. The app was mostly vibe-coded with Claude Code, but not one-shot. I added features and fixed bugs by using it, as I spent way too much time in the terminal over the last few months. Try it out with `uvx faceoff` (requires uv).
Saturday, 18 April 2026
New top story on Hacker News: Show HN: AI Subroutines – Run automation scripts inside your browser tab
Show HN: AI Subroutines – Run automation scripts inside your browser tab
7 by arjunchint | 0 comments on Hacker News.
We built AI Subroutines in rtrvr.ai. Record a browser task once, save it as a callable tool, replay it at: zero token cost, zero LLM inference delay, and zero mistakes. The subroutine itself is a deterministic script composed of discovered network calls hitting the site's backend as well as page interactions like click/type/find. The key architectural decision: the script executes inside the webpage itself, not through a proxy, not in a headless worker, not out of process. The script dispatches requests from the tab's execution context, so auth, CSRF, TLS session, and signed headers get added to all requests and propagate for free. No certificate installation, no TLS fingerprint modification, no separate auth stack to maintain. During recording, the extension intercepts network requests (MAIN-world fetch/XHR patch + webRequest fallback). We score and trim ~300 requests down to ~5 based on method, timing relative to DOM events, and origin. Volatile GraphQL operation IDs are detected and force a DOM-only fallback before they break silently on the next run. The generated code combines network calls with DOM actions (click, type, find) in the same function via an rtrvr.* helper namespace. Point the agent at a spreadsheet of 500 rows and with just one LLM call parameters are assigned and 500 Subroutines kicked off. Key use cases: - record sending IG DM, then have reusable and callable routine to send DMs at zero token cost - create routine getting latest products in site catalog, call it to get thousands of products via direct graphql queries - setup routine to file EHR form based on parameters to the tool, AI infers parameters from current page context and calls tool - reuse routine daily to sync outbound messages on LinkedIn/Slack/Gmail to a CRM using a MCP server We see the fundamental reason that browser agents haven't taken off is that for repetitive tasks going through the inference loop is unnecessary. Better to just record once, and get the LLM to generate a script leveraging all the possible ways to interact with a site and the wider web like directly calling backed API's, interacting with the DOM, and calling 3P tools/APIs/MCP servers.
7 by arjunchint | 0 comments on Hacker News.
We built AI Subroutines in rtrvr.ai. Record a browser task once, save it as a callable tool, replay it at: zero token cost, zero LLM inference delay, and zero mistakes. The subroutine itself is a deterministic script composed of discovered network calls hitting the site's backend as well as page interactions like click/type/find. The key architectural decision: the script executes inside the webpage itself, not through a proxy, not in a headless worker, not out of process. The script dispatches requests from the tab's execution context, so auth, CSRF, TLS session, and signed headers get added to all requests and propagate for free. No certificate installation, no TLS fingerprint modification, no separate auth stack to maintain. During recording, the extension intercepts network requests (MAIN-world fetch/XHR patch + webRequest fallback). We score and trim ~300 requests down to ~5 based on method, timing relative to DOM events, and origin. Volatile GraphQL operation IDs are detected and force a DOM-only fallback before they break silently on the next run. The generated code combines network calls with DOM actions (click, type, find) in the same function via an rtrvr.* helper namespace. Point the agent at a spreadsheet of 500 rows and with just one LLM call parameters are assigned and 500 Subroutines kicked off. Key use cases: - record sending IG DM, then have reusable and callable routine to send DMs at zero token cost - create routine getting latest products in site catalog, call it to get thousands of products via direct graphql queries - setup routine to file EHR form based on parameters to the tool, AI infers parameters from current page context and calls tool - reuse routine daily to sync outbound messages on LinkedIn/Slack/Gmail to a CRM using a MCP server We see the fundamental reason that browser agents haven't taken off is that for repetitive tasks going through the inference loop is unnecessary. Better to just record once, and get the LLM to generate a script leveraging all the possible ways to interact with a site and the wider web like directly calling backed API's, interacting with the DOM, and calling 3P tools/APIs/MCP servers.
Friday, 17 April 2026
Thursday, 16 April 2026
Wednesday, 15 April 2026
Tuesday, 14 April 2026
New top story on Hacker News: Show HN: A memory database that forgets, consolidates, and detects contradiction
Show HN: A memory database that forgets, consolidates, and detects contradiction
9 by pranabsarkar | 2 comments on Hacker News.
Vector databases store memories. They don't manage them. After 10k memories, recall quality degrades because there's no consolidation, no forgetting, no conflict resolution. Your AI agent just gets noisier. YantrikDB is a cognitive memory engine — embed it, run it as a server, or connect via MCP. It thinks about what it stores: consolidation collapses duplicate memories, contradiction detection flags incompatible facts, temporal decay with configurable half-life lets unimportant memories fade like human memory does. Single Rust binary. HTTP + binary wire protocol. 2-voter + 1-witness HA cluster via Docker Compose or Kubernetes. Chaos-tested failover, runtime deadlock detection (parking_lot), per-tenant quotas, Prometheus metrics. Ran a 42-task hardening sprint last week — 1178 core tests, cargo-fuzz targets, CRDT property tests, 5 ops runbooks. Live on a 3-node Proxmox homelab cluster with multiple tenants. Alpha — primary user is me, looking for the second one.
9 by pranabsarkar | 2 comments on Hacker News.
Vector databases store memories. They don't manage them. After 10k memories, recall quality degrades because there's no consolidation, no forgetting, no conflict resolution. Your AI agent just gets noisier. YantrikDB is a cognitive memory engine — embed it, run it as a server, or connect via MCP. It thinks about what it stores: consolidation collapses duplicate memories, contradiction detection flags incompatible facts, temporal decay with configurable half-life lets unimportant memories fade like human memory does. Single Rust binary. HTTP + binary wire protocol. 2-voter + 1-witness HA cluster via Docker Compose or Kubernetes. Chaos-tested failover, runtime deadlock detection (parking_lot), per-tenant quotas, Prometheus metrics. Ran a 42-task hardening sprint last week — 1178 core tests, cargo-fuzz targets, CRDT property tests, 5 ops runbooks. Live on a 3-node Proxmox homelab cluster with multiple tenants. Alpha — primary user is me, looking for the second one.
Monday, 13 April 2026
Sunday, 12 April 2026
Saturday, 11 April 2026
Friday, 10 April 2026
New top story on Hacker News: Launch HN: Twill.ai (YC S25) – Delegate to cloud agents, get back PRs
Launch HN: Twill.ai (YC S25) – Delegate to cloud agents, get back PRs
15 by danoandco | 1 comments on Hacker News.
Hey HN, we're Willy and Dan, co-founders of Twill.ai ( https://twill.ai/ ). Twill runs coding CLIs like Claude Code and Codex in isolated cloud sandboxes. You hand it work through Slack, GitHub, Linear, our web app or CLI, and it comes back with a PR, a review, a diagnosis, or a follow-up question. It loops you in when it needs your input, so you stay in control. Demo: https://www.youtube.com/watch?v=oyfTMXVECbs Before Twill, building with Claude Code locally, we kept hitting three walls 1. Parallelization: two tasks that both touch your Docker config or the same infra files are painful to run locally at once, and manual port rebinding and separate build contexts don't scale past a couple of tasks. 2. Persistence: close your laptop and the agent stops. We wanted to kick off a batch of tasks before bed and wake up to PRs. 3. Trust: giving an autonomous agent full access to your local filesystem and processes is a leap, and a sandbox per task felt safer to run unattended. All three pointed to the same answer: move the agents to the cloud, give each task its own isolated environment. So we built what we wanted. The first version was pure delegation: describe a task, get back a PR. Then multiplayer, so the whole team can talk to the same agent, each in their own thread. Then memory, so "use the existing logger in lib/log.ts, never console.log" becomes a standing instruction on every future task. Then automation: crons for recurring work, event triggers for things like broken CI. This space is crowded. AI labs ship their own coding products (Claude Code, Codex), local IDEs wrap models in your editor, and a wave of startups build custom cloud agents on bespoke harnesses. We take the following path: reuse the lab-native CLIs in cloud sandboxes. Labs will keep pouring RL into their own harnesses, so they only get better over time. That way, no vendor lock-in, and you can pick a different CLI per task or combine them. When you give Twill a task, it spins up a dedicated sandbox, clones your repo, installs dependencies, and invokes the CLI you chose. Each task gets its own filesystem, ports, and process isolation. Secrets are injected at runtime through environment variables. After a task finishes, Twill snapshots the sandbox filesystem so the next run on the same repo starts warm with dependencies already installed. We chose this architecture because every time the labs ship an improvement to their coding harness, Twill picks up the improvement automatically. We’re also open-sourcing agentbox-sdk, https://ift.tt/rOfJw6D , an SDK for running and interacting with agent CLIs across sandbox providers. Here’s an example: a three-person team assigned Twill to a Linear backlog ticket about adding a CSV import feature to their Rails app. Twill cloned the repo, set up the dev environment, implemented the feature, ran the test suite, took screenshots and attached them to the PR. The PR needed one round of revision, which they requested through Github. For more complex tasks, Twill asks clarifying questions before writing code and records a browser session video (using Vercel's Webreel) as proof of work. Free tier: 10 credits per month (1 credit = $1 of AI compute at cost, no markup), no credit card. Paid plans start at $50/month for 50 credits, with BYOK support on higher tiers. Free pro tier for open-source projects. We’d love to hear how cloud coding agents fit into your workflow today, and if you try Twill, what worked, what broke, and what’s still missing.
15 by danoandco | 1 comments on Hacker News.
Hey HN, we're Willy and Dan, co-founders of Twill.ai ( https://twill.ai/ ). Twill runs coding CLIs like Claude Code and Codex in isolated cloud sandboxes. You hand it work through Slack, GitHub, Linear, our web app or CLI, and it comes back with a PR, a review, a diagnosis, or a follow-up question. It loops you in when it needs your input, so you stay in control. Demo: https://www.youtube.com/watch?v=oyfTMXVECbs Before Twill, building with Claude Code locally, we kept hitting three walls 1. Parallelization: two tasks that both touch your Docker config or the same infra files are painful to run locally at once, and manual port rebinding and separate build contexts don't scale past a couple of tasks. 2. Persistence: close your laptop and the agent stops. We wanted to kick off a batch of tasks before bed and wake up to PRs. 3. Trust: giving an autonomous agent full access to your local filesystem and processes is a leap, and a sandbox per task felt safer to run unattended. All three pointed to the same answer: move the agents to the cloud, give each task its own isolated environment. So we built what we wanted. The first version was pure delegation: describe a task, get back a PR. Then multiplayer, so the whole team can talk to the same agent, each in their own thread. Then memory, so "use the existing logger in lib/log.ts, never console.log" becomes a standing instruction on every future task. Then automation: crons for recurring work, event triggers for things like broken CI. This space is crowded. AI labs ship their own coding products (Claude Code, Codex), local IDEs wrap models in your editor, and a wave of startups build custom cloud agents on bespoke harnesses. We take the following path: reuse the lab-native CLIs in cloud sandboxes. Labs will keep pouring RL into their own harnesses, so they only get better over time. That way, no vendor lock-in, and you can pick a different CLI per task or combine them. When you give Twill a task, it spins up a dedicated sandbox, clones your repo, installs dependencies, and invokes the CLI you chose. Each task gets its own filesystem, ports, and process isolation. Secrets are injected at runtime through environment variables. After a task finishes, Twill snapshots the sandbox filesystem so the next run on the same repo starts warm with dependencies already installed. We chose this architecture because every time the labs ship an improvement to their coding harness, Twill picks up the improvement automatically. We’re also open-sourcing agentbox-sdk, https://ift.tt/rOfJw6D , an SDK for running and interacting with agent CLIs across sandbox providers. Here’s an example: a three-person team assigned Twill to a Linear backlog ticket about adding a CSV import feature to their Rails app. Twill cloned the repo, set up the dev environment, implemented the feature, ran the test suite, took screenshots and attached them to the PR. The PR needed one round of revision, which they requested through Github. For more complex tasks, Twill asks clarifying questions before writing code and records a browser session video (using Vercel's Webreel) as proof of work. Free tier: 10 credits per month (1 credit = $1 of AI compute at cost, no markup), no credit card. Paid plans start at $50/month for 50 credits, with BYOK support on higher tiers. Free pro tier for open-source projects. We’d love to hear how cloud coding agents fit into your workflow today, and if you try Twill, what worked, what broke, and what’s still missing.
Thursday, 9 April 2026
Wednesday, 8 April 2026
Tuesday, 7 April 2026
Monday, 6 April 2026
New top story on Hacker News: Show HN: GovAuctions lets you browse government auctions at once
Show HN: GovAuctions lets you browse government auctions at once
20 by player_piano | 13 comments on Hacker News.
I've long been into finding deals on government auction sites (seizures, surplus sales etc.) - right now for example San Diego DHS is selling 26 tons of lead shot, with bidding starting at $1,000 ¯\_(ツ)_/¯ It has historically been extremely tedious though: scanning dozens of janky sites which have interminable page loading times; back buttons take you all the way back to the homepage etc. The site I built - GovAuctions - lets you search every government surplus auction at once. You can filter by location, category, and price, save items to a watchlist, and get alerts when new auctions match what you're looking for. Let me know what you think, if you have any suggestions, and if you find any deals in your area!
20 by player_piano | 13 comments on Hacker News.
I've long been into finding deals on government auction sites (seizures, surplus sales etc.) - right now for example San Diego DHS is selling 26 tons of lead shot, with bidding starting at $1,000 ¯\_(ツ)_/¯ It has historically been extremely tedious though: scanning dozens of janky sites which have interminable page loading times; back buttons take you all the way back to the homepage etc. The site I built - GovAuctions - lets you search every government surplus auction at once. You can filter by location, category, and price, save items to a watchlist, and get alerts when new auctions match what you're looking for. Let me know what you think, if you have any suggestions, and if you find any deals in your area!
Sunday, 5 April 2026
Saturday, 4 April 2026
Friday, 3 April 2026
Thursday, 2 April 2026
Wednesday, 1 April 2026
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